John R. Birge

Our objective is to determine a scheduling policy that minimizes regret, which is the difference in expected abandonment cost between a proposed policy, that does not have knowledge of model primitives, and a benchmark policy, that has full knowledge of model primitives. The difficulty is that the state space is very complex, because in order for the system to be Markovian, we must track: (i) the time elapsed since the last arrival for each class; (ii) the amount of time each customer in service has been in service; and (iii) the amount of time each customer in queue has spent waiting. We propose a policy that uses a Learnthen-Schedule (LTS) algorithm that we develop. We analyze the performance of the LTS policy against the benchmark a?-rule (that pri

REVISION: Learning the Scheduling Policy in Time-Varying Multiclass Many Server Queues with Abandonment
Date Posted:Mon, 09 May 2022 09:34:05 -0500
We consider a learning variant of a canonical scheduling problem in a multiclass many server queue with abandonment (specifically, the M_t/M/N+M and the GI/GI/N+GI queues). The objective is to minimize the long-run average class-dependent expected linear holding and abandonment costs when the class-dependent model parameters (arrival rates, service rates and abandonment rates) are a priori unknown. The difficulty is that even when parameters are known, characterizing an optimal scheduling policy appears intractable. Fortunately, the simple cµ/? rule, that prioritizes classes in accordance with a static ranking that depends on the costs, the service rates, and the abandonment rates, is asymptotically optimal as the arrival rates and number of servers become large. Then, our task is to learn the service and abandonment rates well enough to determine an optimal static priority ranking for the classes, and we can benchmark our performance by defining the regret relative to the cµ/? ...

REVISION: Trade Credit Late Payment and Industry Structure
Date Posted:Fri, 18 Mar 2022 09:19:03 -0500
Problem Definition: Trade credit is essential for short-term financing in supply chains. While existing empirical and theoretical research focuses on how upstream suppliers provide trade credit, little attention has been paid in the literature to the payment behavior of downstream customers owing to the paucity of data. By leveraging unique trade credit payment data from Dun & Bradstreet (D&B), this paper is the first empirical study of firms’ delayed payments on trade credits, supporting the flexibility benefit of trade credit and filling an important gap in the literature.

Methodology/Results: We show that a firm’s late payment behavior is positively associated with its market power position and downstream cost-shifting incentive. To provide further support, we use an instrumental variable approach to examine a possible causal interpretation of our results and conduct various robustness tests (e.g., varying alternative measures, using different time windows, and ...

REVISION: Markdown Policies for Demand Learning with Forward-looking Customers
Date Posted:Sun, 05 Dec 2021 20:09:23 -0600
We consider the markdown pricing problem of a firm that sells a product to a mixture of myopic and forward-looking customers. The firm faces an uncertainty about the customers' forward-looking behavior, arrival pattern, and valuations for the product, which we collectively refer to as the demand model. Over a multiperiod selling season, the firm sequentially marks down the product's price and makes demand observations to learn about the underlying demand model. Because forward-looking customers create an intertemporal dependency, we identify that the keys to achieving good profit performance are: (i) judiciously accumulating information on the demand model, and (ii) preserving the market size in early sales periods. Based on these, we construct and analyze markdown policies that exhibit near-optimal performance under a wide variety of forward-looking customer behaviors.

REVISION: Controlling Epidemic Spread: Reducing Economic Losses with Targeted Closures
Date Posted:Thu, 18 Nov 2021 12:49:15 -0600
Data on population movements can be helpful in designing targeted policy responses to curb epidemic spread. However, it is not clear how to exactly leverage such data and how valuable they might be for the control of epidemics. To explore these questions we study a spatial epidemic model that explicitly accounts for population movements, and propose an optimization framework for obtaining targeted policies that restrict economic activity in different neighborhoods of a city at different levels. We focus on COVID-19 and calibrate our model using the mobile phone data that capture individuals’ movements within New York City (NYC). We use these data to illustrate that targeting can allow for substantially higher employment levels than uniform (city-wide) policies when applied to reduce infections across a region of focus. In our NYC example (which focuses on the control of the disease in April 2020), our main model illustrates that appropriate targeting achieves a reduction in ...

REVISION: The Impact of COVID-19 on Supply Chain Credit Risk
Date Posted:Mon, 15 Nov 2021 13:27:48 -0600
Global supply chains expose firms to multi-regional risks while also providing a buffer against local shocks. The recent COVID-19 pandemic and its differential impact on different regions in the world provide an opportunity to explore these effects. We examine multi-regional supply chain risk by focusing on credit risk as measured by CDS spreads and US-China supply chain networks. We find that local risks propagate through global supply chains to other regions. CDS spreads for firms with Chinese supply chain partners increase by 8-9 percent due to supply chain disruptions during the pandemic-related economic shutdown period in China, and the spreads decrease by 12-20 percent when the supply chain activities resume during the economic re-opening period. The household demand channel plays an important role in this risk propagation. We find that supply chain activity resumption is insufficient to decrease credit risk in sectors that cater to local households when the local economy ...

Aggregating Distributed Energy Resources: Efficiency and Market Power
Date Posted:Tue, 28 Sep 2021 06:37:04 -0500
The rapid expansion of distributed energy resources (DERs) is one of the most significant changes to electricity systems around the world. Examples of DERs include solar panels, small natural gas-fueled generators, combined heat and power plants, etc. Due to the small supply capacities of these DERs, it is impractical for them to participate directly in the wholesale electricity market. We study in this paper two efficient aggregation models. In the first aggregation model, a profit-maximizing aggregator procures electricity from DERs, and sells them in the wholesale market. We show that this model preserves full market efficiency, i.e., the social welfare achieved by the aggregation model is the same as that when DERs participate directly in the wholesale market. In the second aggregation model, a uniform two-part pricing policy is applied to DER owners, while the aggregator becomes fully regulated but is guaranteed positive profit. It is shown that this second model again achieves full market efficiency. Furthermore, we show that DER aggregation can also leads to a reduction on the market power of conventional generators. All arguments are supplemented with illustrative examples.

George Bernard Dantzig: The Pioneer of Linear Optimization
Date Posted:Thu, 02 Sep 2021 20:11:25 -0500
George Dantzig introduced the world to the power of optimization, creating trillions of dollars of value and saving countless years of life across the globe. In this laudation, John Birge describes the fascinating life and incredible accomplishments of a scholar whose footprints led the way to almost everything the global economy produces.

New: George Bernard Dantzig: The Pioneer of Linear Optimization
Date Posted:Thu, 02 Sep 2021 11:16:38 -0500
George Dantzig introduced the world to the power of optimization, creating trillions of dollars of value and saving countless years of life across the globe. In this laudation, John Birge describes the fascinating life and incredible accomplishments of a scholar whose footprints led the way to almost everything the global economy produces.

REVISION: Credit Shock Propagation Along Supply Chains: Evidence from the CDS Market
Date Posted:Tue, 20 Jul 2021 11:45:01 -0500
Using a panel of Credit Default Swap (CDS) spreads and supply chain links, we observe that both favorable and unfavorable credit shocks propagate through supply chains in the CDS market. Particularly, the three-day cumulative abnormal CDS spread change (CASC) is 63 basis points for firms whose customers experienced a CDS up-jump event (an adverse credit shock). The value is 74 basis points if their suppliers experienced a CDS up-jump event. The corresponding three-day CASC values are -36 and -38 basis points, respectively, for firms whose customers and suppliers, respectively, experienced an extreme CDS down-jump event (a favorable credit shock). These effects are approximately twice as large for adverse credit shocks originating from natural disasters. Credit shock propagation is absent in inactive supply chains, and is amplified if supply-chain partners are followed by the same analysts. Industry competition and financial linkages between supply chain partners, such as trade credit ...

REVISION: The Impact of COVID-19 on Supply Chain Credit Risk
Date Posted:Sun, 13 Jun 2021 08:54:40 -0500
Global supply chains expose firms to multi-regional risks, but also provide benefits by creating a buffer against local shocks. The COVID-19 pandemic and its differential impact on different parts of the world provide an opportunity for insight into supply chain credit risk, and how operational and structural characteristics of global supply chains affect this risk. In this paper, we examine supply chain credit risk during different phases of the COVID-19 pandemic by focusing on Credit Default Swap (CDS) spreads and US-China supply chain links. CDS spreads reflect both the probability of default and expected loss given default, and are available with daily frequency, which allows the assessment of supply chain partners’ credit risk in a timely manner. We find that CDS spreads for firms with China supply chain partners increase with the economic shutdown in China during the pandemic, and the spreads go down when the economic activity resumed with the re-opening in China. We consider ...

REVISION: Credit Shock Propagation Along Supply Chains: Evidence from the CDS Market
Date Posted:Sun, 13 Jun 2021 08:40:17 -0500
Using a panel of Credit Default Swap (CDS) spreads and supply chain links, we observe that both favorable and unfavorable credit shocks propagate through supply chains in the CDS market. Particularly, the three-day cumulative abnormal CDS spread change (CASC) is 63 basis points for firms whose customers experienced a CDS up-jump event (an adverse credit shock). The value is 74 basis points if their suppliers experienced a CDS up-jump event. The corresponding three-day CASC values are -36 and -38 basis points, respectively, for firms whose customers and suppliers, respectively, experienced an extreme CDS down-jump event (a favorable credit shock). These effects are approximately twice as large for adverse credit shocks originating from natural disasters. Credit shock propagation is absent in inactive supply chains, and is amplified if supply-chain partners are followed by the same analysts. Industry competition and financial linkages between supply chain partners, such as trade credit ...

To Interfere or Not To Interfere: Information Revelation and Price-Setting Incentives in a Multiagent Learning Environment
Date Posted:Fri, 11 Jun 2021 14:19:39 -0500
We consider a platform in which multiple sellers offer their products for sale over a time horizon of T periods. Each seller sets its own price. The platform collects a fraction of the sales revenue and provides price-setting incentives to the sellers to maximize its own revenue. The demand for each seller?s product is a function of all sellers? prices and some customer features. Initially, neither the platform nor the sellers know the demand function, but they can learn about it through sales observations: each seller observes its own sales, whereas the platform observes all sellers? sales as well as the customer feature information. We measure the platform?s performance by comparing its expected revenue with the full-information optimal revenue, and design policies that enable the platform to manage information revelation and price-setting incentives. Perhaps surprisingly, a simple ?do-nothing? policy does not always exhibit poor revenue performance and can perform exceptionally well under certain conditions. With a more conservative policy that reveals information to make price-setting incentives more effective, the platform can always protect itself from large revenue losses caused by demand model uncertainty. We develop a strategic-reveal-and-incentivize policy that combines the benefits of the aforementioned policies and thereby achieves asymptotically optimal revenue performance as T grows large.

REVISION: Dynamic Selling Mechanisms for Product Differentiation and Learning
Date Posted:Thu, 10 Jun 2021 07:07:30 -0500
We consider a firm that designs a vertically differentiated product line for a population of cus- tomers with heterogeneous quality sensitivities. The firm faces an uncertainty about the cost of quality, and we formulate this uncertainty as a belief distribution on a set of cost models. Over a time horizon of T periods, the firm can dynamically adjust its menu and make noisy observations on the underlying cost model through customers’ purchasing decisions. We characterize how optimal product differentiation depends on the “informativeness” of quality choices, formally measured by a contrast-to-noise ratio defined on the firm’s feasible quality set. Based on this, we design a minimum quality standard (MQS) policy that mimics the salient features of the optimal product differentiation policy and prove that the MQS policy is near-optimal. We also prove that, if there exists a certain continuum of informative quality choices, then even a myopic policy that makes no attempt to learn ...

REVISION: Disruption and Rerouting in Supply Chain Networks
Date Posted:Tue, 01 Jun 2021 11:11:25 -0500
We study systemic risk in a supply chain network where firms are connected through purchase orders. Firms can be hit by cost or demand shocks, possibly leading to defaults. These shocks propagate through the supply chain network via input-output linkages between buyers and suppliers. Firms endogenously take contingency plans to mitigate the impact generated from disruptions. They reroute undelivered orders to alternative buyers and switch excess demand to different suppliers. We show that, as long as firms have large initial equity buffers, network fragility is low if both buyer and supplier diversification is low. We argue that horizontal mergers may lead to a more fragile network if firms have small initial equity buffers. We find that a single sourcing strategy is beneficial for a firm only if the default probability of the firm's supplier is low. Otherwise, a multiple sourcing strategy is ex-post more cost effective for a firm.

Modeling Investment Behavior and Risk Propagation in Financial Networks
Date Posted:Tue, 18 May 2021 21:38:44 -0500
Connections among institutions in the global financial network create the potential for risk to propagate and for failures to cascade as successive institutions fail. As conditions, such as capital requirements change, institutions may modify their behavior in ways that can fundamentally change the relationships among institutions and lead to substantially different failure dynamics. Increasing capital requirements can, for example, paradoxically increase the potential for failures to propagate by altering the intensity of relationships and risk exposures. Predicting such outcomes and directing policies to reduce overall systemic risk requires modeling of institutional responses to environmental conditions. This paper discusses an approach based on inverse optimization of relationship decisions subject to capital constraints. A model of cascading failures and data from national debt cross-holdings illustrate the approach and demonstrate how changing capital requirements may lead to distinct differences in the sequences and extent of failures.

New: Modeling Investment Behavior and Risk Propagation in Financial Networks
Date Posted:Tue, 18 May 2021 12:42:27 -0500
Connections among institutions in the global financial network create the potential for risk to propagate and for failures to cascade as successive institutions fail. As conditions, such as capital requirements change, institutions may modify their behavior in ways that can fundamentally change the relationships among institutions and lead to substantially different failure dynamics. Increasing capital requirements can, for example, paradoxically increase the potential for failures to propagate by altering the intensity of relationships and risk exposures. Predicting such outcomes and directing policies to reduce overall systemic risk requires modeling of institutional responses to environmental conditions. This paper discusses an approach based on inverse optimization of relationship decisions subject to capital constraints. A model of cascading failures and data from national debt cross-holdings illustrate the approach and demonstrate how changing capital requirements may lead ...

REVISION: Spatial Price Integration in Commodity Markets with Capacitated Transportation Networks
Date Posted:Mon, 17 May 2021 09:26:25 -0500
Spatial price integration is extensively studied in commodity markets as a means of examining the degree of integration between regions of a geographically diverse market. Many commodity markets that are commonly studied are supported by a well-defined transportation network, such as the network of pipelines in oil and gas markets. In this paper, we analyze the relationship between spatial price integration, i.e., the distribution of prices across geographically distinct locations in the market, and the features of the underlying transportation network. We characterize this relationship and show that price integration is strongly influenced by the characteristics of the transportation network, especially when there are capacity constraints on links in the network. Our results are summarized using a price decomposition which explicitly isolates the influences of market forces (supply and demand), transportation costs and capacity constraints among a set of equilibrium prices. We use ...

REVISION: Enhancing Regulatory Decision-Making for Postmarket Drug Safety
Date Posted:Thu, 11 Mar 2021 14:53:45 -0600
The U.S. Food and Drug Administration (FDA) regulates the approval and safe public use of pharmaceutical products in the United States. The FDA uses postmarket surveillance systems to monitor drugs already on the market; a drug found to be associated with an increased risk of adverse events (ADEs) is subject to a recall or a warning. A flawed postmarket decision-making process can have unintended consequences for patients; creates uncertainty among providers and affect their prescribing practices; and subjects the FDA to unfavorable public scrutiny. The FDA’s current pharmacovigilance process suffers from several shortcomings (for example, a high under-reporting rate), often resulting in incorrect or untimely decisions. Thus, there is a need for robust, data-driven approaches to support and enhance regulatory decision-making in the context of postmarket pharmacovigilance. We propose such an approach. Our empirical approach has several appealing features - it employs large, reliable, ...

REVISION: Foundations and Trends at the Interface of Finance, Operations, and Risk Management
Date Posted:Tue, 23 Feb 2021 05:27:53 -0600
In this work we define the characteristics of the interface of finance, operations, and risk management (iFORM) research and provide examples of iFORM research questions. We illustrate why this is an interesting area and discuss where the two disciplines overlap in a meaningful way. Our goal is to lower the entry cost for new researchers by providing primers on (1) key finance results and papers that OM researchers endeavoring to enter into this field must know; (2) key OM results and papers that finance researchers endeavoring to enter into this field must know. Furthermore, we offer our perspective on resources to help readers to accelerate their iFORM research and on how to write, publish, and referee iFORM papers.

Interface of Operations and Finance: A Tutorial
Date Posted:Thu, 18 Feb 2021 15:38:48 -0600
We present two tutorials: (1) a finance tutorial for OM researchers and (2) an OM tutorial for finance researchers. We complement the textbook treatment of important ideas from one discipline with examples of applications to the other discipline. Our goal is to lower the entry cost for new researchers interested in problems at the interface of the two disciplines.

REVISION: Controlling Epidemic Spread: Reducing Economic Losses with Targeted Closures
Date Posted:Mon, 30 Nov 2020 03:54:26 -0600
Data on population movements can be helpful in designing targeted policy responses to curb epidemic spread. We study a spatial epidemic model, which explicitly accounts for population movements, and propose an optimization framework for obtaining targeted policies that restrict economic activity in different neighborhoods of a city at different levels. We focus on COVID-19 and calibrate our model using the mobile phone data that capture individuals’ movements within New York City (NYC). We show that appropriate targeting achieves a reduction in infections in all neighborhoods while resuming 23.1%–42.4% of the baseline non-teleworkable employment in NYC. By contrast, uniform (city-wide) restriction policies that achieve the same policy goal permit 3.92 to 6.25 times less non-teleworkable employment. Our targeting framework gives policy makers an approach for curbing the spread of epidemics while limiting unemployment.

REVISION: The Impact of COVID-19 on Supply Chain Credit Risk
Date Posted:Fri, 13 Nov 2020 02:54:29 -0600
We examine how supply chain activity reflects into credit risk during different phases of the COVID-19 pandemic by focusing on CDS spreads and US-China supply chain links. We find considerable effects on credit risk propagation. CDS spreads for firms with China supply chain partners increase with supply chain disruptions during the economic shutdown period of the pandemic, and the spreads go down when the economic activity resumes with re-opening in China. The household demand channel is an important driver of this supply chain credit risk behavior. Supply chain activity resumption is not sufficient to decrease credit risk in sectors that cater to households when the local economy suffers from dampened household spending due to economic shutdowns. Having a more global customer base, on the other hand, mitigates the local household demand shock effects. While firm leverage and supply chain duration magnify supply chain driven credit risk during the pandemic, cash holdings, growth ...

Foundations and Trends at the Interface of Finance, Operations, and Risk Management
Date Posted:Wed, 04 Nov 2020 23:01:30 -0600
In this work we define the characteristics of the interface of finance, operations, and risk management (iFORM) research and provide examples of iFORM research questions. We illustrate why this is an interesting area and discuss where the two disciplines overlap in a meaningful way. Our goal is to lower the entry cost for new researchers by providing primers on (1) key finance results and papers that OM researchers endeavoring to enter into this field must know; (2) key OM results and papers that finance researchers endeavoring to enter into this field must know. Furthermore, we offer our perspective on resources to help readers to accelerate their iFORM research and on how to write, publish, and referee iFORM papers.

REVISION: Foundations and Trends at the Interface of Finance, Operations, and Risk Management
Date Posted:Wed, 04 Nov 2020 13:03:34 -0600
In this work we define the characteristics of the interface of finance, operations, and risk management (iFORM) research and provide examples of iFORM research questions. We illustrate why this is an interesting area and discuss where the two disciplines overlap in a meaningful way. Our goal is to lower the entry cost for new researchers by providing primers on (1) key finance results and papers that OM researchers endeavoring to enter into this field must know; (2) key OM results and papers that finance researchers endeavoring to enter into this field must know. Furthermore, we offer our perspective on resources to help readers to accelerate their iFORM research and on how to write, publish, and referee iFORM papers.

The Hidden World of Trade Credit: The Flexibility Role of Late Payments
Date Posted:Mon, 12 Oct 2020 19:40:45 -0500
Trade credit research mainly focuses on the suppliers' provision of trade credit, leaving downstream firms' payment behavior less explored. This study offers a fresh perspective within this area in examining trade credit late payment behavior among downstream customers. By leveraging the unique trade credit payment data from Dun & Bradstreet (D&B), our analysis reveals that late payment behavior is positively correlated with a firm's downstream cost-shifting incentives. We also demonstrate that firms strategically determine the duration of their trade credit payment delays. Moreover, we provide evidence that suppliers rarely take legal actions against prevalent short-term late payments. Finally, we show that more late payment are associated with higher inventory turnovers but lower profitability. Our findings have important implications for both managers and regulators: Firms can utilize trade credit's payment flexibility for working capital management, but should avoid exploiting suppliers with excessive late payments. Regulators should not over-regulate short-term overdue payments as they may reflect behavior that enhances supply chain coordination, as only long-term delays may damage relationships and harm suppliers. In sum, our findings provide fresh insights into the role of flexibility in trade credit, particularly in terms of extended payment, which provides valuable information for future research and business practice.

REVISION: Trade Credit Late Payment and Industry Structure
Date Posted:Mon, 12 Oct 2020 10:43:00 -0500
Problem definition: Trade credit studies pay little attention to firms’ late payment behavior due to the lack of extensive panel data. From the perspective of industry structure, this paper is the first to empirically study firms’ trade credit late payment.

Academic/Practical Relevance: While the trade credit research has focused on credit issuance and terms of suppliers, the information content of trade credit is incomplete without understanding payment behavior of customers.
Methodology: We acquire a unique trade credit payment dataset from Dun & Bradstreet (D&B) Global Trade Plan to study how industry structure shapes the way firms delay payments to their suppliers.

Results: We show that a firm’s late payment behavior is positively associated with market power and downstream cost-shifting, and firms strategically choose to whom and for how long to delay their trade credit payment. We employ an instrumental variable approach to establish causality and ...

Disruption and Rerouting in Supply Chain Networks
Date Posted:Sat, 12 Sep 2020 17:48:39 -0500
We study systemic risk in a supply chain network where firms are connected through purchase orders. Firms can be hit by cost or demand shocks, which can cause defaults. These shocks propagate through the supply chain network via input-output linkages between buyers and suppliers. Firms endogenously take contingency plans to mitigate the impact generated from disruptions. We show that, as long as firms have large initial equity buffers, network fragility is low if both buyer and supplier diversification is low. We find that a single sourcing strategy is beneficial for a firm only if the default probability of the firm's supplier is low. Otherwise, a multiple sourcing strategy is ex-post more cost effective for a firm.

REVISION: Disruption and Rerouting in Supply Chain Networks
Date Posted:Sat, 12 Sep 2020 08:49:40 -0500
We study systemic risk in a supply chain network where firms are connected through purchase orders. Firms can be hit by cost or demand shocks, possibly leading to defaults. These shocks propagate through the supply chain network via input-output linkages between buyers and suppliers. Firms endogenously take contingency plans to mitigate the impact generated from disruptions. They reroute undelivered orders to alternative buyers and switch excess demand to different suppliers. We show that, as long as firms have large initial equity buffers, network fragility is low if both buyer and supplier diversification is low. We argue that vertical mergers reduce network fragility by decreasing contagion across tiers, while horizontal mergers may lead to a more fragile network if a non-systemically important firm fundamentally defaults.

REVISION: The Impact of COVID-19 on Supply Chain Credit Risk
Date Posted:Tue, 01 Sep 2020 04:28:31 -0500
We examine how supply chain activity reflects into credit risk during different phases of the COVID-19 pandemic by focusing on CDS spreads and US-China supply chain links. We find considerable effects on credit risk propagation. CDS spreads for firms with China supply chain partners increase with supply chain disruptions during the economic shutdown period of the pandemic, and the spreads go down when the economic activity resumes with re-opening in China. The household demand channel is an important driver of this supply chain credit risk behavior. Supply chain activity resumption is not sufficient to decrease credit risk in sectors that cater to households when the local economy suffers from dampened household spending due to economic shutdowns. Having a more global customer base, on the other hand, mitigates the local household demand shock effects. While firm leverage and supply chain duration magnify supply chain driven credit risk during the pandemic, cash holdings, growth ...

REVISION: Enhancing Regulatory Decision-Making for Postmarket Drug Safety
Date Posted:Wed, 12 Aug 2020 03:13:29 -0500
The U.S. Food and Drug Administration (FDA) regulates the approval and safe public use of pharmaceutical products in the United States. The FDA uses postmarket surveillance systems to monitor drugs already on the market; a drug found to be associated with an increased risk of adverse events (ADEs) is subject to a recall or a warning. A flawed postmarket decision-making process can have unintended consequences for patients; creates uncertainty among providers and affect their prescribing practices; and subjects the FDA to unfavorable public scrutiny. The FDA’s current pharmacovigilance process suffers from several shortcomings (for example, a high under-reporting rate), often resulting in incorrect or untimely decisions. Thus, there is a need for robust, data-driven approaches to support and enhance regulatory decision-making in the context of postmarket pharmacovigilance. We propose such an approach. Our empirical approach has several appealing features - it employs large, reliable, ...

REVISION: Dynamic Pricing of Fashionable Products with C2C Marketplaces and Strategic Consumers
Date Posted:Tue, 21 Jul 2020 03:16:52 -0500
This paper studies the influence of C2C resale marketplaces on pricing decisions and revenue performance of a capacitated seller selling high-tech or fashion products. We consider a monopolist seller sells fashionable products to consumers over two periods. Consumers who purchased early can resell their used units in online marketplaces later if their realized valuations turn out to be low. Additionally, consumers can strategically choose when (the first period or the second period) and where (from the seller or from the marketplace) to purchase. We characterize strategic consumers' purchasing equilibrium, the equilibrium market-clearing price for the resale marketplace, and the seller's optimal pricing decisions. First, we demonstrate that when the seller is capacitated with limited inventory, the resale marketplace will always benefit the seller. The seller can further strengthen the benefit by designing products with superior quality, a long-lasting valuation, and through ...

REVISION: The Impact of COVID-19 on Supply Chain Credit Risk
Date Posted:Thu, 02 Jul 2020 07:29:59 -0500
We examine how supply chain activity reflects into credit risk during different phases of the COVID-19 by focusing on CDS spreads and US-China supply chain links. We find considerable effects on credit risk. During the economic shutdown of the pandemic, CDS spreads increase with supply chain disruptions and spreads go down when the activity resumes with re-opening of the economy. The household demand channel is an important driver of how supply chains reflect in credit risk. Supply chain activity resumption is not sufficient in sectors that cater to households when the local economy suffers from dampened household demand due to economic shutdowns. Such effects are not observed for sectors that cater more to businesses. While firm leverage, product market competition, and supply chain duration magnify the impact of supply chain activity on credit risk during the pandemic, cash holdings, capital redeployability, growth opportunities, and investment-grade rating moderate such effects.

The Impact of COVID-19 on Supply Chain Credit Risk
Date Posted:Wed, 01 Jul 2020 15:55:53 -0500
While global supply chains provide firms with a buffer against local shocks, they expose firms to multi-regional risks. The COVID-19 pandemic and its differential impact on different regions in the world offer an opportunity to explore these effects. We investigate the multi-regional supply chain risk by focusing on credit risk measured by abnormal CDS spreads and US-China supply chain networks. Our evidence shows that local risks propagate through global supply chains to other regions. Using a matched sample, we find that abnormal CDS spreads for firms with Chinese supply chain partners increase by 12-13 percent relative to the average raw CDS spreads due to supply chain disruptions during the economic shutdown in China, and the abnormal CDS spreads decrease by 9-13 percent relative to the average raw CDS spreads when the supply chain activities resume in China. We also find that having a more global customer base can mitigate the effects of local household demand shocks. Lastly, we discover that firm size, supply chain network centrality, cash holdings, inventory, strong credit ratings, capital redeployability, and the number of segments increase resilience to global supply chain shocks, while financial leverage, operational leverage, and market competition weaken supply chain resilience.

REVISION: The Impact of COVID-19 on Supply Chain Credit Risk
Date Posted:Wed, 01 Jul 2020 06:56:43 -0500
We examine how supply chain activity reflects into credit risk during different phases of the COVID-19 by focusing on CDS spreads and US-China supply chain links. We find considerable effects on credit risk. During the economic shutdown of the pandemic, CDS spreads increase with supply chain disruptions and spreads go down when the activity resumes with re-opening of the economy. The household demand channel is an important driver of how supply chains reflect in credit risk. Supply chain activity resumption is not sufficient in sectors that cater to households when the local economy suffers from dampened household demand due to economic shutdowns. Such effects are not observed for sectors that cater more to businesses. While firm leverage, product market competition, and supply chain duration magnify the impact of supply chain activity on credit risk during the pandemic, cash holdings, capital redeployability, growth opportunities, and investment-grade rating moderate such effects.

REVISION: Controlling Epidemic Spread: Reducing Economic Losses with Targeted Closures
Date Posted:Wed, 20 May 2020 02:33:42 -0500
We propose a spatial epidemic spread model to study the COVID-19 epidemic. In our model, a city consists of multiple neighborhoods, each of which has five disease compartments (susceptible/exposed/infected clinical/infected subclinical/recovered). Due to the movement of individuals across neighborhoods (e.g., commuting to work), the infections in one neighborhood can trigger infections in others. We consider the problem of a planner who reduces the economic activity in a targeted way to curb the spread of the epidemic. We focus both on the regime with a small number of infections and the regime with a large number of infections, and provide a framework for obtaining the policies that induce the lowest economic costs.

We use the available data on individuals’ movements, level of economic activity in different neighborhoods, and the state of the epidemic to apply our framework to the control of the epidemic in NYC. Our results indicate that targeted closures can achieve the same ...

REVISION: Dynamic Selling Mechanisms for Product Differentiation and Learning
Date Posted:Fri, 15 May 2020 04:24:18 -0500
We consider a firm that designs a vertically differentiated product line for a population of cus- tomers with heterogeneous quality sensitivities. The firm faces an uncertainty about the cost of quality, and we formulate this uncertainty as a belief distribution on a set of cost models. Over a time horizon of T periods, the firm can dynamically adjust its menu and make noisy observations on the underlying cost model through customers’ purchasing decisions. We characterize how optimal product differentiation depends on the “informativeness” of quality choices, formally measured by a contrast-to-noise ratio defined on the firm’s feasible quality set. Based on this, we design a minimum quality standard (MQS) policy that mimics the salient features of the optimal product differentiation policy and prove that the MQS policy is near-optimal. We also prove that, if there exists a certain continuum of informative quality choices, then even a myopic policy that makes no attempt to learn ...

Dynamic Learning in Strategic Pricing Games
Date Posted:Tue, 12 May 2020 20:55:48 -0500
In monopoly pricing situations, firms should optimally vary prices to learn demand. The variation must be sufficiently high to ensure complete learning. In competitive situations, however, varying prices provides information to competitors and may reduce the value of learning. Such situations may arise in the pricing of new products such as pharmaceuticals and digital goods. This paper shows that firms in competition can learn efficiently in certain equilibrium actions which involve adding noise to myopic estimation and best-response strategies. The paper then discusses how this may not be the case when actions reveal information quickly to competitors. The paper provides a setting where this effect can be strong enough to stop learning so that firms optimally reduce any variation in prices and choose not to learn demand. The result can be that the selling firms achieve a collaborative outcome instead of a competitive equilibrium. The result has implications for policies that restrict price changes or require disclosures.

New: Dynamic Learning in Strategic Pricing Games
Date Posted:Tue, 12 May 2020 11:56:07 -0500
In monopoly pricing situations, firms should optimally vary prices to learn demand. The variation must be sufficiently high to ensure complete learning. In competitive situations, however, varying prices provides information to competitors and may reduce the value of learning. Such situations may arise in the pricing of new products such as pharmaceuticals and digital goods. This paper shows that firms in competition can learn efficiently in certain equilibrium actions which involve adding noise to myopic estimation and best-response strategies. The paper then discusses how this may not be the case when actions reveal information quickly to competitors. The paper provides a setting where this effect can be strong enough to stop learning so that firms optimally reduce any variation in prices and choose not to learn demand. The result can be that the selling firms achieve a collaborative outcome instead of a competitive equilibrium. The result has implications for policies that ...

REVISION: Controlling Epidemic Spread: Reducing Economic Losses with Targeted Closures
Date Posted:Tue, 12 May 2020 04:00:08 -0500
We propose a spatial epidemic spread model to study the COVID-19 epidemic. In our model, a city consists of multiple neighborhoods, each of which has five disease compartments (susceptible/exposed/infected clinical/infected subclinical/recovered). Due to the movement of individuals across neighborhoods (e.g., commuting to work), the infections in one neighborhood can trigger infections in others. We consider the problem of a planner who reduces the economic activity in a targeted way to curb the spread of the epidemic. We focus both on the regime with a small number of infections and the regime with a large number of infections, and provide a framework for obtaining the policies that induce the lowest economic costs.

We use the available data on individuals’ movements, level of economic activity in different neighborhoods, and the state of the epidemic to apply our framework to the control of the epidemic in NYC. Our results indicate that targeted closures can achieve the same ...

Controlling Epidemic Spread: Reducing Economic Losses with Targeted Closures
Date Posted:Wed, 06 May 2020 20:34:50 -0500
Data on population movements can be helpful in designing targeted policy responses to curb epidemic spread. However, it is not clear how to exactly leverage such data and how valuable they might be for the control of epidemics. To explore these questions we study a spatial epidemic model that explicitly accounts for population movements, and propose an optimization framework for obtaining targeted policies that restrict economic activity in different neighborhoods of a city at different levels. We focus on COVID-19 and calibrate our model using the mobile phone data that capture individuals? movements within New York City (NYC). We use these data to illustrate that targeting can allow for substantially higher employment levels than uniform (city-wide) policies when applied to reduce infections across a region of focus. In our NYC example (which focuses on the control of the disease in April 2020), our main model illustrates that appropriate targeting achieves a reduction in infections in all neighborhoods while resuming 23.1%?42.4% of the baseline non- teleworkable employment level. By contrast, uniform restriction policies that achieve the same policy goal permit 3.92 to 6.25 times less non-teleworkable employment. Our optimization framework demonstrates the potential of targeting to limit the economic costs of unemployment while curbing the spread of an epidemic.

REVISION: Controlling Epidemic Spread: Reducing Economic Losses with Targeted Closures
Date Posted:Wed, 06 May 2020 11:34:50 -0500
We propose a spatial epidemic model to study the COVID-19 epidemic. In our model, a city consists of multiple neighborhoods, each of which has five disease compartments (susceptible/exposed/infected clinical/infected subclinical/recovered). Due to the movement of individuals across neighborhoods (e.g., commuting to work), the infections in one neighborhood can trigger infections in others. We consider the problem of a planner who reduces the economic activity in a targeted way to curb the spread of the epidemic. We focus both on the regime with a small number of infections and the regime with a large number of infections, and provide a framework for obtaining the policies that induce the lowest economic costs.

We use the available data on individuals' movements, level of economic activity in different neighborhoods, and the state of the epidemic to apply our framework to the control of the epidemic in NYC. Our results indicate that targeted closures can achieve the same policy ...

REVISION: Dynamic Learning and Market Making in Spread Betting Markets With Informed Bettors
Date Posted:Mon, 20 Apr 2020 10:43:57 -0500
We study the profit maximization problem of a market maker in a spread betting market. In this market, the market maker quotes cutoff lines for the outcome of a certain future event as “prices,” and bettors bet on whether the event outcome exceeds the cutoff lines. Anonymous bettors with heterogeneous strategic behavior and information levels participate in the market. The market maker has limited information on the event outcome distribution, aiming to extract information from the market (i.e., “learning”) while guarding against an informed bettor’s strategic manipulation (i.e., “bluff-proofing”). We show that Bayesian policies that ignore bluffing are typically vulnerable to the informed bettor’s strategic manipulation, resulting in exceedingly large profit losses for the market maker as well as market inefficiency. We develop and analyze a novel family of policies, called inertial policies, that balance the tradeoff between learning and bluff-proofing. We construct a simple ...

Spatial Price Integration in Commodity Markets with Capacitated Transportation Networks
Date Posted:Mon, 23 Mar 2020 14:27:53 -0500
Spatial price integration is extensively studied in commodity markets as a means of examining the degree of integration between regions of a geographically diverse market. Many commodity markets that are commonly studied are supported by a well-defined transportation network, such as the network of pipelines in oil and gas markets. In this paper, we analyze the relationship between spatial price integration, i.e., the distribution of prices across geographically distinct locations in the market, and the features of the underlying transportation network. We characterize this relationship and show that price integration is strongly influenced by the characteristics of the transportation network, especially when there are capacity constraints on links in the network. Our results are summarized using a price decomposition which explicitly isolates the influences of market forces (supply and demand), transportation costs and capacity constraints among a set of equilibrium prices. We use these theoretical insights to develop a unique discrete optimization methodology to capture spatiotemporal price variations indicative of underlying network bottlenecks. We apply the methodology to gasoline prices in the southeastern U.S., where the methodology effectively characterizes the effects of a series of well-documented network disruptions on market prices, providing important implications for operations and supply chain management.

REVISION: Spatial Price Integration in Commodity Markets with Capacitated Transportation Networks
Date Posted:Mon, 23 Mar 2020 05:28:13 -0500
Spatial price integration is extensively studied in commodity markets as a means of examining the degree of integration between regions of a geographically diverse market. Many commodity markets that are commonly studied are supported by a well-defined transportation network, such as the network of pipelines in oil and gas markets. In this paper, we analyze the relationship between spatial price integration, i.e., the distribution of prices across geographically distinct locations in the market, and the features of the underlying transportation network. We characterize this relationship and show that price integration is strongly influenced by the characteristics of the transportation network, especially when there are capacity constraints on links in the network. Our results are summarized using a price decomposition which explicitly isolates the influences of market forces (supply and demand), transportation costs and capacity constraints among a set of equilibrium prices. We use ...

REVISION: Credit Shock Propagation Along Supply Chains: Evidence from the CDS Market
Date Posted:Mon, 02 Mar 2020 09:41:21 -0600
Using a panel of Credit Default Swap (CDS) spreads and supply chain links, we observe that both favorable and unfavorable credit shocks propagate through supply chains in the CDS market. Particularly, the three-day cumulative abnormal CDS spread change (CASC) is 63 basis points for firms whose customer experienced a CDS up-jump event (that is, an adverse credit shock). The value is 74 basis points if a supplier experienced a CDS up-jump event. The corresponding three-day CASC values are -36 and -38 basis points, respectively, for firms whose customer and supplier,respectively, experienced an extreme CDS down-jump event (that is, a favorable credit shock). Such effects do not exist in inactive supply chains. The credit shock propagation is substantially more pronounced if supply-chain partners are followed by the same analysts. Industry competition and financial linkages between supply chain partners, such as trade credit and large sales exposure, amplify the shock propagation along ...

REVISION: Dynamic Pricing of Fashionable Products with C2C Marketplaces and Strategic Consumers
Date Posted:Sun, 19 Jan 2020 06:28:31 -0600
This paper studies the influence of C2C resale marketplaces on pricing decisions and revenue performance of a capacitated seller selling high-tech or fashion products. We consider a monopolist seller sells fashionable products to consumers over two periods. Consumers who purchased early can resell their used units in online marketplaces later if their realized valuations turn out to be low. Additionally, consumers can strategically choose when (the first period or the second period) and where (from the seller or from the marketplace) to purchase. We characterize strategic consumers' purchasing equilibrium, the equilibrium market-clearing price for the resale marketplace, and the seller's optimal pricing decisions. First, we demonstrate that when the seller is capacitated with limited inventory, the resale marketplace will always benefit the seller. The seller can further strengthen the benefit by designing products with superior quality, a long-lasting valuation, and through ...

REVISION: An Approximation Approach for Response Adaptive Clinical Trial Design
Date Posted:Wed, 15 Jan 2020 04:00:50 -0600
Multi-armed bandit (MAB) problems, typically modeled as Markov decision processes (MDPs), exemplify the learning vs. earning tradeoff. An area that has motivated theoretical research in MAB designs is the study of clinical trials, where the application of such designs has the potential to significantly improve patient outcomes. However, for many practical problems of interest, the state space is intractably large, rendering exact approaches to solving MDPs impractical. In particular, settings that require multiple simultaneous allocations lead to an expanded state and action-outcome space, necessitating the use of approximation approaches. We propose a novel approximation approach that combines the strengths of multiple methods: grid-based state discretization, value function approximation methods, and techniques for a computationally efficient implementation. The hallmark of our approach is the accurate approximation of the value function that combines linear interpolation with ...

REVISION: Optimal Commissions and Subscriptions in Networked Markets
Date Posted:Sat, 14 Sep 2019 05:19:35 -0500
We consider a platform that charges commission rates and subscription fees to sellers and buyers for facilitating transactions but does not directly control the transaction prices, which are determined by the traders. Buyers and sellers are divided into types, and we represent the compatibility between different types using a bipartite network. Traders are heterogeneous in terms of their valuations, and different types have possibly different value distributions. Buyers may have additional value for trading with some seller types. The platform chooses commissions-subscriptions to maximize its revenues. Two salient features of most online platforms are that they do not dictate the transaction prices, and use commissions/subscriptions for extracting revenues. We shed light on how these commissions/subscriptions should be set in networked markets.

Using tools from convex optimization and combinatorics, we obtain tractable methods for computing the optimal ...

REVISION: Markdown Policies for Demand Learning with Forward-looking Customers
Date Posted:Tue, 06 Aug 2019 16:40:05 -0500
We consider the markdown pricing problem of a firm that sells a product to a mixture of myopic and forward-looking customers. The firm faces an uncertainty about the customers' forward-looking behavior, arrival pattern, and valuations for the product, which we collectively refer to as the demand model. Over a multiperiod sales season, the firm sequentially marks down the product's price and makes demand observations to learn the underlying demand model. Because forward-looking customers create an intertemporal dependency, we identify that the keys to achieving good profit performance are: (i) judiciously accumulating information on the demand model, (ii) preserving the market size in early sales periods, and (iii) limiting the impact of the firm's learning on the forward-looking customers. Based on these, we construct and analyze markdown policies that exhibit near-optimal performance under a wide variety of forward-looking customer behaviors. Moreover, contrary to common intuition, ...

REVISION: An Approximation Approach for Response Adaptive Clinical Trial Design
Date Posted:Fri, 14 Jun 2019 17:49:16 -0500
Multi-armed bandit (MAB) problems, typically modeled as Markov decision processes (MDPs), exemplify the learning vs. earning tradeoff. An area that has motivated theoretical research in MAB designs is the study of clinical trials, where the application of such designs has the potential to significantly improve patient outcomes. However, for many practical problems of interest, the state space is intractably large, rendering exact approaches to solving MDPs impractical. In particular, settings that require multiple simultaneous allocations lead to an expanded state and action-outcome space, necessitating the use of approximation approaches. We propose a novel approximation approach that combines the strengths of multiple methods: grid-based state discretization, value function approximation methods, and techniques for a computationally efficient implementation. The hallmark of our approach is the accurate approximation of the value function that combines linear interpolation with ...

REVISION: Optimal Commissions and Subscriptions in Networked Markets
Date Posted:Mon, 06 May 2019 18:36:28 -0500
We consider a platform that charges commission rates and subscription fees to sellers and buyers for facilitating transactions but does not directly control the transaction prices, which are determined by the traders. Buyers and sellers are divided into types, and we represent the compatibility between different types using a bipartite network. Traders are heterogeneous in terms of their valuations, and different types have possibly different value distributions. Buyers may have additional value for trading with some seller types. The platform chooses commissions-subscriptions to maximize its revenues. Two salient features of most online platforms are that they do not dictate the transaction prices, and use commissions/subscriptions for extracting revenues. We shed light on how these commissions/subscriptions should be set in networked markets.

Using tools from convex optimization and combinatorics, we obtain tractable methods for computing the optimal ...

REVISION: Dynamic Pricing of Fashionable Products with C2C Marketplaces and Strategic Consumers
Date Posted:Mon, 01 Apr 2019 07:18:41 -0500
This paper studies the influence of C2C resale marketplaces on pricing decisions and revenue performance of a capacitated seller selling high-tech or fashion products. We consider a monopolist seller sells fashionable products to consumers over two periods. Consumers who purchased early can resell their used units in online marketplaces later if their realized valuations turn out to be low. Additionally, consumers can strategically choose when (the first period or the second period) and where (from the seller or from the marketplace) to purchase. We characterize strategic consumers' purchasing equilibrium, the equilibrium market-clearing price for the resale marketplace, and the seller's optimal pricing decisions. First, we demonstrate that when the seller is capacitated with limited inventory, the resale marketplace will always benefit the seller. The seller can further strengthen the benefit by designing products with superior quality, a long-lasting valuation, and through ...

REVISION: An Approximation Approach for Response Adaptive Clinical Trial Design
Date Posted:Wed, 09 Jan 2019 06:12:49 -0600
Multi-armed bandit (MAB) problems, typically modeled as Markov decision processes (MDPs), exemplify the exploration vs. exploitation tradeoff. An area that has motivated theoretical research in MAB designs is the study of clinical trials, where the application of such designs has the potential to significantly improve patient outcomes. However, for many practical problems of interest, the state space is intractably large, rendering exact approaches to solving MDPs impractical. In particular, settings with latency in observing outcomes that require multiple simultaneous allocations lead to an expanded state and action-outcome space, necessitating the use of approximation approaches. We propose a novel approximation approach that combines the strengths of multiple methods: grid-based state discretization, value function approximation methods, and techniques for a computationally efficient implementation. The hallmark of our approach lies in the accurate approximation of the value ...

REVISION: Dynamic Pricing of Fashionable Products with C2C Marketplaces and Strategic Consumers
Date Posted:Tue, 18 Dec 2018 19:05:37 -0600
Problem Definition: This paper studies how online C2C resale marketplaces influence pricing decisions and revenue performance of a capacitated seller selling high-tech or fashion products, and how such influences can be strengthened or weakened.

Academic/Practical Relevance: The resale marketplace has flourished with the emerging digital platforms. With the existence of strategic consumers behavior, how the resale marketplace impacts the capacitated seller's pricing and how this impact is influenced by the product design and market composition have not been explored in the prior research.

Methodology: In a dynamic setting, we established strategic consumers' purchasing equilibrium, derived analytical results for the seller's optimal pricing decisions, and identified conditions under which the resale marketplace benefits the seller. Numerical studies are used to quantify the impact of the marketplace.

Results: We analytically characterize strategic ...

Markdown Policies for Demand Learning with Forward-looking Customers
Date Posted:Sat, 15 Dec 2018 19:26:48 -0600
We consider the markdown pricing problem of a firm that sells a product to a mixture of myopic and forward-looking customers. The firm faces an uncertainty about the customers' forward-looking behavior, arrival pattern, and valuations for the product, which we collectively refer to as the demand model. Over a multiperiod selling season, the firm sequentially marks down the product's price and makes demand observations to learn about the underlying demand model. Because forward-looking customers create an intertemporal dependency, we identify that the keys to achieving good profit performance are: (i) judiciously accumulating information on the demand model, and (ii) preserving the market size in early sales periods. Based on these, we construct and analyze markdown policies that exhibit near-optimal performance under a wide variety of forward-looking customer behaviors.

REVISION: Markdown Policies for Demand Learning and Strategic Customer Behavior
Date Posted:Sat, 15 Dec 2018 09:28:04 -0600
We consider the markdown pricing problem of a firm that sells a product to a mixture of myopic and forward-looking customers. The firm faces an uncertainty about the customers' forward-looking behavior, arrival pattern, and valuations for the product, which we collectively refer to as the demand model. Over a multiperiod sales season, the firm sequentially marks down the product’s price and makes demand observations to learn the underlying demand model. Since the customers' forward-looking behavior creates an intertemporal dependency, we identify that the keys to achieving good profit performance are: (i) judiciously accumulating information on the demand model, (ii) preserving the market size in early sales periods, and (iii) limiting the impact of the firm’s learning on the customers' forward-looking behavior. Contrary to common intuition, we show that strategic customer behavior can improve the performance of a learning policy: if the customers are forward-looking, the firm’s ...

Dynamic Pricing of Fashionable Products with C2C Marketplaces and Strategic Consumers
Date Posted:Sat, 08 Dec 2018 22:29:34 -0600
This paper studies the influence of C2C resale marketplaces on pricing decisions and revenue performance of a capacitated seller selling high-tech or fashion products. We consider a monopolist seller sells fashionable products to consumers over two periods. Consumers who purchased early can resell their used units in online marketplaces later if their realized valuations turn out to be low. Additionally, consumers can strategically choose when (the first period or the second period) and where (from the seller or from the marketplace) to purchase. We characterize strategic consumers' purchasing equilibrium, the equilibrium market-clearing price for the resale marketplace, and the seller's optimal pricing decisions. First, we demonstrate that when the seller is capacitated with limited inventory, the resale marketplace will always benefit the seller. The seller can further strengthen the benefit by designing products with superior quality, a long-lasting valuation, and through cultivating early markets. Second, we show that with high initial inventory, the seller benefits from the marketplace only when the first-period market size is comparatively smaller than that of the second period. Under such a scenario, the seller is better off designing fashion-oriented products with acceptable quality and attracting more non-tech-savvy consumers who typically arrive and purchase late. Finally, we show that a Buy-Back Program, through which consumers can sell their used units back to the

REVISION: Dynamic Pricing of Fashionable Products with C2C Marketplaces and Strategic Consumers
Date Posted:Sat, 08 Dec 2018 12:30:38 -0600
Problem Definition: This paper studies how online C2C resale marketplaces influence pricing decisions and revenue performance of a capacitated seller selling high-tech or fashion products, and how such influences can be strengthened or weakened.

Academic/Practical Relevance: The resale marketplace has flourished with the emerging digital platforms. With the existence of strategic consumers behavior, how the resale marketplace impacts the capacitated seller's pricing and how this impact is influenced by the product design and market composition have not been explored in the prior research.

Methodology: In a dynamic setting, we established strategic consumers' purchasing equilibrium, derived analytical results for the seller's optimal pricing decisions, and identified conditions under which the resale marketplace benefits the seller. Numerical studies are used to quantify the impact of the marketplace.

Results: We analytically characterize strategic ...

Dynamic Learning and Market Making in Spread Betting Markets With Informed Bettors
Date Posted:Fri, 07 Dec 2018 21:13:06 -0600
We study the profit maximization problem of a market maker in a spread betting market. In this market, the market maker quotes cutoff lines for the outcome of a certain future event as ?prices,? and bettors bet on whether the event outcome exceeds the cutoff lines. Anonymous bettors with heterogeneous strategic behavior and information levels participate in the market. The market maker has limited information on the event outcome distribution, aiming to extract information from the market (i.e., ?learning?) while guarding against an informed bettor?s strategic manipulation (i.e., ?bluff-proofing?). We show that Bayesian policies that ignore bluffing are typically vulnerable to the informed bettor?s strategic manipulation, resulting in exceedingly large profit losses for the market maker as well as market inefficiency. We develop and analyze a novel family of policies, called inertial policies, that balance the tradeoff between learning and bluff-proofing. We construct a simple instance of this family which (i) enables the market maker to achieve a near-optimal profit loss and (ii) eventually yields market efficiency.

REVISION: Dynamic Learning and Market Making in Spread Betting Markets With Informed Bettors
Date Posted:Fri, 07 Dec 2018 11:14:08 -0600
We study the profit maximization problem of a market maker in a spread betting market for a future event. Anonymous bettors with heterogeneous strategic behavior and information levels participate in the market. The market maker is initially uninformed of the event outcome distribution, aiming to extract information from the market (i.e., "learning") while guarding against an informed bettor's strategic manipulation via bets (i.e., "bluff-proofing"). We show that Bayesian policies that ignore bluffing are typically vulnerable to the informed bettor's manipulation. We propose a novel family of policies, called inertial policies, that balance the tradeoff between learning and bluff-proofing, achieving an expected regret up to a logarithmic factor of the number of bets.

REVISION: Credit Risk Propagation Along Supply Chains: Evidence from the CDS Market
Date Posted:Mon, 29 Oct 2018 04:42:45 -0500
We find that credit risk propagates through multiple supply chain tiers for both positive and negative credit shocks. Specifically, rating and industry-adjusted CDS spreads change by 44-71 bps for the first tier. Strong propagation persists for 2nd and 3rd tiers for adverse shocks, but attenuates for favorable shocks. Such effects are not observed for inactive supply chain links. Credit risk propagation is magni- fied with longer-term supply-chain relations, trade credit, differentiated products, and leverage, but is moderated with investment grade rating and high inventory. Credit risk propagation is stronger for supply chain partners followed by the same analysts.

Optimal Commissions and Subscriptions in Networked Markets
Date Posted:Fri, 26 Oct 2018 19:55:50 -0500
We consider a platform that charges commission rates and subscription fees to sellers and buyers for facilitating transactions but does not directly control the transaction prices, which are determined by the traders. Buyers and sellers are divided into types, and we represent the compatibility between different types using a bipartite network. Traders are heterogeneous in terms of their valuations, and different types have possibly different value distributions. Buyers may have additional value for trading with some seller types. The platform chooses commissions-subscriptions to maximize its revenues. Two salient features of most online platforms are that they do not dictate the transaction prices, and use commissions/subscriptions for extracting revenues. We shed light on how these commissions/subscriptions should be set in networked markets.

Using tools from convex optimization and combinatorics, we obtain tractable methods for computing the optimal commissions/subscriptions and provide insights on revenues and welfare. We provide a tractable convex optimization formulation to calculate the revenue-maximizing commissions/subscriptions, and establish that, typically, different types should be charged different commissions/subscriptions depending on their network positions. We establish that the latter result holds even when the traders on each side have identical value distributions, and in this setting we provide lower and upper bounds on the platform?s revenues in

REVISION: Optimal Commissions and Subscriptions in Networked Markets
Date Posted:Fri, 26 Oct 2018 10:55:50 -0500
Two salient features of most online platforms are that they do not dictate the transaction prices, and use commissions/subscriptions for extracting revenues. We consider a platform that charges commission rates and subscription fees to sellers and buyers for facilitating transactions, but does not directly control the transaction prices, which are determined by the traders. Buyers and sellers are divided into types, and we represent the compatibility between different types using a bipartite network. Traders are heterogeneous in terms of their valuations, and different types have possibly different value distributions. The platform chooses commissions-subscriptions to maximize its revenues. We provide a convex optimization formulation to calculate the revenue-maximizing commissions/subscriptions, and establish that, typically, different types should be charged different commissions/subscriptions depending on their network positions. We establish lower and upper bounds on the ...

REVISION: Optimal Dynamic Product Development and Launch for a Network of Customers
Date Posted:Wed, 17 Oct 2018 14:43:47 -0500
We consider a firm that dynamically chooses its effort to develop a product for a network of customers represented by a connected graph. The technology of the product evolves as a real-valued stochastic process that depends on the firm’s dynamic efforts over time. In addition to dynamically choosing its development effort, the firm chooses when to launch or abandon the product. If the firm launches the product, the firm also chooses a selling price, a promotional price and a target customer to offer promotion. Once the target customer adopts the product, the product diffuses over the customer network based on the topology of the graph and the selling price. The product provides local network benefits to its adopters. The expected local network benefit of adoption is proportional to the number of neighbor customers that have already adopted the product. In a continuous-time setting, we explicitly solve the firm’s jointly-optimal development, launch and post-launch strategies for any ...

REVISION: Dynamic Selling Mechanisms for Product Differentiation and Learning
Date Posted:Sun, 07 Oct 2018 06:23:40 -0500
We consider a firm that designs a vertically differentiated product line for a population of cus- tomers with heterogeneous quality sensitivities. The firm faces an uncertainty about the cost of quality, and we formulate this uncertainty as a belief distribution on a set of cost models. Over a time horizon of T periods, the firm can dynamically adjust its menu and make noisy observations on the underlying cost model through customers’ purchasing decisions. We characterize how optimal product differentiation depends on the “informativeness” of quality choices, formally measured by a contrast-to-noise ratio defined on the firm’s feasible quality set. Based on this, we design a minimum quality standard (MQS) policy that mimics the salient features of the optimal product differentiation policy and prove that the MQS policy is near-optimal. We also prove that, if there exists a certain continuum of informative quality choices, then even a myopic policy that makes no attempt to learn ...

REVISION: Risk Factors That Explain Stock Returns: A Non-Linear Factor Pricing Model
Date Posted:Wed, 05 Sep 2018 02:57:17 -0500
The value of an equity investment can be framed as an embedded call option on a firm’s assets. The embedded call option creates a non-linear relationship between stock returns and underlying risk factors; however, such option value and the impact of this non-linearity are often underestimated or overlooked in most asset pricing studies. In this paper, we use the forward-looking measure of default risk in CDS spreads and an associated quadratic term as a non-linear asset pricing model to explain and predict individual stock returns. Notably, in this model, the intercept a disappears in predictions of future risk-adjusted stock returns. The model also provides an alternative to Fama and French (1993)’s three-factor model in substituting functions of our factors for size and value.

An Approximation Approach for Response Adaptive Clinical Trial Design
Date Posted:Wed, 01 Aug 2018 08:07:13 -0500
Multi-armed bandit (MAB) problems, typically modeled as Markov decision processes (MDPs), exemplify the learning vs. earning tradeoff. An area that has motivated theoretical research in MAB designs is the study of clinical trials, where the application of such designs has the potential to significantly improve patient outcomes. However, for many practical problems of interest, the state space is intractably large, rendering exact approaches to solving MDPs impractical. In particular, settings that require multiple simultaneous allocations lead to an expanded state and action-outcome space, necessitating the use of approximation approaches. We propose a novel approximation approach that combines the strengths of multiple methods: grid-based state discretization, value function approximation methods, and techniques for a computationally efficient implementation. The hallmark of our approach is the accurate approximation of the value function that combines linear interpolation with bounds on interpolated value and the addition of a learning component to the objective function. Computational analysis on relevant datasets shows that our approach outperforms existing heuristics (e.g. greedy and upper confidence bound family of algorithms) as well as a popular Lagrangian-based approximation method, where we find that the average regret improves by up to 58.3%. A retrospective implementation on a recently conducted phase 3 clinical trial shows that our design could have reduced the n

REVISION: An Approximation Approach for Response Adaptive Clinical Trial Design
Date Posted:Tue, 31 Jul 2018 23:07:15 -0500
Multi-armed bandit (MAB) problems, typically modeled as Markov decision processes (MDPs), exemplify the exploration vs. exploitation tradeoff. An area that has motivated theoretical research in MAB designs is the study of clinical trials, where the application of such designs has the potential to significantly improve patient outcomes and reduce drug development costs. However, for many practical problems of interest, the state space is intractably large, rendering exact approaches to solving MDPs impractical. In particular, settings with latency in observing outcomes that require multiple simultaneous randomizations, as in most practical clinical trials, lead to an expanded state and action-outcome space, necessitating the use of approximation approaches. We propose a novel approximation approach that combines the strengths of multiple methods: grid-based state discretization, value function approximation methods, and techniques for a computationally efficient implementation. The ...

REVISION: Optimal Dynamic Product Development and Launch for a Network of Customers
Date Posted:Tue, 31 Jul 2018 10:11:06 -0500
We consider a firm that dynamically chooses its effort to develop a product for a network of customers represented by a connected graph. The technology of the product evolves as a real-valued stochastic process that depends on the firm’s dynamic efforts over time. In addition to dynamically choosing its development effort, the firm chooses when to launch or abandon the product. If the firm launches the product, the firm also chooses a selling price, a promotional price and a target customer to offer promotion. Once the target customer adopts the product, the product diffuses over the customer network based on the topology of the graph and the selling price. The product provides local network benefits to its adopters. The expected local network benefit of adoption is proportional to the number of neighbor customers that have already adopted the product. In a continuous-time setting, we explicitly solve the firm’s jointly-optimal development, launch and post-launch strategies for any ...

REVISION: Credit Risk Propagation Along Supply Chains: Evidence from the CDS Market
Date Posted:Wed, 27 Jun 2018 13:11:03 -0500
We find that credit risk propagates through multiple supply chain tiers for both positive and negative credit shocks. Specifically, we show sizeable rating and industry- adjusted CDS spread changes of 44-71 bps at the first tier of supply chains in response to extreme CDS jumps. The reaction to adverse credit shocks persists for 2nd and 3rd tiers, but attenuates for favorable shocks. The effects of credit shocks on supply chain partners disappear when supply chain links are inactive and are magnified with longer- term supply-chain relations, trade credit, sales contribution, differentiated products, and customer leverage. Risk propagation is moderated when a customer is investment grade or has more inventory. Furthermore, credit risk propagation is considerably stronger for supply chain partners followed by the same analysts.

REVISION: Trade Credit, Risk Sharing, and Inventory Financing Portfolios
Date Posted:Tue, 06 Mar 2018 10:56:21 -0600
As an integrated part of a supply contract, trade credit has intrinsic connections with supply chain coordination and inventory management. Using a model that explicitly captures the interaction of firms' operations decisions, financial constraints, and multiple financing channels (bank loans and trade credit), this paper attempts to better understand the risk-sharing role of trade credit -- that is, how trade credit enhances supply chain efficiency by allowing the retailer to partially share the demand risk with the supplier. Within this role, in equilibrium, trade credit is an indispensable external source for inventory financing, even when the supplier is at a disadvantageous position in managing default relative to a bank. Specifically, the equilibrium trade credit contract is net terms when the retailer's financial status is relatively strong. Accordingly, trade credit is the only external source that the retailer uses to finance inventory. By contrast, if the retailer's cash ...

Credit Shock Propagation Along Supply Chains: Evidence from the CDS Market
Date Posted:Mon, 04 Dec 2017 09:42:05 -0600
Using a panel of Credit Default Swap (CDS) spreads and supply chain links, we observe that both favorable and unfavorable credit shocks propagate through supply chains in the CDS market. Particularly, the three-day cumulative abnormal CDS spread change (CASC) is 63 basis points for firms whose customers experienced a CDS up-jump event (an adverse credit shock). The value is 74 basis points if their suppliers experienced a CDS up-jump event. The corresponding three-day CASC values are ?36 and ?38 basis points, respectively, for firms whose customers and suppliers, respectively, experienced an extreme CDS down-jump event (a favorable credit shock). These effects are approximately twice as large for adverse credit shocks originating from natural disasters. Credit shock propagation is absent in inactive supply chains, and is amplified if supply-chain partners are followed by the same analysts. Industry competition and financial linkages between supply chain partners, such as trade credit and large sales exposure, amplify the shock propagation along supply chains. Strong shock propagation persists through second and third supply-chain tiers for adverse shocks but attenuates for favorable shocks.

REVISION: Credit Risk Propagation Along Supply Chains: Evidence from the CDS Market
Date Posted:Sun, 03 Dec 2017 23:42:06 -0600
We find that credit risk propagates through multiple supply chain tiers for both positive and negative credit shocks. Specifically, we show sizeable rating and industry-adjusted CDS spread changes of 44-71 bps at the first tier, continuing for 2nd and 3rd tier supply chain partners for bad credit shocks but attenuating for good ones. We also find that credit risk contagion disappears with inactive supply chain links. The contagion is magnified with longer-term supply-chain relations, trade credit, sales contribution, differentiated products, and customer leverage, while it is moderated when a customer is investment grade or has more inventory.

Risk Factors That Explain Stock Returns: A Non-Linear Factor Pricing Model
Date Posted:Tue, 31 Oct 2017 10:46:01 -0500
The value of an equity investment can be framed as an embedded call option on a firm?s assets. The embedded call option creates a non-linear relationship between stock returns and underlying risk factors; however, such option value and the impact of this non-linearity are often underestimated or overlooked in most asset pricing studies. In this paper, we use the forward-looking measure of default risk in CDS spreads and an associated quadratic term as a non-linear asset pricing model to explain and predict individual stock returns. Notably, in this model, the intercept ? disappears in predictions of future risk-adjusted stock returns. The model also provides an alternative to Fama and French (1993)?s three-factor model in substituting functions of our factors for size and value.

REVISION: Default Risk Premia and a Non-Linear Asset Pricing Model
Date Posted:Tue, 31 Oct 2017 01:46:01 -0500
The value of an equity investment can be framed as an embedded call option on a firm's assets. The embedded call option creates a non-linear relationship between stock returns and underlying risk factors; however, such option value is often underestimated or overlooked in most assets pricing studies. In addition, losses given default create discontinuities in the value of these options that are differentially determined for firms with distinct characteristics, such as size and value. In this paper, we introduce a non-linear equity pricing model that includes these aspects of potential default consequences and which seems to explain and predict individual stock returns (both on a monthly and daily basis). The model can be used as an alternative to Fama and French (1993)'s three-factor model in substituting functions of default and loss for size and value. More importantly, the intercept disappears when our model is used to predict future risk-adjusted stock returns. Further ...

REVISION: Optimal Dynamic Product Development and Launch for a Network of Customers
Date Posted:Sun, 22 Oct 2017 10:32:30 -0500
We consider a firm that dynamically chooses its effort to develop a product for a network of customers represented by a connected graph. The technology of the product evolves as a real-valued stochastic process that depends on the firm’s dynamic efforts over time. In addition to dynamically choosing its development effort, the firm chooses when to launch or abandon the product. If the firm launches the product, the firm also chooses a selling price, a promotional price and a target customer to offer promotion. Once the target customer adopts the product, the product diffuses over the customer network based on the topology of the graph and the selling price. The product provides local network benefits to its adopters. The expected local network benefit of adoption is proportional to the number of neighbor customers that have already adopted the product. In a continuous-time setting, we explicitly solve the firm’s jointly-optimal development, launch and post-launch strategies for any ...

REVISION: Strategic Commitment to a Production Schedule with Uncertain Supply and Demand: Renewable Energy in Day-Ahead Electricity Markets
Date Posted:Sun, 22 Oct 2017 10:29:16 -0500
We consider a day-ahead electricity market that consists of multiple competing renewable firms (e.g., wind generators) and conventional firms (e.g., coal-fired power plants) in a discrete-time setting. The market is run in every period, and all firms submit their price-contingent production schedules in every day-ahead market. Following the clearance of a day-ahead market, in the next period, each (renewable) firm chooses its production quantity (after observing its available supply). If a firm produces less than its cleared day-ahead commitment, the firm pays an undersupply penalty in proportion to its underproduction. We explicitly characterize firms’ equilibrium strategies by introducing and analyzing a supply function competition model. The purpose of an undersupply penalty is to improve reliability by motivating each firm to commit to quantities it can produce in the following day. We prove that in equilibrium, imposing or increasing a market-based undersupply penalty rate in a ...

REVISION: Dynamic Selling Mechanisms for Product Differentiation and Learning
Date Posted:Fri, 20 Oct 2017 14:58:02 -0500
We consider a firm that designs a vertically differentiated product line for a population of customers with heterogeneous quality sensitivities. The firm faces an uncertainty about the cost of quality, and we formulate this uncertainty as a belief distribution on a set of cost models. Over a time horizon of T periods, the firm can dynamically adjust its menu and make noisy observations on the underlying cost model through customers' purchasing decisions. We characterize how optimal product differentiation depends on the "informativeness" of quality choices, formally measured by a contrast-to-noise ratio defined on the firm’s feasible quality set. We prove that, if there exist informative quality choices, then the optimal product differentiation policy improves the quality offering to accelerate information accumulation and exercises the strongest quality improvement on less quality-sensitive customers. We design a minimum quality standard (MQS) policy that mimics the aforementioned ...

REVISION: Inverse Optimization for the Recovery of Market Structure from Market Outcomes: An Application to the MISO Electricity Market
Date Posted:Thu, 18 May 2017 05:36:34 -0500
We propose an inverse optimization based methodology to determine market structure from commodity and transportation prices. The methods are appropriate for locational marginal price based electricity markets where prices are shadow prices in the centralized optimization used to clear the market. We apply the inverse optimization methodology to outcome data from the Midcontinent ISO electricity market (MISO) and, under noise-free assumptions, recover parameters of transmission and related constraints that are not revealed to market participants but explain the price variation. We demonstrate and evaluate analytical uses of the recovered structure including reconstruction of the pricing mechanism and investigations of locational market power through the transmission constrained residual demand derivative. Prices generated from the reconstructed mechanism are highly correlated to actual MISO prices under a wide variety of market conditions. In a case study, the residual demand ...

REVISION: Risk Sensitive Asset Management and Cascading Defaults
Date Posted:Wed, 22 Feb 2017 10:18:17 -0600
We consider an optimal risk-sensitive portfolio allocation problem accounting for the possibility of cascading defaults. Default events have an impact on the distress state of the surviving stocks in the portfolio. We study the recursive system of non-Lipschitz quasi-linear parabolic HJB-PDEs associated with the value function of the control problem in the different default states of the economy. We show the existence of a classical solution to this system via super-sub solution techniques and give an explicit characterization of the optimal feedback strategy in terms of the value function. We prove a verification theorem establishing the uniqueness of the solution. A numerical analysis indicates that the investor accounts for contagion effects when making investment decisions, reduces his risk exposure as he becomes more sensitive to risk, and that his strategy depends non-monotonically on the aggregate risk level.

REVISION: Trade Credit, Risk Sharing, and Inventory Financing Portfolios
Date Posted:Wed, 08 Feb 2017 08:56:17 -0600
As an integrated part of a supply contract, trade credit has intrinsic connections with supply chain coordination and inventory management. Using a model that explicitly captures the interaction of firms' operations decisions, financial constraints, and multiple financing channels (bank loans and trade credit), this paper attempts to better understand the risk-sharing role of trade credit -- that is, how trade credit enhances supply chain efficiency by allowing the retailer to partially share the demand risk with the supplier. Within this role, in equilibrium, trade credit is an indispensable external source for inventory financing, even when the supplier is at a disadvantageous position in managing default relative to a bank. Specifically, the equilibrium trade credit contract is net terms when the retailer's financial status is relatively strong. Accordingly, trade credit is the only external source that the retailer uses to finance inventory. By contrast, if the retailer's cash ...

REVISION: When Customers Anticipate Liquidation Sales: Managing Operations under Financial Distress
Date Posted:Mon, 16 Jan 2017 01:04:19 -0600
The presence of strategic customers may force an already financially distressed firm into a death spiral: Sensing the firm's financial difficulty, customers may wait strategically for deep discounts in liquidation sales. In turn, such waiting lowers the firm's profitability and increases the firm's bankruptcy risk. Using a two-period model to capture these dynamics, this paper identifies customers' strategic waiting behavior as a source of a firm's cost of financial distress. We also find that customers' anticipation of bankruptcy can be self-fullling: When customers anticipate a high bankruptcy probability, they prefer to delay their purchases, making the firm more likely to go bankrupt than when customers anticipate a low probability of bankruptcy. Such behavior has important operational and financial implications. First, the firm acts more conservatively when either facing more severe financial distress or a large share of strategic customers. As its financial situation ...

Online Appendix to 'When Customers Anticipate Liquidation Sales: Managing Operations Under Financial Distress'
Date Posted:Thu, 12 Jan 2017 13:39:56 -0600
This is the online appendix for When Customers Anticipate Liquidation Sales: Managing Operations under Financial Distress. The full paper is available here: http://ssrn.com/abstract=2652994.

New: Online Appendix to 'When Customers Anticipate Liquidation Sales: Managing Operations Under Financial Distress'
Date Posted:Thu, 12 Jan 2017 03:39:56 -0600
This is the online appendix for When Customers Anticipate Liquidation Sales: Managing Operations under Financial Distress. The full paper is available here: http://ssrn.com/abstract=2652994.

REVISION: When Customers Anticipate Liquidation Sales: Managing Operations under Financial Distress
Date Posted:Wed, 11 Jan 2017 08:19:31 -0600
The presence of strategic customers may force an already financially distressed firm into a death spiral: Sensing the firm's financial difficulty, customers may wait strategically for deep discounts in liquidation sales. In turn, such waiting lowers the firm's profitability and increases the firm's bankruptcy risk. Using a two-period model to capture these dynamics, this paper identifies customers' strategic waiting behavior as a source of a firm's cost of financial distress. We also find that customers' anticipation of bankruptcy can be self-fullling: When customers anticipate a high bankruptcy probability, they prefer to delay their purchases, making the firm more likely to go bankrupt than when customers anticipate a low probability of bankruptcy. Such behavior has important operational and financial implications. First, the firm acts more conservatively when either facing more severe financial distress or a large share of strategic customers. As its financial situation ...

Optimal Dynamic Product Development and Launch for a Network of Customers
Date Posted:Wed, 16 Nov 2016 11:17:42 -0600
We consider a firm that dynamically chooses its effort to develop a product for a network of customers represented by a connected graph. The technology of the product evolves as a real-valued stochastic process that depends on the firm?s dynamic efforts over time. In addition to dynamically choosing its development effort, the firm chooses when to launch or abandon the product. If the firm launches the product, the firm also chooses a selling price, a promotional price and a target customer to offer promotion. Once the target customer adopts the product, the product diffuses over the customer network based on the topology of the graph and the selling price. The product provides local network benefits to its adopters. The expected local network benefit of adoption is proportional to the number of neighbor customers that have already adopted the product. In a continuous-time setting, we explicitly solve the firm?s jointly-optimal development, launch and post-launch strategies for any connected network. We introduce metrics that allow ordering customer networks with respect to the firm?s optimal expected discounted profit, launch technology and consumer surplus. We also analyze various extensions, including multiple target customers, heterogeneity in customer demand, and heterogeneity in benefit distributions.

REVISION: Optimal Dynamic Product Development and Launch For a Network of Customers
Date Posted:Wed, 16 Nov 2016 01:17:43 -0600
We consider a firm that dynamically chooses its effort to develop a product for a network of customers represented by a connected graph. The product’s technological development is governed by a stochastic process dependent on the firm’s efforts. In addition to dynamically choosing its development effort, the firm chooses when to launch or abandon the product. If the firm launches the product, the firm also chooses a selling price, a promotional price and a target customer to offer promotion. Once the target customer adopts the product, the product diffuses over the customer network based on the topology of the graph and the selling price. The product provides local network benefits to its adopters. The expected local network benefit of adoption is proportional to the number of neighbor customers that have already adopted the product. In a continuous-time setting, we explicitly solve the firm’s jointly-optimal development, launch and post-launch strategies for any connected network. ...

REVISION: Risk Sensitive Asset Management and Cascading Defaults
Date Posted:Sun, 23 Oct 2016 12:03:14 -0500
We consider an optimal risk-sensitive portfolio allocation problem accounting for the possibility of cascading defaults. Default events have an impact on the distress state of the surviving stocks in the portfolio. We study the recursive system of non-Lipschitz quasi-linear parabolic HJB-PDEs associated with the value function of the control problem in the different default states of the economy. We show the existence of a classical solution to this system via super-sub solution techniques and give an explicit characterization of the optimal feedback strategy in terms of the value function. We prove a verification theorem establishing the uniqueness of the solution. A numerical analysis indicates that the investor accounts for contagion effects when making investment decisions, reduces his risk exposure as he becomes more sensitive to risk, and that his strategy depends non-monotonically on the aggregate risk level.

REVISION: Trade Credit, Risk Sharing, and Inventory Financing Portfolios
Date Posted:Tue, 20 Sep 2016 13:22:07 -0500
As an integrated part of a supply contract, trade credit has intrinsic connections with supply chain coordination and inventory management. Using a model that explicitly captures the interaction of firms' operations decisions, financial constraints, and multiple external financing channels (bank loans and trade credit), this paper attempts to develop a deeper understanding of the risk-sharing role of trade credit, that is, trade credit enhances supply chain efficiency by allowing the retailer to partially share the demand risk with the supplier. Within this role, in equilibrium, trade credit is an indispensable external source for inventory financing, even when the supplier is at a clearly disadvantageous position in managing default than a bank. Specifically, the equilibrium trade credit contract is net terms when the retailer's financial status is relatively strong. Accordingly, trade credit is the only external source the retailer uses to finance inventory. By contrast, if the ...

REVISION: How Inventory Is (Should Be) Financed: Trade Credit in Supply Chains with Demand Uncertainty and Costs of Financial Distress
Date Posted:Wed, 07 Sep 2016 00:11:50 -0500
A new substantially revised version of this paper under the title of "Trade Credit, Risk Sharing, and Inventory Financing Portfolios" is available for download at: http://ssrn.com/abstract=2746645. As an integrated part of a supply contract, trade credit has intrinsic connections with supply chain contracting and inventory management. Using a model that explicitly captures the interaction of firms’ operations decisions and financial risks, this paper attempts to develop a deeper understanding of trade credit from an operational perspective. Revolving around the question of what role trade credit plays in channel coordination and inventory financing, we demonstrate that with demand uncertainty, trade credit enhances supply chain efficiency by serving as a risk-sharing mechanism. When offering trade credit, the supplier balances its impact on operational profit and costs of financial distress. Facing a trade credit contract, the retailer ...

REVISION: How Inventory Is (Should Be) Financed: Trade Credit in Supply Chains with Demand Uncertainty and Costs of Financial Distress
Date Posted:Sat, 03 Sep 2016 05:03:49 -0500
As an integrated part of a supply contract, trade credit has intrinsic connections with supply chain contracting and inventory management. Using a model that explicitly captures the interaction of firms’ operations decisions and financial risks, this paper attempts to develop a deeper understanding of trade credit from an operational perspective. Revolving around the question of what role trade credit plays in channel coordination and inventory financing, we demonstrate that with demand uncertainty, trade credit enhances supply chain efficiency by serving as a risk-sharing mechanism. When offering trade credit, the supplier balances its impact on operational profit and costs of financial distress. Facing a trade credit contract, the retailer finances inventory using a portfolio of cash, trade credit, and short-term debt, where the structure of this inventory financing portfolio depends on the retailer’s financing need and bargaining power. Additionally, our model suggests that ...

REVISION: Dynamic Selling Mechanisms for Product Differentiation and Learning
Date Posted:Mon, 08 Aug 2016 10:22:14 -0500
We consider a firm that designs a menu of vertically differentiated products for a population of customers with heterogeneous quality sensitivities. The firm faces an uncertainty about production costs, and we formulate this uncertainty as a belief distribution on a set of cost models. Over a time horizon of T periods, the firm can dynamically adjust its menu and make noisy observations on the underlying cost model through customers’ purchasing decisions. We characterize how optimal product differentiation depends on the “informativeness” of quality choices, formally measured by a contrast-to-noise ratio defined on the firm’s feasible quality set. We prove that, if there exist informative quality choices, then the optimal product differentiation policy improves the product quality to accelerate information accumulation and exercises the most extreme experimentation on less quality-sensitive customers. We design a minimum quality standard (MQS) policy that mimics the aforementioned ...

REVISION: When Customers Anticipate Liquidation Sales: Managing Operations under Financial Distress
Date Posted:Wed, 06 Jul 2016 16:58:16 -0500
The presence of strategic customers may force an already financially distressed firm into a death spiral: Sensing the firm's financial difficulty, customers may wait strategically for deep discounts in liquidation sales. In turn, such waiting lowers the firm's profitability and increases the firm's bankruptcy risk. Using a two-period model to capture these dynamics, this paper identifies customers' strategic waiting behavior as a source of a firm's cost of financial distress. We also find that customers' anticipation of bankruptcy can be self-fulfilling: When customers anticipate a high bankruptcy probability, they prefer to delay their purchases, making the firm more likely to go bankrupt than when customers anticipate a low probability of bankruptcy. Such behavior has important operational and financial implications. First, the firm acts more conservatively when either facing more severe financial distress or a large share of strategic customers. As its financial situation ...

Risk Sensitive Asset Management and Cascading Defaults
Date Posted:Wed, 20 Apr 2016 18:17:08 -0500
We consider an optimal risk-sensitive portfolio allocation problem accounting for the possibility of cascading defaults. Default events have an impact on the distress state of the surviving stocks in the portfolio. We study the recursive system of non-Lipschitz quasi-linear parabolic HJB-PDEs associated with the value function of the control problem in the different default states of the economy. We show the existence of a classical solution to this system via super-sub solution techniques and give an explicit characterization of the optimal feedback strategy in terms of the value function. We prove a verification theorem establishing the uniqueness of the solution. A numerical analysis indicates that the investor accounts for contagion effects when making investment decisions, reduces his risk exposure as he becomes more sensitive to risk, and that his strategy depends non-monotonically on the aggregate risk level.

REVISION: Risk Sensitive Asset Management and Cascading Defaults
Date Posted:Wed, 20 Apr 2016 09:17:08 -0500
We consider an optimal risk-sensitive portfolio allocation problem, which explicitly accounts for the interaction between market and credit risk. The investor allocates his wealth on a portfolio of stocks, which can default sequentially and cause distress to the remaining stocks in the portfolio. This leads to a recursive dependence between the non-Lipschitz quasi-linear parabolic HJB-PDEs associated with the default states of the portfolio. We show the existence of a classical solution to this system via super-sub solution techniques and give an explicit characterization of the optimal feedback strategy. We prove a verification theorem establishing the uniqueness of the solution. A numerical analysis indicates that the investor accounts for contagion effects when making investment decisions, reduces his risk exposure and extracted utility as he becomes more sensitive to risk, and that his strategy depends non-monotonically on the aggregate risk level.

Trade Credit, Risk Sharing, and Inventory Financing Portfolios
Date Posted:Mon, 14 Mar 2016 02:47:37 -0500
As an integrated part of a supply contract, trade credit has intrinsic connections with supply chain coordination and inventory management. Using a model that explicitly captures the interaction of firms' operations decisions, financial constraints, and multiple financing channels (bank loans and trade credit), this paper attempts to better understand the risk-sharing role of trade credit -- that is, how trade credit enhances supply chain efficiency by allowing the retailer to partially share the demand risk with the supplier. Within this role, in equilibrium, trade credit is an indispensable external source for inventory financing, even when the supplier is at a disadvantageous position in managing default relative to a bank. Specifically, the equilibrium trade credit contract is net terms when the retailer's financial status is relatively strong. Accordingly, trade credit is the only external source that the retailer uses to finance inventory. By contrast, if the retailer's cash level is low, the supplier offers two-part terms, inducing the retailer to finance inventory with a portfolio of trade credit and bank loans. Further, a deeper early-payment discount is offered when the supplier is relatively less efficient in recovering defaulted trade credit, or the retailer has stronger market power. Trade credit allows the supplier to take advantage of the retailer's financial weakness, yet it may also benefit both parties when the retailer's cash is reasonably high. Finally, u

REVISION: Trade Credit and Inventory Financing Portfolios
Date Posted:Sun, 13 Mar 2016 17:47:37 -0500
As an integrated part of a supply contract, trade credit has intrinsic connections with supply chain coordination and inventory management. Using a model that explicitly captures the interaction of firms' operations decisions, financial constraints, and multiple external financing channels (e.g. bank loans and trade credit), this paper attempts to develop a deeper understanding of trade credit from an operational perspective. We find that, in the presence of demand uncertainty, trade credit enhances supply chain efficiency by serving as a risk-sharing mechanism. When offering trade credit, the supplier balances operational profit against the cost of financial distress. Under the optimal endogenous trade credit contract, the retailer finances inventory according to a pecking order of cash, trade credit, and short-term debt, despite short-term debt's seniority to trade credit in the event of retailer default. Furthermore, we find that the optimal trade credit terms and the resulting ...

Enhancing Regulatory Decision-Making for Postmarket Drug Safety
Date Posted:Sun, 03 Jan 2016 19:46:22 -0600
The U.S. Food and Drug Administration (FDA) regulates the approval and safe public use of pharmaceutical products in the United States. The FDA uses postmarket surveillance systems to monitor drugs already on the market; a drug found to be associated with an increased risk of adverse events (ADEs) is subject to a recall or a warning. A flawed postmarket decision-making process can have unintended consequences for patients; creates uncertainty among providers and affect their prescribing practices; and subjects the FDA to unfavorable public scrutiny. The FDA?s current pharmacovigilance process suffers from several shortcomings (for example, a high under-reporting rate), often resulting in incorrect or untimely decisions. Thus, there is a need for robust, data-driven approaches to support and enhance regulatory decision-making in the context of postmarket pharmacovigilance. We propose such an approach. Our empirical approach has several appealing features - it employs large, reliable, and relevant longitudinal databases; it uses methods firmly established in literature; and it addresses selection bias and endogeneity concerns. Our approach can used to both (i) independently validate existing safety concerns relating to a drug, such as those emanating from existing surveillance systems; and (ii) perform a holistic safety assessments by evaluating a drug's association with other ADEs to which the users may be susceptible. We illustrate the utility of our approach by applying it r

REVISION: Quality Management Using Data Analytics: An Application to Pharmaceutical Regulation
Date Posted:Sun, 03 Jan 2016 09:46:23 -0600
The U.S. government regulates consumer products through its various federal agencies. One such agency is the Food and Drug Administration (FDA) that governs the approval and safe public use of pharmaceutical products. If a drug is found unsafe, the FDA can issue a recall or a black box warning (BBW). This regulatory decision directly affects an operational decision: providers' production technology, affecting their treatment choices. Existing methods for monitoring drug safety are geared towards identifying unknown adverse drug reactions (ADRs) and suffer from several shortcomings such as reliance on limited data. There is a lack of data-driven approaches to evaluate a drug's association with a specific ADR. We propose a data-driven approach that fills this gap. We demonstrate the workings of our approach using a controversial BBW on a diabetes drug that warned prescribers of an increased risk of heart attack and cardiovascular mortality with the drug. Our findings, based on a large ...

REVISION: Response-Adaptive Designs for Clinical Trials: Simultaneous Learning from Multiple Patients
Date Posted:Fri, 01 Jan 2016 03:35:12 -0600
Clinical trials have traditionally followed a fixed design, in which randomization probabilities of patients to various treatments remains fixed throughout the trial and specified in the protocol. The primary goal of this static design is to learn about the efficacy of treatments. Response-adaptive designs, on the other hand, allow clinicians to use the learning about treatment effectiveness to dynamically adjust randomization probabilities of patients to various treatments as the trial progresses. An ideal adaptive design is one where patients are treated as effectively as possible without sacrificing the potential learning or compromising the integrity of the trial. We propose such a design, termed Jointly Adaptive, that uses forward-looking algorithms to fully exploit learning from multiple patients simultaneously. Compared to the best existing implementable adaptive design that employs a multiarmed bandit framework in a setting where multiple patients arrive sequentially, we show ...

REVISION: Supply Chain Network Structure and Firm Returns
Date Posted:Fri, 06 Nov 2015 12:28:33 -0600
The complexity and opacity of the network of interconnections among firms and their supply chains inhibits understanding of the impact of management decisions concerning the boundaries of the firm and the number and intensity of its relationships with suppliers and customers. Using recently available data on the relationships of public US firms, this paper investigates the effects of supply chain connections on firm performance as reflected in stock returns. The paper finds that supply chain structure is closely related to firm returns at two levels, a first-order effect from direct connections and a second-order impact from systemic exposures through the network. For the first order effect, using a cross-sectional data set of the supply chain network and monthly returns, we show that a firm’s return can be explained by its concurrent supplier returns, concurrent customer returns, own momentum, and supplier momentum, whereas customer momentum has little impact. A long-short equity ...

Strategic Commitment to a Production Schedule with Uncertain Supply and Demand: Renewable Energy in Day-Ahead Electricity Markets
Date Posted:Tue, 20 Oct 2015 18:43:36 -0500
We consider a day-ahead electricity market that consists of multiple competing renewable firms (e.g., wind generators) and conventional firms (e.g., coal-fired power plants) in a discrete-time setting. The market is run in every period, and all firms submit their price-contingent production schedules in every day-ahead market. Following the clearance of a day-ahead market, in the next period, each (renewable) firm chooses its production quantity (after observing its available supply). If a firm produces less than its cleared day-ahead commitment, the firm pays an undersupply penalty in proportion to its underproduction. We explicitly characterize firms? equilibrium strategies by introducing and analyzing a supply function competition model. The purpose of an undersupply penalty is to improve reliability by motivating each firm to commit to quantities it can produce in the following day. We prove that in equilibrium, imposing or increasing a market-based undersupply penalty rate in a period can result in a strictly larger renewable energy commitment at all prices in the associated day-ahead market, and can lead to lower equilibrium reliability in all periods with probability 1. We also show in an extension that firms with diversified technologies result in lower equilibrium reliability than single-technology firms in all periods with probability 1.

REVISION: Strategic Commitment to a Production Schedule with Supply and Demand Uncertainty: Renewable Energy in Day-Ahead Electricity Markets
Date Posted:Tue, 20 Oct 2015 09:43:36 -0500
Motivated by the increase in variable renewable energy (such as wind and solar) generation, we study a day-ahead electricity market that consists of finitely many competing firms, each facing supply uncertainty. Each firm commits to a price-contingent production schedule in the day-ahead market, and chooses its actual production quantity after the day-ahead market is cleared and the firm’s available supply is realized. If a firm produces less than its cleared production commitment, the firm pays an undersupply penalty in proportion to its underproduction. We investigate two cases regarding overproduction: each firm either receives a credit or pays a penalty in proportion to its overproduction. Using differential equations theory, we explicitly characterize the firms’ committed production schedules and actual production strategies in equilibrium with and without subsidies. The purpose of an undersupply penalty is to improve system reliability by motivating each firm to commit to a ...

Local Discontinuous Galerkin Method for Portfolio Optimization with Transaction Costs
Date Posted:Thu, 15 Oct 2015 19:31:30 -0500
We study the continuous time portfolio selection problem over a finite horizon for an investor who maximizes the expected utility of terminal wealth and faces transaction costs. The portfolio consists of a risk-free asset, and a risky asset whose price is modeled as a geometric Brownian motion. The problem can be formulated as a stochastic singular control or an impulse control problem depending on whether the transaction costs are of proportional or fixed type. Due to the intractability of the problem, modelers resort to numerical methods to obtain approximations of solutions to the problem. In this paper we propose a stable and high-order computational scheme to solve this problem, which is capable of handling any form of transaction costs. Specifically, we implement the Local Discontinuous Galerkin (LDG) Finite Element Method (FEM) to solve the resulting convection-diffusion Partial Differential Equation (PDE), and obtain error estimates for the LDG method. Moreover, we prove the convergence of the scheme. Our numerical experiments show the order of accuracy of the LDG method and illustrate the optimal policies under various kinds of transaction costs.

New: Local Discontinuous Galerkin Method for Portfolio Optimization with Transaction Costs
Date Posted:Thu, 15 Oct 2015 10:31:31 -0500
We study the continuous time portfolio selection problem over a finite horizon for an investor who maximizes the expected utility of terminal wealth and faces transaction costs. The portfolio consists of a risk-free asset, and a risky asset whose price is modeled as a geometric Brownian motion. The problem can be formulated as a stochastic singular control or an impulse control problem depending on whether the transaction costs are of proportional or fixed type. Due to the intractability of the problem, modelers resort to numerical methods to obtain approximations of solutions to the problem. In this paper we propose a stable and high-order computational scheme to solve this problem, which is capable of handling any form of transaction costs. Specifically, we implement the Local Discontinuous Galerkin (LDG) Finite Element Method (FEM) to solve the resulting convection-diffusion Partial Differential Equation (PDE), and obtain error estimates for the LDG method. Moreover, we prove the ...

REVISION: Operational Strategies in the Presence of Consumer-Driven Bankruptcy Risk
Date Posted:Mon, 21 Sep 2015 03:13:46 -0500
Financially distressed retailers often face a vicious feedback loop between their financial health and customer demand: sensing the firm’s financing distress, customers may strategically wait for deep discounts in liquidation sales. Such waiting in turn lowers the retailer’s profitability and aggravates the retailer’s financial difficulty. Using a parsimonious model, we characterize such strategic waiting behavior and the retailer’s optimal operational response. We find that customers’ anticipation of bankruptcy can become a self-fulfilling prophecy: when consumers predict the probability of bankruptcy to be low, they prefer to purchase early, and when they anticipate a high bankruptcy probability, they prefer to delay their purchases, making the retailer more likely to go bankrupt. In the presence of multiple rational expectations equilibria, the one that hurts the retailer and social welfare the most is more appealing to strategic consumers. Facing such behavior, the retailer ...

REVISION: Operations Strategies in the Presence of Consumer-Driven Bankruptcy Risk
Date Posted:Sat, 19 Sep 2015 09:36:20 -0500
Financially distressed retailers often face a vicious feedback loop between their financial health and customer demand: sensing the firm’s financing distress, customers may strategically wait for deep discounts in liquidation sales. Such waiting in turn lowers the retailer’s profitability and aggravates the retailer’s financial difficulty. Using a parsimonious model, we characterize such strategic waiting behavior and the retailer’s optimal operational response. We find that customers’ anticipation of bankruptcy can become a self-fulfilling prophecy: when consumers predict the probability of bankruptcy to be low, they prefer to purchase early, and when they anticipate a high bankruptcy probability, they prefer to delay their purchases, making the retailer more likely to go bankrupt. In the presence of multiple rational expectations equilibria, the one that hurts the retailer and social welfare the most is more appealing to strategic consumers. Facing such behavior, the retailer ...

When Customers Anticipate Liquidation Sales: Managing Operations under Financial Distress
Date Posted:Sun, 30 Aug 2015 20:38:34 -0500
The presence of strategic customers may force an already financially distressed firm into a death spiral: Sensing the firm's financial difficulty, customers may wait strategically for deep discounts in liquidation sales. In turn, such waiting lowers the firm's profitability and increases the firm's bankruptcy risk. Using a two-period model to capture these dynamics, this paper identifies customers' strategic waiting behavior as a source of a firm's cost of financial distress. We also find that customers' anticipation of bankruptcy can be self-fullling: When customers anticipate a high bankruptcy probability, they prefer to delay their purchases, making the firm more likely to go bankrupt than when customers anticipate a low probability of bankruptcy. Such behavior has important operational and financial implications. First, the firm acts more conservatively when either facing more severe financial distress or a large share of strategic customers. As its financial situation deteriorates, the firm lowers inventory alone when financial distress is mild or only a small share of customers are strategic and lowers both inventory and price in the presence of severe financial distress and a large fraction of strategic customers. Under optimal price and inventory decisions, strategic waiting accounts for a large part of the firm's total cost of financial distress, although a larger proportion of strategic customers may result in a lower probability of bankruptcy. In addition to invent

REVISION: Operations Strategies in the Presence of Consumer-Driven Bankruptcy Risk
Date Posted:Sun, 30 Aug 2015 11:38:35 -0500
Financially distressed retailers often face a vicious feedback loop between their financial health and customer demand: sensing the firm’s financing distress, customers may strategically wait for deep discounts in liquidation sales. Such waiting in turn lowers the retailer’s profitability and aggravates the retailer’s financial difficulty. Using a parsimonious model, we characterize such strategic waiting behavior and the retailer’s optimal operational response. We find that customers’ anticipation of bankruptcy can become a self-fulfilling prophecy: when consumers predict the probability of bankruptcy to be low, they prefer to purchase early, and when they anticipate a high bankruptcy probability, they prefer to delay their purchases, making the retailer more likely to go bankrupt. In the presence of multiple rational expectations equilibria, the one that hurts the retailer and social welfare the most is more appealing to strategic consumers. Facing such behavior, the retailer ...

REVISION: Response-Adaptive Designs for Clinical Trials: Simultaneous Learning from Multiple Patients
Date Posted:Sat, 01 Aug 2015 23:55:04 -0500
Clinical trials have traditionally followed a fixed design, in which randomization probabilities of patients to various treatments remains fixed throughout the trial and specified in the protocol. The primary goal of this static design is to learn about the efficacy of treatments. Response-adaptive designs, on the other hand, allow clinicians to use the learning about treatment effectiveness to dynamically adjust randomization probabilities of patients to various treatments as the trial progresses. An ideal adaptive design is one where patients are treated as effectively as possible without sacrificing the potential learning or compromising the integrity of the trial. We propose such a design, termed Jointly Adaptive, that uses forward-looking algorithms to fully exploit learning from multiple patients simultaneously. Compared to the best existing implementable adaptive design that employs a multiarmed bandit framework in a setting where multiple patients arrive sequentially, we show ...

Inverse Optimization for the Recovery of Market Structure from Market Outcomes: An Application to the MISO Electricity Market
Date Posted:Sun, 31 May 2015 17:44:37 -0500
We propose an inverse optimization based methodology to determine market structure from commodity and transportation prices. The methods are appropriate for locational marginal price based electricity markets where prices are shadow prices in the centralized optimization used to clear the market. We apply the inverse optimization methodology to outcome data from the Midcontinent ISO electricity market (MISO) and, under noise-free assumptions, recover parameters of transmission and related constraints that are not revealed to market participants but explain the price variation. We demonstrate and evaluate analytical uses of the recovered structure including reconstruction of the pricing mechanism and investigations of locational market power through the transmission constrained residual demand derivative. Prices generated from the reconstructed mechanism are highly correlated to actual MISO prices under a wide variety of market conditions. In a case study, the residual demand derivative is shown to be correlated with coefficients of certain transmission constraints.

REVISION: Inverse Optimization for the Recovery of Market Structure from Market Outcomes: An Application to the MISO Electricity Market
Date Posted:Sun, 31 May 2015 08:44:38 -0500
We propose an inverse optimization based methodology to determine market structure from the locational pricing of a commodity. The methodology requires that the market optimally allocates goods and that locational prices correspond to shadow prices of this optimization problem. As a case-in-point, we study locational marginal price based electricity markets where prices are determined using the results of a centralized optimization for clearing the market. We apply the inverse optimization methodology to outcome data from the Midcontinent ISO electricity market and uncover transmission constraints that are not revealed to market participants but explain the price variation. We demonstrate analytical uses of the recovered structure including reconstruction of the pricing mechanism and identifying locational residual demand derivatives which have managerial applications not limited to optimization of bidding strategies and estimation of the value of capacity investments. To broaden the ...

REVISION: Dynamic Pricing and Product Differentiation with Cost Uncertainty and Learning
Date Posted:Tue, 10 Feb 2015 08:33:54 -0600
Motivated by applications in the health insurance industry, we consider a seller who designs and sells a set of vertically differentiated products to a population of quality-sensitive customers. The seller’s business environment entails an uncertainty about production costs. We characterize the seller’s optimal price-quality schedule in the cases of: (a) static cost uncertainty, and (b) dynamic learning about cost uncertainty through noisy observations on an underlying cost curve. We prove that the seller’s optimal quality allocations in (a) and (b) stand in stark contrast: While a seller facing static cost uncertainty degrades the quality in its product offering, a dynamically learning seller improves the quality of its products to accelerate information accumulation. In the case of dynamic learning, we prove that the seller exercises the most extreme experimentation on less quality-sensitive customers. We also extend our results to the cases of commonly used regulations in health ...

REVISION: Response-Adaptive Designs for Clinical Trials: Simultaneous Learning from Multiple Patients
Date Posted:Fri, 30 Jan 2015 05:10:31 -0600
Clinical trials have traditionally followed a fixed design, in which randomization probabilities of patients to various treatments remains fixed throughout the trial and specified in the protocol. The primary goal of this static design is to learn about the efficacy of treatments. Response-adaptive designs, on the other hand, allow clinicians to use the learning about treatment effectiveness to dynamically adjust randomization probabilities of patients to various treatments as the trial progresses. An ideal adaptive design is one where patients are treated as effectively as possible without sacrificing the potential learning or compromising the integrity of the trial. We propose such a design, termed Jointly Adaptive, that uses forward-looking algorithms to fully exploit learning from multiple patients simultaneously. Compared to the best existing implementable adaptive design that employs a multiarmed bandit framework in a setting where multiple patients arrive sequentially, we show ...

REVISION: The Supply Chain Effects of Bankruptcy
Date Posted:Fri, 09 Jan 2015 02:26:44 -0600
This paper examines how a firm's financial distress and the legal environment regarding the ease of bankruptcy reorganization can alter product market competition and supplier-buyer relationships. We identify three effects, predation, bail-out, and abetment, that can change firms' behavior from their actions in the absence of financial distress. The predation effect increases competition before potential bankruptcy as the non-distressed competitor behaves as if it has some first-mover advantage, which could benefit a supplier with price control. The bailout effect reflects the supplier's incentive to grant the distressed firm concessions to preserve competition, improving supply chain efficiency and providing support for the exclusivity rule in Chapter 11 of the United States Bankruptcy Code when the supplier and the distressed firm are financially linked. The abetment effect is that the supplier may deliberately abet the competitor's predation, leading to increased operational ...

REVISION: A Model for Tax Advantages of Portfolios with Many Assets
Date Posted:Thu, 08 Jan 2015 01:43:02 -0600
Taxable portfolios present challenges for optimization models with even a limited number of assets. Holding many assets, however, has a distinct tax advantage over holding few assets. In this paper, we develop a model that takes an extreme view of a portfolio as a continuum of assets to gain the broadest possible advantage from holding many assets. We find the optimal strategy for trading in this portfolio in the absence of transaction costs and develop bounding approximations on the optimal value. We compare the results in a simulation study to a portfolio consisting only of a market index and show that the multi-asset portfolio's tax advantage can lead either to significant consumption or bequest increases.

REVISION: The Supply Chain Effects of Bankruptcy
Date Posted:Thu, 08 Jan 2015 01:37:13 -0600
This paper examines how a firm's financial distress and the legal environment regarding the ease of bankruptcy reorganization can alter product market competition and supplier-buyer relationships. We identify three effects, predation, bail-out, and abetment, that can change firms' behavior from their actions in the absence of financial distress. The predation effect increases competition before potential bankruptcy as the non-distressed competitor behaves as if it has some first-mover advantage, which could benefit a supplier with price control. The bailout effect reflects the supplier's incentive to grant the distressed firm concessions to preserve competition, improving supply chain efficiency and providing support for the exclusivity rule in Chapter 11 of the United States Bankruptcy Code when the supplier and the distressed firm are financially linked. The abetment effect is that the supplier may deliberately abet the competitor's predation, leading to increased operational ...

Dynamic Selling Mechanisms for Product Differentiation and Learning
Date Posted:Tue, 25 Nov 2014 21:36:29 -0600
We consider a firm that designs a vertically differentiated product line for a population of cus- tomers with heterogeneous quality sensitivities. The firm faces an uncertainty about the cost of quality, and we formulate this uncertainty as a belief distribution on a set of cost models. Over a time horizon of T periods, the firm can dynamically adjust its menu and make noisy observations on the underlying cost model through customers? purchasing decisions. We characterize how optimal product differentiation depends on the ?informativeness? of quality choices, formally measured by a contrast-to-noise ratio defined on the firm?s feasible quality set. Based on this, we design a minimum quality standard (MQS) policy that mimics the salient features of the optimal product differentiation policy and prove that the MQS policy is near-optimal. We also prove that, if there exists a certain continuum of informative quality choices, then even a myopic policy that makes no attempt to learn exhibits near-optimal profit performance. This stands in stark contrast to the poor performance of myopic policies in pricing and learning problems in the absence of product differentiation. Finally, we extend our results to the case where the firm simultaneously learns the customers? quality sensitivity distribution as well as the cost model.

REVISION: Dynamic Pricing and Product Differentiation with Cost Uncertainty and Learning
Date Posted:Tue, 25 Nov 2014 11:36:30 -0600
Motivated by applications in health insurance industry, we consider a seller who designs and sells a set of vertically differentiated products to a population of quality-sensitive customers. The seller’s business environment entails an uncertainty about production costs. We characterize the seller’s optimal price-quality schedule in the cases of: (a) static cost uncertainty, and (b) dynamic learning about cost uncertainty through noisy observations on an underlying cost curve. We prove that the seller’s optimal quality allocations in (a) and (b) stand in stark contrast: While a seller facing static cost uncertainty degrades the quality in its product offering, a dynamically learning seller improves the quality of its products to accelerate information accumulation. In the case of dynamic learning, we prove that the seller exercises the most extreme experimentation on less quality-sensitive customers. We also extend our results to the cases of commonly used regulations in health ...

Robustness of Renewable Energy Support Schemes Facing Uncertainty and Regulatory Ambiguity
Date Posted:Sat, 13 Sep 2014 21:10:35 -0500
Renewable portfolio standards, feed-in-tariffs, and market premia are widely used policy instruments to promote investments in renewable energy sources. Regulators continuously evaluate these instruments along the main electricity policy objectives of affordability, reliability, and sustainability. We develop a quantitative approach to assess these policies and their robustness to exogenous changes along these dimensions using a long-term dynamic capacity investment model. We compare their robustness in the light of uncertain renewable feed-in and ambiguous future regulation. We implement the robustness analysis employing different risk measures and find that renewable portfolio standards deliver most robust results, while feed-in-tariffs achieve target renewable buildup rates at least cost.

New: Robustness of Renewable Energy Support Schemes Facing Uncertainty and Regulatory Ambiguity
Date Posted:Sat, 13 Sep 2014 12:10:35 -0500
Renewable portfolio standards, feed-in-tariffs, and market premia are widely used policy instruments to promote investments in renewable energy sources. Regulators continuously evaluate these instruments along the main electricity policy objectives of affordability, reliability, and sustainability. We develop a quantitative approach to assess these policies and their robustness to exogenous changes along these dimensions using a long-term dynamic capacity investment model. We compare their robustness in the light of uncertain renewable feed-in and ambiguous future regulation. We implement the robustness analysis employing different risk measures and find that renewable portfolio standards deliver most robust results, while feed-in-tariffs achieve target renewable buildup rates at least cost.

REVISION: How Inventory Is (Should Be) Financed: Trade Credit in Supply Chains with Demand Uncertainty and Costs of Financial Distress
Date Posted:Mon, 21 Jul 2014 07:23:20 -0500
As an integrated part of a supply contract, trade credit has intrinsic connections with supply chain contracting and inventory management. Using a model that explicitly captures the interaction of firms’ operations decisions and financial risks, this paper attempts to develop a deeper understanding of trade credit from an operational perspective. Revolving around the question of what role trade credit plays in channel coordination and inventory financing, we demonstrate that with demand uncertainty, trade credit enhances supply chain efficiency by serving as a risk-sharing mechanism. When offering trade credit, the supplier balances its impact on operational profit and costs of financial distress. Facing a trade credit contract, the retailer finances inventory using a portfolio of cash, trade credit, and short-term debt, where the structure of this inventory financing portfolio depends on the retailer’s financing need and bargaining power. Additionally, our model suggests that ...

REVISION: Response-Adaptive Designs for Clinical Trials: Simultaneous Learning from Multiple Patients
Date Posted:Sat, 05 Jul 2014 03:12:50 -0500
Clinical trials have traditionally followed a fixed design, in which patient allocation to treatments is fixed throughout the trial and specified in the protocol. The primary goal of this static design is to learn about the efficacy of treatments. Response-adaptive designs, on the other hand, allow clinicians to use the learning about treatment effectiveness to dynamically adjust patient allocation to treatments as the trial progresses. An ideal adaptive design is one where patients are treated as effectively as possible without sacrificing potential learning or compromising the integrity of the trial. We propose such a design, one that uses forward-looking algorithms to fully exploit learning from multiple patients simultaneously. Compared to the best existing implementable adaptive design (e.g. using Berry, 1978), we show that our proposed design improves patient outcomes by up to 8.6% under a set of considered scenarios. Further, we demonstrate our design's effectiveness using ...

REVISION: How Inventory is (Should Be) Financed: Trade Credit in Supply Chains with Demand Uncertainty and Costs of Financial Distress
Date Posted:Tue, 01 Jul 2014 12:32:58 -0500
As an integrated part of a supply contract, trade credit has intrinsic connections with supply chain contracting and inventory management. Using a model that explicitly captures the interaction of firms’ operations decisions and financial risks, this paper attempts to develop a deeper understanding of trade credit from an operational perspective. Revolving around the question of what role trade credit plays in channel coordination and inventory financing, we demonstrate that with demand uncertainty, trade credit enhances supply chain efficiency by serving as a risk-sharing mechanism. When offering trade credit, the supplier balances its impact on operational profit and costs of financial distress. Facing a trade credit contract, the retailer finances inventory using a portfolio of cash, trade credit, and short-term debt, where the structure of this inventory financing portfolio depends on the retailer’s financing need and bargaining power. Additionally, our model suggests that ...

REVISION: The Supply Chain Effects of Bankruptcy
Date Posted:Tue, 01 Jul 2014 12:31:16 -0500
This paper examines how a firm's financial distress and the legal environment regarding the ease of bankruptcy reorganization can alter product market competition and supplier-buyer relationships. We identify three effects, predation, bail-out, and abetment, that can change firms' behavior from their actions in the absence of financial distress. The predation effect increases competition before potential bankruptcy as the non-distressed competitor behaves as if it has some first-mover advantage, which could benefit a supplier with price control. The bailout effect reflects the supplier's incentive to grant the distressed firm concessions to preserve competition, improving supply chain efficiency and providing support for the exclusivity rule in Chapter 11 of the United States Bankruptcy Code when the supplier and the distressed firm are financially linked. The abetment effect is that the supplier may deliberately abet the competitor's predation, leading to increased operational ...

Index Tracking and Enhanced Indexation Using a Parametric Approach
Date Posted:Thu, 19 Jun 2014 22:58:25 -0500
Based on the work of Brandt et al. (2009), we formulate an index tracking and enhanced indexation model using a parametric approach. The portfolio weights are modeled as functions of assets characteristics and similarity measures of the assets with the index to track. This approach permits handling nonlinear and nonconvex objectives functions that are difficult to incorporate in existing index tracking and enhanced indexation models. Additionally, this approach gives the investor more information about the portfolio holdings since the optimization is performed over portfolio strategies. Finally, an empirical implementation and an analysis of selected characteristics are presented for the S&P500 index.

New: Index Tracking and Enhanced Indexation Using a Parametric Approach
Date Posted:Thu, 19 Jun 2014 13:58:25 -0500
Based on the work of Brandt et al. (2009), we formulate an index tracking and enhanced indexation model using a parametric approach. The portfolio weights are modeled as functions of assets characteristics and similarity measures of the assets with the index to track. This approach permits handling nonlinear and nonconvex objectives functions that are difficult to incorporate in existing index tracking and enhanced indexation models. Additionally, this approach gives the investor more information about the portfolio holdings since the optimization is performed over portfolio strategies. Finally, an empirical implementation and an analysis of selected characteristics are presented for the S&P500 index.

REVISION: Portfolio Optimization Under Generalized Hyperbolic Skewed t Distribution and Exponential Utility
Date Posted:Sat, 14 Jun 2014 08:01:54 -0500
In this paper, we show that if asset returns follow a generalized hyperbolic skewed t distribution, the investor has exponential utility function and a riskless asset is available, the optimal portfolio weights can be found either in closed-form or using a successive approximation scheme. We also derive lower bounds for the certainty equivalent return generated by the optimal portfolios. Finally, we present a study of the performance of mean-variance analysis and Taylor's series expected utility expansion (up to the fourth moment) to compute optimal portfolios in this framework.

REVISION: Portfolio Optimization Under Generalized Hyperbolic Skew Student's t-Distribution and Exponential Utility
Date Posted:Thu, 12 Jun 2014 11:12:52 -0500
In this paper, we show that if asset returns follow a generalized hyperbolic skewed t distribution, the investor has exponential utility function and a riskless asset is available, the optimal portfolio weights can be found either in closed-form or using a successive approximation scheme. We also derive lower bounds for the certainty equivalent return generated by the optimal portfolios. Finally, we present a study of the performance of mean-variance analysis and Taylor's series expected utility expansion (up to the fourth moment) to compute optimal portfolios in this framework.

Assessing the Long-Term Effects of Bank Policies
Date Posted:Wed, 28 May 2014 09:26:11 -0500
Current risk management frameworks are not suitable for testing the long-term implications of balance sheet policies. The current established methodologies, such as interest rate gap analysis or Credit Value-at-Risk are short-term and do not give a clear picture of the long-term risk of a bank. Backtesting is also difficult, given that the data is limited, thus not producing risk measures for long-term policies. Therefore, simulation is the best option for assessing the long-term implications of regulatory and management choices. In a previous note, we developed a scenario generation tool for simulating the long-term behavior of balance sheets of banks. In this note, we use this framework to quantify the long-term impact on risk and return of the leverage ratio, the core deposit ratio, operating costs and interest rates. As Basel III is being implemented, our tests confirm the benefits of holding a cushion of capital above the leverage ratio limit of 3%, a policy similar to the one followed by banks under previous regulation. Also, banks should consider setting a conservative limit for the core deposit ratio.

New: Assessing the Long-Term Effects of Bank Policies
Date Posted:Wed, 28 May 2014 00:26:12 -0500
Current risk management frameworks are not suitable for testing the long-term implications of balance sheet policies. The current established methodologies, such as interest rate gap analysis or Credit Value-at-Risk are short-term and do not give a clear picture of the long-term risk of a bank. Backtesting is also difficult, given that the data is limited, thus not producing risk measures for long-term policies. Therefore, simulation is the best option for assessing the long-term implications of regulatory and management choices. In a previous note, we developed a scenario generation tool for simulating the long-term behavior of balance sheets of banks. In this note, we use this framework to quantify the long-term impact on risk and return of the leverage ratio, the core deposit ratio, operating costs and interest rates. As Basel III is being implemented, our tests confirm the benefits of holding a cushion of capital above the leverage ratio limit of 3%, a policy similar to the one ...

REVISION: Supply Chain Network Structure and Firm Returns
Date Posted:Fri, 18 Apr 2014 08:18:28 -0500
The complexity and opacity of the network of interconnections among firms and their supply chains inhibits understanding of the impact of management decisions concerning the boundaries of the firm and the number and intensity of its relationships with suppliers and customers. Using recently available data on the relationships of public US firms, this paper investigates the effects of supply chain connections on firm performance as reflected in stock returns. The paper finds that supply chain structure is closely related to firm returns at two levels, a first-order effect from direct connections and a second-order impact from systemic exposures through the network. For the first order effect, using a cross-sectional data set of the supply chain network and monthly returns, we show that a firm’s return can be explained by its concurrent supplier returns, concurrent customer returns, own momentum, and supplier momentum, whereas customer momentum has little impact. A long-short equity ...

The Structural Impact of Renewable Portfolio Standards and Feed-in-Tariffs on Electricity Markets
Date Posted:Tue, 01 Apr 2014 09:28:20 -0500
Renewable energy sources (RES) capacity has grown globally at a rapid rate benefiting from multiple support schemes such as renewable portfolio standards (RPS), feed-in-tariffs (FIT), and market premia (MP). While research concentrated on comparing the effectiveness of these policy instruments in driving RES investment, the focus is increasingly shifting towards assessments of the structural impact of these schemes on electricity markets. RES support schemes are continuously being assessed on how they help achieve the three main objectives of electricity policy, i.e., the affordability, reliability, and sustainability of electricity supply. In this work, we quantitatively compare RPS, FIT and MP schemes in these three dimensions by assessing their future impact on electricity prices, on generation portfolios and security of supply as well as on carbon emissions. We simulate the impact of all three support schemes using a long-term capacity expansion model with an hourly granularity for a time horizon of 60 years. We find that all support schemes increase RES penetration and thereby help reduce CO2 emissions. However, MP and FIT schemes can achieve these effects at lower cost, while RPS schemes deliver more robust results. Our findings provide insights to regulators, utilities and investors on the consequences of current regulation and possible alternatives.

New: The Structural Impact of Renewable Portfolio Standards and Feed-in-Tariffs on Electricity Markets
Date Posted:Tue, 01 Apr 2014 00:28:21 -0500
Renewable energy sources (RES) capacity has grown globally at a rapid rate benefiting from multiple support schemes such as renewable portfolio standards (RPS), feed-in-tariffs (FIT), and market premia (MP). While research concentrated on comparing the effectiveness of these policy instruments in driving RES investment, the focus is increasingly shifting towards assessments of the structural impact of these schemes on electricity markets. RES support schemes are continuously being assessed on how they help achieve the three main objectives of electricity policy, i.e., the affordability, reliability, and sustainability of electricity supply. In this work, we quantitatively compare RPS, FIT and MP schemes in these three dimensions by assessing their future impact on electricity prices, on generation portfolios and security of supply as well as on carbon emissions. We simulate the impact of all three support schemes using a long-term capacity expansion model with an hourly granularity ...

REVISION: Portfolio Optimization Under Generalized Hyperbolic Skew Student's t-Distribution and Exponential Utility
Date Posted:Sat, 29 Mar 2014 11:39:21 -0500
In this paper, we show that if asset returns follow a generalized hyperbolic skewed t distribution, the investor has exponential utility function and a riskless asset is available, the optimal portfolio weights can be found either in closed-form or using a successive approximation scheme. We also derive lower bounds for the certainty equivalent return generated by the optimal portfolios. Finally, we present a study of the performance of mean-variance analysis and Taylor's series expected utility expansion (up to the fourth moment) to compute optimal portfolios in this framework.

Supply Chain Network Structure and Firm Returns
Date Posted:Mon, 27 Jan 2014 09:37:47 -0600
The complexity and opacity of the network of interconnections among firms and their supply chains inhibits understanding of the impact of management decisions concerning the boundaries of the firm and the number and intensity of its relationships with suppliers and customers. Using recently available data on the relationships of public US firms, this paper investigates the effects of supply chain connections on firm performance as reflected in stock returns. The paper finds that supply chain structure is closely related to firm returns at two levels, a first-order effect from direct connections and a second-order impact from systemic exposures through the network. For the first order effect, using a cross-sectional data set of the supply chain network and monthly returns, we show that a firm?s return can be explained by its concurrent supplier returns, concurrent customer returns, own momentum, and supplier momentum, whereas customer momentum has little impact. A long-short equity strategy based on the supplier momentum yields monthly abnormal returns of 56 basis points. This result implies investors? limited attention to supplier firms relative to customer firms and gradual diffusion of information downstream as opposed to upstream in the supply chain. For the second-order effect, we find a market anomaly by grouping firms according to their centrality in the supply chain. Specifically, manufacturing firms that are more central in the network earn lower returns, while logist

REVISION: Supply Chain Network Structure and Firm Returns
Date Posted:Sun, 26 Jan 2014 23:37:48 -0600
The complexity and opacity of the network of interconnections among firms and their supply chains inhibits understanding of the impact of management decisions concerning the boundaries of the firm and the number and intensity of its relationships with suppliers and customers. Using recently available data on the relationships of public US firms, this paper investigates the effects of supply chain connections on firm performance as reflected in stock returns. The paper finds that supply chain structure is closely related to firm returns at two levels, a first-order effect from direct connections and a second-order impact from systemic exposures through the network. For the first order effect, using a cross-sectional data set of the supply chain network and monthly returns, we show that a firm’s return can be explained by its concurrent supplier returns, concur- rent customer returns, own momentum, and supplier momentum, whereas customer momentum has little impact. A long-short equity ...

REVISION: Response-Adaptive Designs for Clinical Trials: Simultaneous Learning from Multiple Patients
Date Posted:Mon, 06 Jan 2014 05:45:33 -0600
Clinical trials have traditionally followed a fixed design, in which patient allocation to treatments is fixed throughout the trial and specified in the protocol. The primary goal of this static design is to learn about the efficacy of treatments. Response-adaptive designs, on the other hand, allow clinicians to use the learning about treatment effectiveness to dynamically adjust patient allocation to treatments as the trial progresses. An ideal adaptive design is one where patients are treated as effectively as possible without sacrificing potential learning or compromising the integrity of the trial. We propose such a design, one that uses forward-looking algorithms to fully exploit learning from multiple patients simultaneously. Compared to the best existing implementable adaptive design (e.g. using Berry, 1978), we show that our proposed design improves patient outcomes by up to 8.6% under a set of considered scenarios. Further, we demonstrate our design's effectiveness using ...

REVISION: The Supply Chain Effect of Bankruptcy Reorganization
Date Posted:Sun, 23 Jun 2013 13:17:36 -0500
Bankruptcy reorganization is a costly legal process designed to relieve operationally viable companies from their financial obligations. It allows the bankrupt firm to avoid liquidation and to continue creating value through operations. Focusing on the interaction of supply chain structures and the cost of reorganization, this paper studies the influence of bankruptcy reorganization on both ex ante and ex post operations and performances of the financially distressed firm, its competitor, and ...

REVISION: The Supply Chain Effect of Bankruptcy Reorganization
Date Posted:Sat, 15 Jun 2013 16:19:08 -0500
Bankruptcy reorganization is a costly legal process designed to relieve operationally viable companies from their financial obligations. It allows the bankrupt firm to avoid liquidation and to continue creating value through operations. Focusing on the interaction of supply chain structures and the cost of reorganization, this paper studies the influence of bankruptcy reorganization on both ex ante and ex post operations and performances of the financially distressed firm, its competitor, and ...

REVISION: The Supply Chain Effect of Bankruptcy Reorganization
Date Posted:Mon, 13 May 2013 04:38:42 -0500
Bankruptcy reorganization is a costly legal process designed to relieve operationally viable companies from their financial obligations. It allows the bankrupt firm to avoid liquidation and to continue creating value through operations. Focusing on the interaction of supply chain structures and the cost of reorganization, this paper studies the influence of bankruptcy reorganization on both ex ante and ex post operations and performances of the financially distressed firm, its competitor, and ...

REVISION: The Supply Chain Effect of of Bankruptcy Reorganization
Date Posted:Fri, 10 May 2013 12:06:09 -0500
Bankruptcy reorganization is a costly legal process designed to relieve operationally viable companies from their financial obligations. It allows the bankrupt firm to avoid liquidation and continue creating value through operations. Focusing on the interaction of supply chain structures and the cost of reorganization, this paper studies the influence of bankruptcy reorganization on both ex-ante and ex-post operations and performances of the financially distressed firm, its competitor, and its ...

REVISION: How Inventory is (Should Be) Financed: Trade Credit in Supply Chains with Demand Uncertainty and Cos
Date Posted:Tue, 12 Feb 2013 15:23:47 -0600
As an integrated part of a supply contract, trade credit has intrinsic connections with supply chain contracting and inventory management. Using a model that explicitly captures the interaction of firms’ operations decisions and financial risks, this paper attempts to develop a deeper understanding of trade credit from an operational perspective. Revolving around the question of what role trade credit plays in channel coordination and inventory financing, we demonstrate that with demand ...

REVISION: Fully Adaptive Designs for Clinical Trials: Simultaneous Learning from Multiple Patients
Date Posted:Fri, 10 Aug 2012 16:21:04 -0500
Clinical trials have traditionally followed a fixed design where allocation of patients to a given treatment is purely random. Such trials are static in the sense that a protocol is developed and executed with no modifications during the course of the trial. The primary goal of this traditional design is to maximize learning about the efficacy of treatments at the end of the trial. Adaptive designs, on the other hand, allow clinicians to learn about treatment effectiveness during the course of ...

Response-Adaptive Designs for Clinical Trials: Simultaneous Learning from Multiple Patients
Date Posted:Fri, 10 Aug 2012 15:45:59 -0500
Clinical trials have traditionally followed a fixed design, in which randomization probabilities of patients to various treatments remains fixed throughout the trial and specified in the protocol. The primary goal of this static design is to learn about the efficacy of treatments. Response-adaptive designs, on the other hand, allow clinicians to use the learning about treatment effectiveness to dynamically adjust randomization probabilities of patients to various treatments as the trial progresses. An ideal adaptive design is one where patients are treated as effectively as possible without sacrificing the potential learning or compromising the integrity of the trial. We propose such a design, termed Jointly Adaptive, that uses forward-looking algorithms to fully exploit learning from multiple patients simultaneously. Compared to the best existing implementable adaptive design that employs a multiarmed bandit framework in a setting where multiple patients arrive sequentially, we show that our proposed design improves health outcomes of patients in the trial by up to 8.6%, in expectation, under a set of considered scenarios. Further, we demonstrate our design's effectiveness using data from a recently conducted stent trial. This paper also adds to the general understanding of such models by showing the value and nature of improvements over heuristic solutions for problems with short delays in observing patient outcomes. We do this by showing the relative performance of these s

REVISION: Long-Term Bank Balance Sheet Management: Estimation and Simulation of Risk-Factors
Date Posted:Wed, 01 Aug 2012 00:55:56 -0500
We propose a dynamic framework which encompasses the main risks in balance sheets of banks in an integrated fashion. Our contributions are fourfold: 1) solving a simple one-period model that describes the optimal bank policy under credit risk; 2) estimating the long-term stochastic processes underlying the risk factors in the balance sheet, taking into account the credit and interest rate cycles; 3) simulating several scenarios for interest rates and charge-offs; and 4) describing the ...

REVISION: Long-Term Bank Balance Sheet Management: Estimation and Simulation of Risk-Factors
Date Posted:Fri, 22 Jun 2012 11:41:51 -0500
We propose a dynamic framework which encompasses the main risks in balance sheets of banks in an integrated fashion. Our contributions are fourfold: 1) solving a simple one-period model that describes the optimal bank policy under credit risk; 2) estimating the long-term stochastic processes underlying the risk factors in the balance sheet, taking into account the credit and interest rate cycles; 3) simulating several scenarios for interest rates and charge-offs; and 4) describing the ...

Long-Term Bank Balance Sheet Management: Estimation and Simulation of Risk-Factors
Date Posted:Fri, 22 Jun 2012 00:00:00 -0500
We propose a dynamic framework which encompasses the main risks in balance sheets of banks in an integrated fashion. Our contributions are fourfold: 1) solving a simple one-period model that describes the optimal bank policy under credit risk; 2) estimating the long-term stochastic processes underlying the risk factors in the balance sheet, taking into account the credit and interest rate cycles; 3) simulating several scenarios for interest rates and charge-offs; and 4) describing the equations that govern the evolution of the balance sheet in the long run. The models that we use address momentum and the interaction between different rates. Our results enable simulation of bank balance sheets over time given a bank's lending strategy and provides a basis for an optimization model to determine bank asset-liability management strategy endogenously.

The Supply Chain Effects of Bankruptcy
Date Posted:Sun, 10 Jun 2012 21:31:48 -0500
This paper examines how a firm's financial distress and the legal environment regarding the ease of bankruptcy reorganization can alter product market competition and supplier-buyer relationships. We identify three effects, predation, bail-out, and abetment, that can change firms' behavior from their actions in the absence of financial distress. The predation effect increases competition before potential bankruptcy as the non-distressed competitor behaves as if it has some first-mover advantage, which could benefit a supplier with price control. The bailout effect reflects the supplier's incentive to grant the distressed firm concessions to preserve competition, improving supply chain efficiency and providing support for the exclusivity rule in Chapter 11 of the United States Bankruptcy Code when the supplier and the distressed firm are financially linked. The abetment effect is that the supplier may deliberately abet the competitor's predation, leading to increased operational disadvantages for the distressed firm before bankruptcy. Together these effects stress that a firm's bankruptcy potential can hurt its competitors and benefit its suppliers/customers. They also provide guidelines for firms' operational decisions in such situations, a rationale for observed firm actions surrounding bankruptcies, and motivation for policies supporting reorganization and relaxing broad enforcement of non-discriminatory pricing regulations.

REVISION: Trade Credit in Supply Chains: Multiple Creditors and Priority Rules
Date Posted:Sun, 10 Jun 2012 16:27:39 -0500
Priority rules determine the order of repayment when the debtor cannot repay all of his debt. In this paper, we study how different priority rules influence trade credit usage and supply chain efficiency when multiple creditors are present. We find that with only demand risk, when the wholesale price is exogenous, trade credit with high priority can lead to high chain efficiency, yet trade credit with low priority allows more retailers to obtain trade credit and suppliers to gain higher profits.

REVISION: The Impact of Bankruptcy Reorganization on Operational Competitiveness and Supply Chain Performance
Date Posted:Sun, 10 Jun 2012 03:05:22 -0500
Using a supply chain model that explicitly captures the bankruptcy process governed by the debtor-oriented U.S. Bankruptcy Code, this paper explores how bankruptcy reorganization influences the bankrupt firm, its creditors and other parties in the supply chain both before and after bankruptcy filing. The model consists of four players: two downstream firms which engage in Cournot competition; a monopoly upstream supplier that sells to both of them; and a group of creditors to one of the ...

New: Firm Profitability, Inventory Volatility, and Capital Structure
Date Posted:Tue, 23 Aug 2011 12:41:34 -0500
Traditional theories of capital structure imply a consistent relationship between firm profitability and firm leverage. Empirical data, however, suggest that the relationship is not monotonic. In the cross-section of firms, non-profitable firms become significantly more leveraged as losses decrease; profitable firms become significantly less leveraged as profits increase until a point where the most profitable firms have again significantly greater leverage as profits increase. In this paper, ...

Firm Profitability, Inventory Volatility, and Capital Structure
Date Posted:Tue, 23 Aug 2011 00:00:00 -0500
Traditional theories of capital structure imply a consistent relationship between firm profitability and firm leverage. Empirical data, however, suggest that the relationship is not monotonic. In the cross-section of firms, non-profitable firms become significantly more leveraged as losses decrease; profitable firms become significantly less leveraged as profits increase until a point where the most profitable firms have again significantly greater leverage as profits increase. In this paper, we present an extension of a model of Xu and Birge (2004) that is consistent with these observations. The model assumes that firms make debt and production scale decisions that depend on fixed costs necessary to maintain operations, variable costs of production, and volatility in future demand forecasts. In addition to predicting the convex relationship between profit margins and leverage that appears in the data, the model also predicts decreasing inventory volatility for non-profitable firms followed by increasing inventory volatility for profitable firms, which is also a statistically significant result in the data. These observations are consistent with a model of firms that make early price and quantity commitments in advance of demand realization as in the classical news vendor model of operations.

REVISION: Trade Credit in Supply Chains: Multiple Creditors and Priority Rules
Date Posted:Sun, 15 May 2011 08:28:45 -0500
Priority rules determine the order of repayment when the debtor cannot repay all of his debt. In this paper, we study how different priority rules influence trade credit usage and supply chain efficiency when multiple creditors are present. We find that with only demand risk, when the wholesale price is exogenous, trade credit with high priority can lead to high chain efficiency, yet trade credit with low priority allows more retailers to obtain trade credit and suppliers to gain higher profits.

Trade Credit in Supply Chains: Multiple Creditors and Priority Rules
Date Posted:Sun, 15 May 2011 00:00:00 -0500
Priority rules determine the order of repayment when the debtor cannot repay all of his debt. In this paper, we study how different priority rules influence trade credit usage and supply chain efficiency when multiple creditors are present. We find that with only demand risk, when the wholesale price is exogenous, trade credit with high priority can lead to high chain efficiency, yet trade credit with low priority allows more retailers to obtain trade credit and suppliers to gain higher profits. When the supplier has control of the wholesale price, however, we show that the supplier should extend unlimited trade credit with net terms. We also study the case when demand risk mingles with other risks, especially those with longer terms. Under this setting, we show several scenarios when the optimal trade credit policy should change according to different risks and that, in general, trade credit with low priority results in high chain efficiency. Finally, we use empirical data to show that, at an aggregate level, trade credit usage reacts to changes in the law according to our theory.

How Inventory Is (Should Be) Financed: Trade Credit in Supply Chains with Demand Uncertainty and Costs of Financial Distress
Date Posted:Wed, 05 Jan 2011 20:24:28 -0600
A new substantially revised version of this paper under the title of "Trade Credit, Risk Sharing, and Inventory Financing Portfolios" is available for download at: http://ssrn.com/abstract=2746645. As an integrated part of a supply contract, trade credit has intrinsic connections with supply chain contracting and inventory management. Using a model that explicitly captures the interaction of firms? operations decisions and financial risks, this paper attempts to develop a deeper understanding of trade credit from an operational perspective. Revolving around the question of what role trade credit plays in channel coordination and inventory financing, we demonstrate that with demand uncertainty, trade credit enhances supply chain efficiency by serving as a risk-sharing mechanism. When offering trade credit, the supplier balances its impact on operational profit and costs of financial distress. Facing a trade credit contract, the retailer finances inventory using a portfolio of cash, trade credit, and short-term debt, where the structure of this inventory financing portfolio depends on the retailer?s financing need and bargaining power. Additionally, our model suggests that financial diversification, that is, employing multiple financing sources, provides an alternative explanation for the use of factoring in accounts receivable management and the decentralization of some supply chains. Finally, using a sample of firm-level data f

REVISION: How Inventory is (Should Be) Financed: Trade Credit in Supply Chains with Demand Uncertainty and Cos
Date Posted:Wed, 05 Jan 2011 18:57:08 -0600
As an integrated part of a supply contract, trade credit has intrinsic connections with supply chain contracting and inventory management. Using a model that explicitly captures the interaction of firms’ operations decisions and financial risks, this paper attempts to develop a deeper understanding of trade credit from an operational perspective. Revolving around the question of what role trade credit plays in channel coordination and inventory financing, we demonstrate that with demand ...

REVISION: How Inventory is (Should Be) Financed: Trade Credit in Supply Chains with Demand Uncertainty and Cos
Date Posted:Wed, 05 Jan 2011 18:57:08 -0600
As an integrated part of a supply contract, trade credit has intrinsic connections with supply chain contracting and inventory management. Using a model that explicitly captures the interaction of firms’ operations decisions and financial risks, this paper attempts to develop a deeper understanding of trade credit from an operational perspective. Revolving around the question of what role trade credit plays in channel coordination and inventory financing, we demonstrate that with demand ...

REVISION: Index Tracking and Enhanced Indexation Using a Parametric Approach
Date Posted:Tue, 03 Nov 2009 06:07:08 -0600
Based on the work of Brandt, Santa-Clara and Valkanov (2009), we formulate an index tracking and enhanced indexation model using a parametric approach. The portfolio weights are modeled as functions of assets characteristics and similarity measures of the assets with the index to track. This approach permits to handle non-linear and non-convex objectives functions that are common in index tracking and enhanced indexation. An empirical implementation and analysis of the characteristics are ...

REVISION: Portfolio Optimization Under Generalized Hyperbolic Skew Student's t-Distribution and Exponen
Date Posted:Mon, 21 Sep 2009 19:15:43 -0500
In this paper, we show that if the asset returns follow a generalized hyperbolic (GH) skewed t distribution and the investor has exponential utility function, the optimal portfolio weights can be found either in closed-form or using a successive approximation scheme. Additionally, we present a study of the performance of the sample average approximation (SAA) and the expected utility expansion (up to the fourth moment) to compute optimal portfolios in this framework.

REVISION: Optimal Investment and Production Across Markets with Stochastic Exchange Rates
Date Posted:Thu, 18 Jun 2009 09:42:30 -0500
All multinational firms face foreign exchange fluctuations, which can create unstable cash flows and even bankruptcy. Managers of these firms face critical questions over how to reduce the risk due to income and expenses in multiple currencies. Traditional financial risk controls include futures and options, but firms also can use operational controls, such as foreign production capacity. In this paper, we study these alternatives for a simplified single-product firm operating in a home ...

REVISION: Portfolio Optimization Under Generalized Hyperbolic Skew Student's t-Distribution and Exponen
Date Posted:Tue, 26 May 2009 10:20:07 -0500
In this paper, we show that if the returns of the assets have a GH skewed t distribution and the investor has exponential utility function, the optimal portfolio weights resemble the results from elliptical distribution with a correction from skewness. The optimal portfolio weights can be found with almost no extra work compared with the traditional mean-variance approach for elliptical distributions.

Portfolio Optimization Under Generalized Hyperbolic Skewed t Distribution and Exponential Utility
Date Posted:Sun, 24 May 2009 20:39:54 -0500
In this paper, we show that if asset returns follow a generalized hyperbolic skewed t distribution, the investor has exponential utility function and a riskless asset is available, the optimal portfolio weights can be found either in closed-form or using a successive approximation scheme. We also derive lower bounds for the certainty equivalent return generated by the optimal portfolios. Finally, we present a study of the performance of mean-variance analysis and Taylor's series expected utility expansion (up to the fourth moment) to compute optimal portfolios in this framework.

REVISION: Portfolio Optimization Under Generalized Hyperbolic Skew Student's t-Distribution and Exponen
Date Posted:Sun, 24 May 2009 12:17:03 -0500
In this paper, we show that if the returns of the assets have a GH skewed t distribution and the investor has exponential utility function, the optimal portfolio weights resemble the results from elliptical distribution with a correction from skewness. The optimal portfolio weights can be found with almost no extra work compared with the traditional mean-variance approach for elliptical distributions.

REVISION: Index Tracking and Enhanced Indexation Using a Parametric Approach
Date Posted:Sun, 05 Apr 2009 14:35:09 -0500
Computational complexity and empirical results of the classic integer programming approach for index tracking and enhanced indexation motivate the use of a parametric model based on the work of Brandt, Santa-Clara and Valkanov (2008). The weights are modeled as functions of assets characteristics and similarity measures of the assets with the index. This approach permits to handle non-linear and non-convex objectives functions that are common in index tracking and enhanced indexation. An ...

Index Tracking and Enhanced Indexation Using a Parametric Approach
Date Posted:Sun, 05 Apr 2009 00:00:00 -0500
Based on the work of Brandt, Santa-Clara and Valkanov (2009), we formulate an index tracking and enhanced indexation model using a parametric approach. The portfolio weights are modeled as functions of assets characteristics and similarity measures of the assets with the index to track. This approach permits to handle non-linear and non-convex objectives functions that are common in index tracking and enhanced indexation. An empirical implementation and analysis of the characteristics are presented for the S&P500 index.

New: Discrete-Time Optimization of Consumption and Investment Decisions Given Intolerance for a Decline i
Date Posted:Tue, 19 Feb 2008 14:24:46 -0600
We extend Samuelson's (1969) discrete-time dynamic consumption and investment optimization problem to the case where the investor is intolerant of any decline in her standard of living. This constraint represents a strong form of habit formation such that the consumption rate is non-decreasing over time. To achieve this objective, the investor first guarantees a consumption perpetuity at the current consumption rate and then allocates the remaining wealth under a state-dependent, adjusted ...

Discrete-Time Optimization of Consumption and Investment Decisions Given Intolerance for a Decline in Standard of Living
Date Posted:Tue, 19 Feb 2008 00:00:00 -0600
We extend Samuelson's (1969) discrete-time dynamic consumption and investment optimization problem to the case where the investor is intolerant of any decline in her standard of living. This constraint represents a strong form of habit formation such that the consumption rate is non-decreasing over time. To achieve this objective, the investor first guarantees a consumption perpetuity at the current consumption rate and then allocates the remaining wealth under a state-dependent, adjusted coefficient of relative risk aversion. We study the impact of the length of the time interval on the optimal consumption and investment policies. This effect has implications for investors considering investments in assets, such as hedge funds and private equity, that have restrictions on trading intervals.

REVISION: A Model for Tax Advantages of Portfolios with Many Assets
Date Posted:Sat, 03 Sep 2005 21:08:56 -0500
Taxable portfolios present challenges for optimization models with even a limited number of assets. Holding many assets, however, has a distinct tax advantage over holding few assets. In this paper, we develop a model that takes an extreme view of a portfolio as a continuum of assets to gain the broadest possible advantage from holding many assets. We find the optimal strategy for trading in this portfolio in the absence of transaction costs and develop bounding approximations on the optimal ...

Equity Valuation, Production, and Financial Planning: A Stochastic Programming Approach
Date Posted:Sun, 23 Jan 2005 03:45:34 -0600
Most of the operations management literature assumes that the firm can always finance production decisions at an optimal level or borrow at a constant interest rate; however, operational decisions are constrained by limited capital and often critically depend on external financing. This paper proposes an integrated corporate planning model, which extends the forecasting-based discount dividend pricing method into an optimization-based valuation framework to make production and financial ...

Operational Decisions, Capital Structure, and Managerial Compensation: A News Vendor Perspective
Date Posted:Sun, 23 Jan 2005 03:34:07 -0600
While firm growth critically depends on financing ability and access to external capital, the operations management literature seldom considers the effects of financial constraints on the firms' operational decisions. Another critical assumption in traditional operations models is that corporate managers always act in the firm owners' best interests. Managers are, however, agents of the owners of the company, whose interests are often not aligned with those of equity-holders or debt-holders; ...

Joint Production and Financing Decisions: Modeling and Analysis
Date Posted:Sun, 23 Jan 2005 03:19:59 -0600
This paper develops models to make production and financing decisions simultaneously in the presence of demand uncertainty and market imperfections. While the Modigliani and Miller propositions demonstrate that a firm's investment and financing decisions can be made independently in a perfect capital market, our models illustrate how a firm's production decisions are affected by the existence of financial constraints. We analyze the interactions between a firm's production and financing ...

REVISION: Optimal Investment and Production Across Markets with Stochastic Exchange Rates
Date Posted:Sun, 23 Jan 2005 03:09:30 -0600
All multinational firms face foreign exchange fluctuations, which can create unstable cash flows and even bankruptcy. Managers of these firms face critical questions over how to reduce the risk due to income and expenses in multiple currencies. Traditional financial risk controls include futures and options, but firms also can use operational controls, such as foreign production capacity. In this paper, we study these alternatives for a simplified single-product firm operating in a home ...

Comparisons of Alternative Quasi-Monte Carlo Sequences for American Option Pricing
Date Posted:Wed, 29 Dec 2004 08:12:21 -0600
Quasi-Monte Carlo sequences have been shown to provide accurate option price approximations for a variety of options. In this paper, we apply quasi-Monte Carlo sequences in a duality approach to value American options. We compare the results using different low discrepancy sequences and estimate error bounds and computational effort. The results demonstrate the value of sequences using expansions of irrationals.

Error Bounds for Quasi-Monte Carlo Methods in Option Pricing
Date Posted:Wed, 29 Dec 2004 02:45:43 -0600
The classic error bounds for quasi-Monte Carlo approximation follow the Koksma-Hlawka inequality based on the assumption that the integrand has finite variation. Unfortunately, not all functions have this property. In particular, integrands for common applications in finance, such as option pricing, do not typically have bounded variation. In contrast to this lack of theoretical precision, quasi-Monte Carlo methods perform quite well empirically. This paper provides some theoretical ...