Faculty & Research

Niels Gormsen

Niels Gormsen

Neubauer Family Assistant Professor of Finance

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5807 South Woodlawn Avenue
Chicago, IL 60637

Niels Joachim Gormsen joins Chicago Booth as a Neubauer Assistant Professor of Finance in 2018. His research interests include financial economics and empirical asset pricing. His research was awarded the AQR Top Finance Graduate award in 2018. At Booth he will teach investments. Previously, he taught corporate finance at Copenhagen Business School.

Gormsen earned a PhD in financial economics, an MSc in advanced economics and finance, and a BSc in international business all from Copenhagen Business School. During his graduate studies at Copenhagen Business School, he spent time as a visiting scholar at Harvard University and Columbia University.

Outside of academia, Gormsen enjoys sailing. In 2011, he won the Youth World Championship in the Olympic 49er dinghy.


2018 - 2019 Course Schedule

Number Name Quarter
35000 Investments 2019 (Winter)

REVISION: Time Variation of the Equity Term Structure
Date Posted: Dec  05, 2018
I document that the term structure of holding-period equity returns is counter-cyclical: it is downward sloping in good times, but upward sloping in bad times. This new stylized fact implies that long-maturity risk plays a central role in asset price fluctuations, consistent with theories of long-run risk and habit, but these theories cannot explain the average downward slope. At the same time, the cyclical variation is inconsistent with recent models constructed to match the average downward slope. I present the theoretical source of the puzzle and suggest a new model as a resolution. My model also shows that the counter-cyclical term structure has implications for real activity, which I verify empirically: in bad times, long-duration firms decrease their investment and capital-to-labor ratio relative to short-duration firms.

REVISION: Betting Against Correlation: Testing Theories of the Low-Risk Effect
Date Posted: Jun  21, 2018
We test whether the low-risk effect is driven by (a) leverage constraints and thus risk should be measured using beta vs. (b) behavioral effects and thus risk should be measured by idiosyncratic risk. Beta depends on volatility and correlation, where only volatility is related to idiosyncratic risk. We introduce a new betting against correlation (BAC) factor that is particularly suited to differentiate between leverage constraints vs. lottery explanations. BAC produces strong performance in the US and internationally, supporting leverage constraint theories. Similarly, we construct the new factor SMAX to isolate lottery demand, which also produces positive returns. Consistent with both leverage and lottery theories contributing to the low-risk effect, we find that BAC is related to margin debt while idiosyncratic risk factors are related to sentiment.

REVISION: Conditional Risk
Date Posted: Jan  25, 2018
We present a new direct methodology to study conditional risk, that is, the extra return compensation for time-variation in risk. We show theoretically that the conditional part of the CAPM can be captured by augmenting the standard market model with a conditional-risk factor, which is a specific market timing strategy. Both in the U.S. and global sample covering 23 countries, all major equity risk factors load on our conditional-risk factor, implying that each factor has a higher conditional market beta when the market risk premium is high or the market variance is low. Accordingly, these factor returns can be partly explained by conditional risk. Studying the economic drivers of these results, we find evidence that conditional risk arises from variation in discount rate betas (not cash flow betas) due to the endogenous effects of arbitrage trading.

New: Higher-Moment Risk
Date Posted: Nov  15, 2017
We show how the market's higher order moments can be estimated ex ante using methods based on Martin (2017). These ex ante higher order moments predict future realized higher order moments, whereas trailing realized moments have little predictive power. Higher-moment risks move together in the sense that skewness becomes more negative when kurtosis becomes more positive. In addition, higher-moment risk is high when volatility is low, suggesting that risk doesn't go away - it hides in the tails. Higher-moment risk has significant implications for investors; for example, the tail loss probability of a volatility-targeting investor varies from 3.6% to 9.7%, entirely driven by changes in higher-moment risk. We empirically analyze the economic drivers of these risks, such as financial intermediary leverage, market and funding illiquidity, and potential bubbles.