REVISION: Coronavirus: Impact on Stock Prices and Growth Expectations
We use data from the aggregate equity market and dividend futures to quantify how investors’ expectations about economic growth across horizons evolve in response to the coronavirus outbreak and subsequent policy responses. Dividend futures, which are claims to dividends on the aggregate stock market in a particular year, can be used to directly compute a lower bound on growth expectations across maturities or to estimate expected growth using a simple forecasting model. We show how the actual forecast and the bound evolve over time. As of March 25, our forecast of annual growth in dividends is down 28% in the US and 22% in the EU, and our forecast of GDP growth is down by 2.2% in the US and 2.8% in the EU. The lower bound on the change in expected dividends is -38% in the US and -49% in the EU on the 2-year horizon. The lower bound is model free and forward looking. There are signs of catch-up growth from year 3 to year 10. News about economic relief programs on March 13 appear to ...
REVISION: Higher-Moment Risk
We use a new method to estimate ex ante higher order moments of stock market returns from option prices. Even and odd number higher order moments are strongly negatively correlated, creating periods where the return distribution is riskier because it is more left-skewed and fat tailed. The higher-moment risk increases in good times when variance is lower and prices are higher. This time variation is inconsistent with disaster-based models where disaster risk, and thus higher-moment risk, peaks in bad times. The variation in higher-moment risk also has important implications for investors as it causes the probability of a three-sigma loss on the market portfolio to vary from 0.7% to 1.9% percent over the sample, peaking in calm periods such as just before the onset of the financial crisis.
REVISION: Conditional Risk in Global Stock Returns
We estimate the premium associated with time-varying market betas without using rolling betas or instruments. Instead, we use a new conditional-risk factor, which is a market timing strategy defined as the unexpected return on the market times the ex ante price of risk. The factor is a powerful tool for documenting a global effect of conditional risk on stock returns: across 23 developed countries, all major equity risk factors load on our conditional-risk factor with the right sign, meaning their alpha can partly be explained by the time variation in their market betas. The conditional-risk factor explains 50% more alpha than traditional methods that use rolling betas to capture conditional risk.
REVISION: Duration-Driven Returns
We propose a duration-based explanation for the major equity risk factors, including value, profitability, investment, low risk, and payout factors. Both in the US and globally, these factors invest in firms that earn most of their cash flows in the near future. The factors could therefore all be driven a premium on near-future cash flows. We test this hypothesis using a novel dataset of single-stock dividend futures, which are claims on annual dividends of individual firms. Consistent with our hypothesis, risk-adjusted returns are higher on near- than on distant-future cash flows. In addition, firm-level characteristics do not predict returns on the cash flows once controlling for maturity.
REVISION: Time Variation of the Equity Term Structure
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. The counter-cyclical variation is 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. More generally, any one-factor model will fail to explain both the average downward slope and the counter-cyclical variation. I therefore introduce a new model with two priced risk factors to solve the puzzle.
REVISION: Betting Against Correlation: Testing Theories of the Low-Risk Effect
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.