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We introduce a new approach to estimating long-term aggregate discount rates using the cross section of earnings and book values to explain current stock prices and extract expected market returns. The proposed discount rate measure is countercyclical. Shocks to it account for nearly half of historical market return variation; in contrast, shocks to other discount rate measures account for no more than 2%. It dominates other measures in explaining time-series variation in returns on duration-sorted portfolios and delivers out-of-sample predictability that exceeds that afforded by other expected return measures and predictive variables. It also performs well in international equity markets. [ABSTRACT FROM AUTHOR]

We document that stocks that experience sudden increases in idiosyncratic volatility underperform otherwise similar stocks in the future, and we propose that this phenomenon can be explained by the Miller conjecture [Miller E (1977) Risk, uncertainty, and divergence of opinion. J. Finance 32(4):1151-1168]. We show that volatility shocks can be traced to unusual firm-level news flow, which temporarily increases the level of investor disagreement about the firm value. At the same time, volatility shocks pose a barrier to short selling, preventing pessimistic investors from expressing their views. In the presence of divergent opinions and short-selling constraints, prices initially reflect optimistic views but adjust downward in the future as investors' opinions converge.

A correction is presented to the article "The Macroeconomic Uncertainty Premium in the Corporate Bond Market" which appeared in the August 2021 issue.

Drawing upon more than 12 million observations over the period from 1996 to 2020, we find that allowing for nonlinearities significantly increases the out-of-sample performance of option and stock characteristics in predicting future option returns. The nonlinear machine learning models generate statistically and economically sizable profits in the long-short portfolios of equity options even after accounting for transaction costs. Although option-based characteristics are the most important standalone predictors, stock-based measures offer substantial incremental predictive power when considered alongside option-based characteristics. Finally, we provide compelling evidence that option return predictability is driven by informational frictions and option mispricing. Authors have furnished an Internet Appendix , which is available on the Oxford University Press Web site next to the link to the final published paper online. [ABSTRACT FROM AUTHOR]

We introduce a measure of regret for stock market investors and investigate its cross-sectional asset pricing implications. According to our regret-based framework, investors experience regret due to not achieving the highest possible return from a similar set of stock investments, and equity portfolios with high regret generate 6.84% more annualized alpha than portfolios with low regret. Using investor-trading activity of 78,000 households at a large U.S.-based brokerage firm, we develop an investor-based regret index and show that this household-level regret measure predicts stock returns in a similar way to our proposed regret measure. We also show that regret is not spanned by established risk or behavioral factors that have been documented to be robust predictors of equity returns. This paper was accepted by David Sraer, finance. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2022.03389. [ABSTRACT FROM AUTHOR]

While it is established that idiosyncratic volatility is negatively priced in the cross-section of stock returns, the relation between idiosyncratic volatility and hedge fund returns is largely unexplored. We document that hedge funds with high idiosyncratic volatility earn higher future risk-adjusted returns of 6 percent p.a. than hedge funds with low idiosyncratic volatility. The outperformance arises because hedge funds trade high idiosyncratic volatility stocks wisely. They pick high volatility stocks when they are underpriced and short-sell high volatility stocks when they are overpriced. Our results support the notion that hedge funds' idiosyncratic volatility is a measure of managerial skill. [ABSTRACT FROM AUTHOR]

We examine how time-series volatility of book-to-market (UNC) is priced in equity returns and the relative contributions of its book volatility (variations in earnings and book value) and market volatility components (shocks in required return). UNC captures valuation risk, so stocks with high valuation risk earn higher return. An investment strategy long in high-UNC firms and short in low-UNC firms generates 8.5% annual risk-adjusted return. UNC valuation risk premium is driven by outperformance of high-UNC firms facing higher information risk and is not explained by established risk factors and firm characteristics. This paper was accepted by Agostino Capponi, finance. Funding: The authors acknowledge financial support from Spain's Ministry of Education [Grant EC02011-24928], Generalitat de Catalonia [Grant 2014-SGR-1079], Banc Sabadell, the Bank of Cyprus, and the European Social Fund. M. El Hefnawy acknowledges financial support from the Spanish Ministry of Science and Innovation [Grant PID2021-128994NA-I00]. Supplemental Material: The data files and online appendix are available at https://doi.org/10.1287/mnsc.2023.4888. [ABSTRACT FROM AUTHOR]

This paper investigates hedge funds’ ability to time industry-specific returns and shows that funds’ timing ability in the manufacturing industry improves their future performance, probability of survival, and ability to attract more capital. The results indicate that the best industry-timing hedge funds in the manufacturing sector have the highest return exposure to earnings surprises. This, together with persistently sticky earnings surprises, transparent information environment in regards to earnings releases, and large post-earnings-announcement drift in the manufacturing industry, explain to a great extent why best-timing hedge funds can generate significantly larger future returns compared to worst-timing hedge funds [ABSTRACT FROM AUTHOR]

We examine the role of macroeconomic uncertainty in the cross section of corporate bonds and find a significant uncertainty premium for both investment-grade (IG) (0.40% per month) and non-investment-grade (NIG) (0.81% per month) bonds. The economic-uncertainty premium declines as we progressively remove downgraded bonds, indicating that the premium represents an increase in required returns for bonds with higher credit and macroeconomic risk. The economic-uncertainty premia vary across equities and bonds in a manner consistent with the heterogeneous risk-aversion levels of dominant players in equities (retail investors) versus bonds (institutional investors). [ABSTRACT FROM AUTHOR]