Given the complex and controversial nature of short-selling regulation, we review the academic literature and provide insights for policy makers and academics. We organize the complex history of short-selling regulation into three areas: trading restrictions, securities lending regulations, and disclosure requirements. We identify, analyze, and discuss 45 distinct regulations promulgated from 1896 to 2021, primarily by reviewing the academic literature and the data sources employed. We provide several insights regarding the effectiveness of regulatory approaches and the wider impact of short-selling regulation on markets.

We explore the impact of investors’ beliefs on cryptocurrency demand and prices using new individual-level survey data and a structural characteristics-based demand model with differentiated cryptocurrencies and heterogeneous investors. We show that younger individuals with lower incomes are more optimistic about the future value of cryptocurrencies, as are late investors. We identify the model combining observable beliefs with an instrumental variable strategy that exploits variation in the production of different cryptocurrencies. Counterfactual analyses quantify the impact on portfolio allocations and equilibrium prices of (i) (regulating) entry of late optimistic investors, and (ii) growing concerns among investors about the sustainability of energy-intensive proof-of-work cryptocurrencies

This paper revisits the relation between systematic skewness and returns, showing two main findings. First, the systematic skewness premium in individual stocks is time varying. When either skewness preference or systematic skewness is above rather than below the median, the premium is 4% higher. The combined effect of the two induces time variation in the premium of about 7%. Second, systematic skewness has significant additional explanatory power in explaining returns relative to most common characteristics, except size and momentum. These two results imply that skewness preference is an important determinant of expected returns providing a possible rationale for size and momentum

We provide evidence that investors with leverage constraints demand leverage for the sake of leverage. We study the equity closed-end fund (CEF) market and document a strong positive relation between fund leverage and CEF premiums, indicating that investors pay a relative premium for leverage. We perform a quasi-natural experiment and identify leverage as a causal driver of the premium. Leverage changes do not signal improved fund performance. Instead, the only benefit to investors of increased leverage is amplified exposure via greater volatility and risk exposure. We supply external validity by relating our results to the betting-against-beta factor.

Regulations introduce significant fixed costs and add to operating leverage. Fixed regulatory costs that contribute to operating leverage should generate a risk premium. To explore whether such a premium exists, we introduce a measure of “regulatory operating leverage” that reflects the importance of fixed regulatory costs in a firm’s cost structure. Regulatory operating leverage predicts stock returns in the cross-section, and a zero-cost high-low regulatory operating leverage strategy generates positive and significant risk-adjusted return. Finally, the impact of regulatory operating leverage on returns is due to the (systematic) risk contribution of fixed regulatory costs.

We implement a dynamically regularized, bootstrapped two-stage out-of-sample parametric portfolio policy to evaluate characteristics’ efficacy in the conditional stock return-generating process in the metric of expected power utility. Traditional characteristics, such as momentum and size afforded large utility gains before 1999. These opportunities have since vanished. Overfitting—imprecision in weight estimation—is correlated with the optimal portfolio’s variance. Therefore, it is not a problem for power utility investors with coefficients of relative aversion greater than four. For more risk-tolerant investors, we successfully reduce estimation error by increasing the curvature of the loss function relative to the investor’s utility function.

This paper studies the extent to which macro-finance term structure models are susceptible to predictive uncertainty. We propose a general form of arbitrage-free models and quantify the relative importance of unpredictable priced risk variance, as well as macro-finance model uncertainty and learning uncertainty in predictability. Predictive performance and relative contributions of uncertainty sources are dynamically measured based on Bayesian methods, revealing dominating priced risk variance and other important uncertainty sources at different points in time. Macro-finance model uncertainty is high for near-term forward spread forecasts and contributes up to 87% of predictive uncertainty prior to recessions, implying strong dispersion in the information content of macro variables when forming near-term monetary policy expectations

We show how the Joslin, Singleton, and Zhu (2011) factor extraction approach to estimating the Gaussian term structure model can be modified to handle the interest rate lower bound without the approximations used in other approaches. This drastically reduces the computation time and produces more robust estimates of the lower bound parameter and the shadow rate. It makes feasible the extensive specification search necessary to allow for unspanned factors as in Joslin, Priebsch, and Singleton (2014), allowing the term structure model to be used to better assess the effects of policy on the term premium and market expectations.

This paper reappraises the usefulness of combining individual forecasts for predicting the U.S. equity premium. For comparison, we also consider penalized regression and dimension reduction approaches. We fail to find evidence of predictive ability in recent decades, regardless of the forecasting method used. Further analysis shows that an increase in the correlation of individual forecast errors is an important factor in the declining performance of combination forecasts

Central clearing counterparties (CCPs) were established to mitigate default losses resulting from counterparty risk in derivatives markets. In a parsimonious model, we show that clearing benefits are unevenly distributed across market participants. Loss sharing rules determine who wins or loses from clearing. Current rules disproportionately benefit market participants with flat portfolios. Instead, those with directional portfolios are relatively worse off, consistent with their reluctance to voluntarily use central clearing. Alternative loss sharing rules can address cross-sectional disparities in clearing benefits. However, we show that CCPs may favor current rules to maximize fee income, with externalities on clearing participation.