There is ample empirical literature centering on the effectiveness of foreign exchange intervention (FXI). Given the mix of objectives and country-heterogeneity, the general lack of consensus thus far is no surprise. We shed light on this debate by conducting the first comprehensive meta-analysis in the FXI literature, with 279 reported effects that stem from 74 distinct empirical studies. We cover estimations conducted in 19 countries across five decades. Overall, our meta-survey reports an average depreciation of domestic currency of 1% and a reduction of exchange rate volatility of 0.6%, in response to a $1 billion US dollar purchase. Results are qualitatively confirmed but smaller in size under fixed and random-effect estimations. When narrowing in on different economic factors, we find that effects are magnified for cases consistent with the monetary trilemma (greater if financial openness and monetary independence are low). Effects are also larger in emerging than advanced economies, when banking crises remain mild, and when interventions are large in size and are announced.

Loss aversion is one of the most widely used concepts in behavioral economics. We conduct a large-scale, interdisciplinary meta-analysis to systematically accumulate knowledge from numerous empirical estimates of the loss aversion coefficient reported from 1992 to 2017. We examine 607 empirical estimates of loss aversion from 150 articles in economics, psychology, neuroscience, and several other disciplines. Our analysis indicates that the mean loss aversion coefficient is 1.955 with a 95 percent probability that the true value falls in the interval [1.820, 2.102]. We record several observable characteristics of the study designs. Few characteristics are substantially correlated with differences in the mean estimates.

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

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