In contrast with theoretical predictions, high-fee active equity funds generate worse net-of-expenses performance. We show that this fee-performance puzzle is driven by the preference of high-fee funds for stocks with low operating profitability and high investment rates, characteristics associated with low expected returns. After controlling for exposures to profitability and investment factors, we find high-fee funds significantly outperform low-fee funds before expenses and achieve similarly poor net-of-fees performance. In resolving the fee-performance puzzle, our findings provide support to the theoretical prediction that net alphas are unrelated to fees and challenge the common advice to prefer low-fee funds over high-fee counterparts.

We provide the first systematic asset pricing analysis of one of the main safe asset categories, the repurchase agreement (repo). Based on the temporal and cross-sectional variation in short-term rates, we form a carry that, together with a market factor, prices these near-money assets in a linear pricing model. The carry depicts heterogeneity in nonpecuniary convenience yields of collateral assets and increases in the safety premium and the liquidity premium reflecting opportunity cost. Our carry helps explain the cross-section of short-term rates, as well as of long-term bond returns after accounting for standard bond pricing factors.

I find that short interest significantly and negatively predicts aggregate stock returns in 24 of 32 countries examined. This predictability survives out-of-sample tests, persists outside of recessions, and is not subsumed by other well-known return predictors. The results indicate that short interest contains valuable information for forecasting international market returns that is distinct and more powerful than that of other available predictors. However, the predictive power of short interest varies over time and across regions. It is higher around economic downturns when margin requirements tighten and in regions where short selling is constrained by regulations or equity lending market frictions.

This paper develops a general equilibrium model and provides empirical support that the market volatility-of-volatility (VOV) predicts market returns and drives the time-varying volatility risk. In asset pricing tests with the market, volatility, and VOV as factors, the risk premium on VOV is statistically and economically significant and robust. Market and volatility risks are not priced in unconditional models, but, consistent with theory, their factor loadings, conditional on VOV, are priced. The pricing impact of VOV strengthens during market crashes, suggesting that VOV is particularly relevant during market turmoil, when investors demand increased compensation for VOV risk

We model how investors allocate between asset managers, managers choose portfolios of multiple securities, fees are set, and security prices are determined. Investors are indifferent between higher-cost informed managers and lower-cost uninformed managers, interpreted as passive managers as their portfolio is linked to the " expected market portfolio." We make precise Samuelson's dictum by showing that active investors reduce micro-inefficiencies more than they do macro-inefficiencies. In fact, all inefficiency arises from systematic factors when the number of assets is large. Further, we show how the costs of active and passive investing affect macro- and micro-efficiency, fees, and assets managed by active and passive managers. Our findings help explain the rise of delegated asset management and the resultant changes in financial markets.

We analyze the supply-side disruptions associated with COVID-19. We find that sectors in which a higher fraction of the workforce is not able to work remotely experienced greater declines in employment and expected revenue growth, worse stock market performance, and higher likelihood of default. The stock market overweights low-exposure industries. Thus, our findings cast light on the disconnect between stock market indices and aggregate outcomes. We combine these ex ante heterogeneous industry exposures with daily financial market data to create a stock return portfolio that tracks news about the supply-side disruptions resulting from the pandemic

I find that approximately 30% of price fluctuations in the Fama-French size and value factors are nonfundamental price pressures driven by correlated fund flows, which generate price movements that revert over time. Is this really demand-based price pressure? I show that the price effects happen exclusively in periods when mutual funds place trades, a fact that is difficult to explain using traditional mechanisms such as unobserved investor preference changes. The estimated price elasticity is also consistent with other studies. Overall, my findings show that a sizable fraction of size and value factor movements do not represent economic risk.

We illustrate the role of left tail dependence—left tail mean (LTM)—in equity risk premium (ERP) predictability. LTM measures the average of pairwise left tail dependency among major equity sectors incorporating shocks imperceptible at the aggregate level. LTM, as well as the variance risk premium, significantly predicts the ERP in and out of sample, which is not the case with commonly used predictors. We find this predictability is the result of procyclical shocks’ reversals in a stable business cycle. This paper contributes to the ongoing debate on ERP predictability

We empirically study how inventory constraints of underwriters affect corporate bond offerings. Using underwriter-insurer-level transaction data, we find that a more constrained underwriter is more likely to place a bond and increases the allocation in the primary market to an insurer with a stronger preexisting relationship. The same underwriter is also more likely to buy back part of an allocation from the same insurer within 6 to 12 months after an offering. Overall, by “parking” inventory to relationship investors in the primary market, underwriters mitigate the effect of their inventory constraints on firms’ bond financing costs.

Marketing capability refers to a firm’s ability to optimally deploy and integrate different marketing inputs to achieve high sales at low cost. This paper examines whether the value of marketing capability is incorporated into stock returns. High-level marketing capability predicts better future operating performance and stock returns. A marketing capability-based long-short portfolio strategy earns an average annual return of 5.16%, a substantial portion of which is earned over subsequent earnings announcements. The return predictability is stronger in stocks with higher valuation uncertainty and lower investor attention. Investors underreact to the value-relevant, but difficult to process, information embedded in marketing capability