I revisit the question of whether active mutual funds generate value. Standard performance measures based on fund alpha compare after-cost fund returns with the before-cost returns of paper factor portfolios. In practice, investors pay bid–ask spreads and price impact costs when they trade securities directly. Using cost-aware benchmarks, I credit mutual funds for the trading costs their investors would otherwise face on their outside option. I propose a new measure of cost-aware value added (CAVA) that captures this execution margin. The average fund generates up to $9.51 million per year in CAVA. Of this amount, $7.18 million comes from recognizing benchmark trading costs. The gain over standard value added is largest for value and blend funds and persists when benchmarks use low-fee ETFs. Capital concentrates in funds with positive CAVA, including funds that account for 21% of aggregate assets but have negative standard value added. I provide a methodology to recover the cost-aware benchmark required to compute CAVA from a simple regression with lasso and ridge penalties for bid-ask spreads and price impact, respectively.

Abstract: Short-term return reversal is one of the most robust asset-pricing anomalies, and is commonly linked to liquidity provision. We decompose individual firm stock returns into two distinct components: SYS, the component of returns that can be linked to systematic risk and public information releases; and an orthogonal residual RES. The RES component reverses, while the SYS component exhibits continuation. Moreover, the residual reversals are highly asymmetric: positive residual shocks reverse much more slowly than negative shocks. A return factor based on asymmetric idiosyncratic reversal subsumes the idiosyncratic volatility factor and a broad set of other short-horizon anomalies. Our findings suggest that asymmetric idiosyncratic reversal (AIR) is the primary driver of short-term return predictability.

Abstract: This paper studies how trading costs affect discount-rate estimates from characteristic-based factor models. Standard factors rebalance fully in response to changes in characteristics, which is suboptimal when trading is costly. I propose transaction-cost-aware (TCA) factors that optimize the trade-off between tracking exposure to priced characteristics and minimizing trading costs. TCA factors produce tangency portfolios closer to the feasible efficient frontier: Correcting construction inefficiencies in standard factors increases net-of-cost squared Sharpe ratios by up to 150%. TCA construction is most beneficial for high-turnover factors, such as momentum, that otherwise carry minimal weight in the net tangency portfolio. These results suggest that asset pricing tests based on cost-unaware factors may incorrectly reject characteristics due to construction inefficiencies.