Abstract In this paper, we forecast industry returns out-of-sample using the cross-section of book-to-market (BM) ratios and investigate whether investors can exploit this predictability in portfolio allocation. Cash-flow and return forecasting regressions show that cross-industry BM ratios contain significant predictive information beyond aggregate and industry-specific BM ratios. Forecast combination methods based on industry BM ratios generate significant out-of-sample predictability for many industries. Real-time portfolio-rotation strategies that buy industries with high predicted returns and short industries with low predicted returns based on combination forecasts earn significant alpha with respect to standard asset pricing models net of transaction costs.

We investigate whether transaction costs, arbitrage risk, and short-sale impediments explain the abnormal returns of volatility-managed equity portfolios. Even using six cost-mitigation strategies, after transaction costs, volatility management of asset-pricing factors besides the market return generally produces zero abnormal returns and significantly reduces Sharpe ratios. In contrast, abnormal returns of the volatility-managed market portfolio are robust to transaction costs and concentrated in the most easily arbitraged stocks, those with low arbitrage risk and impediments to short selling. Moreover, the managed market strategy only provides superior performance when sentiment is high, consistent with prior theory that sentiment traders underreact to volatility.

ABSTRACT Failing to account for transaction costs materially impacts inferences drawn when evaluating asset pricing models, biasing tests in favor of those employing high‐cost factors. Ignoring transaction costs, Hou, Xue, and Zhang (2015, Review of Financial Studies , 28, 650–705) q ‐factor model and Barillas and Shanken (2018, The Journal of Finance , 73, 715–754) six‐factor models have high maximum squared Sharpe ratios and small alphas across 205 anomalies. They do not, however, come close to spanning the achievable mean‐variance efficient frontier. Accounting for transaction costs, the Fama and French (2015, Journal of Financial Economics , 116, 1–22; 2018, Journal of Financial Economics , 128, 234–252) five‐factor model has a significantly higher squared Sharpe ratio than either of these alternative models, while variations employing cash profitability perform better still.

This paper shows that leading theories of the beta anomaly fail to explain the anomaly’s conditional performance. Abnormal returns and Sharpe ratios of betting-against-beta (BAB) factors rise following months with below-median realized volatility, even controlling for mispricing, limits to arbitrage, lottery preferences, analyst disagreement, and sentiment. Moreover, the leverage constraints theory counterfactually predicts that market and BAB Sharpe ratios increase with volatility. We further show that institutional investors shift their demand from high- to low-beta stocks as volatility increases, and the resulting price impact is sufficient to explain the difference in abnormal BAB returns between high- and low-volatility states.