Frazzini and Pedersen’s (2014) Betting Against Beta (BAB) factor is based on the same basic idea as Blacks’(1972) beta-arbitrage, but its astonishing performance has generated academic interest and made it highly influential with practitioners. This performance is driven by non-standard procedures used in its construction that effectively, but non-transparently, equal weight stock returns. For each dollar invested in BAB, the strategy commits on average $1.05 to stocks in the bottom 1% of total market capitalization. BAB earns positive returns after accounting for transaction costs, but earns these by tilting toward profitability and investment. Predictable biases resulting from Frazzini and Pedersen’s non-standard beta estimation procedure drive results presented as evidence supporting BAB’s underlying theory.

We create a parsimonious monetary policy exposure (MPE) index based on observable firm characteristics that previous studies link to how stocks react to monetary policy. Our index successfully captures stocks’ responses to both conventional and unconventional monetary policy. Stocks whose prices react more positively to expansionary monetary policy (high-MPE stocks) earn lower average returns. This result is consistent with the notion that high-MPE stocks provide a hedge against bad economic shocks, to which the Federal Reserve responds with expansionary monetary policy. A long-short trading strategy designed to exploit this effect achieves an annualized Sharpe Ratio of 0.77.

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.

We zero in on the expected returns of long-short portfolios based on 204 stock market anomalies by accounting for i) effective bid–ask spreads, ii) post-publication effects, and iii) the modern era of trading technology that began in the early 2000s. Net of these effects, the average anomaly’s expected return is a measly 4 bps per month. The strongest anomalies net, at best, 10 bps after controlling for data mining. Several methods for combining anomalies net around 20 bps. Expected returns are negligible despite cost mitigations that produce impressive net returns in-sample and the omission of additional trading costs, like price impact.