Binsbergen, Han, and Lopez-Lira (2023) predict analysts’ forecast errors using a random forest model. A strategy that trades against this model’s predictions earns a monthly alpha of 1.54% ($ t $-value = 5.84). This estimate represents a large improvement over studies using classical statistical methods. We attribute the difference to a look-ahead bias. Removing the bias erases the alpha. Linear models yield as accurate forecasts and superior trading profits. Neither alternative machine learning models nor combinations thereof resurrect the predictability. We discuss the state of research into the term structure of analysts’ forecasts and its causal relationship with returns.

Factors display strong cross-sectional momentum that subsumes momentum in industries and other portfolio characteristics. The profits of all these momentum strategies—based on factors, industries, and other characteristics—significantly correlate with each other and therefore likely emanate from the same source. If factors display momentum, so will any set of portfolios with cross-sectional variation in factor loadings. Consistent with factors being at the root of momentum, we find that momentum in industry-neutral factors explains industry momentum, but industry momentum explains none of the factor momentum. Cross-sectional factor momentum concentrates in the first few highest-eigenvalue factors and is distinct from time-series factor momentum.

Stock market participation is monotonically related to IQ, controlling for wealth, income, age, and other demographic and occupational information. The high correlation between IQ and participation exists even among the affluent. Supplemental data from siblings, studied with an instrumental variables approach and regressions that control for family effects, demonstrate that IQ's influence on participation extends to females and does not arise from omitted familial and nonfamilial variables. High‐IQ investors are more likely to hold mutual funds and larger numbers of stocks, experience lower risk, and earn higher Sharpe ratios. We discuss implications for policy and finance research.

A strategy that selects stocks based on their historical same‐calendar‐month returns earns an average return of 13% per year. We document similar return seasonalities in anomalies, commodities, and international stock market indices, as well as at the daily frequency. The seasonalities overwhelm unconditional differences in expected returns. The correlations between different seasonality strategies are modest, suggesting that they emanate from different systematic factors. Our results suggest that seasonalities are not a distinct class of anomalies that requires an explanation of its own, but rather that they are intertwined with other return anomalies through shared systematic factors.

Book value of equity consists of two economically different components: retained earnings and contributed capital. We predict that book-to-market strategies work because the retained earnings component of the book value of equity includes the accumulation and, hence, the averaging of past earnings. Retained earnings-to-market predicts the cross section of average returns in U.S. and international data and subsumes book-to-market. Contributed capital-to-market has no predictive power. We show that retained earnings-to-market, and, by extension, book-to-market, predicts returns because it is a good proxy for underlying earnings yield (Ball, 1978; Berk, 1995) and not because book value represents intrinsic value.

Accruals are the non-cash component of earnings. They represent adjustments made to cash flows to generate a profit measure largely unaffected by the timing of receipts and payments of cash. Prior research uncovers two anomalies: expected returns increase in profitability and decrease in accruals. We show that cash-based operating profitability (a measure that excludes accruals) outperforms measures of profitability that include accruals. Further, cash-based operating profitability subsumes accruals in predicting the cross section of average returns. An investor can increase a strategy’s Sharpe ratio more by adding just a cash-based operating profitability factor to the investment opportunity set than by adding both an accruals factor and a profitability factor that includes accruals.

Gross profit scaled by book value of total assets predicts the cross section of average returns. Novy-Marx (2013) concludes that it outperforms other measures of profitability such as bottom line net income, cash flows, and dividends. One potential explanation for the measure׳s predictive ability is that its numerator (gross profit) is a cleaner measure of economic profitability. An alternative explanation lies in the measure׳s deflator. We find that net income equals gross profit in predictive power when they have consistent deflators. Deflating profit by the book value of total assets results in a variable that is the product of profitability and the ratio of the market value of equity to the book value of total assets, which is priced. We then construct an alternative measure of profitability, operating profitability, which better matches current expenses with current revenue. This measure exhibits a far stronger link with expected returns than either net income or gross profit. It predicts returns as far as ten years ahead, seemingly inconsistent with irrational pricing explanations.