Kewei Hou

Industry Concentration and Average Stock Returns

Firms in more concentrated industries earn lower returns, even after controlling for size, book-to-market, momentum, and other return determinants. Explanations based on chance, measurement error, capital structure, and persistent in-sample cash flow shocks do not explain this finding. Drawing on work in industrial organization, we posit that either barriers to entry in highly concentrated industries insulate firms from undiversifiable distress risk, or firms in highly concentrated industries are less risky because they engage in less innovation, and thereby command lower expected returns. Additional time-series tests support these risk-based interpretations. Copyright 2006 by The American Finance Association.

Market Frictions, Price Delay, and the Cross-Section of Expected Returns

We parsimoniously characterize the severity of market frictions affecting a stock using the delay with which its price responds to information. The most delayed firms command a large return premium not explained by size, liquidity, or microstructure effects. Moreover, delay captures part of the size effect, idiosyncratic risk is priced only among the most delayed firms, and earnings drift is monotonically increasing in delay. Frictions associated with investor recognition appear most responsible for the delay effect. The very small segment of delayed firms, comprising only 0.02% of the market, generates substantial variation in average returns, highlighting the importance of frictions. Copyright 2005, Oxford University Press.

The Accrual Anomaly: Risk or Mispricing?

We document considerable return comovement associated with accruals after controlling for other common factors. An accrual-based factor-mimicking portfolio has a Sharpe ratio of 0.16, higher than that of the market factor or the SMB and HML factors of Fama and French (1993). In time series regressions, a model that includes the Fama-French factors and the additional accrual factor captures the accrual anomaly in average returns. However, further time series and cross-sectional tests indicate that it is the accrual characteristic rather than the accrual factor loading that predicts returns. These findings favor a behavioral explanation for the accrual anomaly.

A Comparison of New Factor Models

This paper compares the Hou, Xue, and Zhang (2014) q-factor model and the Fama and French (2014a) five-factor model on both conceptual and empirical grounds. Four concerns cast doubt on the five-factor model: The internal rate of return often correlates negatively with the one-period-ahead expected return; the value factor seems redundant in the data; the expected investment tends to correlate positively with the one-period-ahead expected return; and past investment is a poor proxy for the expected investment. Empirically, the four-factor q-model outperforms the five-factor model, especially in capturing price and earnings momentum and profitability anomalies.

Accruals and Aggregate Stock Market Returns

Past research has shown that the level of operating accruals is a negative cross-sectional predictor of stock returns. This paper examines whether the accrual anomaly extends to the aggregate stock market. In contrast with cross-sectional findings, there is no indication that aggregate operating accruals is a negative time series predictor of stock market returns; the relation is strongly positive for the market portfolio and also for several sector and industry portfolios. In addition, innovations in accruals are negatively contemporaneously associated with market returns, suggesting that changes in accruals contain information about changes in discount rates, or that firms manage earnings in response to market-wide undervaluation.

Resurrecting the Size Effect: Firm Size, Profitability Shocks, and Expected Stock Returns

Recent studies report that the size effect in the cross-section of stock returns has disappeared after the early 1980s. This paper shows that the disappearance of the size effect from realized returns can be attributed to unexpected shocks to the profitability of small and big firms. We find that small firms experience large negative profitability shocks after the early 1980s, while big firms experience large positive shocks. As a result, realized returns of small and big firms over this period differ substantially from expected returns. After adjusting for the price impact of profitability shocks, we find that there still is a robust size effect in the cross-section of expected returns. Our results highlight the importance of in-sample cash flow shocks for understanding cross-sectional return predictability.

Accruals and NOA Anomalies: Risk or Mispricing?

We document return comovement based upon accruals and NOA after controlling for other common return factors. The NOA factor has a Sharpe ratio over three times as large as the market factor, and over twice as large as the HML factor of Fama and French (1993). In time series tests, a model that includes the Fama-French factors, an accruals factor, and an NOA factor largely captures the accruals and NOA anomalies. However, further tests indicate that it is the NOA and accruals characteristics rather than factor loadings that predict returns. These findings favor a behavioral explanation for the accruals and NOA anomalies.

The CAPM Strikes Back? An Equilibrium Model with Disasters

Embedding disasters into a general equilibrium model with heterogeneous firms induces strong nonlinearity in the pricing kernel, helping explain the empirical failure of the (consumption) CAPM. Our single-factor model reproduces the failure of the CAPM in explaining the value premium in finite samples without disasters and its relative success in samples with disasters. Due to beta measurement errors, the estimated beta-return relation is flat, consistent with the beta “anomaly,” even though the true beta-return relation is strongly positive. Finally, the consumption CAPM fails in simulations, even though a nonlinear model with the true pricing kernel holds exactly by construction.

Political Uncertainty and Commodity Prices

Using a comprehensive sample of 87 commodities, we examine the effect of political uncertainty on commodity prices. We show that political uncertainty surrounding U.S. presidential elections has a significant negative impact on commodity prices worldwide, likely due to shrinking demand before the elections. On average, commodity prices decline by 6.4% in the quarter leading up to U.S. elections. This effect holds true for gold, and is stronger for close elections and elections during recessions. On the other hand, political uncertainty in commodity producing countries with little demand pushes commodity prices up by 5.4% in the quarter before their national elections.

Systemic default and return predictability in the Stock and Bond Markets

Using a structural model of default, we construct a measure of systemic default defined as the probability that many firms default at the same time. Our estimation accounts for correlations in defaults between firms through common exposures to shocks. The systemic default measure spikes during recession periods and is strongly correlated with traditional credit-related macroeconomic measures such as the default spread and VIX. Furthermore, our measure predicts future equity and corporate bond index returns, particularly at the one-year horizon, and even after controlling for many traditional return predictors such as the dividend yield, default spread, inflation, and tail risk. These predictability results are robust to out-of-sample tests.