Leverage and the Cross‐Section of Equity Returns

Building on theoretical asset pricing literature, we examine the role of market risk and the size, book‐to‐market (BTM), and volatility anomalies in the cross‐section of unlevered equity returns. Compared with levered (stock) returns, unlevered market beta plays a more important role in explaining the cross‐section of unlevered equity returns, even after controlling for size and BTM. The size effect is weakened, while the value premium and the volatility puzzle virtually disappear for unlevered returns. We show that leverage induces heteroskedasticity in returns. Unlevering returns removes this pattern, which is otherwise difficult to address by controlling for leverage in regressions.     

The Cross‐Section of Credit Risk Premia and Equity Returns

We explore the link between a firm's stock returns and credit risk using a simple insight from structural models following Merton ( 1974 ): risk premia on equity and credit instruments are related because all claims on assets must earn the same compensation per unit of risk. Consistent with theory, we find that firms' stock returns increase with credit risk premia estimated from CDS spreads. Credit risk premia contain information not captured by physical or risk‐neutral default probabilities alone. This sheds new light on the “distress puzzle”—the lack of a positive relation between equity returns and default probabilities—reported in previous studies.     

Financial Distress and the Cross‐section of Equity Returns

We explicitly consider financial leverage in a simple equity valuation model and study the cross‐sectional implications of potential shareholder recovery upon resolution of financial distress. Our model is capable of simultaneously explaining lower returns for financially distressed stocks, stronger book‐to‐market effects for firms with high default likelihood, and the concentration of momentum profits among low credit quality firms. The model further predicts (i) a hump‐shaped relationship between value premium and default probability, and (ii) stronger momentum profits for nearly distressed firms with significant prospects for shareholder recovery. Our empirical analysis strongly confirms these novel predictions.     

Sparse Signals in the Cross‐Section of Returns

This paper applies the Least Absolute Shrinkage and Selection Operator (LASSO) to make rolling one‐minute‐ahead return forecasts using the entire cross‐section of lagged returns as candidate predictors. The LASSO increases both out‐of‐sample fit and forecast‐implied Sharpe ratios. This out‐of‐sample success comes from identifying predictors that are unexpected, short‐lived, and sparse. Although the LASSO uses a statistical rule rather than economic intuition to identify predictors, the predictors it identifies are nevertheless associated with economically meaningful events: the LASSO tends to identify as predictors stocks with news about fundamentals.     

Organization Capital and the Cross‐Section of Expected Returns

Organization capital is a production factor that is embodied in the firm's key talent and has an efficiency that is firm specific. Hence, both shareholders and key talent have a claim to its cash flows. We develop a model in which the outside option of the key talent determines the share of firm cash flows that accrue to shareholders. This outside option varies systematically and renders firms with high organization capital riskier from shareholders' perspective. We find that firms with more organization capital have average returns that are 4.6% higher than firms with less organization capital.     

Financial Intermediaries and the Cross‐Section of Asset Returns

Financial intermediaries trade frequently in many markets using sophisticated models. Their marginal value of wealth should therefore provide a more informative stochastic discount factor (SDF) than that of a representative consumer. Guided by theory, we use shocks to the leverage of securities broker‐dealers to construct an intermediary SDF. Intuitively, deteriorating funding conditions are associated with deleveraging and high marginal value of wealth. Our single‐factor model prices size, book‐to‐market, momentum, and bond portfolios with an R² of 77% and an average annual pricing error of 1%—performing as well as standard multifactor benchmarks designed to price these assets.     

Failure Risk and the Cross‐Section of Hedge Fund Returns

Modeling a hedge fund's probability of failure with a dynamic logit regression, I find that the probability of a fund's failure has a significantly negative effect on the fund's future returns. A quintile portfolio with the highest failure probability underperforms a quintile portfolio with the lowest failure probability by 5% to 6% per year from 1997 to 2012. The results are robust to the definition of hedge fund failure and controlling for a large set of risk factors and fund characteristics. Moreover, the negative effect of failure probability on future fund returns is stronger for funds with weak share restrictions.     

Good‐Specific Habit Formation and the Cross‐Section of Expected Returns

I study asset prices in a general equilibrium framework in which agents form habits over individual varieties of goods rather than over an aggregate consumption bundle. Goods are produced by monopolistically competitive firms whose elasticities of demand depend on consumers' habit formation. Firms that produce goods with a high habit level relative to consumption have low demand elasticities, set high prices for their product, have low expected returns on their stock, and have low asset pricing betas and stock return volatilities. I find supportive evidence for these predictions in the data.     

Aggregate Jump and Volatility Risk in the Cross‐Section of Stock Returns

We examine the pricing of both aggregate jump and volatility risk in the cross‐section of stock returns by constructing investable option trading strategies that load on one factor but are orthogonal to the other. Both aggregate jump and volatility risk help explain variation in expected returns. Consistent with theory, stocks with high sensitivities to jump and volatility risk have low expected returns. Both can be measured separately and are important economically, with a two‐standard‐deviation increase in jump (volatility) factor loadings associated with a 3.5% to 5.1% (2.7% to 2.9%) drop in expected annual stock returns.     

Luck versus Skill in the Cross‐Section of Mutual Fund Returns

The aggregate portfolio of actively managed U.S. equity mutual funds is close to the market portfolio, but the high costs of active management show up intact as lower returns to investors. Bootstrap simulations suggest that few funds produce benchmark‐adjusted expected returns sufficient to cover their costs. If we add back the costs in fund expense ratios, there is evidence of inferior and superior performance (nonzero true α) in the extreme tails of the cross‐section of mutual fund α estimates.     

On the Stability of the Cross‐Section of Expected Stock Returns in the Cross‐Section: Understanding the Curious Role of Share Turnover

In this paper, we shed further light on cross‐sectional predictors of stock return performance. Specifically, we explore whether the cross‐section of expected stock returns is robust within stock groups sorted by past monthly return. We find that the book/market and momentum effects are remarkably robust to sorting on past returns. However, share turnover is negatively related to future returns for stocks with abnormally low stock price performance in the recent past, but postively related to returns for well‐performing stocks. This casts doubt on the use of turnover as a liquidity proxy, but is consistent with turnover being a proxy for momentum trading which pushes prices in the direction of past price movements. Our results are robust to both NYSE/AMEX and Nasdaq stocks, and also robust to stratifying the sample by time period.     

Expected versus Ex Post Profitability in the Cross‐Section of Industry Returns

Asset pricing theory predicts a positive cross‐sectional relation between expected profitability and expected returns. However, empirical studies typically use lagged ex post profitability as a proxy for expected profitability. In this article, we use out‐of‐sample combination forecasts to estimate expected industry‐level operating profit, gross profit, operating cash flow, and net income. We then construct real‐time industry‐rotation strategies based on high and low expected profitability. For each measure except gross profit, these predicted‐profitability strategies earn significant alpha net of transaction costs and outperform strategies based on ex post profitability.     

Cross‐Section of Expected Returns and Extreme Returns: The Role of Investor Attention and Risk Preferences

Previous work finds a negative and significant relation between the maximum daily return over the past one month and expected future stock returns. We determine that this effect is more pronounced for stocks that achieve their maximum daily returns toward the end of the month and stocks that are associated with capital losses show greater reversals. These results suggest the effect is related to investor attention and risk preferences.     

Volatility Risk Premium,Risk Aversion,and the Cross‐Section of Stock Returns

We test if innovations in investor risk aversion are a priced factor in the stock market. Using 25 portfolios sorted on book‐to‐market and size as test assets, our new factor together with the market factor explains 64% of the variation in average returns compared to 60% for the Fama‐French model. The new factor is generally significant with an estimated risk premium close to its time series mean also when industry portfolios and portfolios sorted on previous returns are augmented to the test assets.     

Connecting Optimal Capital Investment and Equity Returns

Economic theory predicts a contemporaneous correlation between equity returns and investment growth that is only weakly present in the data. By modifying the firm's production function to include a lag between investment decisions and expenditures, and after correcting for the temporal aggregation of investment, I find the predicted correlation to be present in the data. I estimate the model for 31 industries and find that investment returns are highly correlated with the industry portfolio equity returns. Further, the portion of investment returns orthogonal to equity returns is associated positively with changes in profitability and negatively with lagged differences between equity and investment returns.     

Industry‐Specific Human Capital,Idiosyncratic Risk,and the Cross‐Section of Expected Stock Returns

Human capital is one of the largest assets in the economy and in theory may play an important role for asset pricing. Human capital is heterogeneous across investors. One source of heterogeneity is industry affiliation. I show that the cross‐section of expected stock returns is primarily affected by industry‐level rather than aggregate labor income risk. Furthermore, when human capital is excluded from the asset pricing model, the resulting idiosyncratic risk may appear to be priced. I find that the premium for idiosyncratic risk documented by several empirical studies depends on the covariance between stock and human capital returns.     

Explaining the Cross‐Section of Stock Returns in Japan: Factors or Characteristics?

Japanese stock returns are even more closely related to their book-to-market ratios than are their U.S. counterparts, and thus provide a good setting for testing whether the return premia associated with these characteristics arise because the characteristics are proxies for covariance with priced factors. Our tests, which replicate the Daniel and Titman (1997) tests on a Japanese sample, reject the Fama and French (1993) three-factor model, but fail to reject the characteristic model.     

Market Segmentation and Cross‐predictability of Returns

We present evidence supporting the hypothesis that due to investor specialization and market segmentation, value‐relevant information diffuses gradually in financial markets. Using the stock market as our setting, we find that (i) stocks that are in economically related supplier and customer industries cross‐predict each other's returns, (ii) the magnitude of return cross‐predictability declines with the number of informed investors in the market as proxied by the level of analyst coverage and institutional ownership, and (iii) changes in the stock holdings of institutional investors mirror the model trading behavior of informed investors.     

Stock Liquidity Risk and the Cross‐sectional Earnings‐Returns Relationship

We argue that a higher sensitivity to aggregate market‐wide liquidity shocks (i.e., a higher liquidity risk) implies a tendency for a stock's price to converge to fundamentals. We test this intuition within the framework of the earnings‐returns relationship. We find a positive liquidity risk effect on the relationship between return and expected change in earnings. This effect on the earnings‐returns relationship is distinct from the negative effect observed for stock illiquidity level. Notably, the liquidity risk effect is evident (absent) during periods of neutral/low (high) aggregate market liquidity. We also show that the liquidity risk effect is dominant in firms that: (a) are of intermediate size; (b) are of intermediate book‐to‐market; and (c) are profit making.     

Intraday Patterns in the Cross‐section of Stock Returns

Motivated by the literature on investment flows and optimal trading, we examine intraday predictability in the cross‐section of stock returns. We find a striking pattern of return continuation at half‐hour intervals that are exact multiples of a trading day, and this effect lasts for at least 40 trading days. Volume, order imbalance, volatility, and bid‐ask spreads exhibit similar patterns, but do not explain the return patterns. We also show that short‐term return reversal is driven by temporary liquidity imbalances lasting less than an hour and bid‐ask bounce. Timing trades can reduce execution costs by the equivalent of the effective spread.