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.     

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.     

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.     

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.     

Investment and the Cross‐Section of Equity Returns

The data show that, upon being hit by adverse profitability shocks, large public firms have ample latitude to divest their least productive assets, reducing the risk faced by shareholders and the returns that they are likely to demand. In the one‐factor production‐based asset pricing model, when the frictions to capital adjustment are shaped to respect the evidence on investment, the model‐generated cross‐sectional dispersion of returns is only a small fraction of that documented in the data. Our conclusions hold even when operating or labor leverage is modeled in ways shown to be promising in the extant literature.     

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.     

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.     

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.     

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.     

Managerial Incentives,Fraud, and Monitoring

In response to equity compensation contracts that encourage managers to commit fraud as well as provide productive effort, owners may choose to monitor the manager to limit the fraud. We examine the firm owners’ incentives to perform both ex ante monitoring, such as internal controls, and ex post monitoring, such as audits, in a model that includes the reputational damages caused when a fraud is discovered. We provide conditions under which the owner prefers either more or less monitoring, and examine the effect of additional monitoring on the optimal equity package and equilibrium level of fraud.     

Market Expectations in the Cross‐Section of Present Values

Returns and cash flow growth for the aggregate U.S. stock market are highly and robustly predictable. Using a single factor extracted from the cross‐section of book‐to‐market ratios, we find an out‐of‐sample return forecasting R² of 13% at the annual frequency (0.9% monthly). We document similar out‐of‐sample predictability for returns on value, size, momentum, and industry portfolios. We present a model linking aggregate market expectations to disaggregated valuation ratios in a latent factor system. Spreads in value portfolios’ exposures to economic shocks are key to identifying predictability and are consistent with duration‐based theories of the value premium.     

Asset Write-Offs—Managerial Incentives and Macroeconomic Factors

The objective of this study is to explain what firm-specific and macroeconomic factors are likely to influence the asset write-off decision in Singapore, where upward revaluations, unlike in the U.S., are also permitted. The focus is on write-offs relating to two main asset categories, namely, fixed assets and long-term investments. Data on seventy-eight firms listed on the Stock Exchange of Singapore Mainboard were collected from 1983 to 1997. Results of cross-sectional and time-series analyses identify relevant macroeconomic factors to be unemployment rate, GDP growth rate and occupancy rate of properties, and firm-specific factors to be profitability and a change of board chairman.     

Managerial Incentives and Corporate Fraud: The Sources of Incentives Matter

Operating performance and stock return results imply that managerswho commit fraud anticipate large stock price declines if theywere to report truthfully, which would cause greater lossesfor managerial stockholdings than for options because of differencesin convexity. Fraud firms have significantly greater incentivesfrom unrestricted stockholdings than control firms do, and unrestrictedstockholdings are their largest incentive source. Our resultsemphasize the importance of the shape and vesting status ofincentive payoffs in providing incentives to commit fraud. Fraudfirms also have characteristics that suggest a lower likelihoodof fraud detection, which implies lower expected costs of fraud.     

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.     

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.     

Performance Incentives within Firms: The Effect of Managerial Responsibility

We show that top management incentives vary by responsibility. For oversight executives, pay‐performance incentives are $1.22 per thousand dollar increase in shareholder wealth higher than for divisional executives. For CEOs, incentives are $5.65 higher than for divisional executives. Incentives for the median top management team are substantial at $32.32. CEOs account for 42 to 58 percent of aggregate team incentives. For divisional executives, the pay–divisional performance sensitivity is positive and increasing in the precision of divisional performance and the pay–firm performance sensitivity is decreasing in the precision of divisional performance. These results support principal–agent models with multiple signals of managerial effort.     

Cross‐region and cross‐sector asset allocation with regimes

Cross‐region and cross‐sector asset allocation decisions are one of the most fundamental issues in international equity portfolio management. Equity returns exhibit higher volatilities and correlations, and lower expected returns, in bear markets compared to bull markets. However, static mean–variance analysis fails to capture this salient feature of equity returns. We accommodate the nonlinearity of returns using a regime switching model across both regions and sectors. The regime‐dependent asset allocation potentially adds value to the traditional static mean–variance allocation. In addition, optimal allocation across sectors provide greater benefits compared to international diversification, which is characterized by higher returns, lower risks, lower correlations with the world market and a higher Sharpe ratio.     

The Performance of Hedge Funds: Risk, Return, and Incentives

Hedge funds display several interesting characteristics that may influence performance, including: flexible investment strategies, strong managerial incentives, substantial managerial investment, sophisticated investors, and limited government oversight. Using a large sample of hedge fund data from 1988–1995, we find that hedge funds consistently outperform mutual funds, but not standard market indices. Hedge funds, however, are more volatile than both mutual funds and market indices. Incentive fees explain some of the higher performance, but not the increased total risk. The impact of six data-conditioning biases is explored. We find evidence that positive and negative survival-related biases offset each other.