Cross-section Of Stock Returns Research Articles

Employment Growth, Liquidity Risk, and the Cross-section of Stock Returns

Employment growth (EG) is likely related to liquidity fundamentals of investment opportunities, firm health, and information environment. This, in turn, implies that liquidity risk may play a role in explaining the relation between employment growth and stock returns. We explain the link between employment growth and liquidity risk with a parsimonious model and find strong empirical evidence supporting our conjecture. Stocks of high-EG firms are more liquid and exposed to lower liquidity risk than stocks of low-EG firms. After adjusting for liquidity risk, employment growth loses its power to predict returns.

Option-Implied variance asymmetry and the cross-section of stock returns

We find a positive relationship between individual stocks’ implied variance asymmetry, defined as the difference between upside and downside risk-neutral semivariances extracted from out-of-money options, and future stock returns. The high-minus-low hedge portfolio earns the excess return of 0.90% (0.67%) per month in equal-weighted (value-weighted) returns. We show that implied variance asymmetry provides a neat measure of risk-neutral skewness and outperforms the standard risk-neutral skewness in predicting the cross-section of future stock returns. Risk-based equilibrium asset pricing models can not explain such a positive relationship, which instead can be potentially explained by information asymmetry and informed trading.

Good Volatility, Bad Volatility, and the Cross Section of Stock Returns

Based on intraday data for a large cross section of individual stocks and newly developed econometric procedures, we decompose the realized variation for each of the stocks into separate so-called realized up and down semi-variance measures, or “good” and “bad” volatilities, associated with positive and negative high-frequency price increments, respectively. Sorting the individual stocks into portfolios based on their normalized good minus bad volatilities results in economically large and highly statistically significant differences in the subsequent portfolio returns. These differences remain significant after controlling for other firm characteristics and explanatory variables previously associated with the cross section of expected stock returns.

Investor sentiment, investor crowded-trade behavior, and limited arbitrage in the cross section of stock returns

We examine the interaction effects of investor sentiment, investor crowded-trade behavior, and limited arbitrage on the cross section of stock returns. This paper presents strong evidence to reveal that investor crowded-trade behavior will increase stock prices greatly (little) among stocks with positive (negative) investor sentiment; and investor sentiment will increase (not increase) stock prices among stocks with extreme seller-initiated (buyer-initiated) crowded-trade behavior. Furthermore, this paper finds that the benchmark-adjusted returns are positive among stocks with relatively positive investor sentiment and buyer-initiated crowded-trade behavior, and negative among stocks with relatively negative investor sentiment and seller-initiated crowded-trade behavior. Finally, this paper demonstrates that limited arbitrage plays more roles on stocks with pessimistic sentiment and seller-initiated crowded-trade behavior. Taken together, this paper confirms the combined effects of investor sentiment and investor crowded-trade behavior on the cross section of stock returns, and further explores the moderating effect of limited arbitrage.

The memory of stock return volatility: Asset pricing implications

We examine long memory volatility in the cross-section of stock returns. We show that long memory volatility is widespread in the United States and that the degree of memory can be related to firm characteristics, such as market capitalization, book-to-market ratio, prior performance, and price jumps. Long memory volatility is negatively priced in the cross-section. Buying stocks with shorter memory and selling stocks with longer memory in volatility generates significant excess returns of 1.71% per annum. Consistent with theory, we find that the volatility of stocks with longer memory is more predictable than stocks with shorter memory. This makes the latter more uncertain, which is compensated for with higher average returns.

Factor Models for Portfolio Selection in Large Dimensions: The Good, the Better and the Ugly

Abstract This paper injects factor structure into the estimation of time-varying, large-dimensional covariance matrices of stock returns. Existing factor models struggle to model the covariance matrix of residuals in the presence of time-varying conditional heteroskedasticity in large universes. Conversely, rotation-equivariant estimators of large-dimensional time-varying covariance matrices forsake directional information embedded in market-wide risk factors. We introduce a new covariance matrix estimator that blends factor structure with time-varying conditional heteroskedasticity of residuals in large dimensions up to 1000 stocks. It displays superior all-around performance on historical data against a variety of state-of-the-art competitors, including static factor models, exogenous factor models, sparsity-based models, and structure-free dynamic models. This new estimator can be used to deliver more efficient portfolio selection and detection of anomalies in the cross-section of stock returns.

Is Firm-Level Political Exposure Priced?

The effect of economy-wide political uncertainty on stock market returns is well documented in the literature. However, in order to take a stand on the relation between firm-specific political risk and the cross-section of stock returns, we need a measure independent of those returns. Using a machine-learning based firm-specific measure of political risk, we show that political risk is priced in the cross-section of stock returns. On average, a one standard deviation increase in a firm's political risk is associated with a 0.5% to 1.0% increase in their annual returns. Using a related non-price measure that captures the mean of a firm's political-shocks, we disentangle whether the asset pricing implications of political risk stem from news about the discount rate or future cash flows. We further show that political risk is priced only for firms that do not actively manage political risk. Finally, using a natural language processing (NLP) enabled measure of risk associated with political topics, we examine how (and to what extent) sub-components of political risk are priced.

Tomorrow's Fish and Chip Paper? Slowly incorporated News and the Cross-section of Stock Returns

The link between news and investor decision making is widely discussed in the literature. Utilising unique U.S. firm-level news data between 1979 and 2016, we document a cross-sectional difference in the speed of the diffusion of information contained in news. We distinguish news articles as being either slowly or quickly incorporated into contemporaneous stock prices. The return spread between stocks classified according to these two types of news yields a statistically significant profit of 139 basis points per month. This abnormal return cannot be explained by other well-known risk factors and is robust when allowing for trading costs. Overall, our research refines the role of news regarding information dissemination in the financial markets.

Persistent Predictors and the Cross-Section of Stock Returns

We show that when returns are predictable, persistent predictors, known to bias time-series predictive regressions, also bias the estimation of the cross-sectional moments of asset return distribution, especially the variance-covariance matrix of returns. Our findings, further, suggest that the underlying persistence levels together with sample size influence the conclusions of asset pricing tests. We define a test statistic that is immune to this bias and shows consistent results across the persistence levels and sample sizes.

News, Skewness, and the Cross-section of Stock Returns

News, Skewness, and the Cross-section of Stock Returns

Dispersion of beliefs, ambiguity, and the cross-section of stock returns

Abstract We examine whether ambiguity is priced in the cross-section of expected stock returns. Using the cross-sectional dispersion in real-time forecasts of real GDP growth as a measure for ambiguity, we find that high ambiguity beta stocks earn lower future returns relative to low ambiguity beta stocks. This negative predictive relation between the ambiguity beta and future returns is consistent with theory, which predicts the marginal utility of consumption to rise when ambiguity is high. We further show that the ambiguity premium remains significant after controlling for exposures to expected real GDP growth, VIX, and financial market dislocations index.

Social Media Effect, Investor Recognition and the Cross-Section of Stock Returns

Investor recognition affects cross-sectional stock returns. In informationally incomplete markets, investors have limited recognition of all securities, and their holding of stocks with low recognition requires compensation for being imperfectly diversified. Using the number of posts on the Chinese social media platform Guba to measure investor recognition of stocks, this paper provides a direct test of Merton's investor recognition hypothesis. We find a significant social media premium in the Chinese stock market. We further find that including a social media factor based on this premium significantly improves the explanatory power of Fama-French factor models of cross-sectional stock returns, and these results are robust when we control for the mass media effect and liquidity effect. Finally, we find that investment strategies based on the social media factor earn sizable risk-adjusted returns, which signifies the importance of the social media premium in portfolio management.

Time-Series and Cross-Sectional Stock Return Forecasting: New Machine Learning Methods

This paper extends the machine learning methods developed in Han et al. (2019) for forecasting cross-sectional stock returns to a time-series context. The methods use the elastic net to refine the simple combination return forecast from Rapach et al. (2010). In a time-series application focused on forecasting the US market excess return using a large number of potential predictors, we find that the elastic net refinement substantively improves the simple combination forecast, thereby providing one of the best market excess return forecasts to date. We also discuss the cross-sectional return forecasts developed in Han et al. (2019), highlighting how machine learning methods can be used to improve combination forecasts in both the time-series and cross-sectional dimensions. Overall, because many important questions in finance are related to time-series or cross-sectional return forecasts, the machine learning methods discussed in this paper should provide valuable tools to researchers and practitioners alike.

Horizontal Industry Relationships and Return Predictability

It has been documented that vertical customer-supplier links between industries are the basis for strong cross-sectional stock return predictability (Menzly and Ozbas (2010)). We show that robust predictability also arises from horizontal links between industries, i.e., from the fact that industries are competitors or offer products, which are substitutes for each other. These horizontally linked industries exhibit positively correlated fundamentals. The signal derived from this type of connectedness is the basis for significant alpha in sorted portfolio strategies, and informed investors take the related information into account when they form their portfolios. We thus provide evidence of return predictability based on a new type of economic links between industries not captured in previous studies.

Return Dispersion and Fund Performance: Australia – the Land of Opportunity?

We examine the relation between cross-sectional stock return dispersion and active fund performance in Australia, drawing on the concept that higher return dispersion provides greater opportunity for skilled managers to generate value. In contrast with findings using US data that outperformance occurs only for the most active funds and in the highest return dispersion environments, Australian data show that active funds earn positive returns when return dispersion is in the moderate-to-high range. We also find meaningful differences between large-cap funds, where outperformance is modest and significant only for the most active funds, and small-cap funds, where active returns are larger in magnitude and do not depend greatly on fund activeness. Applying a switching strategy between active funds and passive investment reveals that investors would be advantaged by retaining exposure to active funds in Australia in all but low return dispersion months in the absence of buy-sell spreads. However, imposing spreads eliminates the added value from switching, concluding in favour of maintaining an active exposure regardless of the dispersion environment.

Financial Constraints, Stock Liquidity, and Stock Returns

We examine the different impacts of stock liquidity on the stock returns across financially constrained and unconstrained firms due to different levels of information asymmetry. Our results show that financial constraints are highly correlated with liquidity and liquidity risk. More importantly, stock liquidity is a significant determinant of the cross-sectional stock returns for financially constrained firms, but it is insignificant for unconstrained firms. In addition, stock liquidity is a main driver of the different relations between financial constraints and stock returns. The liquidity premium accounts for the positive constraint premium, but it cannot be subsumed by the constraint premium.

Predictability of Order Imbalance, Market Quality and Equity Cost of Capital

We study the effect of the predictability of order imbalance on market quality. We measure the degree of predictability by using the predictive likelihood from a dynamic linear model where the dependent variable is the day-ahead order imbalance. Empirically, we show that increasing order imbalance predictability corresponds to significantly higher market liquidity and efficiency. This positive relationship is economically significant: a long-short portfolio based on past predictability generates significant risk-adjusted returns. Predictability of order imbalance measures a cost of asymmetric information that is not captured by traditional measures of adverse selection. The risk factor that is associated with asymmetric information is priced in the cross-section of stock returns, controlling for a variety of conventional sources of systematic risk. These results suggest the existence of a tight link between market microstructure features affecting order imbalance predictability and both market quality and the cost of capital of firms.

Cash Flow, Profitability, and Investment in the Cross-Section of Stock Returns

Cash Flow, Profitability, and Investment in the Cross-Section of Stock Returns

Transitory prices, resiliency, and the cross-section of stock returns

This paper suggests a new measure of stock market resiliency and demonstrates that resiliency is a dimension of liquidity that generates cross-sectional variations in stock returns. Resiliency is defined as quickness of the transitory price recovery from a liquidity shock. Using the Beveridge-Nelson decomposition and the spectral analysis in the frequency domain, we measure resiliency as the speed of mean reversion of transitory price components. Our main finding is that a zero-investment portfolio long in low-resiliency stocks and short in high-resiliency stocks earns significant abnormal returns. We also find that our resiliency measure is complementary to existing liquidity measures.

Patent-to-Market Premium

A firm’s patent-to-market (PTM) ratio refers to the percentage of a firm’s market value that is attributable to its patent market value. A hedging portfolio based on PTM ratio generates a monthly return of 71 basis points. The CAPM cannot be rejected for firms with low PTM ratios, but is rejected for firms with high PTM ratios. PTM ratio is a priced factor distinct from known factors in the cross-section of stock returns. PTM ratio is positively associated with future profitability. Our analysis suggests that real option is the channel through which PTM ratio predicts future stock returns.