Cross-section Of Stock Returns Research Articles

Economic policy uncertainty in OFDI host countries and the cross-section of stock returns

This study examines the association between foreign economic policy uncertainty (FEPU) originating from the host countries (regions) of outward foreign direct investment (OFDI) and expected stock returns. We construct a novel variable, FEPU, based on the year-end OFDI amounts of Chinese listed multinational enterprises (MNEs) and the EPU indices of 23 host countries (regions). Our findings reveal that stocks with higher FEPU outperform those with lower FEPU by 4.96 % annually. Beyond predicting short-term expected returns, FEPU also exhibits strong positive predictive power for firms’ long-term cumulative returns. Additionally, through mechanism tests, we demonstrate that this excess return is attributable to compensation for risk premium. Unlike the majority of studies that focus solely on the impact of EPU within a single country or region, we examine the cross-border implications of EPU. This approach offers an innovative perspective on the pricing of MNEs’ stocks by characterizing the exposure to external EPU.

Gold, platinum, and mutual fund flows

Huang and Kilic (2019) demonstrate that gold to platinum price ratio (GP), which proxies for tail risk in the economy, is a priced risk factor in the cross-section of stock returns. We document that GP negatively affects the mutual fund flows of the active equity funds. In cross-sectional regressions, we find that funds with high betas with respect to the change in GP (βΔGP) have larger future fund flows, as such funds provide a hedge against economic distress. Further, βΔGP helps predict the future performance of the fund in the next few quarters. βΔGP also relates negatively to the downside risk of the fund, implying that funds could potentially reduce their left-tail risk by tilting towards securities with above average βΔGP. We also examine the flows to active corporate bond funds and passive funds. While these effects of GP are largely observable for passive funds, they are not as strongly observable for corporate bond funds.

Choices Matter When Training Machine Learning Models for Return Prediction

Applying machine learning to cross-sectional stock return prediction requires careful consideration of modeling choices. Common approaches that fail to account for heterogeneity or imbalanced stock representation in training data can lead to suboptimal performance. I study two strategies to address these issues: training group-specific models and predicting relative returns. Both approaches yield similar economic improvements over models trained on the full cross-section of US stock returns, with value-weighted trading strategies benefiting significantly. The findings underscore the importance of aligning machine learning modeling decisions with desired economic outcomes and provide guidance for researchers and practitioners seeking robust machine learning models.

MD&A tone and stock returns

This paper examines the impact of the MD&A (Management’s Discussion and Analysis) tone on cross-sectional stock returns in the unique context of Chinese A-share companies. We find a significant and positive relationship between the MD&A tone and Chinese stock returns after controlling for quantitative financial metrics. Our results suggest that the MD&A tone provides additional value that further enhances the information quality in MD&A disclosures and market responses. Furthermore, we observe that the textual tone effect is more pronounced for firms with lower institutional ownership, lower financial transparency, in less competitive market environments, and both the information factor and the sentiment factor of MD&A tone positively affect stock returns. Thus, we provide convincing evidence that MD&A narratives can serve as an effective communication tool to facilitate disclosure and mitigate information asymmetry between corporations and investors.

Ambiguity, limited market participation, and the cross-sectional stock return

Using expected utility under uncertain probability theory (EUUP, Izhakian, 2017, 2020), we study whether the ambiguity related to individual stocks is priced in the Chinese A-share market and the mechanism behind the ambiguity premium phenomenon. Theoretically, when the asset price is within a specific range, ambiguity-averse investors refrain from participating in trading. As asset ambiguity increases, high ambiguity-averse investors exit the market, with those with lower aversion remaining in the market (limited market participation phenomenon). Investors who remain in the market due to lower ambiguity aversion are willing to accept a low ambiguity premium. Empirically, we measure monthly ambiguity using the volatility of daily stock return distributions within a month and find the following: (1) The equal-weighted average returns of the most ambiguous portfolios (top 20%) are significantly lower than those of the least ambiguous portfolios (bottom 20%). (2) Ambiguity still significantly negatively affects the cross-sectional stock return after controlling for common firm characteristics. (3) The higher the ambiguity, the lower the future trading activity, supporting our theoretical predictions. These findings reveal the mechanism of the negative ambiguity premium in the Chinese A-share market. This study provides new ideas for building a factor-pricing model suitable for the A-share market and a fresh perspective for preventing systemic financial risk.

Hedge funds and the positive idiosyncratic volatility effect

Abstract While it is established that idiosyncratic volatility is negatively priced in the cross-section of stock returns, the relation between idiosyncratic volatility and hedge fund returns is largely unexplored. We document that hedge funds with high idiosyncratic volatility earn higher future risk-adjusted returns of 6 percent p.a. than hedge funds with low idiosyncratic volatility. The outperformance arises because hedge funds trade high idiosyncratic volatility stocks wisely. They pick high volatility stocks when they are underpriced and short-sell high volatility stocks when they are overpriced. Our results support the notion that hedge funds’ idiosyncratic volatility is a measure of managerial skill.

Investor clientele and intraday patterns in the cross section of stock returns

Investor clientele and intraday patterns in the cross section of stock returns

From macro to micro: Sparse macroeconomic risks and the cross-section of stock returns

Our study presents novel evidence on the pricing effectiveness of macroeconomic risks at the firm level. We employ a sparse PCA approach to aggregate macroeconomic variables from the FRED-MD database and obtain the first eight components, namely inflation, housing, spreads, production, employment, personal income, yields, and credit. We then construct the corresponding firm-level macroeconomic risk exposures as the sensitivity of the individual stock returns to the sparse economic components. Our research yields three main findings: (i) macro betas cannot be fully captured by common firm fundamentals, indicating the unique information in macro betas; (ii) the betas of inflation, production, personal income, yields, and credit are strong predictors of future stock returns beyond other micro risks; and (iii) behavioral mispricing theory, especially arbitrage frictions and investor sentiment, can help explain the macro beta premium.

Social forecasting: Online social opinion and the cross-section of stock returns

We construct a monthly measure for online social opinions on stocks in the Chinese market by extracting textual data from the internet. By implementing a “social forecasting” strategy, we find a significantly positive alpha of 0.544% per month based on the model of Fama-French's (2015) five-factor plus Carhart's (1997) momentum, and 0.636% per month based on Liu et al.'s (2018) Chinese four-factor model. This anomaly is driven by the mispricing of underexplored information since online social opinions are a strong predictor of firms' earning surprise and are strengthened when there are information frictions, such as low investor attention, high arbitrage costs, high proportions of sentiment-driven trades, and low percentages of institutional investors. Our results are robust to a series of tests and remain unchanged after taking the “factor zoo” into consideration.

Measuring and Testing Systemic Risk from the Cross-Section of Stock Returns

Measuring and Testing Systemic Risk from the Cross-Section of Stock Returns

Realized semibetas in the Australian stock market

ABSTRACT This study examines the explanatory power of the CAPM and downside risk asset pricing models (the downside beta and the realized semibeta models) for the next-month firm-level cross-sectional stock return variation in the Australian stock market. We show that the CAPM beta, downside beta, semibeta BetaNP, and semibeta BetaNN negatively predict future stock returns, which is inconsistent with the findings in the original study by Bollerslev, Patton, and Quaedvlieg (2022). The BetaNN measures the individual stock movement in the same direction as the downward stock market, while BetaNP measures individual stock downward with the upward stock market. These findings are robust, not subsumed by conventional cross-sectional asset pricing factors, and consistent with the existing Australian downside risk study.

Higher order moments of stock market volatility and the cross section of stock returns

This study investigates whether risks captured by higher order moments of stock market volatility are priced in the cross section of stocks. These moments are estimated from daily snapshots of VIX option prices and, therefore, are conditional and forward-looking. Empirical results show that exposure to kurtosis of aggregate volatility (KOV) is a significant cross-sectional determinant of stock returns. The KOV risk premium is negative and robust to controlling for exposures to alternative measures of economic uncertainty as well as stock characteristics.

The change in salience and the cross-section of stock returns: Empirical evidence from China A-shares

This study explores the relationship between stocks' change in salience (CS) and the cross-section of stock returns. We find that the change in salience can negatively predict future stock returns. Investors, attracted by salient attributes of choices, overestimate prominent rising trends in stock historical return performance and extrapolate, leading stocks with prominent rising trends to be overpriced and earning lower subsequent returns. In terms of potential mechanisms, we find that costly arbitrage and lottery preference are two underlying mechanisms behind the mispricing of CS. The CS effect is more pronounced among stocks with greater costly arbitrage and greater lottery preference. Moreover, both institutional and individual investors are shown to generate distortions of expectations induced by salient thinking. Our findings are robust after considering common risk factors, shell contamination, short-term reversals, the prospect theory, and investor attention.

Lottery demand, weather and the cross-section of stock returns

We investigate the role that weather-induced mood plays in underperformance of lottery stocks. We hypothesize that during pleasant weather conditions, investors are more likely to become risk-taking and optimistic, and invest more in lottery stocks. This results in overpricing and lower expected return for these stocks. We use sky cloud cover as a proxy for the weather condition and show that the MAX effect mainly exists following sunny weather; however, there is less evidence for the MAX anomaly following cloudy weather. Our finding is robust to using alternative constructions of MAX and skewness measures as well as other proxies for weather condition such as rain, wind and temperature.

Default risk and stock returns: From a perspective of measurement errors

The risk-return tradeoff underlies the conceptual framework for the asset pricing model and investment decisions under the efficient markets hypothesis. Many empirical study, however, demonstrate negative relation between credit risk and cross-section of stock returns. The main effort of this study is to attempt detangling the puzzle from the perspective of measurement error by representing the first treatment of a wide class of nonlinear models with discrete variables using instruments. We found measurement error has great impact on the relationship between stock returns and firm's risk of default. In addition, this relation found to be crucially dependent on credit cycles and firm financial status.

Pension expenses, risk, and implications for stock returns

As life expectancy rises, firms' pension expenses for employees rise as well. This study shows that from around the beginning of the current millennium, US firms' annual mean expenditure on their employee pensions has increased substantially. This expense has become a burden on firms and presents a risk for their activity, which is not diversifiable. An increase in the newly suggested measure causes an increase in the average cross-sectional stock returns. This effect is both statistically and economically significant. Moreover, the effect is sustainable for a variety of robustness tests, both in-sample and out-of-sample.

Price of Regulations: Regulatory Costs and the Cross-section of Stock Returns

Abstract Regulations introduce significant fixed costs and add to operating leverage. Fixed regulatory costs that contribute to operating leverage should generate a risk premium. To explore whether such a premium exists, we introduce a measure of “regulatory operating leverage” that reflects the importance of fixed regulatory costs in a firm’s cost structure. Regulatory operating leverage predicts stock returns in the cross-section, and a zero-cost high-low regulatory operating leverage strategy generates positive and significant risk-adjusted return. Finally, the impact of regulatory operating leverage on returns is due to the (systematic) risk contribution of fixed regulatory costs.

Forecasting stock prices changes using long-short term memory neural network with symbolic genetic programming

This study introduces an augmented Long-Short Term Memory (LSTM) neural network architecture, integrating Symbolic Genetic Programming (SGP), with the objective of forecasting cross-sectional price returns across a comprehensive dataset comprising 4500 listed stocks in the Chinese market over the period from 2014 to 2022. Using the S&P Alpha Pool Dataset for China as basic input, this architecture incorporates data augmentation and feature extraction techniques. The result of this study demonstrates significant improvements in Rank Information coefficient (Rank IC) and IC information ratio (ICIR) by 1128% and 5360% respectively when it is applied to fundamental indicators. For technical indicators, the hybrid model achieves a 206% increase in Rank IC and an impressive surge of 2752% in ICIR. Furthermore, the proposed hybrid SGP-LSTM model outperforms major Chinese stock indexes, generating average annualized excess returns of 31.00%, 24.48%, and 16.38% compared to the CSI 300 index, CSI 500 index, and the average portfolio, respectively. These findings highlight the effectiveness of SGP-LSTM model in improving the accuracy of cross-sectional stock return predictions and provide valuable insights for fund managers, traders, and financial analysts.

Owner's Earnings, Cash-based Operating Profits, and Capital Expenditures in the Cross Section of Stock Returns

Owner's Earnings, Cash-based Operating Profits, and Capital Expenditures in the Cross Section of Stock Returns

Clean Energy Premium and Cross-section of Stock Returns

Clean Energy Premium and Cross-section of Stock Returns