Cross-section Of Stock Returns Research Articles

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

Risk-Neutral Higher Moments and the Cross-Section of Stock Returns

Risk-Neutral Higher Moments and the Cross-Section of Stock Returns

Mean-Variance Optimization of Factors and the Cross-Section of Stock Returns

Mean-Variance Optimization of Factors and the Cross-Section of Stock Returns

Oil Price Exposure and the Cross-Section of Stock Returns

Abstract We provide evidence that equity investors are slow to process information about how current oil price changes affect future earnings announcements. Stock prices respond to lagged quarterly oil price changes when firms start announcing earnings in the next quarter. A cross-sectional equity trading strategy that exploits this predictability yields an annualized Sharpe ratio of 0.50. Our oil-response forecast strategy earns especially high returns after large absolute oil price changes, in recessions or bear markets, and during peak earnings season. The predictability we document is consistent with limited attention, is not driven by risk factor exposure, and survives several robustness tests.

Portfolio optimization and valuation capability of multi-factor models: an observational evidence from Dhaka stock exchange

The main goal of this study is to examine the return explanation strengths of the Carhart four-factor, the Fama–French three-factor, and the single-factor models in the context of the Bangladeshi stock market. We, therefore, reveal the risk-adjusted returns, test the valuation capability of multi-factor models, and estimate optimal portfolio weights of stocks listed in DSE under the DSE30 index. Our findings demonstrate that large capitalization firms that have low or medium book-to-market (B/M) ratios produce more concentrated returns than their counterparts, resulting in greater earnings per unit of total, systematic, and downside risks. Furthermore, we discover that each factorial value has an impressive capacity to explain the market excess returns; however, the influence of factor values on the cross-section of stock returns is somewhat contradictory. In particular, the momentum factor is unable to describe the cross-section excess returns, whereas the risk premium, size, and value factors have a significant impact on the cross-section excess returns. Finally, we find that a large-cap firm with a low B/M ratio is suitable for risk-seeking investors; in contrast, a small-cap firm with a low B/M ratio is appropriate for lower risk tolerance investors. Moreover, our empirical outcomes have noteworthy implications for private companies, investors, and policymakers.

The cross-sectional stock return predictions via quantum neural network and tensor network

In this paper, we investigate the application of quantum and quantum-inspired machine learning algorithms to stock return predictions. Specifically, we evaluate the performance of quantum neural network, an algorithm suited for noisy intermediate-scale quantum computers, and tensor network, a quantum-inspired machine learning algorithm, against classical models such as linear regression and neural networks. To evaluate their abilities, we construct portfolios based on their predictions and measure investment performances. The empirical study on the Japanese stock market shows the tensor network model achieves superior performance compared to classical benchmark models, including linear and neural network models. Though the quantum neural network model attains the lowered risk-adjusted excess return than the classical neural network models over the whole period, both the quantum neural network and tensor network models have superior performances in the latest market environment, which suggests capability of model’s capturing non-linearity between input features.

Risk factors in the Indonesian stock market

This paper identifies the relevant risk factors that determine the cross-section of returns in the Indonesian stock market. We examine 152 factors using the Bayesian framework developed in Jensen et al. (2022). Our results show that size, value, quality, and profitability are the characteristics themes that explain future cross-sectional stock returns during the period 1991–2022. Momentum is not significant. We document differences in factor returns for stocks that adhere to Sharia law (i.e. Islamic finance principles). Value and size return patterns occur across all stocks, but significant posterior alphas for quality and profitability reliably exist only within non-Sharia stocks.

Machine learning, anomalies, and the expected market return: Evidence from China

We investigate whether machine learning (ML) techniques that forecast overall U.S. market returns using cross-sectional stock return anomalies in Dong et al. (2022) are useful for the China equity market. We successfully forecast out-of-sample R2 of the market return in China using a combined version of ordinary least squares and an elastic net model. However, the other four ML methods cannot forecast the market return. Overall, our exercise highlights the potential of ML techniques, but also calls for future research to rule out the possibility of model mining.

Technical analysis as a sentiment barometer and the cross-section of stock returns

This paper explores an unexamined sentiment channel through which technical analysis can add value. We use a spectrum of technical trading strategies to build a daily market sentiment indicator that is highly correlated with other commonly used sentiment measures. This technical-analysis-based sentiment indicator positively predicts near-term returns and is inversely related to long-term returns in the cross-section. Simple trading strategies based on this sentiment indicator yield substantial abnormal returns. These results are consistent with the explanation that lack of synchronization induces rational arbitrageurs to exploit the mispricing before it is corrected.