Cross-section Of Returns Research Articles

The Anna Karenina principle and stock prices

The Anna Karenina principle (AKP) based on the opening line of Leo Tolstoy’s Anna Karenina, “All happy families are all alike; each unhappy family is unhappy in its own way” is applied to financial markets. We test the AKP by defining happy firms as positive return firms and unhappy firms as negative return firms and analyse whether happy firms are more “alike” than unhappy firms. We use return correlations, cross-sectional return and cross-sectional volatility dispersion as measures and find for different stock index constituent samples totalling more than 8000 stocks that the AKP does not hold in general. In contrast, we find that the average return volatility of unhappy firms is larger than that of happy firms. This cross-sectional version of volatility asymmetry also implies that unhappy firms are more stressed than happy firms.

False discoveries in the anomaly research: New insights from the Stock Exchange of Melbourne (1927–1987)

Are equity anomalies a product of p-hacking in the asset pricing literature? To shed new light on this question, we perform a true out-of-sample study of 30 well-known anomalies in the cross-section of returns. We replicate these anomalies in a novel hand-collected dataset of firms listed on the historical Stock Exchange of Melbourne in the years 1926 to 1987. The vast majority of return-predictive signals cannot be confirmed. Those which are observed are commonly driven by small firms with marginal economic significance. Only a handful of anomalies survive our tests, namely, the dividend yield, value uncertainty, and short-term residual reversal effects. Overall, our findings support the view that many anomalies are statistical artifacts resulting from data mining.

Liquidity risk and stock returns: empirical evidence from industrial products and services sector in Bursa Malaysia

This study investigates the impact of liquidity risk on stock returns of 149 firms in the industrial products and services sectors of Bursa Malaysia from January 2000 to December 2018 with a monthly frequency dataset. This study employed the two-stage standard procedures in asset pricing to estimate the significant effect of liquidity risk on industrial products and services stock returns. The results show that the investors require liquidity premium for stocks whose illiquidity co-moves with market illiquidity and market return while shifting their investment to liquid stocks when the market becomes illiquid, thus positive premium for stocks whose return is higher during the illiquid market. It suggests that two liquidity risks, namely commonality in liquidity and the covariances between stock illiquidity and market returns, and aggregate liquidity risk explain the cross-sectional returns variations across stocks in the industrial products and services sector, thus partly support the LCAPM model. We provide evidence on the important role of liquidity risks on the cross-sectional industrial products and services stock returns in Bursa Malaysia in the LCAPM framework. The findings of this study may be useful for investment decision-making and portfolio allocation strategy under the liquid and illiquid securities conditions. For policymakers, understanding the impact of liquidity risks on stock returns for the industrial products and services sectors may help enhance market liquidity for economic growth. Therefore, our findings contribute to the practical and policy implications.

Cross-Sectional Skewness

Abstract What distribution best characterizes the time series and cross-section of individual stock returns? To answer this question, we estimate the degree of cross-sectional return skewness relative to a benchmark that nests many models considered in the literature. We find that cross-sectional skewness in monthly returns far exceeds what this benchmark model predicts. However, cross-sectional skewness in long-run returns in the data is substantially below what the model predicts. We show that fat-tailed idiosyncratic events appear to be necessary to explain skewness in the data. (JEL, G10, G11, G12, G13, G14).

Aggregate liquidity premium and cross-sectional returns: Evidence from China

The Chinese stock market incurs huge illiquidity costs. Liquidity has different aspects but literature rarely measures it from an aggregate perspective. To capture liquidity along various dimensions and more consistently, we propose an aggregate liquidity premium with a partial least squares approach by aggregating information on 12 liquidity-related firm characteristics in the Chinese stock market. The aggregate liquidity predictor generates significantly higher expected stock returns than individual characteristics, remains robust to different information aggregation methods, and reflects the multidimensional feature of liquidity. Behavioral mispricing theory helps explain the aggregate liquidity premium because illiquidity limits arbitrage to correct mispricing. As liquidity is important and traditional factor models limitedly explain it, we develop a new-factor model to capture the aggregate liquidity premium. The aggregate liquidity premium in China indicates that policymakers should improve market liquidity.

Ripples into waves: Trade networks, economic activity, and asset prices

We exploit information in sovereign CDS spreads and the international trade network to provide causal evidence of the propagation of global economic shocks. We show that trade links are an important source of shock transmission using the natural experiments of the Japanese tsunami and the COVID-19 lockdown in China. We then confirm more general and gradual information flows along the trade network by showing extensive country-level credit/equity cross-sectional return predictability. News about country fundamentals flows primarily from importers to exporters, depends on both direct and indirect links in the trade network, and is magnified by the exporting country's financial vulnerability.

Constructing long-short stock portfolio with a new listwise learn-to-rank algorithm

Factor strategies have gained growing popularity in industry with the fast development of machine learning. Usually, multi-factors are fed to an algorithm for some cross-sectional return predictions, which are then further used to construct a long-short portfolio. Instead of predicting the value of the stock return, emerging studies predict a ranked stock list using the mature learn-to-rank technology. In this study, we propose a new listwise learn-to-rank loss function which aims to emphasize both the top and the bottom of a rank list. Our loss function, motivated by the long-short strategy, is endogenously shift-invariant and can be viewed as a direct generalization of ListMLE. Under different transformation functions, our loss can lead to consistency with binary classification loss or permutation level 0-1 loss. A probabilistic explanation for our model is also given as a generalized Plackett-Luce model. Based on a dataset of 68 factors in the China A-share market from 2006 to 2019, our empirical study has demonstrated the strength of our method which achieves an out-of-sample annual return of 38% with Sharpe ratio 2.

What Do We Know About Corporate Bond Returns?

Recently, there has been a fast-growing literature on the determinants of corporate bond returns, in particular, the driving force of cross-sectional return variation. In this review, we first survey recent empirical studies on this important topic. We discuss cross-sectional evidence as well as time-series evidence. We then present a model-based analysis of individual corporate bond returns using the structural approach for credit risk modeling. We show, among other things, that the expected corporate bond return implied by the Merton model predicts 1-month-ahead corporate bond returns in the cross section.

Higher moments anomaly: evidence from the Indian equity market

PurposeThe study investigates the impact of higher moments on cross-sectional returns in the Indian equity market.Design/methodology/approachUsing the daily data of 3,085 Bombay Stock Exchange-listed stocks spanning over 20 years from January 2000 to December 2019, the study evaluates the relationship between higher moments (skewness and kurtosis) and stock returns at individual stock and portfolio levels. The variations in the returns of the equal-weighted and the value-weighted portfolios are analysed, where the portfolios are constructed by sorting the stocks on skewness and kurtosis. The returns are adjusted for five common factors – market excess-returns, size, value, momentum and illiquidity, to controls other cross-sectional effects. Besides, the study employs Fama-MacBeth cross-sectional regression and time-series tests of higher moments as robustness measures.FindingsThe study presents higher moments anomaly in the Indian equity market. Contrary to what is expected based on a risk-averse rational agent model, a robust positive relationship is observed between the skewness and stock returns. The relationship between the kurtosis and stock returns is negative, albeit statistically weak. These results are robust for the Fama-MacBeth cross-sectional regression and time-series tests.Originality/valueIt is among the earlier attempts to investigate the pricing impact of higher moments at different levels of asset prices in an emerging market. Besides the standard portfolio methodology for explaining cross-sectional variations, the study also employs the time-series tests for higher moment factors, hence provides more robust results. Results have wider implications for asset pricing in emerging markets and highlight many issues for further research.

Comovement and return predictability in asset markets: An experiment with two Lucas trees

Abstract Using a laboratory experiment, we investigate whether comovement can emerge between two risky assets, despite their fundamentals not being correlated. The ‘Two trees’ asset pricing model developed by Cochrane et al. (2007) guides our experimental design and its predictions serve as our source of hypotheses. The model makes time-series and cross-section return predictions following a shock to one of the two assets’ dividend distributions. As the model predicts, we observe (1) positive contemporaneous correlation between the two assets, (2) positive autocorrelation in the shocked asset, and (3) time-series and cross-sectional return predictability from the dividend-price ratio. In line with the rational foundations of the model, the model's predictions have stronger support in markets with relatively sophisticated agents.

Navigating the factor zoo around the world: an institutional investor perspective

The literature on cross-sectional stock return predictability has documented over 450 factors. We take the perspective of an institutional investor and navigate this zoo of factors by focusing on the evidence relevant to the practicalities of factor-based investment strategies. Establishing a sound theoretical rationale is key to identifying “true” factors, and we emphasize the need to recognize data-mining concerns that may cast doubt on the relevance of many factors. From a practical investment perspective, much of the factor evidence documented by academics may be more apparent than real. The performance of many factors is dependent on the inclusion of small- and micro-cap stocks in academic studies, although such stocks would likely be excluded from the real investment universe due to illiquidity and transaction costs. Nevertheless, a parsimonious set of factors emerges in equities and other asset classes, including currencies, fixed income, and commodities. These factors can serve as meaningful ingredients to factor-based portfolio construction.

Contagion and Return Predictability in Asset Markets: An Experiment With Two Lucas Trees

Using a laboratory experiment, we investigate whether comovement can emerge between two risky assets, despite their fundamentals not being correlated. The ‘Two trees’ asset pricing model developed by Cochrane et al. (2007) guides our experimental design and its predictions serve as our source of hypotheses. The model makes time-series and cross-section return predictions following a shock to one of the two assets’ dividend distributions. As the model predicts, we observe (1) positive contemporaneous correlation between the two assets, (2) positive autocorrelation in the shocked asset, and (3) time-series and cross-sectional return predictability from the dividend-price ratio. In line with the rational foundations of the model, the model's predictions have stronger support in markets with relatively sophisticated agents.

Environmental, Social, and Governance (ESG) Investing Risk and Return Analysis of Egyptian Sustainable Equity Index

The present paper aims to compare risk and return characteristics of ESG index with those of conventional benchmark market index: EGX30 for a period from June 2007 to September 2020. We apply GARCH models not only to examine the volatility features of each index but also to investigate effect of exogenous shocks to stock market including global financial crisis in 2008 (GFC), political uncertainty after Egyptian revolution in 2011 (ER2011) and most recently potential effects of covid-19 pandemic. The results reveal that the daily compounded returns of the ESG Index are not statistically different from those of the EGX30 index, however, annualized returns of the ESG Index have been better than the returns of the EGX30 index. Interestingly, the annualized returns of ESG index outperform those of EGX30 index during the exogenous shock periods. Conversely, the ESG returns tend to underperform EGX30 returns during normal time periods, except the pre-Egyptian revolution period. We find that volatility of ESG index returns is more persistent than that of benchmark index, but the returns of benchmark index has larger leverage effect than those of ESG index. The findings have three important practical implications. Firstly, portfolio managers could follow ESG investing to diversify their portfolios and maximize returns. Secondly, firms listed in Egyptian exchange should adopt more environmental, social, governance issues in their policies to maximize firm value. Thirdly, asset pricing models could be extended to include ESG premium factor in explaining cross-sectional returns in the Egyptian exchange.

Equity Factor Timing With Kalman Filter

Extensive research literature has shown that equity factor premia is not constant over time. With the broad adoption of factor investing among active managers, to generate differentiated market views and returns, factor or style timing remains as one attractive meaning. We present an adaptive linear factor model to capture time-varying factor risk premia and discuss its application in equity portfolio investment. The model framework that allows time-varying factor returns can potentially capture more returns and allows an investor to understand the changes in cross-sectional factor returns. We estimate model coefficients through a Kalman filter model and discuss the results with an empirical example.

Validating intra-day risk premium in cross-sectional return curves

This paper investigates the cross-sectional asset pricing for intra-day return curves. By introducing a functional Fama-MacBeth regression approach, the validation of the intra-day risk premium associated with the Fama-French Carhart factors is examined. The empirical evidence reveals that these common risk factors show weak explainability to the entire cross-sectional intra-day returns, despite significant risk premiums that are discovered in specific half-hour time-spans in bullish sentiment.

Mood Sensitive Stocks and Sustainable Cross-Sectional Returns During the COVID-19 Pandemic: An Analysis of Day of the Week Effect in the Chinese A-Share Market.

This study examines two stock market anomalies and provides strong evidence of the day-of-the-week effect in the Chinese A-share market during the COVID-19 pandemic. Specifically, we examined the Quality minus Junk (QMJ) strategy return on Monday and FridayQuality stocks mean portfolio deciles that earn higher excess returns. As historical evidences suggest that less distressed/safe stocks earn higher excess returns (Dichev, 1998).. The QMJ factor is similar to the division of speculative and non-speculative stocks described by Birru (2018). Our findings provide evidence that the QMJ strategy gains negative returns on Fridays for both anomalies because the junk side is sensitive to an elevated mood and, thus, performs better than the quality side of portfolios on Friday. Our findings are also consistent with the theory of investor sentiment which asserts that investors are more optimistic when their mood is elevated, and generally individual mood is better on Friday than on other days of the week. Therefore, the speculative stocks earned higher sustainable stock returns during higher volatility in Chinese market due to COVID-19. Intrinsically, new evidence emerges on an inclined strategy to invest in speculative stocks on Fridays during the COVID-19 pandemic to gain sustainable excess returns in the Chinese A-share market.

Firm-specific investor sentiment for the Chinese stock market

Investor sentiment plays a significant role in asset prices, and sieves out safer and speculative stocks, and captures institutional and retail investors’ demand shocks. Previous studies focus on the effects of investor sentiment in the developed stock markets using various sentiment metrics. We propose overnight and over-weekend returns as firm-specific investor sentiment (FSIS) to reflect retail investors’ beliefs about the developing Chinese stock market, and show that FSIS has short-term persistence, a negative relation with intraday returns and price impact, a U-shaped relation with trading activity, an inverse U-shaped relation with long-run performance and a positive impact on cross-sectional returns. FSIS captures the characteristics of the Chinese stock market with short-selling constraints, and market-level information of FSIS explains risk premiums from sentiment-driven mispricing. Our results also reflect the stronger-self remediability after the 2015 crash of the Chinese stock market.

Notional Value Effect in Futures Markets

We examine how the notional value of futures contracts predicts the cross-section of returns within the major asset classes tracking a large number of futures contracts. We find that low notional value contracts outperform high notional value contracts within government bonds, short-term rates, commodities, currencies, and equity indexes. A diversified portfolio of the strategy delivers abnormal returns after controlling for standard asset pricing factors. The strategy is related to value and reversal factors but their explanatory power is low. Differences in liquidity explain a large portion of the cross-section of notional value, where high notional value contracts are more liquid, and subsume the reversal and value factors. Volatility risk can be a partial explanation both cross-sectionally where low notional value contracts exhibit higher volatility and across time where shocks to market volatility decrease strategy returns.

Global Economic Uncertainty and the U.S. Equity Returns

We document the predictive ability and economic significance of global economic policy uncertainty for U.S. equity returns. After orthogonalizing global economic policy uncertainty (global EPU) with respect to the U.S. EPU, we find that it has significant predictive power for aggregate stock returns and returns of portfolios constructed on size, investment, capital expenditure, and foreign sales in 6 to 12-months ahead horizons. For individual companies, we show that global EPU commands an economically significant and negative-valued premium in the cross-section of returns. The stocks of firms that are highly sensitive to global EPU, whether positive or negative, outperform those that are less sensitive to this measure.

To explore the mystery of the idiosyncratic volatility of the A-share market

Return and risk are inevitable topics in financial research. This paper explores the relationship between IVOL (idiosyncratic volatility) and cross-sectional return (risk premium and excess return) of the Chinese A-share market. With the monthly data of 237 months from January 2001 to September 2019 of Ashare of Shanghai and Shenzhen stock exchange, IVOL of each stock by the regressions is conducted through rolling window based on the four factors model of Carhart. Whether there is a significant positive or negative relationship between the IVOL and the cross-sectional return of the stock by combination analysis and crosssection regression are tested in the paper. The research shows that, after excluding the influence of financial crisis and stock disaster, from January 2001 to September 2019, there is a significant positive relationship between the special volatility and cross-sectional return in Chinese A-share market under normal market conditions, and there is no so-called “mystery of the special volatility”.