When the Market Went Viral: COVID-19, Stock Returns, and Firm Characteristics

Abstract We investigate how transaction costs change the number of characteristics that are jointly significant for an investor’s optimal portfolio and, hence, how they change the dimension of the cross-section of stock returns. We find that transaction costs increase the number of significant characteristics from 6 to 15. The explanation is that, as we show theoretically and empirically, combining characteristics reduces transaction costs because the trades in the underlying stocks required to rebalance different characteristics often cancel out. Thus, transaction costs provide an economic rationale for considering a larger number of characteristics than that in prominent asset-pricing models. Authors have furnished an Internet Appendix, which is available on the Oxford University Press Web site next to the link to the final published paper online.

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Firm Characteristics and Long-Run Stock Returns after Corporate Events

SSRN Electronic Journal ◽

10.2139/ssrn.2097989 ◽

2012 ◽

Author(s):

Hendrik (Hank) Bessembinder ◽

Feng Zhang

Keyword(s):

Stock Returns ◽

Firm Characteristics ◽

Long Run

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Macroeconomic Variables, Firm Characteristics and Stock Returns during Good and Bad Times: Evidence from SEA

Asian Journal of Finance & Accounting ◽

10.5296/ajfa.v5i2.4025 ◽

2013 ◽

Vol 5 (2) ◽

pp. 159

Author(s):

Nafis Alam

Keyword(s):

Stock Returns ◽

Firm Characteristics ◽

Macroeconomic Variables

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Stock Illiquidity and Firm Characteristics

Market Microstructure and Liquidity ◽

10.1142/s2382626619500096 ◽

2018 ◽

Vol 04 (03n04) ◽

pp. 1950009

Author(s):

Divya Verma Gakhar ◽

Shweta Kundlia

Keyword(s):

Financial Markets ◽

Stock Returns ◽

Regression Models ◽

Policy Implications ◽

Firm Characteristics ◽

Financial Health ◽

Chi Square ◽

Retail Investors ◽

Multiple Regression Models ◽

Basic Characteristics

Main objective of the study is to analyze firm characteristics which affect stock illiquidity. The paper aims to give suggestions and policy implications to corporates and investors while dealing with investments in illiquid stocks. ANOVA, chi-square tests, correlation analysis, univariate and multiple regression models are employed on Amihud (2002) (Amihud, Y., (2002). Illiquidity and Stock Returns: Cross-Section and Time-Series Effects, Journal of Financial Markets 5, 31–56) illiquidity measure and various firm characteristics. Findings of this paper suggest that firms with illiquid stocks can be characterized with low promoter’s stakes, high leverage, poor financial health, small size and low/negative profitability. The findings of the paper will be of relevance to retail investors who are at the mercy of informed investors. The results portray basic characteristics that an investor should look into before investing in any stock. The study is of value to the investors who are grieved because of the adverse selections and information asymmetry. Moreover, the basic nature of illiquid firms has never been studied.

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Realized Skewness and the Return Predictability

Journal of Derivatives and Quantitative Studies ◽

10.1108/jdqs-01-2016-b0005 ◽

2016 ◽

Vol 24 (1) ◽

pp. 119-152

Author(s):

Myounghwa Sim

Keyword(s):

Stock Returns ◽

Cross Section ◽

Realized Volatility ◽

Return Predictability ◽

Higher Moments ◽

Firm Characteristics ◽

Realized Variance ◽

Negative Relation ◽

Intraday Data ◽

Skewness And Kurtosis

We explore the cross-section of realized variance, skewness, and kurtosis for stock returns obtained from intraday data. We investigate the properties of the realized higher moments, and more importantly, examine relations between the realized moments and subsequent stock returns. We find evidence of a negative relation between realized skewness and next week’s returns. A strategy buying stocks in the lowest realized skewness quintile and selling stocks in the highest realized skewness quintile earns 0.79 percent per week a risk-adjusted basis. Our results on the realized skewness are robust to controls for various firm characteristics such as size and book-to-market. Little evidence exists that either the realized volatility or the realized kurtosis is significantly related to next week’s returns.

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Predicting Stock Returns Using Industry-Relative Firm Characteristics

SSRN Electronic Journal ◽

10.2139/ssrn.213872 ◽

2000 ◽

Cited By ~ 57

Author(s):

Clifford S. Asness ◽

R. Burt Porter ◽

Ross L. Stevens

Keyword(s):

Stock Returns ◽

Firm Characteristics

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Firm Characteristics, Cross-Sectional Regression Estimates, and Asset Pricing Tests

The Review of Asset Pricing Studies ◽

10.1093/rapstu/raz005 ◽

2019 ◽

Vol 10 (2) ◽

pp. 290-334 ◽

Cited By ~ 3

Author(s):

Chris Kirby

Keyword(s):

Asset Pricing ◽

Stock Returns ◽

Nonlinear Effects ◽

Firm Characteristics ◽

Pricing Models ◽

Cross Sectional ◽

Portfolio Returns ◽

Asset Pricing Models ◽

Formidable Challenge ◽

Expected Stock Returns

Abstract I test a number of well-known asset pricing models using regression-based managed portfolios that capture nonlinearity in the cross-sectional relation between firm characteristics and expected stock returns. Although the average portfolio returns point to substantial nonlinearity in the data, none of the asset pricing models successfully explain the estimated nonlinear effects. Indeed, the estimated expected returns produced by the models display almost no variation across portfolios. Because the tests soundly reject every model considered, it is apparent that nonlinearity in the relation between firm characteristics and expected stock returns poses a formidable challenge to asset pricing theory. (JEL G12, C58)

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Arbitrage Portfolios

Review of Financial Studies ◽

10.1093/rfs/hhaa102 ◽

2020 ◽

Cited By ~ 2

Author(s):

Soohun Kim ◽

Robert A Korajczyk ◽

Andreas Neuhierl

Keyword(s):

Asset Pricing ◽

Stock Returns ◽

Predictive Power ◽

Abnormal Returns ◽

Firm Characteristics ◽

Pricing Models ◽

Asset Pricing Models ◽

Maximal Weight ◽

Large Panel ◽

Sharpe Ratios

Abstract We propose a new methodology for forming arbitrage portfolios that utilizes the information contained in firm characteristics for both abnormal returns and factor loadings. The methodology gives maximal weight to risk-based interpretations of characteristics’ predictive power before any attribution is made to abnormal returns. We apply the methodology to simulated economies and to a large panel of U.S. stock returns. The methodology works well in our simulation and when applied to stocks. Empirically, we find the arbitrage portfolio has (statistically and economically) significant alphas relative to several popular asset pricing models and annualized Sharpe ratios ranging from 1.31 to 1.66.

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