Gambling preference and individual equity option returns

Byun, Suk Joon; Kim, Da‐Hea

doi:10.1016/j.jfineco.2016.06.004

Cited by 97 publications

(44 citation statements)

References 64 publications

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“…This result is consistent with the optimistic attitudes of investors in expansions (Blau, 2017;Byun and Kim, 2016). However, there is no significant relationship between kurtosis and subsequent industry returns.…”

Section: Discussionsupporting

confidence: 85%

Higher moments and US industry returns: realized skewness and kurtosis

Chen

Worthington

2021

RAF

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Purpose The purpose of this paper is to examine the relationships between the higher moments of returns (realized skewness and kurtosis) and subsequent returns at the industry level, with a focus on both empirical predictability and practical application via trading strategies. Design/methodology/approach Daily returns for 48 US industries over the period 1970–2019 from Kenneth French’s data library are used to calculate the higher moments and to construct short- and medium-term single-sort trading strategies. The analysis adjusts returns for common risk factors (market, size, value, investment, profitability and illiquidity) to confirm whether conventional asset pricing models can capture these relationships. Findings Past skewness positively relates to subsequent industry returns and this relationship is unexplained by common risk factors. There is also a time-varying effect in which the predictive role of skewness is much stronger over business cycle expansions than recessions, a result consistent with varying investor optimism. However, there is no significant relationship between kurtosis and subsequent industry returns. The analysis confirms robustness using both value- and equal-weighted returns. Research limitations/implications The calculation of realized moments conventionally uses high-frequency intra-day data, regrettably unavailable for industries. In addition, the chosen portfolio-sorting method may omit some information, as it compares only average group returns. Nonetheless, the close relationship between skewness and future returns at the industry level suggests variations in returns unexplained by common risk factors. This enriches knowledge of market anomalies and questions yet again weak-form market efficiency and the validity of conventional asset pricing models. One suggestion is that it is possible to significantly improve the existing multi-factor asset pricing models by including industry skewness as a risk factor. Practical implications Given the relationship between skewness and future returns at the industry level, investors may predict subsequent industry returns to select better-performing funds. They may even construct trading strategies based on return distributions that would generate abnormal returns. Further, as the evaluation of individual stocks also contains industry information, and stocks in industries with better performance earn higher returns, risks related to industry return distributions can also shed light on individual stock picking. Originality/value While there is abundant evidence of the relationships between higher moments and future returns at the firm level, there is little at the industry level. Further, by testing whether there is time variation in the relationship between industry higher moments and future returns, the paper yields novel evidence concerning the asymmetric effect of stock return predictability over business cycles. Finally, the analysis supplements firm-level results focusing only on the decomposed components of higher moments.

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Section: Discussionsupporting

confidence: 85%

Higher moments and US industry returns: realized skewness and kurtosis

Chen

Worthington

2021

RAF

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“…We apply a number of filters in the options data in order to ensure tradability and avoid outliers (e.g., Byun and Kim, 2016;Boyer and Vorkink, 2014). We eliminate all options with any of the following characteristics: i) the underlying asset is an index (Optionmetrics "index flag" is nonzero), ii) the expiration date of the option is at the market open of the last trading day, iii) we control for options with nonstandard settlement, that is, when the number of shares to be delivered is different from 100 (i.e.…”

Section: Filters Of Options Datamentioning

confidence: 99%

Overcoming Arbitrage Limits: Option Trading and Momentum Returns

et al. 2018

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“…Our analysis instead focuses on raw returns. Boyer and Vorkink (2014) and Byun and Kim (2016) investigate the relation between skewness and option returns using the cross section of option returns. Goodman, Neamtiu, and Zhang (2018) find that fundamental accounting information is related to future option returns.…”

Section: Figure 1 Option Leverage As a Function Of Volatility In The mentioning

confidence: 99%

Volatility and Expected Option Returns

Hu¹,

Jacobs

2019

J. Financ. Quant. Anal.

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We analyze the relation between expected option returns and the volatility of the underlying securities. The expected return from holding a call (put) option is a decreasing (increasing) function of the volatility of the underlying. These predictions are supported by the data. In the cross section of equity option returns, returns on call (put) option portfolios decrease (increase) with underlying stock volatility. This finding is not due to cross-sectional variation in expected stock returns. It holds in various option samples with different maturities and moneyness, and is robust to alternative measures of underlying volatility and different weighting methods.

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Gambling preference and individual equity option returns

Cited by 97 publications

References 64 publications

Higher moments and US industry returns: realized skewness and kurtosis

Higher moments and US industry returns: realized skewness and kurtosis

Overcoming Arbitrage Limits: Option Trading and Momentum Returns

Volatility and Expected Option Returns

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