Option trading volume by moneyness, firm fundamentals, and expected stock returns

https://doi.org/10.1016/j.finmar.2021.100648Get rights and content

Highlights

  • Information diffusion from the option market to equities plays an important role in accounting for return predictability in option trading volume.

  • A high option trading volume of all maturity terms, deep-in-the-money put options, and deep-out-of-the-money call options, predicts higher leverage, increases in default risk, negative earnings surprises, and decreases in future profitability.

  • Option trading volume negatively predicts future stock returns.

  • Such predictability is less evident in stocks with high institutional ownership and more analyst coverage.

Abstract

I examine the link between option and equity markets by considering the informational content of the option trading volume with respect to moneyness and maturity when the trade direction is unobserved. A high option trading volume of all maturity terms, deep-in-the-money put options, and deep-out-of-the-money call options, predicts higher leverage, increases in default risk, negative earnings surprises, and decreases in future profitability. In addition, option trading volume negatively predicts future stock returns. Information diffusion from the option market to equities plays an important role in accounting for return predictability in option trading volume. Such predictability is less evident in stocks with high institutional ownership and more analyst coverage.

Introduction

Over the last few decades, the derivative market has rapidly grown to a market that includes multiple assets, not only traditional options, such as forwards, futures, swaps on equities, bonds, commodities, currency, and interest rates, but also exotic instruments, such as collateralized debt obligations (CDOs), credit default swaps (CDSs), and mortgage-backed securities (MBSs). In a complete, competitive, and frictionless perfect market offering equal access to market prices and information, derivatives are redundant assets, and their payoffs can be replicated by the payoff of a portfolio containing underlying assets with known prices (Black and Scholes, 1973; Cox and Ross, 1976). However, in the real world, in the presence of asymmetric information, when the assumption of complete, competitive, and frictionless markets is relaxed, derivatives contribute to market efficiency by producing information about the underlying assets (Grossman, 1988; Back, 1993; Cao, 1999). In recent years, several important studies have documented the information diffusion from equity markets to derivative markets through the channel of option trading volume (Easley et al., 1998; Chakravarty et al., 2004; Pan and Poteshman, 2006; Johnson and So, 2012; Ge et al., 2016).

In this paper, I provide empirical evidence that option trading volume, which is publicly available and nondirectional, reliably forecasts stock returns in the cross-section, even after controlling for various measures of stock order imbalance (Chordia et al., 2002; Easley et al., 2002; Chordia and Subrahmanyam, 2004), option volatility (An et al., 2014), option skewness (Harvey and Siddique, 2000; Xing et al., 2010; Conrad et al., 2013), and the standard anomalies (Stambaugh et al., 2012) for stock returns. I find that option trading volume is associated with firms’ future fundamentals, such as leverage, credit risk, profitability, and earnings surprises. Option trading volume not only significantly and positively predicts increases in future CDS spreads and increases in leverage, but also negatively and significantly predicts future earnings surprises and future profitability. These findings indicate that option trading volume contains valuable information for the cross-section of equity returns that is gradually priced into the equity market. I show that the predictability is stronger for firms with high arbitrage costs. To the best of my knowledge, these links between option trading volume, firm fundamentals, and expected stock returns have not been documented elsewhere.

I construct the option trading volume measure as the option trading volume level adjusted by the stock trading volume. I form different bins with respect to three dimensions: call or put options, option moneyness (strike price), and option maturity (expiration date). I use the Ivy DB OptionMetrics data from January 1996 to December 2019 (24 years, or 288 months in total). In OptionMetrics, option trading volume is nondirectional; that is, the trade direction is unobserved. I find that the publicly available and nondirectional option trading volume of all maturity terms, deep-in-the-money (DITM) put options, and deep-out-of-the-money (DOTM) call options, significantly and negatively predicts cross-sectional stock returns. Stocks ranked in the bottom quintile by option trading volume of very-near-term deep-in-the-money put options outperform those ranked in the top quintile by 1.04% per month (12.48% annualized). Similar results hold for all maturity ranges for deep-in-the-money put options. Stocks ranked in the bottom quintile by option trading volume of very-near-term deep-out-of-the-money call options outperform those ranked in the top quintile by 1.25% per month (15.00% annualized). Similar results hold for all maturity ranges for deep-out-of-the-money call options. The negative relation between option trading volume and average future stock returns is robust to different weighting schemes. The result holds with a Fama-MacBeth regression and is robust to controlling for stock characteristics known to be related to the cross-section of stock returns. The Fama-French factors and the momentum factor hardly explain the significant positive average return of the portfolio that buys low option trading volume stocks and shorts high option trading volume stocks. These results suggest that option trading volume contains useful, fundamental information about the firm.

That the predictability of future stock returns by option trading volume lasts for one month suggests that predictability is unlikely to be due to market microstructure effects (Chakravarty et al., 2004; Muravyev et al., 2013). The predictability of future stock returns by option trading volume is also robust to the different subsample periods and across different market states, such as during periods of low versus high economic activities and low versus high aggregate stock market returns. The predictability of option trading volume on future stock returns is robust to controlling for various stock order balances (Chordia et al., 2002; Easley et al., 2002; Chordia and Subrahmanyam, 2004). The predictability of future stock returns by option trading volume is robust to controlling for various volatility measures (An et al., 2014) and various skewness measures (Harvey and Siddique, 2000; Xing et al., 2010; Conrad et al., 2013). In addition, this predictability is also robust to controlling for the 11 previously documented asset-pricing anomalies used by Stambaugh et al. (2012). These anomalies, which survive the test of the three factors of Fama and French (1993), include financial distress (failure probability and Ohlson's O-score), net stock issues, composite equity issues, total accruals, net operating assets, momentum, gross profit-to-assets, asset growth, return-on-assets, and investment-to-assets.

I extend the examination to the informational content of option trading volume. The option trading volume of all maturity terms DITM put options and DOTM call options, is associated with the firm's leverage, credit risk, and default risk, as well as the firm's earnings surprises and future profitability. I find that the option trading volume significantly and positively predicts increases in both the levels and the changes of the CDS spreads of all maturities when controlling for the standard variables used in the CDS literature (Blanco et al., 2005; Ericsson et al., 2009; Zhang et al., 2009; Han and Zhou, 2015; Han et al., 2017). I also find that option trading volume significantly and positively predicts the future leverage of the firm. Furthermore, the option trading volume negatively and significantly predicts future earnings surprises. Option trading volume also negatively and significantly predicts future profitability. These results indicate that option trading volume contains valuable information about firm fundamentals and that such information is incorporated only gradually into stock prices. I also show that the predictive power of option trading volume for the cross-section of stock returns is stronger for stocks facing high arbitrage costs, such as those with low market capitalization, low stock price, high bid-ask spreads, high idiosyncratic volatility, high analyst dispersion, and high default risk. I find that the outperformance of low option trading volume stocks over high option trading volume stocks is more prominent for stocks with low visibility, such as stocks with low institutional ownership and low analyst coverage. For less visible stocks, the information in the option trading volume of firm fundamentals is even more gradually diffused into the stock prices.

I also consider the case in which short interest in the stock market might be correlated with trading volume in the option market. Studies examining short interest and securities lending establish that stocks with high levels of short interest have lower returns in the future.1 My results show that the predictive power of option trading volume for stock returns is robust even after controlling for the short interest of stocks. Thus, option-trading-volume predictability is different from short-interest predictability, which has been documented in the literature.

The remainder of this paper is organized as follows. In Section 2, I provide a more detailed description of the hypothesis and develops my empirical predictions. In Section 3, I discuss the data and provides summary statistics for the key variables. In Section 4, I present empirical evidence that option trading volume contains useful information about firm fundamentals and thus negatively and significantly predicts the cross-section of expected stock returns. I conclude in Section 5.

Section snippets

Hypothesis development

In Black and Scholes (1973), option contracts are redundant assets that do not affect the price of the underlying assets. The options can be created synthetically by dynamically trading the underlying asset and other assets. Even when the market is dynamically complete in the absence of options, it may become incomplete when an option is traded, and the arbitrage-based pricing formula may not apply in general, because of the asymmetric information that makes it impossible to price options by

Data

The option data for this study originate from the Ivy DB OptionMetrics database. This comprehensive data set contains all U.S.–listed equity call and put options and consists of end-of-day bid and ask quotes for each strike and expiration, implied volatility and option Greeks, and trading volume for my sample period of January 1996 to December 2019. Option trading volumes is measured in number contracts. I match trading volume with the actual day it occurs. To avoid microstructure-related bias,

Empirical tests

This section presents the empirical findings. I first present an analysis of portfolios formed by sorting on option trading volume (Subsection 4.1). A regression analysis of various robustness checks (Subsection 4.2) follows. I then present an analysis of the link between firm fundamentals and option trading volume (Subsection 4.3). Finally, I show that option trading volume predictability is different from short interest predictability (Subsection 4.4).

Conclusion

Using a comprehensive cross-section of option data from the Ivy DB OptionMetrics database that range from January 1996 to December 2019 (24 years, or 288 months in total), I find an economically meaningful link between option and equity markets via option trading volume. I construct the option trading volume measure as the option trading volume level adjusted by stock trading volume. To my knowledge, this paper is the first paper in the literature to examine the relation between option trading

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    I deeply appreciate Robert Geske for his tremendous encouragement and support. I am very grateful for advice and suggestions from Michael Brennan, Bing Han, Richard Roll, Avanidhar Subrahmanyam, and Walter Torous. I also thank OptionMetrics for providing the option data and Markit for providing the CDS data.

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