Home » New Research: Can Options Predict Future Stock Performance?
Can options prices predict future stocks’ returns? There is a growing body of evidence that suggests options prices may convey information that can provide insights into the future price of underlying stocks.
By Professor George Skiadopoulos, Queen Mary University of London and University of Piraeus
In particular, one measure that research has looked at when it comes to assessing the informational content of options is the Risk Neutral Skewness (RNS) of the underlying asset. RNS measures how expensive out-of-the-money (OTM) equity put options are relative to OTM call equity options written on a particular stock. However, so far, research has focused on the informational content of negative RNS. Previous research suggests that stocks with high negative RNS underperform, i.e., they provide a negative alpha. This signal is particularly strong in the case that the underlying stock is overpriced and there exists limits-to-arbitrage in trading it.
Can, then, the reverse be true? Can the options market identify stocks that might be good investments via positive RNS?
New research on “Positive Stock Information in Out-of-the-Money Option Prices”—from Konstantinos Gkionisa, UBS AG; Alexandros Kostakis, University of Liverpool Management School; Przemyslaw S. Stilgerg, Standard Chartered Bank, and myself—leverages OptionMetrics data to examine these questions.
We find that the expensiveness of OTM calls relative to OTM puts, reflected in the positive RNS of the option-implied stock return distribution, can, in fact, reveal positive information for the future stock price, i.e., it predicts stock outperformance. We also offer a trading mechanism for why this is the case and present other factors that when used with RNS can help to identify future positive stock performance.
To arrive at our findings, we leverage the model-free methodology of Bakshi, Kapadia, and Madan, 2003 to calculate RNS and options data set, OptionMetrics. OptionMetrics conveniently provides the entire cross section of equity options across various strikes and maturities (30, 45, 180 days, etc.) for stocks.
We obtain daily data on stocks from CRSP. Our stock universe consists of U.S. common stocks listed on NYSE, NYSE MKT, and NASDAQ.
We confine our sample to the equity options for which we can calculate RNS reliably. We then classify the stocks into 10 portfolios, with those in portfolio one having the lowest RNS values and those in portfolio 10 having the highest. We compute the returns for each stock and portfolio over one week intervals, giving us a time series of returns for each portfolio. From this time series, we compute the alpha, or measure of average returns, to evaluate our investments.
The Findings: Options, RNS, and Stocks
In particular, we find that:
Stocks that have more positive RNS outperform (do better) in the future, even when one controls other risk factors (such as size, value, and momentum factors). We find that the alpha of the portfolio with the highest RNS stocks is big—6% per annum. This alpha is strongly statistically significant as assessed by its t-statistic, exceeding a value of 3 (when typically, it suffices to have a t-stat above 2).
We also provide an explanation on why stocks with a positive RNS outperform. According to our trading mechanism, we conjecture: if the underlying stock is perceived to be underpriced, investors anticipating a subsequent price correction may look to the option market to buy (sell) OTM calls (puts) in order to lever up their positions and maximize their trading profits.
Why might they not exploit their expectation by just trading in the underlying stock directly? When traders recognize an underpriced/inexpensive stock, they will likely buy it, as they expect the price to increase. They can do this by buying the stock directly. However, as the profit is not guaranteed, there is a chance they may lose money and be exposed to the stock’s downside risk. Alternatively, to hedge the stock’s downside risk, they can buy cheap call options to gain the upside. In this case, they will make money if the stock goes up. The call downside risk is also limited because if the stock price goes down and the call option ends up being out-of-the-money, the investor will only lose the call premium, paid to buy the call option in the first place.
In buying either an OTM call option or selling an OTM put option, an investor increases the price of the call option and decreases that of the put option. This is manifested, as a result, in positive RNS.
Therefore, we find that a relatively high positive RNS value becomes a strong signal for subsequent outperformance of the underlying stock.
Moreover, we document that while looking at stocks with the highest positive RNS is a good indicator, simultaneously assessing those that are most underpriced and exposed to downside risk can provide even greater insight to maximize profits.
This is consistent with our proposed trading mechanism: investors will strongly resort to those equity options for which the underlying stocks are underpriced and subject to significant downside risk. As a result, the RNS of those stocks will yield an even greater outperformance.
In our research, we use well-established measures to identify underpriced stocks and their exposure to downside risk.
Another interesting finding is that this outperformance does not last long. While we assume in our research that investors hold their positions in the stock for a one-week investment horizon, the stock price correction happens much quicker. In fact, investors can gain the majority of profit from their trades overnight. What this means for the investor is that as soon as he/she gets into a trading strategy, he/she does not need to keep it open for long. Instead, he/she can take the profits obtained from the strategy overnight and then close his/her position in the option market.
Why is this the case? The market is efficient and recognizes there is a profit or “mispricing” that it quickly corrects. As in the example above, when an investor trades a stock or buys an option, a correction may happen through a delta hedging mechanism from the market maker hedging through trading the underlying stock, thereby having an effect on the underlying stock price. This correction takes place rather fast. Limits to arbitrage, such as short selling constraints, cannot impede this correction because the stock is underpriced. This is in contrast to the case where negative RNS signals underperformance because of overpricing, and this takes much longer to be corrected due to short selling constraints.
This research verifies that RNS can be a trading signal for investors to determine whether to buy a particular stock. It also reveals the importance of considering underpricing and downside risk, alongside RNS–where the full trading mechanism kicks in.
To the best of our knowledge, this is the first time that positive RNS has been verified as a positive trading mechanism, and our proposed trading mechanism as an explanation is novel.
The research is also part of broader literature, from my co-authors and me, examining the informational content of options prices in addressing a number of questions in finance. Additional applications for options data that we have assessed to this end include using OptionMetrics to construct option-based measures to predict GDP growth in economies (of importance to policy makers and firms, and in fact, yes, one can predict economic growth using information from index options prices, published in the leading journal Management Science, 2019) and to determine the leverage of corporate firms (published in Journal of Banking and Finance, 2019).
As with these studies, we find this latest paper to be practical and of importance to academics and practitioners in uncovering additional insights that options data can provide.
To see the full paper, please visit https://www.sciencedirect.com/science/article/abs/pii/S0378426621000704
George Skiadopoulos is Professor of Finance in the School of Economics and Finance, Queen Mary University of London, and in the Department of Banking and Financial Management, University of Piraeus. He is also Director of the Institute of Finance and Financial Regulation (www.iffr.gr) and an Honorary Senior Visiting Fellow at Business School, City University of London.