Predicting Short-Term Stock Returns with Weekly Options Indicators: Comparative Study of Key Market Movers, SPY, and S&P 500 Index

In this paper, we investigate the predictive power of non-price indicators for short-term stock returns for prominent high-volume stocks and SPY using weekly options data. By analyzing open interest and volume distributions, we forecast weekly and monthly aggregated returns around option expirations. We show that these options trading dynamics are crucial predictors of stock returns, even amid market turbulence. Notably, the lagged open interest call and put, as well as call and put volume, retain statistical significance in predicting returns with proper controls. Both in-sample (2013-2022) and out-of-sample (2023) tests confirm the predictors’ robustness, consistently outperforming the S&P 500 and NASDAQ 100 indexes, and the aggregated active trading strategies of the key market movers. Our findings align with the role of options and informed trading on the price discovery of stocks, as demonstrated by Chakravarty et al. [2004, Informed Trading in Stock and Option Markets, Journal of Finance 59(3), 1235–1257]. Integrating traditional variables from Fama and French [2012, Size, Value, and Momentum in International Stock Returns, Journal of Financial Economics 105(3), 457–472; 2015, A Five-Factor Asset Pricing Model, Journal of Financial Economics 116(1), 1–22] and Amihud and Mendelson [1980, Dealership Market — Market-Making with Inventory, Journal of Financial Economics 8, 31–53] further enhances our models’ predictive efficacy. The non-price indicators exhibited significantly enhanced predictive power during the COVID-19 crisis, surpassing their effectiveness under regular market conditions. Additionally, we explore return volatility forecasting using our predictors through GARCH modeling, further highlighting their strategic importance in investment performance.