We investigate the predictive power of non-price indicators for short-term stock returns for prominent high-volume stocks and SPY, leveraging 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, as well as 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). Integrating traditional variables from Fama and French (2012, 2015) and Amihud and Mendelson (1980) further enhances our models’ predictive efficacy. Additionally, we explore return volatility forecasting using our predictors through GARCH modeling, further highlighting their strategic importance in investment performance.