This study examines the short-run predictive capacity of non-price indicators utilizing weekly options data from 2013 to 2022. Analyzing the empirical distributions of open interest and volume, we generate weekly and aggregated monthly return forecasts of “Magnificent 7” stocks and SPDR S&P 500 ETF (SPY) upon weekly option expiration, shaping asset-centric directional trading strategies. Our findings unfolded intriguing insights into the dynamic relationship between options market activity and subsequent stock performance supporting the main result of Goyenko and Zhang (2022) that option characteristics are dominant predictors of stock return. In addition, contrary to the findings of Fodor et al. (2010), we demonstrate that the lagged versions of our primary predictors, OIC and OIP, maintain statistical significance in predicting returns when appropriate controls are applied. The out-of-sample tests further solidified the robustness of the employed predictors, demonstrating their ability to consistently outshine the S&P 500 Index across different market scenarios. Our study also reveals that even with established variables from prior literature, such as those advocated by Fama and French (2012, 2015) and Amihud and Mendelson (1980), incorporated as controls, our predictors maintain considerable predictive efficacy and can be considered for future market research. Our study further extends its purview to the intricate realm of return volatility forecasting, incorporating diverse factors such as open interest and volume-based predictors, company-specific factors, and exponential moving averages, where the regression analyses shed light on the nuanced relationships that contribute to the overall volatility landscape.