Optimizing Investor Confidence: Robo-Advisory Utilizing Open Interest and Options Volume for Precise Large-Cap Stock Price Predictions

Le résumé fourni par la source

This study interrogates the predictive efficacy of open interest and options volume in shaping large-cap stock price trajectories, employing robo-Advisory mechanisms as analytical conduits. Anchored in the burgeoning landscape of financial technologies, the research examines how algorithmic advisory platforms synthesize derivative market indicators to generate precise, investor-centric insights. Utilizing a dataset of 28,088 observations from the National Stock Exchange (NSE) across 2021, the analysis incorporates Ordinary Least Squares regression to evaluate the explanatory capacity of open interest and options volume. Empirical findings substantiate the statistically significant role of both predictors, with open interest (β = 0.54) demonstrating superior predictive potency relative to options volume (β = 0.27). The adjusted R² value of 0.89 underscores the robustness of the model. Theoretically, this investigation enriches discourse on market microstructure by elucidating the informational content embedded within derivatives trading activity. Practically, it advocates robo-advisors as critical mediators capable of demystifying 162 esoteric option metrics for retail investors, thereby democratizing access to sophisticated forecasting tools. The research concludes that integrating open interest and options volume within robo-advisory frameworks not only enhances price discovery mechanisms but also fortifies investor confidence, contributing to market efficiency and inclusivity in emerging economies.

Ce résumé expose les affirmations des auteurs. BNTIC ne l’interprète pas comme une validation indépendante des résultats.