MUMBAI, India, Nov. 2 -- Intellectual Property India has published a patent application (202641125692 A) filed by Sr University on October 22, 2026, for An Ai-Based Predictive System For Assessing The Relationship Between Investor Overconfidence, Trading Intensity, And Stock Portfolio Performance.
Inventors include Mr. Narsimgoju Bhargav; and Dr. Geetha Manoharan.
The application for the patent was published on October 30, 2026, under issue no. 44/2026.
Abstract: AN AI-BASED PREDICTIVE SYSTEM FOR ASSESSING THE RELATIONSHIP BETWEEN INVESTOR OVERCONFIDENCE, TRADING INTENSITY, AND STOCK PORTFOLIO PERFORMANCE ABSTRACT The present invention discloses an artificial intelligence-based predictive system for evaluating how investor overconfidence influences trading intensity and stock portfolio performance. The system integrates historical transaction records, portfolio holdings, market indicators, and behavioral proxies to identify patterns associated with excessive confidence in investment decisions. A data preprocessing module cleans and standardizes inputs, while a behavioral analytics engine estimates overconfidence using indicators such as frequent trading, concentrated positions, and deviations between expected and realized returns. Machine learning models assess nonlinear relationships among behavioral tendencies, trading frequency, transaction costs, risk exposure, and risk- adjusted portfolio outcomes. A predictive module generates forecasts of potential performance deterioration under alternative trading scenarios and identifies investors exhibiting elevated behavioral risk. Explainable analytics highlight the variables contributing to each assessment, enabling transparent interpretation of model outputs. An interactive dashboard presents individualized risk scores, projected outcomes, and decision-support alerts to encourage disciplined portfolio management. The proposed system supports continuous model updating as new data become available, thereby improving responsiveness to changing market conditions and investor behavior without replacing human investment judgment and facilitating evidence-based evaluation of behavioral investment strategies.
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