MUMBAI, India, Nov. 2 -- Intellectual Property India has published a patent application (202641124968 A) filed by Sr University on October 19, 2026, for A Behavioural Analytics System For Modelling Investor Overconfidence And Its Impact On Portfolio Returns Through Trading Intensity.
Inventors include Mr Mohsin Khan; Dr Kafila; and Dr Geetha Manoharan.
The application for the patent was published on October 30, 2026, under issue no. 44/2026.
Abstract: A Behavioural Analytics System for Modelling Investor Overconfidence and Its Impact on Portfolio Returns Through Trading Intensity ABSTRACT 8. The present invention relates to A Behavioural Analytics System for Modelling Investor Overconfidence and Its Impact on Portfolio Returns Through Trading Intensity, designed to analyze investor behavioural biases, trading activities, and investment performance using advanced computational techniques. The system integrates artificial intelligence, machine learning, statistical modelling, and financial data analytics to quantify investor overconfidence and evaluate its relationship with trading intensity and portfolio returns. The proposed system comprises a behavioural data acquisition module, investor overconfidence assessment module, trading intensity measurement module, portfolio performance evaluation module, statistical mediation analysis engine, and predictive analytics module. These interconnected components process investor confidence indicators, transaction frequency, portfolio turnover, trading costs, and risk-adjusted investment returns. The statistical mediation engine examines the direct and indirect relationships between investor overconfidence and portfolio performance through trading intensity. An automated behavioural risk assessment module identifies potentially excessive trading patterns and generates personalized alerts and investment insights. Furthermore, a secure cloud-based platform enables continuous data monitoring, interactive visualization, and integrated financial analysis. The invention aims to improve behavioural risk identification, enhance investor decision-making, reduce unnecessary trading activities, and support disciplined portfolio management through intelligent, data-driven financial analytics and systematic evaluation of behavioural influences on investment outcomes.
Disclaimer: Curated by HT Syndication.
