Hybrid Recommendation and Quantum Intelligence for Stock Investment Analytics

Abstract

Stock investment analytics has become increasingly complex due to market volatility, high-frequency trading activities, large-scale financial datasets, and rapidly changing economic conditions. Traditional investment recommendation systems often struggle to accurately analyze multidimensional financial information and generate reliable investment decisions under uncertain market environments. Recent advancements in artificial intelligence, recommendation systems, and quantum-inspired computing have created new opportunities for developing intelligent investment analytics frameworks capable of improving prediction accuracy and investment performance. This research proposes a Hybrid Recommendation and Quantum Intelligence Framework for Stock Investment Analytics (HRQI-SIA) that integrates financial data analytics, investor behavior modeling, hybrid recommendation systems, quantum-inspired optimization, and intelligent portfolio decision-making mechanisms into a unified architecture. The framework utilizes machine learning-based recommendation engines to analyze historical stock performance, market indicators, technical signals, and investor preferences, while quantum intelligence modules perform large-scale optimization for stock selection and portfolio allocation. The proposed framework dynamically generates personalized investment recommendations and optimizes portfolio performance according to market conditions and risk profiles.