Comparative Analysis of Rule-Based and Data-Driven Investment Strategies using Web-Based Intelligent Systems

Abstract: Retail investors often face challenges in choosing optimal investment strategies due to market volatility and limited access to financial advisory tools. This study proposes an AI-powered web-based investment simulator that models and compares multiple strategies, including Systematic Investment Plan (SIP), Lumpsum, Value Averaging (VA), and advanced variations such as Constant Share and VA Pro Rata. Rule-based AI techniques simulate adaptive investment decisions, while linear regression is explored for predictive analysis of future stock prices. Results highlight comparative returns, risk efficiency, and strategy suitability under varying market conditions. The system demonstrates how intelligent decision support can empower retail investors through interactive dashboards and Excel-based reporting Keywords: Investment Strategies, Rule-Based AI, SIP, Value Averaging, Lumpsum, Financial Decision Support, Regression Forecasting, Web-Based Simulator, Portfolio Analysis, Retail Investors