An AI-Based Stock Forecasting and Advisory Framework Integrating Machine Learning and Sentiment Analysis (Implemented via the SP-07 AI Stock Forecasting Prototype System)

Retail participation in Indian capital markets has grown rapidly in recent years, yet most retail investors still lack access to advanced, AI‑driven tools that synthesize price forecasts, news sentiment, and actionable recommendations into reproducible, investor‑ready reports. Addressing this gap, SP‑07 AI Stock Forecasting introduces a modular, AI‑powered platform focused on the National Stock Exchange (NSE) and Bombay Stock Exchange (BSE), providing short‑ and medium‑term price forecasts (7/14/30 days), combined confidence scores, automated Buy/Sell/Hold recommendations, news informed sentiment scoring, alerting, and downloadable investor reports. The system leverages a hybrid modeling strategy encompassing classical machine learning (Random Forest, Linear Regression), with extensibility for deep learning (LSTM), along with robust news sentiment analysis and a rules‑based recommendation engine. Early prototype results demonstrate directional accuracy of 60–65% in selected backtests. This research paper presents the architecture, design, evaluation, and future potential of SP‑07 AI Stock Forecasting and Advisory Platform,, synthesizing best practices from forecasting, sentiment analysis, and recommendation engine research, and situates the platform within the broader context of intelligent financial advisory systems.