Market-Plus-Pulse: LSTM-Based Equity Price Prediction Framework with Real-Time Web Interface
Year: 2025
Type: article
Abstract: Financial markets exhibit high volatility driven by economic indicators, global events, and investor sentiment, making accurate price prediction crucial for portfolio management and risk assessment. This paper presents a comprehensive framework for equity price prediction using Long Short-Term Memory (LSTM) networks integrated with a Django-based web application. The system incorporates historical market data extraction via APIs, advanced preprocessing techniques including normalization and feature engineering, and a bidirectional LSTM architecture for temporal pattern recognition. Performance evaluation on S&P 500 data demonstrates significant improvements over traditional ARIMA models, achieving 23.7% reduction in RMSE and 19.2% reduction in MAE. The web interface enables real-time prediction, comparative analysis, and risk assessment tools for both institutional and retail investors. This integrated approach bridges the gap between academic research and practical financial technology deployment
Authors: Gagandeep H P, Yashaswini Y
Language: English
Field: Decision Sciences
Domain: Social Sciences
Open Access status: diamond
OpenAlex ID: W4413857252