Advanced Stock Market Prediction Using Long Short-Term Memory Networks: A Comprehensive Deep Learning Framework

TL;DR

This paper presents a deep learning framework using LSTM networks combined with sentiment analysis to improve stock price prediction accuracy for major tech firms, outperforming traditional models and providing real-time visualization tools.

Contribution

The paper introduces a hybrid deep learning approach that integrates LSTM-based time series forecasting with sentiment analysis for enhanced stock market prediction.

Findings

LSTM model achieved a MAPE of 2.72 on test data.

Sentiment analysis improved prediction accuracy.

Web app provides real-time stock forecasts.

Abstract

Predicting stock market movements remains a persistent challenge due to the inherently volatile, non-linear, and stochastic nature of financial time series data. This paper introduces a deep learning-based framework employing Long Short-Term Memory (LSTM) networks to forecast the closing stock prices of major technology firms: Apple, Google, Microsoft, and Amazon, listed on NASDAQ. Historical data was sourced from Yahoo Finance and processed using normalization and feature engineering techniques. The proposed model achieves a Mean Absolute Percentage Error (MAPE) of 2.72 on unseen test data, significantly outperforming traditional models like ARIMA. To further enhance predictive accuracy, sentiment scores were integrated using real-time news articles and social media data, analyzed through the VADER sentiment analysis tool. A web application was also developed to provide real-time visualizations of stock price forecasts, offering practical utility for both individual and institutional investors. This research demonstrates the strength of LSTM networks in modeling complex financial sequences and presents a novel hybrid approach combining time series modeling with sentiment analysis.