News-Driven Stock Price Forecasting in Indian Markets: A Comparative Study of Advanced Deep Learning Models

TL;DR

This paper compares advanced deep learning models for stock price forecasting in Indian markets, integrating sentiment analysis from social media and financial news to improve prediction accuracy.

Contribution

It introduces a comprehensive approach combining multiple deep learning models with sentiment analysis, using 30 years of Indian market data for enhanced stock price prediction.

Findings

LSTM outperforms other models in accuracy

Sentiment analysis improves forecast reliability

Multi-model integration yields better results

Abstract

Forecasting stock market prices remains a complex challenge for traders, analysts, and engineers due to the multitude of factors that influence price movements. Recent advancements in artificial intelligence (AI) and natural language processing (NLP) have significantly enhanced stock price prediction capabilities. AI's ability to process vast and intricate data sets has led to more sophisticated forecasts. However, achieving consistently high accuracy in stock price forecasting remains elusive. In this paper, we leverage 30 years of historical data from national banks in India, sourced from the National Stock Exchange, to forecast stock prices. Our approach utilizes state-of-the-art deep learning models, including multivariate multi-step Long Short-Term Memory (LSTM), Facebook Prophet with LightGBM optimized through Optuna, and Seasonal Auto-Regressive Integrated Moving Average (SARIMA). We further integrate sentiment analysis from tweets and reliable financial sources such as Business Standard and Reuters, acknowledging their crucial influence on stock price fluctuations.