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

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  • Kaushal Attaluri
  • Mukesh Tripathi
  • Srinithi Reddy
  • Shivendra

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.

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  • Kaushal Attaluri & Mukesh Tripathi & Srinithi Reddy & Shivendra, 2024. "News-Driven Stock Price Forecasting in Indian Markets: A Comparative Study of Advanced Deep Learning Models," Papers 2411.05788, arXiv.org.
    • Handle: RePEc:arx:papers:2411.05788
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    References listed on IDEAS

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    1. Taewook Kim & Ha Young Kim, 2019. "Forecasting stock prices with a feature fusion LSTM-CNN model using different representations of the same data," PLOS ONE, Public Library of Science, vol. 14(2), pages 1-23, February.
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