Title: Analysing the effectiveness of financial news sentiments on stock price prediction of 12 Indian sectoral stock indices using a hybrid LSTM-GRU model

Authors: Meera George; R. Murugesan

Addresses: Department of Humanities and Social Sciences, National Institute of Technology, Tiruchirappalli, India ' Department of Humanities and Social Sciences, National Institute of Technology, Tiruchirappalli, India

Abstract: Despite the growing interest in combining news sentiments with historical data to improve stock price prediction, a considerable gap exists in predicting the sectoral stock indices using this methodology. This study addresses this gap by predicting the closing price of 12 Indian sectoral stock indices through a hybrid deep-learning architecture. It employs a hybrid TFIDF-Doc2Vec feature extraction technique and an SVM classifier to extract the financial news sentiments. These sentiments are utilised to create a sentiment Index, combined with historical stock data to predict each sectoral stock index using a hybrid LSTM-GRU model. The study evaluates the effectiveness of financial news sentiments in sectoral stock prediction by comparing models with and without sentiments. Results demonstrate a notable influence of sentiments on the stock price prediction of ten sectoral stock indices with a pronounced impact on the NSEBANK index. This study offers valuable insights for investors in formulating sector-specific trading strategies. It also aids policymakers in market regulation and helps financial analysts improve forecasting models by incorporating financial news sentiments. Future research could explore the integration of multi-source investor sentiments with advanced deep-learning models for even more accurate stock price predictions across diverse sectors.

Keywords: stock price prediction; sectoral stock indices; financial news sentiments; FNSs; hybrid TFIDF-Doc2Vec; hybrid LSTM-GRU.

DOI: 10.1504/IJBIDM.2026.151262

International Journal of Business Intelligence and Data Mining, 2026 Vol.28 No.1, pp.17 - 40

Received: 20 Mar 2024
Accepted: 07 Nov 2024
Published online: 20 Jan 2026 *

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