Improving stock market prediction accuracy using sentiment and technical analysis

Shubham Agrawal (), Nitin Kumar (), Geetanjali Rathee (), Chaker Abdelaziz Kerrache (), Carlos T. Calafate () and Muhammad Bilal ()
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Shubham Agrawal: Netaji Subhas University of Technology
Nitin Kumar: Netaji Subhas University of Technology
Geetanjali Rathee: Netaji Subhas University of Technology
Chaker Abdelaziz Kerrache: Université Amar Telidji de Laghouat
Carlos T. Calafate: Universitat Politècnica de València
Muhammad Bilal: Lancaster University

Electronic Commerce Research, 2025, vol. 25, issue 5, No 24, 4103-4126

Abstract: Abstract The utilization of sentiment analysis as a method for predicting stock market trends has gained significant attention recently, especially during economic crises. This research aims to assess the predictive accuracy of sentiment analysis in the stock market by constructing a reinforced model that integrates both sentiment and technical analysis. While prior studies have concentrated on social media sentiment for stock price prediction, this research introduces an enhanced model that combines sentiment analysis with technical indicators to improve the precision of stock market prediction. The study creates and evaluates predictive models for stock prices and trends using a substantial dataset of tweets from twenty prominent companies. Finally the re-enforced model has been developed and tested on the stock prices of: Apple, General Electric, Ford Motors and Amazon. The deliberate selection of these companies, each representing distinct industry sectors, serves a dual purpose. It not only facilitates a practical evaluation of our model across diverse market conditions but also ensures computational feasibility, allowing for a focused and detailed analysis of the model’s predictive accuracy and reliability in various economic landscapes. The study’s outcomes offer valuable insights into the effectiveness of the reinforced model, which combines sentiment and technical analysis to predict stock market movements, providing a more comprehensive approach to understanding market sentiment’s influence on stock prices. Furthermore, these findings contribute to the existing knowledge on stock market prediction techniques and emphasize the importance of considering multiple factors in decision-making.

Keywords: Stock market prediction; Long short term memory (LSTM); Sentiment analysis; Reinforced model; Historical analysis (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s10660-024-09874-x

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