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Improving stock market prediction accuracy using sentiment and technical analysis

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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.

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Data availability

There is no data associated with this research. In addition, implementation source code will be delivered upon request to the corresponding author Chaker Abdelaziz Kerrache.

Notes

  1. https://github.com/Nitinkumar3399/Batchelor-Thesis-Project-II/blob/main/AMZN.csv.

  2. Twitter developer API resource link: https://developer.twitter.com/en/portal/products/pro.

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Acknowledgements

This work has been partially funded by both the R&D project SERB-SURE SUR/2022/001051 and the R&D project PID2021-122580NB-100, from MCIN/AEI/10.13039/501100011033 and “ERDF A way of making Europe”.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Netaji Subhas University of Technology, New Delhi, India

    Shubham Agrawal, Nitin Kumar & Geetanjali Rathee

  2. Laboratoire d’Informatique et de Mathématiques, Université Amar Telidji de Laghouat, Laghouat, Algeria

    Chaker Abdelaziz Kerrache

  3. Computer Engineering Department (DISCA), Universitat Politècnica de València, Valencia, Spain

    Carlos T. Calafate

  4. School of Computing and Communications, Lancaster University, Lancaster, UK

    Muhammad Bilal

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  1. Shubham Agrawal
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  4. Chaker Abdelaziz Kerrache
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  5. Carlos T. Calafate
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Contributions

Authors equally contributed to this work with more implementation-related efforts from Shubham Agrawal and Nitin Kumar.

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Correspondence to Chaker Abdelaziz Kerrache.

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Agrawal, S., Kumar, N., Rathee, G. et al. Improving stock market prediction accuracy using sentiment and technical analysis. Electron Commer Res (2024). https://doi.org/10.1007/s10660-024-09874-x

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