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Deep Learning for Stock Market Prediction Using Sentiment and Technical Analysis

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Abstract

Machine learning and deep learning techniques are applied by researchers with a background in both economics and computer science, to predict stock prices and trends. These techniques are particularly attractive as an alternative to existing models and methodologies because of their ability to extract abstract features from data. Most existing research approaches are based on using either numerical/economical data or textual/sentimental data. In this article, we use cutting-edge deep learning/machine learning approaches on both numerical/economical data and textual/sentimental data in order not only to predict stock market prices and trends based on combined data but also to understand how a stock's Technical Analysis can be strengthened by using Sentiment Analysis. Using the four tickers AAPL, GOOG, NVDA and S&P 500 Information Technology, we collected historical financial data and historical textual data and we used each type of data individually and in unison, to display in which case the results were more accurate and more profitable. We describe in detail how we analyzed each type of data, and how we used it to come up with our results.

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Data availability

The economic data utilized in this study was sourced from Yahoo Finance. Economic data used in this research is publicly available and can be accessed through Yahoo Finance's platform. The financial_phrasebank dataset referenced in this study was also utilized. The dataset is publicly available.

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Authors and Affiliations

  1. PSL Research University, Paris, France

    Georgios-Markos Chatziloizos

  2. Department of Informatics and Telecommunications, National and Kapodistrian University of Athens, Athens, Greece

    Dimitrios Gunopulos & Konstantinos Konstantinou

Authors
  1. Georgios-Markos Chatziloizos
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  2. Dimitrios Gunopulos
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  3. Konstantinos Konstantinou
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Correspondence to Georgios-Markos Chatziloizos.

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This article is part of the topical collection “Data Science, Technology and Applications” guest edited by Slimane Hammoudi and Christoph Quix.

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Chatziloizos, GM., Gunopulos, D. & Konstantinou, K. Deep Learning for Stock Market Prediction Using Sentiment and Technical Analysis. SN COMPUT. SCI. 5, 446 (2024). https://doi.org/10.1007/s42979-024-02651-5

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