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Forecasting Prices of Shares Listed on the Warsaw Stock Exchange Using Machine Learning

Author

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  • Jóźwicki Rafał

    (Institute of Finance, Department of Finance and Accounting of SME’s, University of Lodz, Lodz, Poland)

Abstract

Objective: The technology developing before our eyes is entering many areas of life and has an increasing influence on shaping human behavior. Undoubtedly, it can be stated that one such area is trading on stock exchanges and other markets that offer investors the opportunity to allocate their capital. Thanks to widespread access to the Internet and the computing capabilities of computers used in the daily activities of investors, the nature of their working has changed significantly, compared to what we observed even 10–15 years ago. At present, stock exchange orders may be placed in person using various types of brokerage investment accounts, which allow the investor to view real-time quotations which opens up a whole new range of opportunities for investorsIts skillful application during the stock market game can positively influence a player’s investment performance.Machine learning is a branch of artificial intelligence and computer science that focuses on using data and algorithms to solve decision-making problems based on large amounts of information. In machine learning, algorithms find patterns and relationships in large data sets and make the best decisions and predictions based on this analysis.

Suggested Citation

  • Jóźwicki Rafał, 2022. "Forecasting Prices of Shares Listed on the Warsaw Stock Exchange Using Machine Learning," Journal of Intercultural Management, Sciendo, vol. 14(3), pages 63-78, September.
    • Handle: RePEc:vrs:joinma:v:14:y:2022:i:3:p:63-78:n:4
    DOI: 10.2478/joim-2022-0012
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    More about this item

    Keywords

    stock market; investment strategies; machine learning;
    All these keywords.

    JEL classification:

    • E - Macroeconomics and Monetary Economics
    • E - Macroeconomics and Monetary Economics
    • G - Financial Economics
    • G - Financial Economics

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