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Communication Papers of the 19th Conference on Computer Science and Intelligence Systems (FedCSIS)

Annals of Computer Science and Information Systems, Volume 41

FedCSIS 2024 Data Science Challenge: Predicting Stock Trends by a Multi-Dimensional Approach

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DOI: http://dx.doi.org/10.15439/2024F7856

Citation: Communication Papers of the 19th Conference on Computer Science and Intelligence Systems (FedCSIS), M. Bolanowski, M. Ganzha, L. Maciaszek, M. Paprzycki, D. Ślęzak (eds). ACSIS, Vol. 41, pages 185–190 ()

Full text

Abstract. Predicting stock market trends is a challenge that is extremely hard to solve. Finding a solution to this challenge has captivated financial analysts, economists, and data scientists for a long time. The volatility and complexity of financial markets make it difficult to obtain accurate predictions. This challenge is the topic of the FedCSIS 2024 competition. In this competition, we have the dataset contains key financial indicators for 300 companies chosen from 11 different sectors of the S\&P 500 index, from 10 years. Each company is described by values of 58 indicators that are derived from its financial statements. The dataset also contains information on 1-year change for each indicator, which can indicate a trend in the considered values. Our mission is to develop a predictive model able to accurately forecast stock trend movements based on the provided financial fundamental data. This paper presents our solution to win the $2^{nd}$ place in the competition.

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