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Predicting Stock Trends Using Common Financial Indicators: A Summary of FedCSIS 2024 Data Science Challenge Held on KnowledgePit.ai Platform

Rakićević Aleksandar M., Milosević Pavle D., Dragović Ivana T., Poledica Ana M., Zukanović Milica M., Janusz Andrzej, Ślęzak Dominik

Annals of Computer Science and Information Systems

2024

Vol. 39

731--737

Predictive analytics aims to empower finance professionals to make data-driven decisions, anticipate customer behavior, and navigate the complexities of the financial landscape. One of the tasks in this domain is the prediction of stock trend movements. The goal of the FedCSIS 2024 Data Science Challenge was to investigate the feasibility of a predictive model that would allow for accurate forecasts of stock trend movements based on the financial fundamental data. Such a model could have a vital role in algorithmic or manual trading, providing trading signals for making decisions about the time and direction of stock trades. We describe the prepared dataset and competition task. We also summarize the outcomes of the competition and provide insights about the most successful ML techniques used by participants. Finally, we discuss our ideas for a continuation of this study.