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

Annals of Computer Science and Information Systems, Volume 39

Forecasting Stock Trends with Feedforward Neural Networks

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DOI: http://dx.doi.org/10.15439/2024F885

Citation: Proceedings of the 19th Conference on Computer Science and Intelligence Systems (FedCSIS), M. Bolanowski, M. Ganzha, L. Maciaszek, M. Paprzycki, D. Ślęzak (eds). ACSIS, Vol. 39, pages 767–771 ()

Full text

Abstract. Stock market prediction stands as a complex and crucial task, pivotal for enhancing the overall stability and efficiency of financial markets by offering essential insights into market movements and trends. In this study, we introduce a simple yet potent model based on feedforward neural networks to tackle this challenge effectively. Our approach leverages advancements in machine learning and deep learning to analyze large datasets of financial statements, demonstrating promising results in forecasting stock trends.

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