Abstract

A turning point in the stock market is the moment when the stock price time series changes direction. Its identification is important both for economic theory and, above all, for the practice of investing in the stock market. In this article, we propose a new concept, based on a broad representation of the market in the form of heat maps visualizing histograms of return rates. We then use convolution neural networks (CNN) to process these images. By extracting and analyzing image-based features, we can classify stock market events and effectively detect potential turning points. We conduct the study on 4 datasets, verifying 9 CNN architectures. The results are promising, we achieved accuracy of up to 97%.

Recommended Citation

Rafało, M. & Szupiluk, R. (2025). Stock Turning Points Prediction Using Convolution Neural Networks with Return Rate Heat MapIn I. Luković, S. Bjeladinović, B. Delibašić, D. Barać, N. Iivari, E. Insfran, M. Lang, H. Linger, & C. Schneider (Eds.), Empowering the Interdisciplinary Role of ISD in Addressing Contemporary Issues in Digital Transformation: How Data Science and Generative AI Contributes to ISD (ISD2025 Proceedings). Belgrade, Serbia: University of Gdańsk, Department of Business Informatics & University of Belgrade, Faculty of Organizational Sciences. ISBN: 978-83-972632-1-5. https://doi.org/10.62036/ISD.2025.77

Paper Type

Short Paper

DOI

10.62036/ISD.2025.77

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Stock Turning Points Prediction Using Convolution Neural Networks with Return Rate Heat Map

A turning point in the stock market is the moment when the stock price time series changes direction. Its identification is important both for economic theory and, above all, for the practice of investing in the stock market. In this article, we propose a new concept, based on a broad representation of the market in the form of heat maps visualizing histograms of return rates. We then use convolution neural networks (CNN) to process these images. By extracting and analyzing image-based features, we can classify stock market events and effectively detect potential turning points. We conduct the study on 4 datasets, verifying 9 CNN architectures. The results are promising, we achieved accuracy of up to 97%.