Predicting Stock Price Movement as an Image Classification Problem
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Abstract (arXiv)
The paper studies intraday price movement of stocks that is considered as an image classification problem. Using a CNN-based model we make a compelling case for the high-level relationship between the first hour of trading and the close. The algorithm managed to adequately separate between the two opposing classes and investing according to the algorithm's predictions outperformed all alternative constructs but the theoretical maximum. To support the thesis, we ran several additional tests. The findings in the paper highlight the suitability of computer vision techniques for studying financial markets and in particular prediction of stock price movements.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 12 of its 103 references that are in the map. First posted on arXiv 2 Mar 2023.
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References · 12
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