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.

翻译：本文研究了将股票日内价格变动视为图像分类问题的情况。我们采用基于CNN的模型，有力地论证了交易首小时与收盘价之间存在高层级关联。该算法能够有效区分两个相反类别，依据算法预测进行投资的表现优于除理论最大值以外的所有替代方案。为支撑这一论点，我们进行了多项补充测试。本文研究成果凸显了计算机视觉技术在金融市场研究，尤其是股票价格变动预测中的适用性。