  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.
