Predicting Stock Price Movement as an Image Classification Problem
This addresses stock price prediction for financial traders, but it is incremental as it applies existing computer vision techniques to a new domain.
The paper tackled predicting intraday stock price movements by framing it as an image classification problem using a CNN-based model, and found that investing based on its predictions outperformed all alternative constructs except the theoretical maximum.
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.