Interpretable image-based deep learning for price trend prediction in ETF markets

Abstract

Image-based deep learning models excel at extracting spatial information from images but their potential in financial applications has not been fully explored. Here we propose the channel and spatial attention convolutional neural network (CS-ACNN) for price trend prediction. It utilizes the attention mechanisms to focus on specific areas of input images that are the most relevant for prices. Using exchange-traded funds (ETF) data from three different markets, we show that CS-ACNN – using images constructed from financial time series – achieves on-par and, in some cases, superior performances compared to models that use time series data only. This holds true for both model classification metrics and investment profitability, and the out-of-sample Sharpe ratios range from 1.57 to 3.03 after accounting for transaction costs. The model learns visual patterns that are consistent with traditional technical analysis, providing an economic rationale for learned patterns and allowing investors to interpret the model.