Interpretable Image-Based Deep Learning for Price Trend Prediction in ETF Markets

41 Pages Posted: 2 Aug 2022 Last revised: 27 Oct 2023

See all articles by Ruixun Zhang

Ruixun Zhang

Peking University; MIT Laboratory for Financial Engineering

Chaoyi Zhao

Peking University

Guanglian Lin

Nankai University

Date Written: July 27, 2022

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.

Keywords: Price trend prediction, Convolutional neural network (CNN), Attention, Image, Interpretability

JEL Classification: G12, G17, C45, C53

Suggested Citation

Zhang, Ruixun and Zhao, Chaoyi and Lin, Guanglian, Interpretable Image-Based Deep Learning for Price Trend Prediction in ETF Markets (July 27, 2022). Available at SSRN: https://ssrn.com/abstract=4173579 or http://dx.doi.org/10.2139/ssrn.4173579

Ruixun Zhang (Contact Author)

Peking University ( email )

5 Yiheyuan Road
Haidian District
Beijing, Beijing 100871
China

MIT Laboratory for Financial Engineering ( email )

100 Main Street
E62-611
Cambridge, MA 02142

Chaoyi Zhao

Peking University ( email )

No. 5 Yiheyuan Road
Beijing
China

HOME PAGE: http://zhaochaoyi.github.io/

Guanglian Lin

Nankai University ( email )

Balitai, 94 weijing Rd
Tianjin, Tianjin 300071

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