Stock price prediction via image-based DCNN with CBAM

Qiao Zhou; Yu Zhang; Ningning Liu; Xin Jin; Shuzhan Li; Yujie Zhou

doi:10.1117/12.2602250

28 October 2021 Stock price prediction via image-based DCNN with CBAM

Qiao Zhou, Yu Zhang, Ningning Liu, Xin Jin, Shuzhan Li, Yujie Zhou

Proceedings Volume 11884, International Symposium on Artificial Intelligence and Robotics 2021; 118840F (2021) https://doi.org/10.1117/12.2602250
Event: International Symposium on Artificial Intelligence and Robotics 2021, 2021, Fukuoka, Japan

Abstract

Stock price prediction has always been an issue full of challenges due to its complex volatile nature. Inspired by the observation that the traders prefer judging the stock trends from the charts, and have concluded useful techniques for predicting stock trends. In this paper, we try to predict stock price trends based on the stock images by using Deep Convolutional Neural Networks (DCNN), and mainly focus on how to generate the stock images and label the image to train the DCNN model. Specifically, on one hand, we transfer the stock time series data to 5 types of stock images, trying to explore effective visual representation for stock data. On the other hand, we label the images in a more meaningful way to train the attention-based DCNN model to build a practical model. Experimental results on the S&P 500 stocks show that the MACD image performs better than other types, and it is hard to predict the stocks’ next day trend. However, the convolutional block attention module (CBAM) can improve the performance of DCNN.

Citation Download Citation

Qiao Zhou, Yu Zhang, Ningning Liu, Xin Jin, Shuzhan Li, and Yujie Zhou "Stock price prediction via image-based DCNN with CBAM", Proc. SPIE 11884, International Symposium on Artificial Intelligence and Robotics 2021, 118840F (28 October 2021); https://doi.org/10.1117/12.2602250

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