Is Deep Learning for Image Recognition Applicable to Stock Market Prediction?

Hyun Sik Sim, Hae In Kim and Jae Joon Ahn

Complexity, 2019, vol. 2019, 1-10

Abstract:

Stock market prediction is a challenging issue for investors. In this paper, we propose a stock price prediction model based on convolutional neural network (CNN) to validate the applicability of new learning methods in stock markets. When applying CNN, 9 technical indicators were chosen as predictors of the forecasting model, and the technical indicators were converted to images of the time series graph. For verifying the usefulness of deep learning for image recognition in stock markets, the predictive accuracies of the proposed model were compared to typical artificial neural network (ANN) model and support vector machine (SVM) model. From the experimental results, we can see that CNN can be a desirable choice for building stock prediction models. To examine the performance of the proposed method, an empirical study was performed using the S&P 500 index. This study addresses two critical issues regarding the use of CNN for stock price prediction: how to use CNN and how to optimize them.

Date: 2019
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Citations: View citations in EconPapers (15)

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Persistent link: https://EconPapers.repec.org/RePEc:hin:complx:4324878

DOI: 10.1155/2019/4324878

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