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A Stock Prediction Model Based on DCNN

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  • Qiao Zhou
  • Ningning Liu

Abstract

The prediction of a stock price has always been a challenging issue, as its volatility can be affected by many factors such as national policies, company financial reports, industry performance, and investor sentiment etc.. In this paper, we present a prediction model based on deep CNN and the candle charts, the continuous time stock information is processed. According to different information richness, prediction time interval and classification method, the original data is divided into multiple categories as the training set of CNN. In addition, the convolutional neural network is used to predict the stock market and analyze the difference in accuracy under different classification methods. The results show that the method has the best performance when the forecast time interval is 20 days. Moreover, the Moving Average Convergence Divergence and three kinds of moving average are added as input. This method can accurately predict the stock trend of the US NDAQ exchange for 92.2%. Meanwhile, this article distinguishes three conventional classification methods to provide guidance for future research.

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  • Qiao Zhou & Ningning Liu, 2020. "A Stock Prediction Model Based on DCNN," Papers 2009.03239, arXiv.org.
    • Handle: RePEc:arx:papers:2009.03239
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    References listed on IDEAS

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    1. Xi Zhang & Yunjia Zhang & Senzhang Wang & Yuntao Yao & Binxing Fang & Philip S. Yu, 2018. "Improving Stock Market Prediction via Heterogeneous Information Fusion," Papers 1801.00588, arXiv.org.
    2. Lu, Tsung-Hsun & Shiu, Yung-Ming & Liu, Tsung-Chi, 2012. "Profitable candlestick trading strategies—The evidence from a new perspective," Review of Financial Economics, Elsevier, vol. 21(2), pages 63-68.
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