IDEAS home Printed from https://ideas.repec.org/p/arx/papers/2009.03239.html
   My bibliography  Save this paper

A Stock Prediction Model Based on DCNN

Author

Listed:
  • 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.

Suggested Citation

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

    Download full text from publisher

    File URL: http://arxiv.org/pdf/2009.03239
    File Function: Latest version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Rosdyana Mangir Irawan Kusuma & Trang-Thi Ho & Wei-Chun Kao & Yu-Yen Ou & Kai-Lung Hua, 2019. "Using Deep Learning Neural Networks and Candlestick Chart Representation to Predict Stock Market," Papers 1903.12258, arXiv.org.
    2. Kamaladdin Fataliyev & Aneesh Chivukula & Mukesh Prasad & Wei Liu, 2021. "Stock Market Analysis with Text Data: A Review," Papers 2106.12985, arXiv.org, revised Jul 2021.
    3. Linyi Yang & Yingpeng Ma & Yue Zhang, 2023. "Measuring Consistency in Text-based Financial Forecasting Models," Papers 2305.08524, arXiv.org, revised Jun 2023.
    4. Zhou, Zhongbao & Gao, Meng & Liu, Qing & Xiao, Helu, 2020. "Forecasting stock price movements with multiple data sources: Evidence from stock market in China," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 542(C).
    5. María José Ayala & Nicolás Gonzálvez-Gallego & Rocío Arteaga-Sánchez, 2024. "Google search volume index and investor attention in stock market: a systematic review," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 10(1), pages 1-29, December.
    6. Huiwen Wang & Wenyang Huang & Shanshan Wang, 2021. "Forecasting open-high-low-close data contained in candlestick chart," Papers 2104.00581, arXiv.org.
    7. Chen, Shi & Bao, Si & Zhou, Yu, 2016. "The predictive power of Japanese candlestick charting in Chinese stock market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 457(C), pages 148-165.
    8. Shengkun Wang & Taoran Ji & Jianfeng He & Mariam Almutairi & Dan Wang & Linhan Wang & Min Zhang & Chang-Tien Lu, 2024. "AMA-LSTM: Pioneering Robust and Fair Financial Audio Analysis for Stock Volatility Prediction," Papers 2407.18324, arXiv.org.
    9. Ashwini Saini & Anoop Sharma, 2022. "Predicting the Unpredictable: An Application of Machine Learning Algorithms in Indian Stock Market," Annals of Data Science, Springer, vol. 9(4), pages 791-799, August.
    10. Karolina Sowinska & Pranava Madhyastha, 2020. "A Tweet-based Dataset for Company-Level Stock Return Prediction," Papers 2006.09723, arXiv.org.
    11. Li-Chen Cheng & Wei-Ting Lu & Benjamin Yeo, 2023. "Predicting abnormal trading behavior from internet rumor propagation: a machine learning approach," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 9(1), pages 1-23, December.
    12. Yihang Fu & Mingyu Zhou & Luyao Zhang, 2024. "DAM: A Universal Dual Attention Mechanism for Multimodal Timeseries Cryptocurrency Trend Forecasting," Papers 2405.00522, arXiv.org.
    13. Zhou, Zhongbao & Gao, Meng & Xiao, Helu & Wang, Rui & Liu, Wenbin, 2021. "Big data and portfolio optimization: A novel approach integrating DEA with multiple data sources," Omega, Elsevier, vol. 104(C).
    14. Francisco de Arriba-P'erez & Silvia Garc'ia-M'endez & Jos'e A. Regueiro-Janeiro & Francisco J. Gonz'alez-Casta~no, 2024. "Detection of financial opportunities in micro-blogging data with a stacked classification system," Papers 2404.07224, arXiv.org.
    15. Omer Berat Sezer & Mehmet Ugur Gudelek & Ahmet Murat Ozbayoglu, 2019. "Financial Time Series Forecasting with Deep Learning : A Systematic Literature Review: 2005-2019," Papers 1911.13288, arXiv.org.
    16. Shangkun Deng & Zhihao Su & Yanmei Ren & Haoran Yu & Yingke Zhu & Chenyang Wei, 2022. "Can Japanese Candlestick Patterns be Profitable on the Component Stocks of the SSE50 Index?," SAGE Open, , vol. 12(3), pages 21582440221, August.
    17. Wen, Danyan & Ma, Chaoqun & Wang, Gang-Jin & Wang, Senzhang, 2018. "Investigating the features of pairs trading strategy: A network perspective on the Chinese stock market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 505(C), pages 903-918.
    18. Juan Benjamín Duarte Duarte & Katherine Julieth Sierra Suárez & Víctor Alfonso Rueda Ortiz, 2015. "Análisis comparativo de eficiencia entre Brasil, México y Estados Unidos," Revista Finanzas y Politica Economica, Universidad Católica de Colombia, vol. 7(2), pages 341-357, July.
    19. Juan Benjamín Duarte Duarte & Juan Manuel Mascareñas Pérez-Iñigo, 2014. "¿Han sido los mercados bursátiles eficientes informacionalmente?," Apuntes del Cenes, Universidad Pedagógica y Tecnológica de Colombia, June.
    20. Chiang, Mi-Hsiu & Chiu, Hsin-Yu & Kuo, Wei-Yu, 2021. "Predictive ability of similarity-based futures trading strategies," Pacific-Basin Finance Journal, Elsevier, vol. 68(C).

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:arx:papers:2009.03239. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: arXiv administrators (email available below). General contact details of provider: http://arxiv.org/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.