IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0252404.html
   My bibliography  Save this article

DPP: Deep predictor for price movement from candlestick charts

Author

Listed:
  • Chih-Chieh Hung
  • Ying-Ju Chen

Abstract

Forecasting the stock market prices is complicated and challenging since the price movement is affected by many factors such as releasing market news about earnings and profits, international and domestic economic situation, political events, monetary policy, major abrupt affairs, etc. In this work, a novel framework: deep predictor for price movement (DPP) using candlestick charts in the stock historical data is proposed. This framework comprises three steps: 1. decomposing a given candlestick chart into sub-charts; 2. using CNN-autoencoder to acquire the best representation of sub-charts; 3. applying RNN to predict the price movements from a collection of sub-chart representations. An extensive study is operated to assess the performance of the DPP based models using the trading data of Taiwan Stock Exchange Capitalization Weighted Stock Index and a stock market index, Nikkei 225, for the Tokyo Stock Exchange. Three baseline models based on IEM, Prophet, and LSTM approaches are compared with the DPP based models.

Suggested Citation

  • Chih-Chieh Hung & Ying-Ju Chen, 2021. "DPP: Deep predictor for price movement from candlestick charts," PLOS ONE, Public Library of Science, vol. 16(6), pages 1-22, June.
    • Handle: RePEc:plo:pone00:0252404
    DOI: 10.1371/journal.pone.0252404
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0252404
    Download Restriction: no
    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0252404&type=printable
    Download Restriction: no
    File URL: https://libkey.io/10.1371/journal.pone.0252404?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Iikka Korhonen, 2019. "Differences in Transition Paths: Russia versus China," ifo DICE Report, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 17(03), pages 17-21, October.
    2. Guosheng Hu & Yuxin Hu & Kai Yang & Zehao Yu & Flood Sung & Zhihong Zhang & Fei Xie & Jianguo Liu & Neil Robertson & Timothy Hospedales & Qiangwei Miemie, 2017. "Deep Stock Representation Learning: From Candlestick Charts to Investment Decisions," Papers 1709.03803, arXiv.org, revised Feb 2018.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Huang, Wenyang & Zhao, Jianyu & Wang, Xiaokang, 2024. "Model-driven multimodal LSTM-CNN for unbiased structural forecasting of European Union allowances open-high-low-close price," Energy Economics, Elsevier, vol. 132(C).
    2. V. Lanzetta, 2024. "Transfer learning for financial data predictions: a systematic review," Papers 2409.17183, arXiv.org.

    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. MohammadAmin Fazli & Parsa Alian & Ali Owfi & Erfan Loghmani, 2021. "RPS: Portfolio Asset Selection using Graph based Representation Learning," Papers 2111.15634, arXiv.org.
    2. Shima Nabiee & Nader Bagherzadeh, 2023. "Stock Trend Prediction: A Semantic Segmentation Approach," Papers 2303.09323, arXiv.org.
    3. Xing Wang & Yijun Wang & Bin Weng & Aleksandr Vinel, 2020. "Stock2Vec: A Hybrid Deep Learning Framework for Stock Market Prediction with Representation Learning and Temporal Convolutional Network," Papers 2010.01197, arXiv.org.
    4. Jungsik Hwang, 2020. "Modeling Financial Time Series using LSTM with Trainable Initial Hidden States," Papers 2007.06848, arXiv.org.
    5. Yash Thesia & Vidhey Oza & Priyank Thakkar, 2022. "A dynamic scenario‐driven technique for stock price prediction and trading," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(3), pages 653-674, April.
    6. Paraje, Guillermo & Colchero, Arantxa & Wlasiuk, Juan Marcos & Sota, Antonio Martner & Popkin, Barry M., 2021. "The effects of the Chilean food policy package on aggregate employment and real wages," Food Policy, Elsevier, vol. 100(C).
    7. Iikka Korhonen, 2019. "Forty Years of Chinese Reforms: An Overview," Comparative Economic Studies, Palgrave Macmillan;Association for Comparative Economic Studies, vol. 61(3), pages 349-358, September.
    8. Ahmet Murat Ozbayoglu & Mehmet Ugur Gudelek & Omer Berat Sezer, 2020. "Deep Learning for Financial Applications : A Survey," Papers 2002.05786, arXiv.org.
    9. Ugirumurera, Juliette & Severino, Joseph & Ficenec, Karen & Ge, Yanbo & Wang, Qichao & Williams, Lindy & Chae, Junghoon & Lunacek, Monte & Phillips, Caleb, 2021. "A modeling framework for designing and evaluating curbside traffic management policies at Dallas-Fort Worth International Airport," Transportation Research Part A: Policy and Practice, Elsevier, vol. 153(C), pages 130-150.

    More about this item

    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:plo:pone00:0252404. 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: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    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.