IDEAS home Printed from https://ideas.repec.org/p/arx/papers/2212.12717.html
   My bibliography  Save this paper

Stock Market Prediction via Deep Learning Techniques: A Survey

Author

Listed:
  • Jinan Zou
  • Qingying Zhao
  • Yang Jiao
  • Haiyao Cao
  • Yanxi Liu
  • Qingsen Yan
  • Ehsan Abbasnejad
  • Lingqiao Liu
  • Javen Qinfeng Shi

Abstract

Existing surveys on stock market prediction often focus on traditional machine learning methods instead of deep learning methods. This motivates us to provide a structured and comprehensive overview of the research on stock market prediction. We present four elaborated subtasks of stock market prediction and propose a novel taxonomy to summarize the state-of-the-art models based on deep neural networks. In addition, we also provide detailed statistics on the datasets and evaluation metrics commonly used in the stock market. Finally, we point out several future directions by sharing some new perspectives on stock market prediction.

Suggested Citation

  • Jinan Zou & Qingying Zhao & Yang Jiao & Haiyao Cao & Yanxi Liu & Qingsen Yan & Ehsan Abbasnejad & Lingqiao Liu & Javen Qinfeng Shi, 2022. "Stock Market Prediction via Deep Learning Techniques: A Survey," Papers 2212.12717, arXiv.org, revised Feb 2023.
    • Handle: RePEc:arx:papers:2212.12717
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/2212.12717
    File Function: Latest version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Burton G. Malkiel, 2003. "The Efficient Market Hypothesis and Its Critics," Working Papers 111, Princeton University, Department of Economics, Center for Economic Policy Studies..
    2. Yawei Li & Shuqi Lv & Xinghua Liu & Qiuyue Zhang & Siew Ann Cheong, 2022. "Incorporating Transformers and Attention Networks for Stock Movement Prediction," Complexity, Hindawi, vol. 2022, pages 1-10, February.
    3. Daiki Matsunaga & Toyotaro Suzumura & Toshihiro Takahashi, 2019. "Exploring Graph Neural Networks for Stock Market Predictions with Rolling Window Analysis," Papers 1909.10660, arXiv.org, revised Nov 2019.
    4. Fuli Feng & Xiangnan He & Xiang Wang & Cheng Luo & Yiqun Liu & Tat-Seng Chua, 2018. "Temporal Relational Ranking for Stock Prediction," Papers 1809.09441, arXiv.org, revised Jan 2019.
    5. repec:pri:cepsud:91malkiel is not listed on IDEAS
    6. Sidra Mehtab & Jaydip Sen, 2020. "Stock Price Prediction Using CNN and LSTM-Based Deep Learning Models," Papers 2010.13891, arXiv.org.
    7. Deli Chen & Yanyan Zou & Keiko Harimoto & Ruihan Bao & Xuancheng Ren & Xu Sun, 2019. "Incorporating Fine-grained Events in Stock Movement Prediction," Papers 1910.05078, arXiv.org.
    8. Xiao-Yang Liu & Hongyang Yang & Jiechao Gao & Christina Dan Wang, 2021. "FinRL: Deep Reinforcement Learning Framework to Automate Trading in Quantitative Finance," Papers 2111.09395, arXiv.org.
    9. Priyank Sonkiya & Vikas Bajpai & Anukriti Bansal, 2021. "Stock price prediction using BERT and GAN," Papers 2107.09055, arXiv.org.
    10. Wenhang Bao & Xiao-yang Liu, 2019. "Multi-Agent Deep Reinforcement Learning for Liquidation Strategy Analysis," Papers 1906.11046, arXiv.org.
    11. Jingyuan Wang & Yang Zhang & Ke Tang & Junjie Wu & Zhang Xiong, 2019. "AlphaStock: A Buying-Winners-and-Selling-Losers Investment Strategy using Interpretable Deep Reinforcement Attention Networks," Papers 1908.02646, arXiv.org.
    12. Eduardo Ramos-P'erez & Pablo J. Alonso-Gonz'alez & Jos'e Javier N'u~nez-Vel'azquez, 2021. "Multi-Transformer: A New Neural Network-Based Architecture for Forecasting S&P Volatility," Papers 2109.12621, arXiv.org.
    13. Wenjie Lu & Jiazheng Li & Yifan Li & Aijun Sun & Jingyang Wang, 2020. "A CNN-LSTM-Based Model to Forecast Stock Prices," Complexity, Hindawi, vol. 2020, pages 1-10, November.
    14. Fama, Eugene F & MacBeth, James D, 1973. "Risk, Return, and Equilibrium: Empirical Tests," Journal of Political Economy, University of Chicago Press, vol. 81(3), pages 607-636, May-June.
    15. Chien Yi Huang, 2018. "Financial Trading as a Game: A Deep Reinforcement Learning Approach," Papers 1807.02787, arXiv.org.
    16. Zhihan Zhou & Liqian Ma & Han Liu, 2021. "Trade the Event: Corporate Events Detection for News-Based Event-Driven Trading," Papers 2105.12825, arXiv.org, revised May 2021.
    17. Antonio Briola & Jeremy Turiel & Riccardo Marcaccioli & Alvaro Cauderan & Tomaso Aste, 2021. "Deep Reinforcement Learning for Active High Frequency Trading," Papers 2101.07107, arXiv.org, revised Aug 2023.
    18. Shuo Sun & Rundong Wang & Bo An, 2021. "Reinforcement Learning for Quantitative Trading," Papers 2109.13851, arXiv.org.
    19. 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.
    20. Haiyao Wang & Jianxuan Wang & Lihui Cao & Yifan Li & Qiuhong Sun & Jingyang Wang & Kai Hu, 2021. "A Stock Closing Price Prediction Model Based on CNN-BiSLSTM," Complexity, Hindawi, vol. 2021, pages 1-12, September.
    21. Zechu Li & Xiao-Yang Liu & Jiahao Zheng & Zhaoran Wang & Anwar Walid & Jian Guo, 2021. "FinRL-Podracer: High Performance and Scalable Deep Reinforcement Learning for Quantitative Finance," Papers 2111.05188, arXiv.org.
    22. Raehyun Kim & Chan Ho So & Minbyul Jeong & Sanghoon Lee & Jinkyu Kim & Jaewoo Kang, 2019. "HATS: A Hierarchical Graph Attention Network for Stock Movement Prediction," Papers 1908.07999, arXiv.org, revised Nov 2019.
    23. Jia Wang & Tong Sun & Benyuan Liu & Yu Cao & Hongwei Zhu, 2021. "CLVSA: A Convolutional LSTM Based Variational Sequence-to-Sequence Model with Attention for Predicting Trends of Financial Markets," Papers 2104.04041, arXiv.org.
    24. Zexin Hu & Yiqi Zhao & Matloob Khushi, 2021. "A Survey of Forex and Stock Price Prediction Using Deep Learning," Papers 2103.09750, arXiv.org.
    25. Zhengyao Jiang & Dixing Xu & Jinjun Liang, 2017. "A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem," Papers 1706.10059, arXiv.org, revised Jul 2017.
    26. Burton G. Malkiel, 2003. "The Efficient Market Hypothesis and Its Critics," Journal of Economic Perspectives, American Economic Association, vol. 17(1), pages 59-82, Winter.
    27. Zhipeng Liang & Hao Chen & Junhao Zhu & Kangkang Jiang & Yanran Li, 2018. "Adversarial Deep Reinforcement Learning in Portfolio Management," Papers 1808.09940, arXiv.org, revised Nov 2018.
    28. Deniz Ersan & Chifumi Nishioka & Ansgar Scherp, 2020. "Comparison of machine learning methods for financial time series forecasting at the examples of over 10 years of daily and hourly data of DAX 30 and S&P 500," Journal of Computational Social Science, Springer, vol. 3(1), pages 103-133, April.
    29. Burton G. Malkiel, 2003. "The Efficient Market Hypothesis and Its Critics," Working Papers 111, Princeton University, Department of Economics, Center for Economic Policy Studies..
    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. Qianwei Ying & Tahir Yousaf & Qurat ul Ain & Yasmeen Akhtar & Muhammad Shahid Rasheed, 2019. "Stock Investment and Excess Returns: A Critical Review in the Light of the Efficient Market Hypothesis," JRFM, MDPI, vol. 12(2), pages 1-22, June.
    2. Xiao-Yang Liu & Jingyang Rui & Jiechao Gao & Liuqing Yang & Hongyang Yang & Zhaoran Wang & Christina Dan Wang & Jian Guo, 2021. "FinRL-Meta: A Universe of Near-Real Market Environments for Data-Driven Deep Reinforcement Learning in Quantitative Finance," Papers 2112.06753, arXiv.org, revised Mar 2022.
    3. Shuo Sun & Rundong Wang & Bo An, 2021. "Reinforcement Learning for Quantitative Trading," Papers 2109.13851, arXiv.org.
    4. González-Sánchez, Mariano, 2022. "Factorial asset pricing models using statistical anomalies," Research in International Business and Finance, Elsevier, vol. 60(C).
    5. Xiao-Yang Liu & Hongyang Yang & Jiechao Gao & Christina Dan Wang, 2021. "FinRL: Deep Reinforcement Learning Framework to Automate Trading in Quantitative Finance," Papers 2111.09395, arXiv.org.
    6. Senarathne Chamil W. & Long Wei, 2019. "Industry Competition and Common Stock Returns," Management Sciences. Nauki o Zarządzaniu, Sciendo, vol. 24(3), pages 24-35, September.
    7. Ben Hambly & Renyuan Xu & Huining Yang, 2023. "Recent advances in reinforcement learning in finance," Mathematical Finance, Wiley Blackwell, vol. 33(3), pages 437-503, July.
    8. Mariia Bondarenko & Karel Brůna, 2021. "The Impact of FX Exposure on the Firm's Stock Market Return," European Financial and Accounting Journal, Prague University of Economics and Business, vol. 2021(1), pages 45-70.
    9. Raehyun Kim & Chan Ho So & Minbyul Jeong & Sanghoon Lee & Jinkyu Kim & Jaewoo Kang, 2019. "HATS: A Hierarchical Graph Attention Network for Stock Movement Prediction," Papers 1908.07999, arXiv.org, revised Nov 2019.
    10. Dionysia Dionysiou, 2015. "Choosing Among Alternative Long-Run Event-Study Techniques," Journal of Economic Surveys, Wiley Blackwell, vol. 29(1), pages 158-198, February.
    11. David M. Ritzwoller & Joseph P. Romano, 2019. "Uncertainty in the Hot Hand Fallacy: Detecting Streaky Alternatives to Random Bernoulli Sequences," Papers 1908.01406, arXiv.org, revised Apr 2021.
    12. Chia-Lin Chang & Jukka Ilomäki & Hannu Laurila & Michael McAleer, 2018. "Long Run Returns Predictability and Volatility with Moving Averages," Risks, MDPI, vol. 6(4), pages 1-18, September.
    13. Bell, Peter N, 2013. "New Testing Procedures to Assess Market Efficiency with Trading Rules," MPRA Paper 46701, University Library of Munich, Germany.
    14. Jitka Veselá & Alžběta Zíková, 2022. "Are the Czech, Polish, German and Dutch markets taking a random walk? [Konají český, polský, německý a nizozemský trh náhodnou procházku?]," Český finanční a účetní časopis, Prague University of Economics and Business, vol. 2022(2), pages 19-38.
    15. Muchnik, Lev & Bunde, Armin & Havlin, Shlomo, 2009. "Long term memory in extreme returns of financial time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 388(19), pages 4145-4150.
    16. Nathan Jensen, 2007. "International institutions and market expectations: Stock price responses to the WTO ruling on the 2002 U.S. steel tariffs," The Review of International Organizations, Springer, vol. 2(3), pages 261-280, September.
    17. Ishani Chaudhuri & Parthajit Kayal, 2022. "Predicting Power of Ticker Search Volume in Indian Stock Market," Working Papers 2022-214, Madras School of Economics,Chennai,India.
    18. Ghada A. Altarawneh & Ahmad B. Hassanat & Ahmad S. Tarawneh & Ahmad Abadleh & Malek Alrashidi & Mansoor Alghamdi, 2022. "Stock Price Forecasting for Jordan Insurance Companies Amid the COVID-19 Pandemic Utilizing Off-the-Shelf Technical Analysis Methods," Economies, MDPI, vol. 10(2), pages 1-18, February.
    19. John Sabelhaus, 2005. "Alternative Methods for Projecting Equity Returns: Implications for Evaluating Social Security Reform Proposals," Risk Management and Insurance Review, American Risk and Insurance Association, vol. 8(1), pages 43-63, March.
    20. Cristi Spulbar & Ramona Birau & Lucian Florin Spulbar, 2021. "A Critical Survey on Efficient Market Hypothesis (EMH), Adaptive Market Hypothesis (AMH) and Fractal Markets Hypothesis (FMH) Considering Their Implication on Stock Markets Behavior," Ovidius University Annals, Economic Sciences Series, Ovidius University of Constantza, Faculty of Economic Sciences, vol. 0(2), pages 1161-1165, December.

    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:2212.12717. 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.