IDEAS home Printed from https://ideas.repec.org/p/arx/papers/2004.01497.html
   My bibliography  Save this paper

Deep learning for Stock Market Prediction

Author

Listed:
  • Mojtaba Nabipour
  • Pooyan Nayyeri
  • Hamed Jabani
  • Amir Mosavi

Abstract

Prediction of stock groups' values has always been attractive and challenging for shareholders. This paper concentrates on the future prediction of stock market groups. Four groups named diversified financials, petroleum, non-metallic minerals and basic metals from Tehran stock exchange are chosen for experimental evaluations. Data are collected for the groups based on ten years of historical records. The values predictions are created for 1, 2, 5, 10, 15, 20 and 30 days in advance. The machine learning algorithms utilized for prediction of future values of stock market groups. We employed Decision Tree, Bagging, Random Forest, Adaptive Boosting (Adaboost), Gradient Boosting and eXtreme Gradient Boosting (XGBoost), and Artificial neural network (ANN), Recurrent Neural Network (RNN) and Long short-term memory (LSTM). Ten technical indicators are selected as the inputs into each of the prediction models. Finally, the result of predictions is presented for each technique based on three metrics. Among all the algorithms used in this paper, LSTM shows more accurate results with the highest model fitting ability. Also, for tree-based models, there is often an intense competition between Adaboost, Gradient Boosting, and XGBoost.

Suggested Citation

  • Mojtaba Nabipour & Pooyan Nayyeri & Hamed Jabani & Amir Mosavi, 2020. "Deep learning for Stock Market Prediction," Papers 2004.01497, arXiv.org.
  • Handle: RePEc:arx:papers:2004.01497
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/2004.01497
    File Function: Latest version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Jiang, Minqi & Liu, Jiapeng & Zhang, Lu & Liu, Chunyu, 2020. "An improved Stacking framework for stock index prediction by leveraging tree-based ensemble models and deep learning algorithms," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 541(C).
    2. Hsieh, David A, 1991. "Chaos and Nonlinear Dynamics: Application to Financial Markets," Journal of Finance, American Finance Association, vol. 46(5), pages 1839-1877, December.
    3. Ali Aali-Bujari & Francisco Venegas-Martínez & Gilberto Pérez-Lechuga, 2017. "Impact of the stock market capitalization and thebanking spread in growth and development in LatinAmerican: A panel data estimation with System GMM," Contaduría y Administración, Accounting and Management, vol. 62(5), pages 3-4, Diciembre.
    4. Basak, Suryoday & Kar, Saibal & Saha, Snehanshu & Khaidem, Luckyson & Dey, Sudeepa Roy, 2019. "Predicting the direction of stock market prices using tree-based classifiers," The North American Journal of Economics and Finance, Elsevier, vol. 47(C), pages 552-567.
    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. Suya Jin & Guiyan Liu & Qifeng Bai, 2023. "Deep Learning in COVID-19 Diagnosis, Prognosis and Treatment Selection," Mathematics, MDPI, vol. 11(6), pages 1-16, March.
    2. King, Juan C. & Dale, Roberto & Amigó, José M., 2024. "Blockchain metrics and indicators in cryptocurrency trading," Chaos, Solitons & Fractals, Elsevier, vol. 178(C).
    3. Mohammed El Amine Senoussaoui & Mostefa Brahami & Issouf Fofana, 2021. "Transformer Oil Quality Assessment Using Random Forest with Feature Engineering," Energies, MDPI, vol. 14(7), pages 1-15, March.
    4. Yiyang Zheng, 2022. "Neural Network and Order Flow, Technical Analysis: Predicting short-term direction of futures contract," Papers 2203.12457, arXiv.org.
    5. Tidor-Vlad Pricope, 2021. "Deep Reinforcement Learning in Quantitative Algorithmic Trading: A Review," Papers 2106.00123, arXiv.org.
    6. Damian Ślusarczyk & Robert Ślepaczuk, 2023. "Optimal Markowitz Portfolio Using Returns Forecasted with Time Series and Machine Learning Models," Working Papers 2023-17, Faculty of Economic Sciences, University of Warsaw.
    7. Zefan Dong & Yonghui Zhou, 2024. "A Novel Hybrid Model for Financial Forecasting Based on CEEMDAN-SE and ARIMA-CNN-LSTM," Mathematics, MDPI, vol. 12(16), pages 1-16, August.
    8. Li-Chen Cheng & Yu-Hsiang Huang & Ming-Hua Hsieh & Mu-En Wu, 2021. "A Novel Trading Strategy Framework Based on Reinforcement Deep Learning for Financial Market Predictions," Mathematics, MDPI, vol. 9(23), pages 1-16, November.
    9. Mufhumudzi Muthivhi & Terence L. van Zyl, 2022. "Fusion of Sentiment and Asset Price Predictions for Portfolio Optimization," Papers 2203.05673, arXiv.org.
    10. Pedro M. Mirete-Ferrer & Alberto Garcia-Garcia & Juan Samuel Baixauli-Soler & Maria A. Prats, 2022. "A Review on Machine Learning for Asset Management," Risks, MDPI, vol. 10(4), pages 1-46, April.
    11. Ioan Mihail Savaniu & Alexandru-Polifron Chiriță & Oana Tonciu & Magdalena Culcea & Ancuta Neagu, 2023. "Neural-Network-Based Time Control for Microwave Oven Heating of Food Products Distributed by a Solar-Powered Vending Machine with Energy Management Considerations," Energies, MDPI, vol. 16(19), pages 1-22, October.
    12. Xiaolu Wei & Yubo Tian & Na Li & Huanxin Peng, 2024. "Evaluating ensemble learning techniques for stock index trend prediction: a case of China," Portuguese Economic Journal, Springer;Instituto Superior de Economia e Gestao, vol. 23(3), pages 505-530, September.
    13. Satya Verma & Satya Prakash Sahu & Tirath Prasad Sahu, 2024. "Two-Stage Hybrid Feature Selection Approach Using Levy’s Flight Based Chicken Swarm Optimization for Stock Market Forecasting," Computational Economics, Springer;Society for Computational Economics, vol. 63(6), pages 2193-2224, June.
    14. Tariq Mahmood & Ibtasam Ahmad & Malik Muhammad Zeeshan Ansar & Jumanah Ahmed Darwish & Rehan Ahmad Khan Sherwani, 2024. "Comparative Study of Long Short-Term Memory (LSTM) and Quantum Long Short-Term Memory (QLSTM): Prediction of Stock Market Movement," Papers 2409.08297, arXiv.org.
    15. Pawan Kumar Singh & Anushka Chouhan & Rajiv Kumar Bhatt & Ravi Kiran & Ansari Saleh Ahmar, 2022. "Implementation of the SutteARIMA method to predict short-term cases of stock market and COVID-19 pandemic in USA," Quality & Quantity: International Journal of Methodology, Springer, vol. 56(4), pages 2023-2033, August.
    16. Priyank Sonkiya & Vikas Bajpai & Anukriti Bansal, 2021. "Stock price prediction using BERT and GAN," Papers 2107.09055, arXiv.org.
    17. Arvind Kumar Sinha & Pradeep Shende, 2024. "Uncertainty Optimization Based Feature Selection Model for Stock Marketing," Computational Economics, Springer;Society for Computational Economics, vol. 63(1), pages 357-389, January.
    18. Juan C. King & Roberto Dale & Jos'e M. Amig'o, 2024. "Blockchain Metrics and Indicators in Cryptocurrency Trading," Papers 2403.00770, 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. Chen, Wei & Zhang, Haoyu & Jia, Lifen, 2022. "A novel two-stage method for well-diversified portfolio construction based on stock return prediction using machine learning," The North American Journal of Economics and Finance, Elsevier, vol. 63(C).
    2. Baoqiang Zhan & Shu Zhang & Helen S. Du & Xiaoguang Yang, 2022. "Exploring Statistical Arbitrage Opportunities Using Machine Learning Strategy," Computational Economics, Springer;Society for Computational Economics, vol. 60(3), pages 861-882, October.
    3. Kian-Ping Lim & Melvin J. Hinich & Venus Khim-Sen Liew, 2005. "Statistical Inadequacy of GARCH Models for Asian Stock Markets," Journal of Emerging Market Finance, Institute for Financial Management and Research, vol. 4(3), pages 263-279, December.
    4. Henriques, Irene & Sadorsky, Perry, 2023. "Forecasting rare earth stock prices with machine learning," Resources Policy, Elsevier, vol. 86(PA).
    5. Dutta, Shantanu & Essaddam, Naceur & Kumar, Vinod & Saadi, Samir, 2017. "How does electronic trading affect efficiency of stock market and conditional volatility? Evidence from Toronto Stock Exchange," Research in International Business and Finance, Elsevier, vol. 39(PB), pages 867-877.
    6. A. Corcos & J-P Eckmann & A. Malaspinas & Y. Malevergne & D. Sornette, 2002. "Imitation and contrarian behaviour: hyperbolic bubbles, crashes and chaos," Quantitative Finance, Taylor & Francis Journals, vol. 2(4), pages 264-281.
    7. Scott C. Linn & Nicholas S. P. Tay, 2007. "Complexity and the Character of Stock Returns: Empirical Evidence and a Model of Asset Prices Based on Complex Investor Learning," Management Science, INFORMS, vol. 53(7), pages 1165-1180, July.
    8. Ren, Fei & Gu, Gao-Feng & Zhou, Wei-Xing, 2009. "Scaling and memory in the return intervals of realized volatility," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 388(22), pages 4787-4796.
    9. Javier Morales & V'ictor Tercero & Fernando Camacho & Eduardo Cordero & Luis L'opez & F-Javier Almaguer, 2014. "Trend and Fractality Assessment of Mexico's Stock Exchange," Papers 1411.3399, arXiv.org.
    10. John T. Barkoulas & Christopher F. Baum & Joseph Onochie, 1997. "A nonparametric investigation of the 90‐day t‐bill rate," Review of Financial Economics, John Wiley & Sons, vol. 6(2), pages 187-198.
    11. Takala, Kari & Virén, Matti, 1995. "Testing nonlinear dynamics, long memory and chaotic behaviour with macroeconomic data," Research Discussion Papers 9/1995, Bank of Finland.
    12. Saqib Farid & Rubeena Tashfeen & Tahseen Mohsan & Arsal Burhan, 2023. "Forecasting stock prices using a data mining method: Evidence from emerging market," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 28(2), pages 1911-1917, April.
    13. Catherine Kyrtsou & Michel Terraza, 2003. "Is it Possible to Study Chaotic and ARCH Behaviour Jointly? Application of a Noisy Mackey–Glass Equation with Heteroskedastic Errors to the Paris Stock Exchange Returns Series," Computational Economics, Springer;Society for Computational Economics, vol. 21(3), pages 257-276, June.
    14. Risso, Wiston Adrián, 2008. "The informational efficiency and the financial crashes," Research in International Business and Finance, Elsevier, vol. 22(3), pages 396-408, September.
    15. repec:zbw:bofrdp:1995_009 is not listed on IDEAS
    16. Urquhart, Andrew & Hudson, Robert, 2013. "Efficient or adaptive markets? Evidence from major stock markets using very long run historic data," International Review of Financial Analysis, Elsevier, vol. 28(C), pages 130-142.
    17. Kian-Ping Lim & Venus Khim-Sen Liew & Hock-Tsen Wong, 2003. "Weak-form Efficient Market Hypothesis, Behavioural Finance and Episodic Transient Dependencies: The Case of the Kuala Lumpur Stock Exchange," Finance 0312012, University Library of Munich, Germany.
    18. Franco Bevilacqua & Adriaan van Zon, 2004. "Random walks and non-linear paths in macroeconomic time series: some evidence and implications," Chapters, in: John Foster & Werner Hölzl (ed.), Applied Evolutionary Economics and Complex Systems, chapter 3, Edward Elgar Publishing.
    19. Semei Coronado-Ram'irez & Pedro Celso-Arellano & Omar Rojas, 2014. "Adaptive Market Efficiency of Agricultural Commodity Futures Contracts," Papers 1412.8017, arXiv.org, revised Mar 2015.
    20. Philip Maymin, 2010. "The Hazards of Propping Up: Bubbles and Chaos," Papers 1002.2282, arXiv.org.
    21. Jialei Jiang & Eun-Mi Park & Seong-Taek Park, 2021. "The Impact of the COVID-19 on Economic Sustainability—A Case Study of Fluctuation in Stock Prices for China and South Korea," Sustainability, MDPI, vol. 13(12), pages 1-17, June.

    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:2004.01497. 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.