IDEAS home Printed from https://ideas.repec.org/p/war/wpaper/2022-25.html
   My bibliography  Save this paper

Daily and intraday application of various architectures of the LSTM model in algorithmic investment strategies on Bitcoin and the S&P 500 Index

Author

Listed:
  • Katarzyna Kryńska

    (University of Warsaw, Faculty of Economic Sciences, Quantitative Finance Research Group)

  • Robert Ślepaczuk

    (University of Warsaw, Faculty of Economic Sciences, Quantitative Finance Research Group, Department of Quantitative Finance)

Abstract

This thesis investigates the use of various architectures of the LSTM model in algorithmic investment strategies. LSTM models are used to generate buy/sell signals, with previous levels of Bitcoin price and the S&P 500 Index value as inputs. Four approaches are tested: two are regression problems (price level prediction) and the other two are classification problems (prediction of price direction). All approaches are applied to daily, hourly, and 15-minute data and are using a walk-forward optimization procedure. The out-of-sample period for the S&P 500 Index is from February 6, 2014 to November 27, 2020, and for Bitcoin it is from January 15, 2014 to December 1, 2020. We discover that classification techniques beat regression methods on average, but we cannot determine if intra-day models outperform inter-day models. We come to the conclusion that the ensembling of models does not always have a positive impact on performance. Finally, a sensitivity analysis is performed to determine how changes in the main hyperparameters of the LSTM model affect strategy performance.

Suggested Citation

  • Katarzyna Kryńska & Robert Ślepaczuk, 2022. "Daily and intraday application of various architectures of the LSTM model in algorithmic investment strategies on Bitcoin and the S&P 500 Index," Working Papers 2022-25, Faculty of Economic Sciences, University of Warsaw.
    • Handle: RePEc:war:wpaper:2022-25
    as

    Download full text from publisher

    File URL: https://www.wne.uw.edu.pl/download_file/2146/0
    File Function: First version, 2022
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Illia Baranochnikov & Robert Ślepaczuk, 2022. "A comparison of LSTM and GRU architectures with novel walk-forward approach to algorithmic investment strategy," Working Papers 2022-21, Faculty of Economic Sciences, University of Warsaw.
    2. Burton G. Malkiel, 2005. "Reflections on the Efficient Market Hypothesis: 30 Years Later," The Financial Review, Eastern Finance Association, vol. 40(1), pages 1-9, February.
    3. Schulmeister, Stephan, 2009. "Profitability of technical stock trading: Has it moved from daily to intraday data?," Review of Financial Economics, Elsevier, vol. 18(4), pages 190-201, October.
    4. Fama, Eugene F, 1970. "Efficient Capital Markets: A Review of Theory and Empirical Work," Journal of Finance, American Finance Association, vol. 25(2), pages 383-417, May.
    5. Wun-Hua Chen & Jen-Ying Shih & Soushan Wu, 2006. "Comparison of support-vector machines and back propagation neural networks in forecasting the six major Asian stock markets," International Journal of Electronic Finance, Inderscience Enterprises Ltd, vol. 1(1), pages 49-67.
    6. Jan Grudniewicz & Robert Ślepaczuk, 2021. "Application of machine learning in quantitative investment strategies on global stock markets," Working Papers 2021-23, Faculty of Economic Sciences, University of Warsaw.
    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. Kamil Kashif & Robert 'Slepaczuk, 2024. "LSTM-ARIMA as a Hybrid Approach in Algorithmic Investment Strategies," Papers 2406.18206, arXiv.org.
    2. Karol Chojnacki & Robert Ślepaczuk, 2023. "This study compares well-known tools of technical analysis (Moving Average Crossover MAC) with Machine Learning based strategies (LSTM and XGBoost) and Ensembled Machine Learning Strategies (LSTM ense," Working Papers 2023-15, Faculty of Economic Sciences, University of Warsaw.

    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. Bartosz Bieganowski & Robert Ślepaczuk, 2024. "Supervised Autoencoder MLP for Financial Time Series Forecasting," Working Papers 2024-03, Faculty of Economic Sciences, University of Warsaw.
    2. Bartosz Bieganowski & Robert 'Slepaczuk, 2024. "Supervised Autoencoders with Fractionally Differentiated Features and Triple Barrier Labelling Enhance Predictions on Noisy Data," Papers 2411.12753, arXiv.org, revised Nov 2024.
    3. Patrick Buckley & Fergal O’Brien, 0. "The effect of malicious manipulations on prediction market accuracy," Information Systems Frontiers, Springer, vol. 0, pages 1-13.
    4. Patrick Buckley & Fergal O’Brien, 2017. "The effect of malicious manipulations on prediction market accuracy," Information Systems Frontiers, Springer, vol. 19(3), pages 611-623, June.
    5. Rocha Filho, Tareísio M. & Rocha, Paulo M.M., 2020. "Evidence of inefficiency of the Brazilian stock market: The IBOVESPA future contracts," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 543(C).
    6. Vasilios Sogiakas, 2017. "Efficiency of the UK Stock Exchange," Journal of Risk & Control, Risk Market Journals, vol. 4(1), pages 51-69.
    7. Stephan Schulmeister, 2014. "A General Financial Transactions Tax. Motives, Effects and Implementation According to the Proposal of the European Commission," WIFO Working Papers 461, WIFO.
    8. Damian Pastor & Pavel Kisela & Viliam Kovac & Tomas Sabol & Viliam Vajda, 2015. "Application Of Market Valuation Models In Portfolio Management," Polish Journal of Management Studies, Czestochowa Technical University, Department of Management, vol. 12(1), pages 154-165, DEcember.
    9. Jürgen Huber & Michael Kirchler & Thomas Stöckl, 2010. "The hot hand belief and the gambler’s fallacy in investment decisions under risk," Theory and Decision, Springer, vol. 68(4), pages 445-462, April.
    10. Joseph D. Prusa & Ryan T. Sagul & Taghi M. Khoshgoftaar, 2019. "Extracting Knowledge from Technical Reports for the Valuation of West Texas Intermediate Crude Oil Futures," Information Systems Frontiers, Springer, vol. 21(1), pages 109-123, February.
    11. Umit Ozmel & Deniz Yavuz & Tim Trombley & Ranjay Gulati, 2020. "Interfirm Ties Between Ventures and Limited Partners of Venture Capital Funds: Performance Effects in Financial Markets," Organization Science, INFORMS, vol. 31(3), pages 698-719, May.
    12. Broere, Mark & Christmann, Robin, 2019. "Takeovers, Shareholder Litigation, and the Free-riding Problem," MPRA Paper 93201, University Library of Munich, Germany.
    13. Milena Kojić & Slobodan Rakić & José Wesley Lima da Silva & Fernando Henrique Antunes de Araujo, 2025. "Sectoral Efficiency and Resilience: A Multifaceted Analysis of S&P Global BMI Indices Under Global Crises," Mathematics, MDPI, vol. 13(4), pages 1-26, February.
    14. Vince Vella & Wing Lon Ng, 2015. "A Dynamic Fuzzy Money Management Approach for Controlling the Intraday Risk‐Adjusted Performance of AI Trading Algorithms," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 22(2), pages 153-178, April.
    15. Hui, Eddie C.M. & Chan, Ka Kwan Kevin, 2019. "Alternative trading strategies to beat “buy-and-hold”," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 534(C).
    16. Stephan Schulmeister, 2007. "The Interaction Between the Aggregate Behaviour of Technical Trading Systems and Stock Price Dynamics," WIFO Working Papers 290, WIFO.
    17. Thanh Huong Nguyen, 2019. "Information and Noise in Stock Markets: Evidence on the Determinants and Effects Using New Empirical Measures," PhD Thesis, Finance Discipline Group, UTS Business School, University of Technology, Sydney, number 7-2019, January-A.
    18. Holmberg, Ulf & Lönnbark, Carl & Lundström, Christian, 2013. "Assessing the profitability of intraday opening range breakout strategies," Finance Research Letters, Elsevier, vol. 10(1), pages 27-33.
    19. Tarcisio M. Rocha Filho & Paulo M. M. Rocha, 2019. "Inefficiency of the Brazilian Stock Market: the IBOVESPA Future Contracts," Papers 1904.09214, arXiv.org.
    20. Cajueiro, Daniel O. & Tabak, Benjamin M., 2006. "Testing for predictability in equity returns for European transition markets," Economic Systems, Elsevier, vol. 30(1), pages 56-78, March.

    More about this item

    Keywords

    machine learning; deep learning; recurrent neural networks; LSTM; algorithmic trading; ensemble investment strategy; intra-day trading; S&P 500 Index; Bitcoin;
    All these keywords.

    JEL classification:

    • C4 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • G13 - Financial Economics - - General Financial Markets - - - Contingent Pricing; Futures Pricing

    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:war:wpaper:2022-25. 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: Marcin Bąba (email available below). General contact details of provider: https://edirc.repec.org/data/fesuwpl.html .

    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.