IDEAS home Printed from https://ideas.repec.org/p/war/wpaper/2024-03.html
   My bibliography  Save this paper

Supervised Autoencoder MLP for Financial Time Series Forecasting

Author

Listed:
  • Bartosz Bieganowski

    (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 paper investigates the enhancement of financial time series forecasting with the use of neural networks through supervised autoencoders, aiming to improve investment strategy performance. It specifically examines the impact of noise augmentation and triple barrier labeling on risk-adjusted returns, using the Sharpe and Information Ratios. The study focuses on the S&P 500 index, EUR/USD, and BTC/USD as the traded assets from January 1, 2010, to April 30, 2022. Findings indicate that supervised autoencoders, with balanced noise augmentation and bottleneck size, significantly boost strategy effectiveness. However, excessive noise and large bottleneck sizes can impair performance, highlighting the importance of precise parameter tuning. This paper also presents a derivation of a novel optimization metric that can be used with triple barrier labeling. The results of this study have substantial policy implications, suggesting that financial institutions and regulators could leverage techniques presented to enhance market stability and investor protection, while also encouraging more informed and strategic investment approaches in various financial sectors.

Suggested Citation

  • Bartosz Bieganowski & Robert Ślepaczuk, 2024. "Supervised Autoencoder MLP for Financial Time Series Forecasting," Working Papers 2024-03, Faculty of Economic Sciences, University of Warsaw.
    • Handle: RePEc:war:wpaper:2024-03
    as

    Download full text from publisher

    File URL: https://www.wne.uw.edu.pl/download_file/3896/0
    File Function: First version, 2024
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Barberis, Nicholas & Thaler, Richard, 2003. "A survey of behavioral finance," Handbook of the Economics of Finance, in: G.M. Constantinides & M. Harris & R. M. Stulz (ed.), Handbook of the Economics of Finance, edition 1, volume 1, chapter 18, pages 1053-1128, Elsevier.
    2. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
    3. Lahmiri, Salim & Bekiros, Stelios, 2020. "Intelligent forecasting with machine learning trading systems in chaotic intraday Bitcoin market," Chaos, Solitons & Fractals, Elsevier, vol. 133(C).
    4. Billah, Baki & King, Maxwell L. & Snyder, Ralph D. & Koehler, Anne B., 2006. "Exponential smoothing model selection for forecasting," International Journal of Forecasting, Elsevier, vol. 22(2), pages 239-247.
    5. Ślepaczuk Robert & Zenkova Maryna, 2018. "Robustness of Support Vector Machines in Algorithmic Trading on Cryptocurrency Market," Central European Economic Journal, Sciendo, vol. 5(52), pages 186-205, January.
    6. 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.
    7. 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.
    8. Mateusz Kijewski & Robert Ślepaczuk, 2020. "Predicting prices of S&P500 index using classical methods and recurrent neural networks," Working Papers 2020-27, Faculty of Economic Sciences, University of Warsaw.
    9. 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.
    10. 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.
    11. Suhwan Ji & Jongmin Kim & Hyeonseung Im, 2019. "A Comparative Study of Bitcoin Price Prediction Using Deep Learning," Mathematics, MDPI, vol. 7(10), pages 1-20, September.
    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. 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.
    2. Stephan Schulmeister, 2007. "The Interaction Between the Aggregate Behaviour of Technical Trading Systems and Stock Price Dynamics," WIFO Working Papers 290, WIFO.
    3. Alizadeh, Amir H. & Thanopoulou, Helen & Yip, Tsz Leung, 2017. "Investors’ behavior and dynamics of ship prices: A heterogeneous agent model," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 106(C), pages 98-114.
    4. Leandro Maciel, 2020. "Technical analysis based on high and low stock prices forecasts: evidence for Brazil using a fractionally cointegrated VAR model," Empirical Economics, Springer, vol. 58(4), pages 1513-1540, April.
    5. Bachar Fakhry & Christian Richter, 2015. "Is the sovereign debt market efficient? Evidence from the US and German sovereign debt markets," International Economics and Economic Policy, Springer, vol. 12(3), pages 339-357, September.
    6. Dhanya Jothimani & Ravi Shankar & Surendra S. Yadav, 2016. "Discrete Wavelet Transform-Based Prediction of Stock Index: A Study on National Stock Exchange Fifty Index," Papers 1605.07278, arXiv.org.
    7. Neely, Christopher J. & Weller, Paul, 2000. "Predictability in International Asset Returns: A Reexamination," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 35(4), pages 601-620, December.
    8. Immonen, Eero, 2015. "A quantitative description for efficient financial markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 433(C), pages 171-181.
    9. Raushan Kumar, 2021. "Predicting Wheat Futures Prices in India," Asia-Pacific Financial Markets, Springer;Japanese Association of Financial Economics and Engineering, vol. 28(1), pages 121-140, March.
    10. Saggese, Pietro & Belmonte, Alessandro & Dimitri, Nicola & Facchini, Angelo & Böhme, Rainer, 2023. "Arbitrageurs in the Bitcoin ecosystem: Evidence from user-level trading patterns in the Mt. Gox exchange platform," Journal of Economic Behavior & Organization, Elsevier, vol. 213(C), pages 251-270.
    11. Mohamed Chikhi & Anne Péguin-Feissolle & Michel Terraza, 2013. "SEMIFARMA-HYGARCH Modeling of Dow Jones Return Persistence," Computational Economics, Springer;Society for Computational Economics, vol. 41(2), pages 249-265, February.
    12. Vivien Lespagnol & Juliette Rouchier, 2015. "What Is the Impact of Heterogeneous Knowledge About Fundamentals on Market Liquidity and Efficiency: An ABM Approach," Lecture Notes in Economics and Mathematical Systems, in: Frédéric Amblard & Francisco J. Miguel & Adrien Blanchet & Benoit Gaudou (ed.), Advances in Artificial Economics, edition 127, pages 105-117, Springer.
    13. Reinhard Ellwanger, Stephen Snudden, 2021. "Predictability of Aggregated Time Series," LCERPA Working Papers bm0127, Laurier Centre for Economic Research and Policy Analysis.
    14. Mu-En Wu & Wei-Ho Chung, 2019. "Empirical Evaluations on Momentum Effects of Taiwan Index Futures via Stop-Loss and Stop-Profit Mechanisms," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 18(02), pages 629-648, March.
    15. Catania, Leopoldo & Proietti, Tommaso, 2020. "Forecasting volatility with time-varying leverage and volatility of volatility effects," International Journal of Forecasting, Elsevier, vol. 36(4), pages 1301-1317.
    16. 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.
    17. repec:ipg:wpaper:2013-020 is not listed on IDEAS
    18. Michael Demmler & Amilcar Orlian Fernández Domínguez, 2021. "Bitcoin and the South Sea Company: A comparative analysis," Revista Finanzas y Politica Economica, Universidad Católica de Colombia, vol. 13(1), pages 197-224, March.
    19. Adina Bărbulescu Robinson & Kapil R. Tuli & Ajay K. Kohli, 2015. "Does Brand Licensing Increase a Licensor's Shareholder Value?," Management Science, INFORMS, vol. 61(6), pages 1436-1455, June.
    20. Tsai, Yi-Cheng & Lei, Chin-Laung & Cheung, William & Wu, Chung-Shu & Ho, Jan-Ming & Wang, Chuan-Ju, 2018. "Exploring the Persistent Behavior of Financial Markets," Finance Research Letters, Elsevier, vol. 24(C), pages 199-220.
    21. Ryan Bartens & Shakill Hassan, 2010. "Value, size and momentum portfolios in real time: the cross section of South African stocks," Australian Journal of Management, Australian School of Business, vol. 35(2), pages 181-202, August.

    More about this item

    Keywords

    machine learning; algorithmic investment strategy; supervised autoencoders; financial time series; trading strategy; risk-adjusted return;
    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:2024-03. 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.