IDEAS home Printed from https://ideas.repec.org/p/arx/papers/2208.07168.html
   My bibliography  Save this paper

AI for trading strategies

Author

Listed:
  • Danijel Jevtic
  • Romain Deleze
  • Joerg Osterrieder

Abstract

In this bachelor thesis, we show how four different machine learning methods (Long Short-Term Memory, Random Forest, Support Vector Machine Regression, and k-Nearest Neighbor) perform compared to already successfully applied trading strategies such as Cross Signal Trading and a conventional statistical time series model ARMA-GARCH. The aim is to show that machine learning methods perform better than conventional methods in the crude oil market when used correctly. A more detailed performance analysis was made, showing the performance of the different models in different market phases so that the robustness of individual models in high and low volatility phases could be examined more closely. For further investigation, these models would also have to be analyzed in other markets.

Suggested Citation

  • Danijel Jevtic & Romain Deleze & Joerg Osterrieder, 2022. "AI for trading strategies," Papers 2208.07168, arXiv.org.
    • Handle: RePEc:arx:papers:2208.07168
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/2208.07168
    File Function: Latest version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Antonio Mucherino & Petraq J. Papajorgji & Panos M. Pardalos, 2009. "Data Mining in Agriculture," Springer Optimization and Its Applications, Springer, number 978-0-387-88615-2, September.
    2. Cao, Jian & Li, Zhi & Li, Jian, 2019. "Financial time series forecasting model based on CEEMDAN and LSTM," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 519(C), pages 127-139.
    3. 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.
    4. Robert J. Shiller, 2003. "From Efficient Markets Theory to Behavioral Finance," Journal of Economic Perspectives, American Economic Association, vol. 17(1), pages 83-104, Winter.
    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. Yoonjae Noh & Jong-Min Kim & Soongoo Hong & Sangjin Kim, 2023. "Deep Learning Model for Multivariate High-Frequency Time-Series Data: Financial Market Index Prediction," Mathematics, MDPI, vol. 11(16), pages 1-18, August.
    2. Thomas Delcey, 2019. "Samuelson vs Fama on the Efficient Market Hypothesis: The Point of View of Expertise [Samuelson vs Fama sur l’efficience informationnelle des marchés financiers : le point de vue de l’expertise]," Post-Print hal-01618347, HAL.
    3. Ariane Szafarz, 2015. "Market Efficiency and Crises:Don’t Throw the Baby out with the Bathwater," Bankers, Markets & Investors, ESKA Publishing, issue 139, pages 20-26, November-.
    4. Vasileiou, Evangelos, 2018. "Is the turn of the month effect an “abnormal normality”? Controversial findings, new patterns and…hidden signs(?)," Research in International Business and Finance, Elsevier, vol. 44(C), pages 153-175.
    5. Steven Pressman, 2018. "Hyman Minsky and behavioral finance," Journal of Behavioral Economics for Policy, Society for the Advancement of Behavioral Economics (SABE), vol. 2(1), pages 33-37, March.
    6. Caporale, Guglielmo Maria & Gil-Alana, Luis & Plastun, Alex, 2018. "Is market fear persistent? A long-memory analysis," Finance Research Letters, Elsevier, vol. 27(C), pages 140-147.
    7. Hervé, Fabrice & Zouaoui, Mohamed & Belvaux, Bertrand, 2019. "Noise traders and smart money: Evidence from online searches," Economic Modelling, Elsevier, vol. 83(C), pages 141-149.
    8. Oscar Bernal Diaz & Astrid Herinckx & Ariane Szafarz, 2014. "Which short-selling regulation is the least damaging to market efficiency? Evidence from Europe," Post-Print CEB, ULB -- Universite Libre de Bruxelles, vol. 37, pages 244-256, March.
    9. Juan Benjamin Duarte Duarte & Leonardo Hernán Talero Sarmiento & Katherine Julieth Sierra Suárez, 2017. "Evaluación del efecto de la psicología del inversionista en un mercado bursátil artificial mediante su grado de eficiencia," Contaduría y Administración, Accounting and Management, vol. 62(4), pages 1345-1360, Octubre-D.
    10. Te Bao & Cars Hommes & Tomasz Makarewicz, 2017. "Bubble Formation and (In)Efficient Markets in Learning‐to‐forecast and optimise Experiments," Economic Journal, Royal Economic Society, vol. 127(605), pages 581-609, October.
    11. Jasman Tuyon & Zamri Ahmada, 2016. "Behavioural finance perspectives on Malaysian stock market efficiency," Borsa Istanbul Review, Research and Business Development Department, Borsa Istanbul, vol. 16(1), pages 43-61, March.
    12. Mattia Guerini & Francesco Lamperti & Mauro Napoletano & Andrea Roventini & Tania Treibich, 2022. "Unconventional monetary policies in an agent-based model with mark-to-market standards," Review of Evolutionary Political Economy, Springer, vol. 3(1), pages 73-107, April.
    13. Gaies, Brahim & Nakhli, Mohamed Sahbi & Sahut, Jean-Michel & Schweizer, Denis, 2023. "Interactions between investors’ fear and greed sentiment and Bitcoin prices," The North American Journal of Economics and Finance, Elsevier, vol. 67(C).
    14. Fabio Della Rossa & Lorenzo Giannini & Pietro DeLellis, 2020. "Herding or wisdom of the crowd? Controlling efficiency in a partially rational financial market," PLOS ONE, Public Library of Science, vol. 15(9), pages 1-16, September.
    15. Kin-Boon Tang & Shao-Jye Wong & Shih-Kuei Lin & Szu-Lang Liao, 2020. "Excess volatility and market efficiency in government bond markets: the ASEAN-5 context," Journal of Asset Management, Palgrave Macmillan, vol. 21(2), pages 154-165, March.
    16. Bekiros, Stelios D., 2010. "Heterogeneous trading strategies with adaptive fuzzy Actor-Critic reinforcement learning: A behavioral approach," Journal of Economic Dynamics and Control, Elsevier, vol. 34(6), pages 1153-1170, June.
    17. Hui, Eddie Chi-Man & Wang, Ziyou, 2015. "Can we predict the property cycle? A study of securitized property market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 426(C), pages 72-87.
    18. Imran Yousaf & Shoaib Ali & Syed Zulfiqar Ali Shah, 2018. "Herding behavior in Ramadan and financial crises: the case of the Pakistani stock market," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 4(1), pages 1-14, December.
    19. Plastun, Alex & Sibande, Xolani & Gupta, Rangan & Wohar, Mark E., 2019. "Rise and fall of calendar anomalies over a century," The North American Journal of Economics and Finance, Elsevier, vol. 49(C), pages 181-205.
    20. Vivien Lespagnol & Juliette Rouchier, 2018. "Trading Volume and Price Distortion: An Agent-Based Model with Heterogenous Knowledge of Fundamentals," Post-Print hal-02084910, HAL.

    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:2208.07168. 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.