IDEAS home Printed from https://ideas.repec.org/a/kap/compec/v58y2021i3d10.1007_s10614-020-10053-x.html
   My bibliography  Save this article

Coalition Feature Interpretation and Attribution in Algorithmic Trading Models

Author

Listed:
  • James V. Hansen

    (Brigham Young University)

Abstract

The ability to correctly interpret a prediction model’s output is critically important in many problem spheres. Accurate interpretation generates user trust in the model, provides insight into how a model may be improved, and supports understanding of the process being modeled. Absence of this capability has constrained algorithmic trading from making use of more powerful predictive models, such as XGBoost and Random Forests. Recently, the adaptation of coalitional game theory has led to the development of consistent methods of determining feature importance for these models (SHAP).This study designs and tests a novel method of integrating the capabilities of SHAP into predictive models for algorithmic trading.

Suggested Citation

  • James V. Hansen, 2021. "Coalition Feature Interpretation and Attribution in Algorithmic Trading Models," Computational Economics, Springer;Society for Computational Economics, vol. 58(3), pages 849-866, October.
    • Handle: RePEc:kap:compec:v:58:y:2021:i:3:d:10.1007_s10614-020-10053-x
    DOI: 10.1007/s10614-020-10053-x
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10614-020-10053-x
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10614-020-10053-x?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Stan Lipovetsky & Michael Conklin, 2001. "Analysis of regression in game theory approach," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 17(4), pages 319-330, October.
    2. Sebastian Bach & Alexander Binder & Grégoire Montavon & Frederick Klauschen & Klaus-Robert Müller & Wojciech Samek, 2015. "On Pixel-Wise Explanations for Non-Linear Classifier Decisions by Layer-Wise Relevance Propagation," PLOS ONE, Public Library of Science, vol. 10(7), pages 1-46, July.
    3. Stephane Mussard & Virginie Terraza, 2008. "The Shapley decomposition for portfolio risk," Applied Economics Letters, Taylor & Francis Journals, vol. 15(9), pages 713-715.
    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. Gabriel Ferrettini & Elodie Escriva & Julien Aligon & Jean-Baptiste Excoffier & Chantal Soulé-Dupuy, 2022. "Coalitional Strategies for Efficient Individual Prediction Explanation," Information Systems Frontiers, Springer, vol. 24(1), pages 49-75, February.
    2. Pelin Ayranci & Phung Lai & Nhathai Phan & Han Hu & Alexander Kolinowski & David Newman & Deijing Dou, 2022. "OnML: an ontology-based approach for interpretable machine learning," Journal of Combinatorial Optimization, Springer, vol. 44(1), pages 770-793, August.
    3. Amini, Mostafa & Bagheri, Ali & Delen, Dursun, 2022. "Discovering injury severity risk factors in automobile crashes: A hybrid explainable AI framework for decision support," Reliability Engineering and System Safety, Elsevier, vol. 226(C).
    4. Kunal Pattanayak & Vikram Krishnamurthy, 2021. "Rationally Inattentive Utility Maximization for Interpretable Deep Image Classification," Papers 2102.04594, arXiv.org, revised Jul 2021.
    5. Borgonovo, Emanuele & Plischke, Elmar & Rabitti, Giovanni, 2024. "The many Shapley values for explainable artificial intelligence: A sensitivity analysis perspective," European Journal of Operational Research, Elsevier, vol. 318(3), pages 911-926.
    6. Haim Shalit, 2021. "The Shapley value decomposition of optimal portfolios," Annals of Finance, Springer, vol. 17(1), pages 1-25, March.
    7. Pera, Rebecca & Viglia, Giampaolo & Furlan, Roberto, 2016. "Who Am I? How Compelling Self-storytelling Builds Digital Personal Reputation," Journal of Interactive Marketing, Elsevier, vol. 35(C), pages 44-55.
    8. Minyoung Lee & Joohyoung Jeon & Hongchul Lee, 2022. "Explainable AI for domain experts: a post Hoc analysis of deep learning for defect classification of TFT–LCD panels," Journal of Intelligent Manufacturing, Springer, vol. 33(6), pages 1747-1759, August.
    9. Stan Lipovetsky, 2021. "Predictor Analysis in Group Decision Making," Stats, MDPI, vol. 4(1), pages 1-14, February.
    10. Hugh Chen & Scott M. Lundberg & Su-In Lee, 2022. "Explaining a series of models by propagating Shapley values," Nature Communications, Nature, vol. 13(1), pages 1-15, December.
    11. Emrah Arbak, 2017. "Identifying the provisioning policies of Belgian banks," Working Paper Research 326, National Bank of Belgium.
    12. Algaba, Encarnación & Béal, Sylvain & Fragnelli, Vito & Llorca, Natividad & Sánchez-Soriano, Joaquin, 2019. "Relationship between labeled network games and other cooperative games arising from attributes situations," Economics Letters, Elsevier, vol. 185(C).
    13. Mark Gromowski & Michael Siebers & Ute Schmid, 2020. "A process framework for inducing and explaining Datalog theories," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 14(4), pages 821-835, December.
    14. Viglia, Giampaolo & Abrate, Graziano, 2017. "When distinction does not pay off - Investigating the determinants of European agritourism prices," Journal of Business Research, Elsevier, vol. 80(C), pages 45-52.
    15. Riccardo Colini-Baldeschi & Marco Scarsini & Stefano Vaccari, 2018. "Variance Allocation and Shapley Value," Methodology and Computing in Applied Probability, Springer, vol. 20(3), pages 919-933, September.
    16. Xingwei Hu, 2020. "A theory of dichotomous valuation with applications to variable selection," Econometric Reviews, Taylor & Francis Journals, vol. 39(10), pages 1075-1099, November.
    17. Patrick S. Hagan & Andrew Lesniewski & Georgios E. Skoufis & Diana E. Woodward, 2021. "Portfolio risk allocation through Shapley value," Papers 2103.05453, arXiv.org.
    18. Antoniadis, Anestis & Lambert-Lacroix, Sophie & Poggi, Jean-Michel, 2021. "Random forests for global sensitivity analysis: A selective review," Reliability Engineering and System Safety, Elsevier, vol. 206(C).
    19. Dmitry Sharapov & Paul Kattuman & Diego Rodriguez & F. Javier Velazquez, 2021. "Using the SHAPLEY value approach to variance decomposition in strategy research: Diversification, internationalization, and corporate group effects on affiliate profitability," Strategic Management Journal, Wiley Blackwell, vol. 42(3), pages 608-623, March.
    20. Haim Shalit, 2020. "The Shapley value of regression portfolios," Journal of Asset Management, Palgrave Macmillan, vol. 21(6), pages 506-512, October.

    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:kap:compec:v:58:y:2021:i:3:d:10.1007_s10614-020-10053-x. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.