IDEAS home Printed from https://ideas.repec.org/a/eee/finlet/v83y2025ics1544612325009195.html
   My bibliography  Save this article

Macro-founded machine learning models for power market price trend detection

Author

Listed:
  • Carlei, Vittorio
  • Furia, Donatella
  • Cascioli, Piera
  • Manenti, Paolo

Abstract

The paper explores the development of a trading strategy in the power market using Artificial Intelligence algorithms to exploit potential information from a large set of indicators. The first part of the document relies on feature construction, analyzing key market factors such as gas storage capacity, withdrawals, and injections to define macroeconomic regimes. The second part introduces additional complexity either testing the granger-causal relationship between gas flow and futures contract returns, either considering temperature-related variables and weather shocks features to expand the training input space of the Machine Learning (ML) models. Finally, the study presents an out-of-sample simulation, comparing ML model performances with a naïve-long only strategy.

Suggested Citation

  • Carlei, Vittorio & Furia, Donatella & Cascioli, Piera & Manenti, Paolo, 2025. "Macro-founded machine learning models for power market price trend detection," Finance Research Letters, Elsevier, vol. 83(C).
    • Handle: RePEc:eee:finlet:v:83:y:2025:i:c:s1544612325009195
    DOI: 10.1016/j.frl.2025.107660
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1544612325009195
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.frl.2025.107660?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

    for a different version of it.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    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:eee:finlet:v:83:y:2025:i:c:s1544612325009195. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/frl .

    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.