IDEAS home Printed from https://ideas.repec.org/a/bla/finmgt/v48y2019i4p1069-1094.html
   My bibliography  Save this article

Machine learning and asset allocation

Author

Listed:
  • Bryan R. Routledge

Abstract

Investors have access to a large array of structured and unstructured data. We consider how these data can be incorporated into financial decisions through the lens of the canonical asset allocation decision. We characterize investor preference for simplicity in models of the data used in the asset allocation decision. The simplicity parameters then guide asset allocation along with the usual risk aversion parameter. We use three distinct and diverse macroeconomic data sets to implement the model to forecast equity returns (the equity risk premium). The data sets we use are (a) price‐dividend ratios, (b) an array of macroeconomic series, and (c) text data from the Federal Reserve's Federal Open Market Committee (FOMC) meetings.

Suggested Citation

  • Bryan R. Routledge, 2019. "Machine learning and asset allocation," Financial Management, Financial Management Association International, vol. 48(4), pages 1069-1094, December.
    • Handle: RePEc:bla:finmgt:v:48:y:2019:i:4:p:1069-1094
    DOI: 10.1111/fima.12303
    as

    Download full text from publisher

    File URL: https://doi.org/10.1111/fima.12303
    Download Restriction: no
    File URL: https://libkey.io/10.1111/fima.12303?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
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Hoang, Daniel & Wiegratz, Kevin, 2022. "Machine learning methods in finance: Recent applications and prospects," Working Paper Series in Economics 158, Karlsruhe Institute of Technology (KIT), Department of Economics and Management.

    More about this item

    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:bla:finmgt:v:48:y:2019:i:4:p:1069-1094. 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: Wiley Content Delivery (email available below). General contact details of provider: https://edirc.repec.org/data/fmaaaea.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.