IDEAS home Printed from https://ideas.repec.org/a/buc/jpredm/v7y2013i3p29-44.html
   My bibliography  Save this article

Multivariate Methods In Assessing The Accuracy Of Prediction Markets Ex Ante Based On Ohe Highest-Price Criterion

Author

Listed:
  • Hung-Wen Lin
  • Chen-yuan Tung
  • Jason Yeh

Abstract

This study successfully establishes the principal component analysis with discriminant analysis (PCA-DA) model to assess the accuracy of contracts in the prediction markets ex ante based on the highest-price criterion. Trained by the xFuture data (7,274 contracts of future events) from 2006-2011, the PCA-DA model shows learning effects and provides 97.72% confidence to predict the outcome of any contract discriminated to the correct prediction group in the Exchange of Future Events. However, we need to greatly improve the low confidence of 19.58% for the PCA-DA model to predict the result of any contract discriminated to the incorrect prediction group.

Suggested Citation

  • Hung-Wen Lin & Chen-yuan Tung & Jason Yeh, 2013. "Multivariate Methods In Assessing The Accuracy Of Prediction Markets Ex Ante Based On Ohe Highest-Price Criterion," Journal of Prediction Markets, University of Buckingham Press, vol. 7(3), pages 29-44.
    • Handle: RePEc:buc:jpredm:v:7:y:2013:i:3:p:29-44
    as

    Download full text from publisher

    File URL: http://ubplj.org/index.php/jpm/article/view/752
    Download Restriction: no
    ---><---

    Citations

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


    Cited by:

    1. Tai, Chung-Ching & Lin, Hung-Wen & Chie, Bin-Tzong & Tung, Chen-Yuan, 2019. "Predicting the failures of prediction markets: A procedure of decision making using classification models," International Journal of Forecasting, Elsevier, vol. 35(1), pages 297-312.

    More about this item

    Keywords

    Principal component analysis; discriminant analysis; PCA-DA model; prediction markets; Exchange of Future Events; degree of market consensus;
    All these keywords.

    JEL classification:

    • L83 - Industrial Organization - - Industry Studies: Services - - - Sports; Gambling; Restaurants; Recreation; Tourism

    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:buc:jpredm:v:7:y:2013:i:3:p:29-44. 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: Dominic Cortis, University of Malta (email available below). General contact details of provider: http://www.ubpl.co.uk/ .

    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.