IDEAS home Printed from https://ideas.repec.org/a/rjr/romjef/vy2019i4p100-123.html
   My bibliography  Save this article

A Multi-Indicator Multi-Output Mixed Frequency Sampling Approach for Stock Index Forecasting

Author

Listed:
  • Yuchen PAN

    (Corresponding Author. Business School at Southwest University of Political Science&Law. No. 399 Baosheng Road, Yubei, Chongqing, 401120, China.)

  • Zhi XIAO

    (Corresponding Author, School of Economics and Business Administration at Chongqing University. No. 174 Shazhengjie, Shapingba, Chongqing, 400044, China.)

  • Xianning WANG

    (School of Economics and Management at Chongqing Normal University. No.37, Middle Road of University Town, Shaping District, Chongqing, 401331, China; Big Data Marketing Research and Applications Center of Chongqing Normal University, No. 37, Middle Road of University Town, Shapingba District, Chongqing, 401331, China.)

  • Daoli YANG

    (E-commerce Department of School of Business Planning at Chongqing Technology and Business University. No. 19, Xuefu Avenue, Nan’an, Chongqing, 400067, China)

Abstract

Compared to stock price index sampled at higher frequency, its indicators are usually sampled at lower frequencies. In practice, with higher frequency variables response to lower frequency variables, we can get multi-output for each period. This paper explores how to construct a multi-indicator multi-output (MIMO) mixed sampling frequency approach for stock price index forecasting. We also consider nonlinear relationship between dependent variables and independent variables in stock market. We establish a new model by applying multiple output support vector machine (MSVM) to modify mixed data sampling (MIDAS). We compare results with other models and make DM tests. The experiment shows that the proposed model performances better.

Suggested Citation

  • Yuchen PAN & Zhi XIAO & Xianning WANG & Daoli YANG, 2019. "A Multi-Indicator Multi-Output Mixed Frequency Sampling Approach for Stock Index Forecasting," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(4), pages 100-123, December.
    • Handle: RePEc:rjr:romjef:v::y:2019:i:4:p:100-123
    as

    Download full text from publisher

    File URL: http://www.ipe.ro/rjef/rjef4_19/rjef4_2019p100-123.pdf
    Download Restriction: no
    ---><---

    More about this item

    Keywords

    Stock price index forecasting; MIMO; MIDAS; MSVM; nonlinearity;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E47 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Forecasting and Simulation: Models and Applications

    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:rjr:romjef:v::y:2019:i:4:p:100-123. 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: Corina Saman (email available below). General contact details of provider: https://edirc.repec.org/data/ipacaro.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.