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Information fusion-based meta-classification predictive modeling for ETF performance

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  • Asil Oztekin

    (University of Massachusetts)

Abstract

This study is aimed at determining the future share net inflows and outflows of Exchange Traded Funds (ETFs). The relationship between net flows is closely related to investor perception of the future and past performance of mutual funds. The net flows for Exchange Traded Funds are expected to be less related to overall fund performance, but rather based on the characteristics of the fund that make it attractive to an individual investor. In order to explore the relationship between investor’s perception of ETFs and subsequent net flows, this study is designed to shed light on the multifaceted linkages between fund characteristics and net flows. A meta-classification predictive modeling approach is designed for the use of large data sets. Then its implementation and results are discussed. A thorough selection of fifteen attributes from each fund, which are the most likely contributors to fund inflows and outflows, is deployed in the analyses. The large data set calls for the use of a robust systematic approach to identifying the attributes of the funds that best predict future inflows and outflows of the fund. The predictive performance of the proposed decision analytic methodology was assessed via the 10-fold cross validation, which yielded very promising results.

Suggested Citation

  • Asil Oztekin, 2018. "Information fusion-based meta-classification predictive modeling for ETF performance," Information Systems Frontiers, Springer, vol. 20(2), pages 223-238, April.
    • Handle: RePEc:spr:infosf:v:20:y:2018:i:2:d:10.1007_s10796-016-9704-4
    DOI: 10.1007/s10796-016-9704-4
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