Using AI for Predicting Cross-section of Expected Returns

DOI
  • Okada Katsuhiko
    Institute of Business and Accounting, Professional, Kwansei Gakuin University
  • Hamuro Yukinobu
    Institute of Business and Accounting, Professional, Kwansei Gakuin University

Bibliographic Information

Other Title
  • AIで探る株価の予測可能性

Abstract

<p>The number of cross-sectional predictors has soared in the last two decades and that made this area of research as “factor zoo”. Recent trend in asset pricing research focus on how we sort out these factors. From practitioner’s viewpoint, factor choice is the most important task for the successful asset management. We discuss the possibility of AI (machine learning and non-linear estimation) in optimizing portfolio given the past factor performance. We also discuss the return predictability of the model which uses data with limited availability.</p>

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Details 詳細情報について

  • CRID
    1390282763099883520
  • NII Article ID
    130007591303
  • DOI
    10.11167/jbef.11.121
  • ISSN
    21853568
  • Text Lang
    ja
  • Data Source
    • JaLC
    • CiNii Articles
    • KAKEN
  • Abstract License Flag
    Disallowed

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