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CORN: Correlation-Driven Nonparametric Learning Approach for Portfolio Selection -- an Online Appendix

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  • Bin Li
  • Dingjiang Huang
  • Steven C. H. Hoi

Abstract

This appendix proves CORN's universal consistency. One of Bin's PhD thesis examiner (Special thanks to Vladimir Vovk from Royal Holloway, University of London) suggested that CORN is universal and provided sketch proof of Lemma 1.6, which is the key of this proof. Based on the proof in Gy\"prfi et al. [2006], we thus prove CORN's universal consistency. Note that the notations in this appendix follows Gy\"orfi et al. [2006].

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  • Bin Li & Dingjiang Huang & Steven C. H. Hoi, 2013. "CORN: Correlation-Driven Nonparametric Learning Approach for Portfolio Selection -- an Online Appendix," Papers 1306.1378, arXiv.org.
    • Handle: RePEc:arx:papers:1306.1378
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    1. László Györfi & Gábor Lugosi & Frederic Udina, 2006. "Nonparametric Kernel‐Based Sequential Investment Strategies," Mathematical Finance, Wiley Blackwell, vol. 16(2), pages 337-357, April.
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