Iterative nonparametric estimation of a log-optimal portfolio selection function

  • Authors:

  • H. Walk;S. Yakowitz

  • Affiliations:

  • Math. Inst. A, Stuttgart Univ.;-

  • Venue:
  • IEEE Transactions on Information Theory
  • Year:
  • 2006

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Abstract

Let stock market vectors form a stationary ergodic sequence. For fixed d ∈ N, a log-optimal portfolio selection function of the past d observed vectors is iteratively estimated on the basis of a training sequence by use of gradients and nonparametric regression. Strong consistency is obtained under a boundedness and α-mixing condition without further assumptions on the distribution