Iterative nonparametric estimation of a log-optimal portfolio selection function

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