• Gyorfi Laszlo
    Department of Computer Science and Information Theory – Budapest University of Technology and Economics
  • FCUL (DEIO) – Campo Grande – Bloco C/6 Piso 4 – Sala 6.4.30 – 14h 30m
  • Sexta-feira, 20 de Junho de 2008

Abstract  We investigate sequential investment strategies for financial markets. Investment strategies are allowed to use information collected from the past of the market and determine, at the beginning of a trading period, a portfolio, that is, a way to distribute their current capital among the available assets. The goal of the investor is to maximize his wealth on the long run without knowing the underlying distribution generating the stock prices. Since accurate statistical modelling of stock market behavior has been known as a notoriously difficult problem, we take an extreme point of view and work with minimal assumptions on the distribution of the time series, i.e., we only assume that the daily price relatives form a stationary process. Under this assumption the asymptotic rate of growth (averaged yield) has a well-defined maximum, called log-optimum, which can be achieved in full knowledge of the distribution of the entire process.

Introduce empirical (data driven) portfolio selection strategy which achieves the best possible average growth rate.


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