Kernel-based semi-log-optimal empirical portfolio selection strategies

L. Györfi, András Urbán, István Vajda

Research output: Contribution to journalArticle

17 Citations (Scopus)

Abstract

The purpose of this paper is to introduce an approximation of the kernel-based logoptimal investment strategy that guarantees an almost optimal rate of growth of the capital under minimal assumptions on the behavior of the market. The new strategy uses much less knowledge on the distribution of the market process. It is analyzed both theoretically and empirically. The theoretical results show that the asymptotic rate of growth well approximates the optimal one that one could achieve with a full knowledge of the statistical properties of the underlying process generating the market, under the only assumption that the market is stationary and ergodic. The empirical results show that the proposed semi-log-optimal and the log-optimal strategies have essentially the same performance measured on past NYSE data.

Original languageEnglish
Pages (from-to)505-516
Number of pages12
JournalInternational Journal of Theoretical and Applied Finance
Volume10
Issue number3
DOIs
Publication statusPublished - May 2007

Fingerprint

Kernel
Portfolio selection
Guarantee
New York Stock Exchange
Investment strategy
Optimal strategy
Approximation
Empirical results
Market process

Keywords

  • Kernel-based empirical portfolio selections
  • Semi-log-optimal portfolios
  • Sequential investment

ASJC Scopus subject areas

  • Economics, Econometrics and Finance(all)

Cite this

Kernel-based semi-log-optimal empirical portfolio selection strategies. / Györfi, L.; Urbán, András; Vajda, István.

In: International Journal of Theoretical and Applied Finance, Vol. 10, No. 3, 05.2007, p. 505-516.

Research output: Contribution to journalArticle

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