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Kelly's Criterion in Portfolio Optimization: A Decoupled Problem
Version 1
: Received: 29 July 2017 / Approved: 31 July 2017 / Online: 31 July 2017 (11:22:09 CEST)
How to cite: Peterson, Z. Kelly's Criterion in Portfolio Optimization: A Decoupled Problem. Preprints 2017, 2017070090. https://doi.org/10.20944/preprints201707.0090.v1 Peterson, Z. Kelly's Criterion in Portfolio Optimization: A Decoupled Problem. Preprints 2017, 2017070090. https://doi.org/10.20944/preprints201707.0090.v1
Abstract
Kelly's Criterion is well known among gamblers and investors as a method for maximizing the returns one would expect to observe over long periods of betting or investing. These ideas are conspicuously absent from portfolio optimization problems in the financial and automation literature. This paper will show how Kelly's Criterion can be incorporated into standard portfolio optimization models. The model developed here combines risk and return into a single objective function by incorporating a risk parameter. This model is then solved for a portfolio of 10 stocks from a major stock exchange using a differential evolution algorithm. Monte Carlo calculations are used to verify the accuracy of the results obtained from differential evolution. The results show that evolutionary algorithms can be successfully applied to solve a portfolio optimization problem where returns are calculated by applying Kelly's Criterion to each of the assets in the portfolio.
Keywords
portfolio optimization; Kelly criterion; differential evolution; mean-variance
Subject
Computer Science and Mathematics, Applied Mathematics
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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