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On Feedback Control in Kelly Betting: An Approximation Approach
Preprint

On Feedback Control in Kelly Betting: An Approximation Approach

arXiv.org
24/05/2020

Abstract

Mathematics - Optimization and Control Quantitative Finance - Computational Finance Quantitative Finance - Mathematical Finance
In this paper, we consider a simple discrete-time optimal betting problem using the celebrated Kelly criterion, which calls for maximization of the expected logarithmic growth of wealth. While the classical Kelly betting problem can be solved via standard concave programming technique, an alternative but attractive approach is to invoke a Taylor-based approximation, which recasts the problem into quadratic programming and obtain the closed-form approximate solution. The focal point of this paper is to fill some voids in the existing results by providing some interesting properties when such an approximate solution is used. Specifically, the best achievable betting performance, positivity of expected cumulative gain or loss and its associated variance, expected growth property, variance of logarithmic growth, and results related to the so-called survivability (no bankruptcy) are provided.

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Details

Title
On Feedback Control in Kelly Betting: An Approximation Approach
Creators
Chung-Han Hsieh
Resource Type
Preprint
Publication Details
arXiv.org
Language
English
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