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Article Dans Une Revue Journal of Statistical Mechanics: Theory and Experiment Année : 2022

Adaptive strategies in Kelly’s horse races model

Armand Despons
  • Fonction : Auteur
Luca Peliti
  • Fonction : Auteur

Résumé

Abstract We formulate an adaptive version of Kelly’s horse model in which the gambler learns from past race results using Bayesian inference. We characterize the cost of this gambling strategy and we analyze the asymptotic scaling of the difference between the growth rate of the gambler and the optimal growth rate, known as the gambler’s regret. We also explain how this adaptive strategy relates to the universal portfolio strategy, and we build improved adaptive strategies in which the gambler exploits the information contained in the bookmaker odds distribution.

Dates et versions

hal-03866477 , version 1 (22-11-2022)

Identifiants

Citer

Armand Despons, Luca Peliti, David Lacoste. Adaptive strategies in Kelly’s horse races model. Journal of Statistical Mechanics: Theory and Experiment, 2022, 2022 (9), pp.093405. ⟨10.1088/1742-5468/ac8e58⟩. ⟨hal-03866477⟩
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