Algorithms for Optimal Allocation of Bets on Many Simultaneous Events

Whitrow, Chris

doi:10.1111/j.1467-9876.2007.00594.x

Cited by 6 publications

(5 citation statements)

References 17 publications

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“…In (Smoczynski and Tomkins, 2010), a closed form solution for the use of the Kelly strategy when betting on horse racing was explored. Another practical extension for betting on multiple simultaneous games was discussed in a number of works (Whitrow, 2007;Grant et al, 2008;Buchen and Grant, 2012), where various approximations for large bet aggregations were proposed.…”

Section: Extensions Of the Formal Strategiesmentioning

confidence: 99%

Optimal sports betting strategies in practice: an experimental review

Uhrín,

Šourek,

Hubáček

et al. 2021

Preprint

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We investigate the most popular approaches to the problem of sports betting investment based on modern portfolio theory and the Kelly criterion. We define the problem setting, the formal investment strategies, and review their common modifications used in practice. The underlying purpose of the reviewed modifications is to mitigate the additional risk stemming from the unrealistic mathematical assumptions of the formal strategies. We test the resulting methods using a unified evaluation protocol for three sports: horse racing, basketball and soccer. The results show the practical necessity of the additional risk-control methods and demonstrate their individual benefits. Particularly, an adaptive variant of the popular "fractional Kelly" method is a very suitable choice across a wide range of settings. sports betting, betting strategies, risk management, bankroll management

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Section: Extensions Of the Formal Strategiesmentioning

confidence: 99%

Optimal sports betting strategies in practice: an experimental review

Uhrín,

Šourek,

Hubáček

et al. 2021

Preprint

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“…Portfolio optimization The approach of splitting the trader's workflow into the two steps of predictive modeling and investment optimization has a long tradition, and has been exploited in absolute majority of works [49,30,56,51,71,18], with some notable exceptions [24,38]. Extracting the parameter estimation out of the portfolio optimization problem then enabled the respective economic research to thrive in an isolated mathematical environment, giving rise to the frameworks of Markowitz [47] and Kelly [32], and their many successors [10,76,71,36,51]. While widely adopted, the optimality of the resulting portfolios is based on rather unrealistic assumptions, which has been progressively criticized by many [27,61,49,57,62,43].…”

Section: Related Workmentioning

confidence: 99%

Beating the market with a bad predictive model

Hubáček¹,

Šír²

2020

Preprint

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It is a common misconception that in order to make consistent profits as a trader, one needs to posses some extra information leading to an asset value prediction more accurate than that reflected by the current market price. While the idea makes intuitive sense and is also well substantiated by the widely popular Kelly criterion, we prove that it is generally possible to make systematic profits with a completely inferior price-predicting model. The key idea is to alter the training objective of the predictive models to explicitly decorrelate them from the market, enabling to exploit inconspicuous biases in market maker's pricing, and profit on the inherent advantage of the market taker. We introduce the problem setting throughout the diverse domains of stock trading and sports betting to provide insights into the common underlying properties of profitable predictive models, their connections to standard portfolio optimization strategies, and the, commonly overlooked, advantage of the market taker. Consequently, we prove desirability of the decorrelation objective across common market distributions, translate the concept into a practical machine learning setting, and demonstrate its viability with real world market data.

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“…We use mean variance optimisation (MVO) [6], Equally weighted portfolio (1/n), Rescaled Kelly (Re. Kelly) [12] [23] [14], Optimal Kelly [25] and K2. All the models are implemented in an online setting, that is, whenever any new data is available all models receive it and recompute and update.…”

Section: )mentioning

confidence: 99%

Constructing smart portfolios from data driven quantitative investment models

Mehra

Prügel-Bennett

Gerding

et al. 2014

2014 IEEE Conference on Computational Intelligence for Financial Engineering &Amp; Economics (CIFEr)

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Abstract-In this paper we present a smart portfolio management methodology, which advances existing portfolio management techniques at two distinct levels. First, we develop a set of investment models that target regimes found in the data over different time horizons. We then build a meta-model which uses the Kelly criterion to determine an optimal allocation over these investment strategies, thus simultaneously capturing regimes operating in the data over different time horizons. Finally, in order to detect changes in the relevant data regime, and hence investment allocations, we use a forecasting algorithm which relies on a Kalman filter. We call our combined method, that uses both the Kelly criterion and the Kalman filter, the K2 algorithm. Using a large-scale historical dataset of both stocks and indices, we show that our K2 algorithm gives better risk adjusted returns in terms of the Sharpe ratio, better average gain to average loss ratio and higher probability of success compared to existing benchmarks, when measured in out-of-sample tests.

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Algorithms for Optimal Allocation of Bets on Many Simultaneous Events

Cited by 6 publications

References 17 publications

Optimal sports betting strategies in practice: an experimental review

Optimal sports betting strategies in practice: an experimental review

Beating the market with a bad predictive model

Constructing smart portfolios from data driven quantitative investment models

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