Growth Versus Security in Dynamic Investment Analysis

MacLean, Leonard C.; Ziemba, William T.; Blazenko, George

doi:10.1142/9789812819192_0016

Cited by 23 publications

(33 citation statements)

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“…In the horseracing context, this amounts to seeing whether financial advantage may be gained by using the Markets Odds alone to forecast racing outcomes, with much of the literature falling into the category of either Odds calibration studies such as 'Favourite-Longshot bias' studies [Hausch et al (1994)], or 'Insider Trading Adjustment' studies [Shin (1991[Shin ( , 1993]. Secondary to this in terms of volume of research articles (but by no means less significant in importance) has been the development in the literature of betting strategies which would be applied in theory if quality D. Edelman ( ) Banking & Finance Unit, University College, Dublin Blackrock, Co. Dublin, Ireland e-mail: david.edelman@ucd.ie odds forecasts were assumed to exist [see Breiman (1961), Kelly (1956), andZiemba et al (1994)]. …”

Section: Horserace Odds Predictionmentioning

confidence: 99%

Adapting support vector machine methods for horserace odds prediction

Edelman¹

2006

Ann Oper Res

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The methodology of Support Vector Machine Methods is adapted in a straightforward manner to enable the analysis of stratified outcomes such as horseracing results. As the strength of the Support Vector Machine approach lies in its apparent ability to produce generalisable models when the dimensionality of the inputs is large relative to the the number of observations, such a methodology would appear to be particularly appropriate in the horseracing context, where often the number of input variables deemed as being potentially relevant can be difficult to reconcile with the scarcity of relevant race results. The methods are applied to a relatively small (200 races in-sample) sample of Australian racing data and tested on 100 races out-of-sample with promising results, especially considering the relatively large number (12) of input variables used.

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Section: Horserace Odds Predictionmentioning

confidence: 99%

Adapting support vector machine methods for horserace odds prediction

Edelman¹

2006

Ann Oper Res

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“…None-the-less, there is a very small probability of losing almost everything. This is also true if one uses traditional Kelly staking on a sequence of events and one should use a fractional Kelly strategy to reduce the risk (MacLean et al, 1992). Another possibility is to use a different utility function, such as U.x/ = −.x −α /.…”

Section: Discussionmentioning

confidence: 99%

“…MacLean et al (1992) showed that an efficient trade-off between growth and security could be achieved by using the so-called 'fractional Kelly' strategy in which a fixed fraction of the player's bankroll is set aside in a riskless asset on each iteration. In effect, this means that the log-optimal bet (or bets) is reduced by some fixed proportion.…”

Section: Utility Functionsmentioning

confidence: 99%

Algorithms for Optimal Allocation of Bets on Many Simultaneous Events

Whitrow

2007

Journal of the Royal Statistical Society Series C: Applied Statistics

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The problem of optimizing a number of simultaneous bets is considered, using primarily log-utility. Stochastic gradient-based algorithms for solving this problem are developed and compared with the simplex method. The solutions may be regarded as a generalization of 'Kelly staking' to the case of many simultaneous bets. Properties of the solutions are examined in two example cases using real odds from sports bookmakers. The algorithms that are developed also have wide applicability beyond sports betting and may be extended to general portfolio optimization problems, with any reasonable utility function. Copyright 2007 Royal Statistical Society.

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“…The mathematical properties of Kelly investing are well documented. An excellent synthesis is contained in MacLean et al (1992). In finance, the Kelly criterion is the basis of "growth-optimal" or "logoptimal" models of investment, portfolio management and asset pricing, which developed out of important works by Breiman (1961), Thorp (1971), Hakansson (1971), Roll (1973), Kraus and Litzenberger (1975), Markowitz (1976) and Rubinstein (1976).…”

Section: Appendix Bmentioning

confidence: 99%

“…Expected log-utility maximization, "log optimal" investment or "Kelly betting" as it is known to professional gamblers, implicitly maximizes the investor's expected (long run average) rate of capital growth (Breiman, 1961;Kelly, 1956;MacLean et al, 1992). 1 When based on physically or objectively "true" probabilities, no other decision rule produces the same wealth over the long run.…”

Section: Introductionmentioning

confidence: 99%

Economic Darwinism: Who has the Best Probabilities?

Johnstone

2006

Theor Decis

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Simulation evidence obtained within a Bayesian model of price-setting in a betting market, where anonymous gamblers queue to bet against a risk-neutral bookmaker, suggests that a gambler who wants to maximize future profits should trade on the advice of the analyst cum probability forecaster who records the best probability score, rather than the highest trading profits, during the preceding observation period. In general, probability scoring rules, specifically the log score and better known â€œBrierâ€\x9D (quadratic) score, are found to have higher probability of ranking rival analysts in predetermined â€œcorrectâ€\x9D order than either (i) the more usual method of counting categorical forecast errors (misclassifications), or (ii) an economic measure of forecasting success, described here as the â€œKelly scoreâ€\x9D and defined as the trading profits accumulated by making log optimal bets (i.e. Kelly betting) against the market maker based on the probability forecasts of the analyst being assessed. This runs counter to the conventional wisdom that financial forecasts are more aptly evaluated in terms of their financial consequences than by an abstract non-monetary measure of statistical accuracy such as the number of misclassifications or a probability score. Copyright Springer 2007bid-ask spread, economic forecast evaluation, Kelly criterion, probability forecasting, probability scoring rules, Kelly score, C11, C44, D40, D81, C52, G11,

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Growth Versus Security in Dynamic Investment Analysis

Cited by 23 publications

References 0 publications

Adapting support vector machine methods for horserace odds prediction

Adapting support vector machine methods for horserace odds prediction

Algorithms for Optimal Allocation of Bets on Many Simultaneous Events

Economic Darwinism: Who has the Best Probabilities?

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