Back on the Track with the Efficient Markets Hypothesis

LOSEY, ROBERT L.; Talbott, John

doi:10.1142/9789812819192_0028

Cited by 2 publications

(2 citation statements)

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“…These variables and corresponding interpretations are listed in Table 5. The aim of this application is to select important variables for interpreting the racetrack betting markets and to investigate the extent to which publicly available information is efficiently incorporated into the payoff of a race, which has been studied by many authors -for example, Losey and Talbott [25], Hausch et al [26], Bolton and Chapman [27], Gabriel and Marsden [28,29], Benter [30], Sung et al [31] and among others.…”

Section: Horse Racing Data Analysismentioning

confidence: 99%

Variable selection for general transformation models with ranking data

Zhang

et al. 2012

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In this paper, we consider variable selection problem for general transformation models with ranking data by penalized maximum log-marginal likelihood approach. We incorporate smoothly clipped absolute deviation (SCAD), lasso and hard thresholding penalties into penalty term. With some conditions and proper penalties, we show that the corresponding penalized estimates are √ n-consistent and enjoy oracle properties. We also propose a three-step Monte Carlo Markov chain stochastic approximation algorithm for our proposed procedures. With the proposed procedure, we not only can select important variables but also are able to estimate corresponding effects. Through some simulation examples and a Hong Kong horse racing data analysis, we illustrate that our proposed procedure uniformly works very well for moderate sample size.

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Section: Horse Racing Data Analysismentioning

confidence: 99%

Variable selection for general transformation models with ranking data

Zhang

et al. 2012

Statistics

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“…The semi-strong form efficiency in racetrack betting markets is an important concept for economic analysis. This has been investigated by many authors (for example, Losey and Talbott [31], Hausch et al [24], Bolton and Chapman [3], Gabriel and Marsden [18,19], Benter [2], and Sung et al [35]). As discussed in Section 1, the effects of Distance preference from past races (X 8 ) and Public betting percentage in current race (X 9 ) may be dependent on Barrial (X 7 ).…”

Section: Horse Racing Datamentioning

confidence: 99%

General partially linear varying-coefficient transformation models for ranking data

2012

Journal of Applied Statistics

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In this paper,we propose a class of general partially linear varying-coefficient transformation models for ranking data. In the models, the functional coefficients are viewed as nuisance parameters and approximated by B-spline smoothing approximation technique. The B-spline coefficients and regression parameters are estimated by rank-based maximum marginal likelihood method. The three-stage Monte Carlo Markov Chain stochastic approximation algorithm based on ranking data is used to compute estimates and the corresponding variances for all the B-spline coefficients and regression parameters. Through three simulation studies and a Hong Kong horse racing data application, the proposed procedure is illustrated to be accurate, stable and practical.

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Back on the Track with the Efficient Markets Hypothesis

Cited by 2 publications

References 0 publications

Variable selection for general transformation models with ranking data

Variable selection for general transformation models with ranking data

General partially linear varying-coefficient transformation models for ranking data

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