Abstract
AbstractThis paper’s primary alternative hypothesis is Ha: profitable exchange-traded horserace betting fund with deterministic payoff exists for acceptable institutional portfolio return—risk. The primary hypothesis challenges the semi-strong efficient market hypothesis applied to horse race wagering. An optimal deterministic betting model (DBM) is derived from the existing stochastic model fundamentals, mathematical pooling principles, and new theorem. The exchange-traded betting fund (ETBF) is derived from force of interest first principles. An ETBF driven by DBM processes conjointly defines the research’s betting strategy. Alpha is excess return above financial benchmark, and invokes betting strategy alpha that is composed of model alpha and fund alpha. The results and analysis from statistical testing of a global stratified data sample of three hundred galloper horse races accepted at the ninety-five percent confidence-level positive betting strategy alpha, to endorse an exchange-traded horse race be...
Highlights
A deterministic betting model for pool wagering is developed from the existing stochastic literature, mathematical pooling principles, and from the development of the multiple system optimization (MSO) theorem over Cn space, to validate the optimal solution generated from the DBM
The MSO theorem states that optimization over an n finite series of complex systems generates a constant real component over each consecutive system
The DBM applied to horse racing optimizes field wagering to determine a feasible, actual payoff—risk trade-off pre-race
Summary
Ri, ..., j and XRi,...,j represent independent, non-identically distributed discrete and continuous random variables, respectively. L-Decomposable model which is a function of the win probabilities of r−1 racers (r ≤ n) These stochastic technical rank-order models use permutation conditional probability to determine expected outcomes for typical horserace betting products, such as win (R1), exacta and quinella R1, R2 , trifecta (R1, R2, R3), and first four events. One approach to determine the optimal win and place bets on r racers (r ≤ n) for a race is the extension of the Kelly criterion (Edelman, 2007) of maximizing the expected logarithmic return for the race, as a function of the market bettor odds and probability forecasts for win, exacta, quinella, trifecta, and additional permutation products as outlined in Equation 3.16.
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