Abstract
We present a stochastic dynamic model of the adjustment of betting odds by bookmakers in a horse-racing betting market. We use optimal stopping theory in a two-horse benchmark model with both informed and noise punters. A costly learning process discloses what information the informed traders possess and a risk-neutral bookmaker selects a stopping time at which the betting odds for each horse in a race are adjusted. Our main finding shows that an increased fraction of informed punters has a non-monotonic effect on the loss per trade to the bookmaker. We also find that as the fraction of noise traders goes up, the learning process is less informative, so that the decision to change the prices for each horse is taken sooner.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
