Noise, Information, and the Favorite-Longshot Bias in Parimutuel Predictions

Abstract

According to the favorite-longshot bias, the expected return on an outcome tends to increase in the fraction of bets laid on that outcome. We derive testable implications for the direction and extent of the bias depending on the ratio of private information to noise present in the market. We link this ratio to observables such as the number of bettors, the number of outcomes, the amount of private information, the level of participation generated by recreational interest in the event, the divisibility of bets, the presence of ex post noise, as well as ex ante asymmetries across outcomes.

How psychological framing affects economic market prices in the lab and field

“…Combining these data sources is useful for the following reasons: General explanations based on trading frictions (35,37) might apply to parimutuel markets but do not apply to the CDA laboratory and field markets; explanations based on information conveyed by the choice of partitions in horse race and economic indicator markets are eliminated by laboratory experiments; and explanations based on low financial stakes and trader experience are eliminated by the parimutuel field markets. Occam's razor therefore favors the only interpretation that applies to all four datasets, which is that PD in individual judgments also influences market prices (cf.…”

“…This tendency is called the "favorite-long-shot bias" and is well established across many countries and decades (34). Several different explanations have been offered, including frictions that prevent equilibration (35), interaction of information and timing (36,37), a desire for positive skewness bets, and overweighting of the low probabilities of long shots winning (34).…”

From Aggregate Betting Data to Individual Risk Preferences

“…In the context of racetrack betting, Jullien and Salanié (2000) and Snowberg and Wolfers (2010) provided evidence showing that a representative agent with nonlinear probability weighting better explains the pattern of prices at the racetrack as compared to an expected utility maximizing representative agent. Ottaviani and Sorensen (2010) and Gandhi and Serrano-Padial (2015) argued that heterogeneity of beliefs and/or information of risk-neutral agents can explain the well-known favoritelongshot bias that characterizes many betting markets. These representative agent studies of betting markets stand in contrast to a strand of research that has emphasized belief heterogeneity as an important determinant of equilibrium in security markets.…”

“…These representative agent studies of betting markets stand in contrast to a strand of research that has emphasized belief heterogeneity as an important determinant of equilibrium in security markets. Ottaviani and Sorensen (2010) and Gandhi and Serrano-Padial (2015) argued that heterogeneity of beliefs and/or information of risk-neutral agents can explain the well-known favoritelongshot bias that characterizes many betting markets. Gandhi and Serrano-Padial furthermore estimated the degree of belief heterogeneity revealed by equilibrium patterns.…”

Information aggregation and belief elicitation in experimental parimutuel betting markets

“…15 See, for example, VaughanWilliams (1999).16 Alternatively, one may compare the market probability with the corresponding posterior probability held by an outsider who can observe the whole sequence of bets (see, for example,Ottaviani and Sørensen, 2010, in a simultaneous betting market game). However, the computation of posterior probabilities requires the existence of theoretical bets.…”