1ESA 2007 World Meeting, Rome

Partition Dependence in Prediction Markets - Evidence from the Lab and from the Field

šUlrich.Sonnemann@wiwi.uni-muenster.de

+49 (0) 251 83-22026

Titelfolie

Ulrich Sonnemann Colin Camerer Craig Fox Thomas Langer Muenster CalTech UCLA Muenster

Outline

1. Motivation & Research Question

2. Experimental/Empirical Evidence

• Lab Study: DAX index, (temperature, sports)

• Field Experiment (NBA Playoffs, Soccer World Cup)

• Field Data (Economic Derivatives)

3. Conclusions

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Prediction Markets and Partition Dependence1. Motivation & Research Question

Prediction Markets

Markets where participants trade in contracts whose payoff depends

on unknown future events.

Iowa Electronic Markets

TradeSports

Siemens deadline (Ortner 98)

„.. market forecasts are typically fairly accurate...“

HP printer sales (Chen, Plott 02)

Survey: Wolfers, Zitzewitz JEP 04

Partition Dependence

Different partitions of the event space can lead to

different probability judgments for the same event

Individual probability judgments depend on specific categorical „partition“, i.e. there is a bias towards the ignorance prior

(bias toward 1/n).

e.g. Fox, Clemen Mgt Sci 05:Subjective Probability Assessment in

Decision Analysis: Partition Dependence and Bias Toward the

Ignorance Prior

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Research Question1. Motivation & Research Question

Are prices in prediction markets (and therefrom inferred probabilities) influenced by the partition of the event space?

Do markets eliminate (or at least mitigate) the partition dependence bias which is observed in individual judgment (and under what circumstances)?

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2. Experimental/Empirical Evidence

Lab Study: DAX index, (temperature, sports)

Study 1

Laboratory Study:

DAX index, (temperature, sports)

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Construction of the assets (for DAX index stimulus)

7328 DAX 7496.99DAX 7327.99 7497 DAX

Asset 2Asset 1 Asset 3

DAX

7328 7497 7647

A claim will pay 100 cts if the DAX closes within the respective interval, otherwise the claim will pay nothing

Lab Study: DAX index, (temperature, sports)2. Experimental/Empirical Evidence

Underlying event:

„DAX close two weeks in the future“

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Treatment 2

7497 DAX 7646.99

Asset 3Asset 1&2 Asset 4

Treatment 1

7328 DAX 7496.99DAX 7327.99 7497 DAX

Asset 2Asset 1 Asset 3&4

7328 7497 7647

DAX

DAX 7496.99 7647 DAX

Lab Study: DAX index, (temperature, sports)2. Experimental/Empirical Evidence

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Lab Study: Main Result2. Experimental/Empirical Evidence

0.5610.152 0.336

0.713

Treatment 1: 1 2 3&4

Treatment 2:

intervals

intervals

3 41&2

p<0.0001Wilcoxon

p<0.0001Wilcoxon

N = 12mean equil.

prices

0.4040.424 0.177

0.581

N = 12mean equil.

prices

0.28912

0.245 34

Bias in Equilibrium Market Prices (DAX index stimulus)

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2. Experimental/Empirical Evidence

Study 2

Field Experiment:

NBA Playoffs, Soccer World Cup

Field Experiment (NBA Playoffs, Soccer World Cup)

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Field Experiment (NBA Playoffs, Soccer World Cup)2. Experimental/Empirical Evidence

Engaging events! NBA Basketball Playoffs 2006 FIFA Soccer World Cup 2006

Large scale study: N = 317 (Germany) + 139 (UCLA) = 456 Max. 20 traders per single market

Long span of continuous open markets (24/7) Playoffs April 22 through June 22, 2006 (~9 weeks)

World Cup May 24 through July 9, 2006 (~6½ weeks) Main Finding:

Evidence for Partition Dependence!

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2. Experimental/Empirical Evidence

Study 3

Field Data:

Economic Derivatives

Field Data (Economic Derivatives)

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Field Data (Economic Derivatives)

Economic Derivatives (by Goldman Sachs and Deutsche Bank) Field Prediction Markets for macroeconomic derivatives such like

growth in non-farm payrolls, retail sales, etc.

Highly professional traders / High stakes ($) Allows derivation of probability density function from prices of digital

options:

2. Experimental/Empirical Evidence

Gürkaynak, Wolfers 06

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We assume the observed probability distribution to be actuallya mixture between • the true probability and • an ignorance prior distribution

Are bias-corrected B(x) more accurate than observed F(x)? Yes: =.6 mean abs error .673 ( =1 .680) Slight correction w/ =.99 improves forecasts (mean forecast error) 53%, 56%, 70%, 54% in four markets (overall n=153, p<.01)

Field Data (Economic Derivatives)2. Experimental/Empirical Evidence

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Observed distribution

True (i.e. unbiased)

distribution

Ignorance prior (1/n)

distribution= * + (1- ) *

F(x) B(x) I(x)

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Conclusions

Partition-dependence exists in prediction markets Evidence from:

Lab: Around 25% in judgment and equilibrium prices Field experiments: Virtual arbitrage 1-6% Field markets: 40% weight on (1/n)?

Market forces seem not to be able to mitigate the systematic distortion in individual judgment!

3. Conclusions

Thank you for your attention!