1esa 2007 world meeting, rome partition dependence in prediction markets - evidence from the lab and...
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1ESA 2007 World Meeting, Rome
Partition Dependence in Prediction Markets - Evidence from the Lab and from the Field
+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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= * + (1- ) *
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!