overconfidence and prediction bias in political stock markets
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Overconfidence and Prediction Bias in Political Stock Markets. Carsten Schmidt (joint work with Michael Berleman, ifo Institute Dresden). The Puzzle. US political stock markets were very successful in predicting the election results - PowerPoint PPT PresentationTRANSCRIPT
School of Business and EconomicsHumboldt University Berlin
Overconfidence and Prediction Bias in Political Stock Markets
Carsten Schmidt (joint work with Michael Berleman, ifo Institute Dresden)
The Puzzle US political stock markets were very successful
in predicting the election results– IEM predict result of the presidential election
Bush/Dukakis 1988 with a MAE of 0.2% (Forsythe et al., 1992, AER)
– Forsythe et al., 1997, JEBO European election markets were not
(significantly) better than polls. Relatively higher MAE compared to US markets.– Netherlands: Jacobsen et al., 2000, EER– Austria: Ortner– Sweden: Bohm and Sonnegard, 1999, ScanJE– Germany: Berlemann und Schmidt (this meta-study)
MAE PSM 1.394, Polls 1.524, (T=1.198, p <0.126)
Driving forces Institutions
– Election system Proportional representation vs. Winner- takes-all
– Polls Adjusted vs. raw data
Market level: market complexity – Empirical contribution (Berg et al., 1997)– Number of different contracts (candidates/parties) is highly
correlated with MAE Contract level: overconfidence Bias
– Theoretical contribution (Jacobsen et al., 2000, EER– Overvaluation of small contracts, undervaluation of relatively large
contracts – Disparity of different contracts– Bias not significant in US data (Forsythe et al., 1999, JEBO)
Trader level– Individual mistakes do not bias prediction in US data
A benchmark: poll prediction In the US poll data is reported raw
– Prediction error of PSM is significant smaller European pollster report corrected data
– Correction is a black box, pollster use different approaches– Prediction error of German PSM is slightly smaller (marginal
significant)
Party Allensbach raw data
Allensbach prediction
Election result
CDU/CSU 38,8 43,5 44,5SPD 46,5 43,5 42,9FDP 11,1 10,0 10,6
Sunday question, German federal election 1980, source: Allensbach
Meta study German data
Method: Empirical meta study Data: Final prediction of all German election
markets (and all corresponding public polls for the election)– Vote share markets– Homogeous in the number of contracts (parties)
CDU,SPD,Grüne,FDP,PDS,Rep,Rest of Field– Different organizer (academia, commercial)
Field data (meta study)German data17 Elections, 34 PSM1990-2003
US data16 Elections,16 PSMBerg et al. (1997)
No of contractsK
5 - 7 2 - 6
Theil coefficient 0.41 0.16
No of Presidential or Federal Elections
4 3
German data: contract level
Prediction error: contract level Criterion
– vi = true vote share of contract i– K = Number of different contracts
Kvv
Kvv iiii
1 if small"" is 1 if large"" is
Prediction error: contract level (2)
What makes markets predict well revisited: market level
Conclusions We find overvaluation of small contracts,
undervaluation of relatively large contracts in German PSM data– Bias not significant in US data (Forsythe et al., 1999
JEBO) Market level
– Market complexity in US data (Berg et al., 1997)– Market complexity constant in German data– Electoral uncertainty and market efficiency
Contract level: overconfidence bias – Jacobsen et al. (2000) EER– Overvaluation of small contracts– Disparity of different contracts (not significant)
Implications for PSM PSM in Europe predict less successful than in
he US because of the diversity of the vote shares and the complexity of the markets
Polls in Europe predict more successful than in the US by correcting the raw data: the poll instrument is not biased by diversity of vote shares and the complexity of the markets
Market design implications– Minimizing number of contracts– Correcting for the diverse vote share bias
Error measures
K
iii vv
KMAE ˆ1
Theory Assumption: Trade is not driven by
different preferences, but by individual information of the traders about the election result
v(1-v) is the unknown, true vote share of party P1(P2)
Each trader receives a private signal si Є [v-ε,v+ε]
Theory (2) Definition p:= p1=1-p2 Buy P1 if market price p1<si Buy P2 if market price p2<1-si In equilibrium p is determined that the
demand for both parties is equal Assumption: traders have the same
endowment E Signal si<p buy E/p contracts P1 Signal si>p buy E/(p-1) contracts P2
Predictions on contract level p=(v+ ε)/(1+2ε)
– Winner of the election if v>1/2 that means p>1/2
– Only if v1=v2=1/2 p is an unbiased estimator v1=v>1/2 p1=p<v=v1, p2=1-p>1-v=v2
– Large parties are undervalued, small parties are overvalued
Predictions Market level
– Mean absolute error (MAE) increases with ε Electoral uncertainty
– MAE increases when the vote shares become more unequal – diversity of the vote shares
Contract level
Number of contracts K=2, ε=0.025
0.0
0.1
0.2
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0.4
0.5
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0.8
0.9
1.0
0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0
v1 p1 Theil
Measure for more than 2 contracts MAE increases when the vote shares
become more unequal Captured for instance by a Theil coefficient
N
iii vKvTheil
1
ln
Number of contracts K=2, ε=0.025
0.0
0.1
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1.0
0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0
v1 p1 Theil