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Overconfidence and Prediction Bias in Political Stock Markets

Overconfidence and Prediction Bias in Political Stock Markets

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Overconfidence and Prediction Bias in Political Stock Markets

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  1. Overconfidence and Prediction Bias in Political Stock Markets Carsten Schmidt (joint work with Michael Berleman, ifo Institute Dresden)

  2. 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)

  3. 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

  4. 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) Sunday question, German federal election 1980, source: Allensbach

  5. 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)

  6. Field data (meta study)

  7. German data: contract level

  8. Prediction error: contract level • Criterion • vi = true vote share of contract i • K = Number of different contracts

  9. Prediction error: contract level (2)

  10. What makes markets predict well revisited: market level

  11. 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)

  12. 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

  13. Error measures

  14. 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 siЄ [v-ε,v+ε]

  15. 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

  16. 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

  17. 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

  18. Number of contracts K=2, ε=0.025

  19. Measure for more than 2 contracts • MAE increases when the vote shares become more unequal • Captured for instance by a Theil coefficient

  20. Number of contracts K=2, ε=0.025