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Overconfidence and Prediction Bias in Political Stock Markets

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

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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

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