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Economic Darwinism: Who has the Best Probabilities?

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  • David Johnstone

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    Abstract

    Simulation evidence obtained within a Bayesian model of price-setting in a betting market, where anonymous gamblers queue to bet against a risk-neutral bookmaker, suggests that a gambler who wants to maximize future profits should trade on the advice of the analyst cum probability forecaster who records the best probability score, rather than the highest trading profits, during the preceding observation period. In general, probability scoring rules, specifically the log score and better known “Brierâ€\x9D (quadratic) score, are found to have higher probability of ranking rival analysts in predetermined “correctâ€\x9D order than either (i) the more usual method of counting categorical forecast errors (misclassifications), or (ii) an economic measure of forecasting success, described here as the “Kelly scoreâ€\x9D and defined as the trading profits accumulated by making log optimal bets (i.e. Kelly betting) against the market maker based on the probability forecasts of the analyst being assessed. This runs counter to the conventional wisdom that financial forecasts are more aptly evaluated in terms of their financial consequences than by an abstract non-monetary measure of statistical accuracy such as the number of misclassifications or a probability score. Copyright Springer 2007

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    Bibliographic Info

    Article provided by Springer in its journal Theory and Decision.

    Volume (Year): 62 (2007)
    Issue (Month): 1 (February)
    Pages: 47-96

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    Handle: RePEc:kap:theord:v:62:y:2007:i:1:p:47-96

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    Web page: http://www.springerlink.com/link.asp?id=100341

    Related research

    Keywords: bid-ask spread; economic forecast evaluation; Kelly criterion; probability forecasting; probability scoring rules; Kelly score; C11; C44; D40; D81; C52; G11;

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    References

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    Cited by:
    1. Hvattum, Lars Magnus & Arntzen, Halvard, 2010. "Using ELO ratings for match result prediction in association football," International Journal of Forecasting, Elsevier, vol. 26(3), pages 460-470, July.
    2. Victor Jose, 2009. "A Characterization for the Spherical Scoring Rule," Theory and Decision, Springer, vol. 66(3), pages 263-281, March.
    3. Lessmann, Stefan & Sung, Ming-Chien & Johnson, Johnnie E.V. & Ma, Tiejun, 2012. "A new methodology for generating and combining statistical forecasting models to enhance competitive event prediction," European Journal of Operational Research, Elsevier, vol. 218(1), pages 163-174.
    4. Grant, Andrew & Johnstone, David, 2010. "Finding profitable forecast combinations using probability scoring rules," International Journal of Forecasting, Elsevier, vol. 26(3), pages 498-510, July.

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