Efficiency Testing of Prediction Markets: Martingale Approach, Likelihood Ratio and Bayes Factor Analysis

This paper studies efficient market hypothesis in prediction markets and the results are illustrated for the in-play football betting market using the quoted odds for the English Premier League. Our analysis is based on the martingale property, where the last quoted probability should be the best pr...

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Main Authors: Mark Richard, Jan Vecer
Format: Article
Language:English
Published: MDPI AG 2021-02-01
Series:Risks
Subjects:
Online Access:https://www.mdpi.com/2227-9091/9/2/31
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spelling doaj-eaa74872a4ad4d5ba0f7cfaba69d83432021-02-02T00:01:00ZengMDPI AGRisks2227-90912021-02-019313110.3390/risks9020031Efficiency Testing of Prediction Markets: Martingale Approach, Likelihood Ratio and Bayes Factor AnalysisMark Richard0Jan Vecer1Frankfurt School of Finance and Management, Adickesallee 32–34, 60322 Frankfurt am Main, GermanyFrankfurt School of Finance and Management, Adickesallee 32–34, 60322 Frankfurt am Main, GermanyThis paper studies efficient market hypothesis in prediction markets and the results are illustrated for the in-play football betting market using the quoted odds for the English Premier League. Our analysis is based on the martingale property, where the last quoted probability should be the best predictor of the outcome and all previous quotes should be statistically insignificant. We use regression analysis to test for the significance of the previous quotes in both the time setup and the spatial setup based on stopping times, when the quoted probabilities reach certain bounds. The main contribution of this paper is to show how a potentially different distributional opinion based on the violation of the market efficiency can be monetized by optimal trading, where the agent maximizes logarithmic utility function. In particular, the trader can realize a trading profit that corresponds to the likelihood ratio in the situation of one market maker and one market taker, or the Bayes factor in the situation of two or more market takers.https://www.mdpi.com/2227-9091/9/2/31prediction marketsefficient market hypothesismartingale testlikelihood ratioBayes factor
collection DOAJ
language English
format Article
sources DOAJ
author Mark Richard
Jan Vecer
spellingShingle Mark Richard
Jan Vecer
Efficiency Testing of Prediction Markets: Martingale Approach, Likelihood Ratio and Bayes Factor Analysis
Risks
prediction markets
efficient market hypothesis
martingale test
likelihood ratio
Bayes factor
author_facet Mark Richard
Jan Vecer
author_sort Mark Richard
title Efficiency Testing of Prediction Markets: Martingale Approach, Likelihood Ratio and Bayes Factor Analysis
title_short Efficiency Testing of Prediction Markets: Martingale Approach, Likelihood Ratio and Bayes Factor Analysis
title_full Efficiency Testing of Prediction Markets: Martingale Approach, Likelihood Ratio and Bayes Factor Analysis
title_fullStr Efficiency Testing of Prediction Markets: Martingale Approach, Likelihood Ratio and Bayes Factor Analysis
title_full_unstemmed Efficiency Testing of Prediction Markets: Martingale Approach, Likelihood Ratio and Bayes Factor Analysis
title_sort efficiency testing of prediction markets: martingale approach, likelihood ratio and bayes factor analysis
publisher MDPI AG
series Risks
issn 2227-9091
publishDate 2021-02-01
description This paper studies efficient market hypothesis in prediction markets and the results are illustrated for the in-play football betting market using the quoted odds for the English Premier League. Our analysis is based on the martingale property, where the last quoted probability should be the best predictor of the outcome and all previous quotes should be statistically insignificant. We use regression analysis to test for the significance of the previous quotes in both the time setup and the spatial setup based on stopping times, when the quoted probabilities reach certain bounds. The main contribution of this paper is to show how a potentially different distributional opinion based on the violation of the market efficiency can be monetized by optimal trading, where the agent maximizes logarithmic utility function. In particular, the trader can realize a trading profit that corresponds to the likelihood ratio in the situation of one market maker and one market taker, or the Bayes factor in the situation of two or more market takers.
topic prediction markets
efficient market hypothesis
martingale test
likelihood ratio
Bayes factor
url https://www.mdpi.com/2227-9091/9/2/31
work_keys_str_mv AT markrichard efficiencytestingofpredictionmarketsmartingaleapproachlikelihoodratioandbayesfactoranalysis
AT janvecer efficiencytestingofpredictionmarketsmartingaleapproachlikelihoodratioandbayesfactoranalysis
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