Predicting the failures of prediction markets: A procedure of decision making using classification models

doi:10.1016/j.ijforecast.2018.04.003

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Title:	Predicting the failures of prediction markets: A procedure of decision making using classification models
Authors:	Chung-Ching Tai;Hung-Wen Lin;Bin-Tzong Chie;Chen-Yuan Tung
Keywords:	Combining forecasts;Support vector machine;Decision trees;Principal component analysis;Discriminant analysis;Imbalanced data;Oversampling;SMOTE
Date:	2019
Issue Date:	2019-03-20 12:10:36 (UTC+8)
Abstract:	Prediction markets have been an important source of information for decision makers due to their high ex post accuracies. Nevertheless, recent failures of prediction markets remind us of the importance of ex ante assessments of their prediction accuracy. This paper proposes a systematic procedure for decision makers to acquire prediction models which may be used to predict the correctness of winner-take-all markets. We commence with a set of classification models and generate combined models following various rules. We also create artificial records in the training datasets to overcome the imbalanced data issue in classification problems. These models are then empirically trained and tested with a large dataset to see which may best be used to predict the failures of prediction markets. We find that no model can universally outperform others in terms of different performance measures. Despite this, we clearly demonstrate a result of capable models for decision makers based on different decision goals.
Relation:	International Journal of Forecasting 35(1), p.297-312
DOI:	10.1016/j.ijforecast.2018.04.003
Appears in Collections:	[Graduate Institute & Department of Industrial Economics] Journal Article

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