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

Predicting the failures of prediction markets: A procedure of decision making using classification models
Predicting the failures of prediction markets: A procedure of decision making using classification models
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.
Combining forecasts, Support vector machine, Decision Trees, Principal Component Analysis, discriminant analysis, Imbalanced data, Oversampling, SMOTE
0169-2070
297-312
Tai, Chung-Ching
b3370b23-7410-4254-99bc-6711046e1095
Lin, Hung-Wen
78917824-d5a4-409c-88d0-2c588f49b59a
Chie, Bin-Tzong
94cc8c70-3ecd-450c-8e3f-33587f2793e5
Tung, Chen-Yuan
f0f76cf4-f077-4aaa-b8f4-1cc751496765
Tai, Chung-Ching
b3370b23-7410-4254-99bc-6711046e1095
Lin, Hung-Wen
78917824-d5a4-409c-88d0-2c588f49b59a
Chie, Bin-Tzong
94cc8c70-3ecd-450c-8e3f-33587f2793e5
Tung, Chen-Yuan
f0f76cf4-f077-4aaa-b8f4-1cc751496765

Tai, Chung-Ching, Lin, Hung-Wen, Chie, Bin-Tzong and Tung, Chen-Yuan (2019) Predicting the failures of prediction markets: A procedure of decision making using classification models. International Journal of Forecasting, 35 (1), 297-312. (doi:10.1016/j.ijforecast.2018.04.003).

Record type: Article

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.

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More information

e-pub ahead of print date: 21 June 2018
Published date: January 2019
Keywords: Combining forecasts, Support vector machine, Decision Trees, Principal Component Analysis, discriminant analysis, Imbalanced data, Oversampling, SMOTE

Identifiers

Local EPrints ID: 434363
URI: https://eprints.soton.ac.uk/id/eprint/434363
ISSN: 0169-2070
PURE UUID: 4e9c1f8c-5dc8-4b80-acfa-29fe219ae94f

Catalogue record

Date deposited: 20 Sep 2019 16:30
Last modified: 20 Sep 2019 16:30

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Contributors

Author: Chung-Ching Tai
Author: Hung-Wen Lin
Author: Bin-Tzong Chie
Author: Chen-Yuan Tung

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