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    题名: Predicting the failures of prediction markets: A procedure of decision making using classification models
    作者: Tai, Chung-Ching
    Lin, Hung-Wen
    Chie, Bin-Tzong
    童振源
    Chen-YuanTung
    贡献者: 國家發展研究所
    关键词: Combining forecasts;Support vector machine;Decision trees;Principal component analysis;Discriminant analysis;Imbalanced data;Oversampling;SMOTE
    日期: 2018-06
    上传时间: 2018-07-24 17:27:37 (UTC+8)
    摘要: 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.
    關聯: International Journal of Forecasting
    数据类型: article
    DOI 連結: https://doi.org/10.1016/j.ijforecast.2018.04.003
    DOI: 10.1016/j.ijforecast.2018.04.003
    显示于类别:[國家發展研究所] 期刊論文

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