English  |  正體中文  |  简体中文  |  全文筆數/總筆數 : 55176/89445 (62%)
造訪人次 : 10658213      線上人數 : 20
RC Version 7.0 © Powered By DSPACE, MIT. Enhanced by NTU Library & TKU Library IR team.
搜尋範圍 查詢小技巧:
  • 您可在西文檢索詞彙前後加上"雙引號",以獲取較精準的檢索結果
  • 若欲以作者姓名搜尋,建議至進階搜尋限定作者欄位,可獲得較完整資料
  • 進階搜尋
    請使用永久網址來引用或連結此文件: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/116030


    題名: Predicting the failures of prediction markets: A procedure of decision making using classification models
    作者: Chung-Ching Tai;Hung-Wen Lin;Bin-Tzong Chie;Chen-Yuan Tung
    關鍵詞: Combining forecasts;Support vector machine;Decision trees;Principal component analysis;Discriminant analysis;Imbalanced data;Oversampling;SMOTE
    日期: 2019
    上傳時間: 2019-03-20 12:10:36 (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 35(1), p.297-312
    DOI: 10.1016/j.ijforecast.2018.04.003
    顯示於類別:[產業經濟學系暨研究所] 期刊論文

    文件中的檔案:

    檔案 描述 大小格式瀏覽次數
    index.html0KbHTML47檢視/開啟
    Predicting the failures of prediction markets A procedure of decision making using classification models.pdf832KbAdobe PDF0檢視/開啟

    在機構典藏中所有的資料項目都受到原著作權保護.

    TAIR相關文章

    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library & TKU Library IR teams. Copyright ©   - 回饋