根據預測市場（prediction market）的文獻，預測選舉已有良好預測準確率，但該準確率是事後的、總體的，而非更有實際價值的事前、個別選舉合約預測的鑒別準確率。本文建構四個鑑別選舉預測市場準確度的模型，在選前針對每個選舉合約的預測準確度進行鑑別。根據預測市場在選前一天提供選舉合約的40個原始變數資訊，Logit模型最能精準判斷那些選舉合約會符合最高價準則的準確預測合約。本文以「2008年總統選舉」、「2009年縣市長選舉」及「2010年五都市長選舉」做為樣本外測試的樣本，使用原始變數的Logit模型之預測力均高於其他模型。Logit模型的樣本外鑑別正確準確率均為100%，但是，Logit模型對於鑑別未正確預測組的預測能力仍須改善。

According to the literature, election prediction markets have excellent accuracy rates of prediction. However, one can only acknowledge the prediction results after the elections and cannot discriminate the accuracy rates of particular election predictions prior to the elections. This paper constructs four models to discriminate the accuracy rate of each election contract prior to the election. According to the information of forty original variables collected from the election contracts in the prediction markets, the Logit model can precisely discriminate which election contracts with the highest price criteria of predictions will be likely correct. In addition to the complete sample model, this paper uses election contracts of the 2008 presidential election, the 2009 magistrate and mayoral elections, and the 2010 five-metropolis mayoral elections as out-of-sample tests. In terms of prediction accuracy, the Logit model using forty original variables is the best among the four discrimination models. The accuracy rates of discrimination of the Logit model for correct predictions are all 100%. Nevertheless, the Logit model's prediction ability for discriminating incorrect prediction groups needs to be improved.