The analysis of human judgment accuracy using decision tree models

In this study, a classification model for predicting human judgment accuracy in Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) was developed using the decision tree model. The training data for decision tree learning was obtained from the questionnaire responses of 22 participants in various fields. Experimental results showed the proposed decision tree model is able to explore the important factors that affect forecasters´ judgment accuracy. These factors including accumulated gain/loss in investment, education level, occupation type, and working experience, which can be used as criteria for evaluating the accuracy of professional consultants.