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Abstract Details
Activity Number:
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360
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Type:
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Contributed
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Date/Time:
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Tuesday, August 2, 2011 : 10:30 AM to 12:20 PM
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Sponsor:
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Business and Economic Statistics Section
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Abstract - #302720 |
Title:
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Binary Prediction to Minimize Total Risk
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Author(s):
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Kentaro Akashi and Yoshinori Kawasaki*+
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Companies:
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Institute of Statistical Mathematics and Institute of Statistical Mathematics
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Address:
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10-3 Midori-cho, Tachikawa, Tokyo, International, 1908562, Japan
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Keywords:
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binary prediction ;
risk minimization ;
optimal cutoff-point ;
minimum prospective interval ;
asymptotic theory
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Abstract:
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Risk involved with financial contracts often can be viewed as uncertainty of binary outcomes. This paper treat risk minimization as the problem of profit maximization, and gives an optimal solution of the cut-off point for binary prediction. This optimality or profit maximization will be asymptotically attained in the sense of convergence in probability. In practice, we have to replace the true parameters inside an indicator function by their estimates. Because indicator functions are discontinuous, apparently it looks non-standard argument. We show, in spite of this, that we can construct an interval for maximized profit, and even minimize it based on asymptotic theories where the MLE is simply plugged in. Simulation results suggest that the finite sample properties of our asymptotic theories are satisfactory. In an empirical analysis using personal loan data of a south German bank, we show the total profit realized by our optimal prediction exceeds the actually observed profit regardless of the settings of loan interest.
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Authors who are presenting talks have a * after their name.
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