基于信息熵的不良资产评估组合预测模型研究

The Combined Forecasting Model of Non-performing Loans Assessment Based on Information Entropy

不良资产价值评估是其处置的前提和关键环节,而不良资产处置定价受诸多因素的影响,目前常用的单一评价方法很难诠释不良资产的价值全貌。因此构建了一种基于信息熵的不良资产组合预测评估模型,通过不同专家运用不同评估方法对不良资产进行估价,分别计算每种评价方法的误差,此误差值运用熵值法计算几种单一评价方法的权重并以此来进行组合预测。该方法能综合单一评价方法的计算结果,大大提高了不良资产价值评估的科学性和准确性。

The disposal of the non - performing loans largely relies on its evaluation, the pricing of which is decided by many factor. Since currently the single assessing method cannot thoroughly clarify such loans, it is necessary to build a combined forecasting model based on information entropy, i. e. with different methods the non - performing loans is evaluated so that the deviation of each method can be carefully calculated under different weight for a combined forecast. In this way the precision of the non - performing loans can be much more improved rather than a single calculating method.