AN INTEGRATED TWO-STAGE METHODOLOGY FOR OPTIMISING THE ACCURACY OF PERFORMANCE CLASSIFICATION MODELS
提出两阶段方法对非银行金融机构进行绩效分类：先优化分组，再用神经网络预测新公司绩效，帮助投资者或监管者评估机构表现。
In this paper we propose a two-stage methodology to classify the non-banking financial institutions (NFIs) based on their financial performance. The first stage of the methodology consists of grouping the companies in similar financial performance classes (e.g.: “good”, “average”, “poor” performance classes). We optimise the allocation of the observations within the performance clusters by applying an enhanced version of an observation re-allocation procedure proposed in our previous work. Next, based on the result of the grouping phase, we construct a performance class variable by attaching a performance label to each data row. Then, in the second phase of our methodology, we propose a feed-forward neural-network classification model that maps the input space to the newly-constructed performance class variable. This model allows us to forecast the performance of new companies as data become available.