A Novel Clustering-Forecast Method With Nonlinear Logo Information Filtering Networks

Abstract

In this paper, we introduced a novel methodology to build a classification-forecast model used for financial risk forewarning. For the first step, we utilize the K–S test, Mann–Whitney U test, and Pearson’s correlation to select variables. Then, we employ CRITIC and fuzzy comprehensive evaluation (FCE) methods to score the risk of listed companies. Following this, self-organizing maps (SOM) clustering is utilized to segment the samples into five distinct risk levels. For the second step, we utilized triangulated maximally filtered graph (TMFG) and maximally filtered clique forest (MFCF) to minimize the number of indicators based on the dependent relationships between variables. These are then combined with Gaussian Markov random field (GMRF) and Copula algorithms to address nonlinear situations, forming what we refer to as the LoGo model. To further enhance the accuracy of LoGo models, we utilize the square Mahalanobis distance to compute the log-likelihoods as part matrix. The results reveal that the enhanced LoGo model with part matrix improves average accuracy by 7% compared with the original models without part matrix, albeit with a tenfold increase in execution time. MFCF demonstrates superior performance over TMFG in linear situations, achieving a 40% higher accuracy. However, under nonlinear circumstances, TMFG only requires half the execution time of MFCF, yet achieves a slightly higher average accuracy. Furthermore, compared with the widely used CNN models, the enhanced LoGo models show superior performance as they achieved closed accuracy in a shorter time.