82 research outputs found
A Model for Recognizing Key Factors and Applications Thereof to Engineering
This paper presents an approach to recognize key factors in data classification. Using collinearity diagnostics to delete the factors of repeated information and Logistic regression significant discriminant to select the factors which can effectively distinguish the two kinds of samples, this paper creates a model for recognizing key factors. The proposed model is demonstrated by using the 2044 observations in finical engineering. The experimental results demonstrate that the 13 indicators such as âmarital status,â ânet income of borrower,â and âEngel's coefficientâ are the key factors to distinguish the good customers from the bad customers. By analyzing the experimental results, the performance of the proposed model is verified. Moreover, the proposed method is simple and easy to be implemented
A big data analytics method for assessing creditworthiness of SMEs:Fuzzy equifinality relationships analysis
Nowadays, many financial institutions are beginning to use Big Data Analytics (BDA) to help them make better credit underwriting decisions, especially for small and medium-sized enterprises (SMEs) with limited financial histories and other information. The various complexities and the equifinality problem of Big Data make it difficult to apply traditionalstatistical techniques to creditworthiness evaluation, or credit scoring. In this study, we extend the existing research in the field of creditworthiness assessment and propose a novel approach based on neighborhood rough sets (NRSs), to evaluate and investigate the complexities and fuzzy equifinality relationships in the presence of Big Data. We utilize a real SME loan dataset from a Chinese commercial bank to generate interval number rules that provide insight into the fuzzy equifinality relationships between borrowersâ demographic information, company financial ratios, loan characteristics, other non-financial information, local macroeconomic indicators and rated creditworthiness level. In addition, the interval number rules are used to predict creditworthiness levels based on test data and the accuracy of the prediction is found to be 75.44%. One of the major advantages of using the proposed BDA approach is that it helps us to reduce complexity and identify equivalence relationships when using Big Data to assess the creditworthiness of SMEs. This study also provides important implications for practices in financial institutions and SMEs
Assessing and predicting small industrial enterprisesâ credit ratings:A fuzzy decision-making approach
Corporate credit-rating assessment plays a crucial role in helping financial institutions make their lending decisions and in reducing the financial constraints of small enterprises. This paper presents a new approach for small industrial enterprisesâ credit-rating assessment using fuzzy decision-making methods, and tests it using real bank loan data from 1,820 small industrial enterprises in China. The procedure of the proposed rating approach includes (1) using triangular fuzzy numbers to quantify the qualitative evaluation indicators; (2) adopting a correlation analysis, univariate analysis and stepping backwards feature selection method to select the input features; (3) employing the best-worst method (BWM) combined with the entropy weight method (EWM), the fuzzy c-means algorithm and the technique for order of preference by similarity to ideal solution (TOPSIS) to classify small enterprises into rating classes; and (4) applying the lattice degree of nearness to predict a new loan applicantâs rating. We also conduct a 10-fold cross-validation to evaluate the predictive performance of our proposed approach. The predictive results demonstrate that our proposed data-processing and feature selection approaches have better accuracy than the alternative approaches in predicting default, offering bankers a new valuable rating system to assist their decision making
Downside and upside risk spillovers from commercial banks into Chinaâs financial system:A new copula quantile regression-based CoVaR model
In this paper, we investigate the downside and upside risk spillovers
from three kinds of commercial banks (state-owned commercial
banks (SOCBs), joint-stock commercial banks (JSCBs) and city
commercial banks (CCBs)) to Chinaâs financial system by proposing
a new copula quantile regression-based CoVaR model. We find
that (i) the dynamic risk spillovers show heterogeneity over time,
specifically that its downward trend is significant after the stock
market disaster in 2015; (ii) JSCBs display the largest risk spillovers,
indicating that JSCBs are the main contributors to systemic risk in
Chinaâs financial system; and (iii) the risk spillovers are not symmetrical,
as the upside risk spillovers are smaller than the downside
risk spillovers. Our results have crucial implications for financial regulators
and investors who want to measure and prevent systemic
financial risk and optimise their investment strategies
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