
Guotai Chi
Guotai Chi is a Professor of Finance, a PhD Supervisor and a doctor of management science and engineering, and he works in the School of Economics and Management of Dalian University of Technology, Dalian 116024, China. His research interest includes asset-liability management, financial risk management, credit rating, and so forth. And his current research is the credit rating theory and method in the big data environment. In the credit rating field, professor Chi has successfully hosted various national sponsored research projects and grants, and has obtained two national invention patents. He has also published more than 150 papers in important academic journals recognized by the Ministry of Management Science of the National Natural Science Foundation of China and more than 50 papers included in SSCI, SCI, EI, ISTP journals.
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Articles by Guotai Chi
Research on listed companies’ credit ratings, considering classification performance and interpretability
This study uses the correlation coefficient and F-test to select the initial features of a credit evaluation system, and then a validity index for a second selection to ensure that the feature system has the optimum ability to discriminate in determining…
A hybrid model for credit risk assessment: empirical validation by real-world credit data
This paper examines which hybridization strategy is more suitable for credit risk assessment in the dynamic financial world.
Determination of weights for an optimal credit rating model based on default and nondefault distance maximization
This study proposes a credit rating model that accurately identifies default and nondefault companies by maximizing intergroup credit score deviations and minimizing intragroup deviations.
An alternative statistical framework for credit default prediction
This study compares the gradient-boosting model with four other well-known classifiers, namely, a classification and regression tree (CART), logistic regression (LR), multivariate adaptive regression splines (MARS) and a random forest (RF).