Dalian University of Technology
Guotai Chi is Professor of Finance and Doctoral Advisor at Dalian University of Technology. His research fields include asset-liability management, financial risk management, credit rating, data mining and artificial intelligence. He is a visiting Professor at various universities in China and has successfully managed numerous national sponsored research projects and grant.
The authors construct a group feature selection method which combines optimal instance selection with weighted comprehensive precision in an effort to improve the performance of prediction models in relation to defaulting firms.
A modified hybrid feature-selection method based on a filter and wrapper approach for credit risk forecasting
This paper proposes the chi-squared with recursive feature elimination method: a means of feature-selection which aims to improve classification performance using fewer features.
Small and medium-sized enterprises’ time to default: an analysis using an improved mixture cure model with time-varying covariates
The authors put forward a method using a support vector machine to enhance the exploration of nonlinear covariate effects if SMEs never default while also considering time-varying and fixed covariates for the incidence and latency of an event.
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…
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.
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).