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Guotai Chi
Dalian University of Technology, Dalian City, China

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Original article
Published: 13 February 2021 in Journal of the Operational Research Society
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In building a predictive credit scoring model, feature selection is an essential pre-processing step that can improve the predictive accuracy and comprehensibility of models. In this study, we select the optimal feature subset based on group feature selection in lieu of the individual feature selection method, to establish a credit scoring model for small manufacturing enterprises. In our methodology, we first select a group of features using the 0-1 programming method, with the objective function of maximising the Gini coefficient (GINI) of the credit score to identify the possibility of default. Then we introduce constraints to remove any redundant features in the same subset, provided they reflect the same information. Finally, we assign weights to different features according to the Gini coefficient, ensuring that the weight of the features reflects their discriminatory power. Our empirical results show that the selection of a set of features more effectively identifies default status than the individual feature selection approach. Moreover, a rating system with more features does not necessarily have better discriminatory power. As the number of features exceeds the optimum number of features selected, the system's discriminatory ability begins to decrease.

ACS Style

Zhipeng Zhang; Guotai Chi; Sisira Colombage; Ying Zhou. Credit scoring model based on a novel group feature selection method: The case of Chinese small-sized manufacturing enterprises. Journal of the Operational Research Society 2021, 1 -17.

AMA Style

Zhipeng Zhang, Guotai Chi, Sisira Colombage, Ying Zhou. Credit scoring model based on a novel group feature selection method: The case of Chinese small-sized manufacturing enterprises. Journal of the Operational Research Society. 2021; ():1-17.

Chicago/Turabian Style

Zhipeng Zhang; Guotai Chi; Sisira Colombage; Ying Zhou. 2021. "Credit scoring model based on a novel group feature selection method: The case of Chinese small-sized manufacturing enterprises." Journal of the Operational Research Society , no. : 1-17.

Research article
Published: 17 January 2021 in Economic Research-Ekonomska Istraživanja
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This paper aims to discover a suitable combination of contemporary feature selection techniques and robust prediction classifiers. As such, to examine the impact of the feature selection method on classifier performance, we use two Chinese and three other real-world credit scoring datasets. The utilized feature selection methods are the least absolute shrinkage and selection operator (LASSO), multivariate adaptive regression splines (MARS). In contrast, the examined classifiers are the classification and regression trees (CART), logistic regression (LR), artificial neural network (ANN), and support vector machines (SVM). Empirical findings confirm that LASSO's feature selection method, followed by robust classifier SVM, demonstrates remarkable improvement and outperforms other competitive classifiers. Moreover, ANN also offers improved accuracy with feature selection methods; LR only can improve classification efficiency through performing feature selection via LASSO. Nonetheless, CART does not provide any indication of improvement in any combination. The proposed credit scoring modeling strategy may use to develop policy, progressive ideas, operational guidelines for effective credit risk management of lending, and other financial institutions. The finding of this study has practical value, as to date, there is no consensus about the combination of feature selection method and prediction classifiers.

ACS Style

Ying Zhou; Mohammad Shamsu Uddin; Tabassum Habib; Guotai Chi; Kunpeng Yuan. Feature selection in credit risk modeling: an international evidence. Economic Research-Ekonomska Istraživanja 2021, 1 -31.

AMA Style

Ying Zhou, Mohammad Shamsu Uddin, Tabassum Habib, Guotai Chi, Kunpeng Yuan. Feature selection in credit risk modeling: an international evidence. Economic Research-Ekonomska Istraživanja. 2021; ():1-31.

Chicago/Turabian Style

Ying Zhou; Mohammad Shamsu Uddin; Tabassum Habib; Guotai Chi; Kunpeng Yuan. 2021. "Feature selection in credit risk modeling: an international evidence." Economic Research-Ekonomska Istraživanja , no. : 1-31.

Journal article
Published: 29 December 2020 in IEEE Access
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This study proposes to address the economic significance of unpaid taxes by using an automatic system for predicting a tax default. Too little attention has been paid to tax default prediction in the past. Moreover, existing approaches tend to apply conventional statistical methods rather than advanced data analytic approaches, including state-of-the-art machine learning methods. Therefore, existing studies cannot effectively detect tax default information in real-world financial data because they fail to take into account the appropriate data transformations and nonlinear relationships between early-warning financial indicators and tax default behavior. To overcome these problems, this study applies diverse feature transformation techniques and state-of-the-art machine learning approaches. The proposed prediction system is validated by using a dataset showing tax defaults and non-defaults at Finnish limited liability firms. Our findings provide evidence for a major role of feature transformation, such as logarithmic and square-root transformation, in improving the performance of tax default prediction. We also show that extreme gradient boosting and the systematically developed forest of multiple decision trees outperform other machine learning methods in terms of accuracy and other classification performance measures. We show that the equity ratio, liquidity ratio, and debt-to-sales ratio are the most important indicators of tax defaults for 1-yearahead predictions. Therefore, this study highlights the essential role of well-designed tax default prediction systems, which require a combination of feature transformation and machine learning methods. The effective implementation of an automatic tax default prediction system has important implications for tax administration and can assist administrators in achieving feasible government expenditure allocations and revenue expansions.

ACS Style

Mohammad Zoynul Abedin; Guotai Chi; Mohammed Mohi Uddin; Shahriare Satu; Imran Khan; Petr Hajek. Tax Default Prediction Using Feature Transformation-Based Machine Learning. IEEE Access 2020, 9, 19864 -19881.

AMA Style

Mohammad Zoynul Abedin, Guotai Chi, Mohammed Mohi Uddin, Shahriare Satu, Imran Khan, Petr Hajek. Tax Default Prediction Using Feature Transformation-Based Machine Learning. IEEE Access. 2020; 9 (99):19864-19881.

Chicago/Turabian Style

Mohammad Zoynul Abedin; Guotai Chi; Mohammed Mohi Uddin; Shahriare Satu; Imran Khan; Petr Hajek. 2020. "Tax Default Prediction Using Feature Transformation-Based Machine Learning." IEEE Access 9, no. 99: 19864-19881.

Research article
Published: 30 November 2020 in International Journal of Finance & Economics
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This paper applies the Random Forest (RF) method for the robust modelling of credit default prediction. This technique has been proven as an efficient classifier and can provide better interpretability in comparison to other classifiers. Using Chines micro‐enterprise credit data set, this study emphasizes the multidimensional analysis of credit risk, such as the whole sample, subsample, and the incremental effect of the group of predictors. To that end, relative variable importance (RVIs) has been presented for all predictors according to the contribution in the prediction accuracy so that to ensure interpretability of the model. The empirical findings confirm that RF technique is reliable and efficient across all of the criteria used in this study. In addition, the examined experimental analysis indicates that non‐traditional variables have a significant effect on the classification accuracy. Thus, this paper recommends some alternative predictors like the legal representative's basic information, internal non‐financial factors, along with traditional financial variables for sustainable model development. The performance is compared from the perspective of five different performance measures. This modelling algorithm can be used by different financial markets participants to measure systematically credit default prediction of individual and institutional customers.

ACS Style

Mohammad S. Uddin; Guotai Chi; Mazin A. M. Al Janabi; Tabassum Habib. Leveraging random forest in micro‐enterprises credit risk modelling for accuracy and interpretability. International Journal of Finance & Economics 2020, 1 .

AMA Style

Mohammad S. Uddin, Guotai Chi, Mazin A. M. Al Janabi, Tabassum Habib. Leveraging random forest in micro‐enterprises credit risk modelling for accuracy and interpretability. International Journal of Finance & Economics. 2020; ():1.

Chicago/Turabian Style

Mohammad S. Uddin; Guotai Chi; Mazin A. M. Al Janabi; Tabassum Habib. 2020. "Leveraging random forest in micro‐enterprises credit risk modelling for accuracy and interpretability." International Journal of Finance & Economics , no. : 1.

Journal article
Published: 03 August 2020 in Mathematics
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In this paper, we propose a new framework of a financial early warning system through combining the unconstrained distributed lag model (DLM) and widely used financial distress prediction models such as the logistic model and the support vector machine (SVM) for the purpose of improving the performance of an early warning system for listed companies in China. We introduce simultaneously the 3~5-period-lagged financial ratios and macroeconomic factors in the consecutive time windows t-3, t-4 and t-5 to the prediction models; thus, the influence of the early continued changes within and outside the company on its financial condition is detected. Further, by introducing lasso penalty into the logistic-distributed lag and SVM-distributed lag frameworks, we implement feature selection and exclude the potentially redundant factors, considering that an original long list of accounting ratios is used in the financial distress prediction context. We conduct a series of comparison analyses to test the predicting performance of the models proposed by this study. The results show that our models outperform logistic, SVM, decision tree and neural network (NN) models in a single time window, which implies that the models incorporating indicator data in multiple time windows convey more information in terms of financial distress prediction when compared with the existing singe time window models.

ACS Style

Dawen Yan; Guotai Chi; Kin Keung Lai. Financial Distress Prediction and Feature Selection in Multiple Periods by Lassoing Unconstrained Distributed Lag Non-linear Models. Mathematics 2020, 8, 1275 .

AMA Style

Dawen Yan, Guotai Chi, Kin Keung Lai. Financial Distress Prediction and Feature Selection in Multiple Periods by Lassoing Unconstrained Distributed Lag Non-linear Models. Mathematics. 2020; 8 (8):1275.

Chicago/Turabian Style

Dawen Yan; Guotai Chi; Kin Keung Lai. 2020. "Financial Distress Prediction and Feature Selection in Multiple Periods by Lassoing Unconstrained Distributed Lag Non-linear Models." Mathematics 8, no. 8: 1275.

Journal article
Published: 03 December 2019 in Economic Modelling
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It is commonly observed that high grade loans with better ratings are often associated with low recoveries if they default (i.e. with relatively high loss-given-default (LGD)). To address the mismatch problem, this paper proposes a credit risk approach by minimizing LGD for higher rated loans as a risk-rating matching standard in the sense that the decreasing LGD from creditors’ perspective is associated with higher credit rating for the borrower. This standard forces customers’ credit rating of each grade to be optimally determined in correspondence to its LGD, which means the LGD of high grade loans tends to be low. The approach is then tested using three credit datasets from China, i.e. credit data from 2044 farmers, 2157 small private businesses and 3111 SMEs. The empirical results show that the proposed approach indeed guides the way to solve the mismatch phenomenon between credit ratings and LGDs in the existing credit rating literature. By optimally determining credit ratings, the findings derived from this paper help provide a valuable reference for bankers, and bond investors to manage their credit risk.

ACS Style

Baofeng Shi; Guotai Chi; Weiping Li. Exploring the mismatch between credit ratings and loss-given-default: A credit risk approach. Economic Modelling 2019, 85, 420 -428.

AMA Style

Baofeng Shi, Guotai Chi, Weiping Li. Exploring the mismatch between credit ratings and loss-given-default: A credit risk approach. Economic Modelling. 2019; 85 ():420-428.

Chicago/Turabian Style

Baofeng Shi; Guotai Chi; Weiping Li. 2019. "Exploring the mismatch between credit ratings and loss-given-default: A credit risk approach." Economic Modelling 85, no. : 420-428.

Journal article
Published: 15 October 2019 in Management Decision
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Purpose The purpose of this paper is to propose a debt rating index system for small industrial enterprises that significantly distinguishes the default state. This debt rating system is constructed using the F-test and correlation analysis method, with the small industrial enterprise loans of a Chinese commercial bank as the data sample. This study establishes the weighting principle for the debt scoring model: “the more significant the default state, the larger is the weight.” The debt rating system for small industrial enterprises is constructed based on the standard “the higher the debt rating, the lower is the loss given default.” Design/methodology/approach In this study, the authors selected indexes that pass the homogeneity of variance test based on the principle that a greater deviation of the default sample’s mean from the whole sample’s mean leads to greater significance in distinguishing the default samples from the non-default samples. The authors removed correlated indexes based on the results of the correlation analysis and constructed a debt rating index system for small industrial enterprises that included 23 indexes. Findings Among the 23 indexes, the weights of 12 quantitative indexes add up to 0.547, while the weights of the remaining 11 qualitative indexes add up to 0.453. That is, in the debt rating of the small industry enterprises, the financial indexes are not capable of reflecting all the debt situations, and the qualitative indexes play a more important role in debt rating. The weights of indexes “X17 Outstanding loans to all assets ratio” and “X59 Date of the enterprise establishment” are 0.146 and 0.133, respectively; both these are greater than 0.1, and the indexes are ranked first and second, respectively. The weights of indexes “X6 EBIT-to- current liabilities ratio,” “X13 Ratio of capital to fixed” and “X78 Legal dispute number” are between 0.07 and 0.09, these indexes are ranked third to fifth. The weights of indexes “X3 Quick ratio” and “X50 Per capital year-end savings balance of Urban and rural residents” are both 0.013, and these are the lowest ranked indexes. Originality/value The data of index i are divided into two categories: default and non-default. A greater deviation in the mean of the default sample from that of the whole sample leads to greater deviation from the non-default sample’s mean as well; thus, the index can easily distinguish the default and the non-default samples. Following this line of thought, the authors select indexes that pass the F-test for the debt rating system that identifies whether or not the sample is default. This avoids the disadvantages of the existing research in which the standard for selecting the index has nothing to do with the default state; further, this presents a new way of debt rating. When the correlation coefficient of two indexes is greater than 0.8, the index with the smaller F-value is removed because of its...

ACS Style

Guotai Chi; Bin Meng. Debt rating model based on default identification. Management Decision 2019, 57, 2239 -2260.

AMA Style

Guotai Chi, Bin Meng. Debt rating model based on default identification. Management Decision. 2019; 57 (9):2239-2260.

Chicago/Turabian Style

Guotai Chi; Bin Meng. 2019. "Debt rating model based on default identification." Management Decision 57, no. 9: 2239-2260.

Articles
Published: 11 October 2019 in Emerging Markets Finance and Trade
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This article empirically investigates the impact of key variables and characteristics on loan loss given default (LGD) of small farmers using data from 28 provinces in China. The default feature of loans is not only a financial issue and a risk management issue but also an exploration of the loan customers’ default rule. In this study, the key variables were selected using an F-test to identify which ones are critical in credit risk management. Then, we use a t-test to obtain the significant characteristics with an impact on LGD. We found that the 30-35-year-old age group, those living in houses with shared ownership, households with two to four workers, and those whose ratio of annual net income to GDP per capita is between 10 and 20 tend to have higher LGD. These results inform bank lenders and policymakers of the most significant factors that influence loan loss default.

ACS Style

Zhichong Zhao; Sisira Colombage; Guotai Chi. Key Variables and Characteristics of Loan Loss Given Default: Empirical Evidence from 28 Provinces in China. Emerging Markets Finance and Trade 2019, 56, 2443 -2460.

AMA Style

Zhichong Zhao, Sisira Colombage, Guotai Chi. Key Variables and Characteristics of Loan Loss Given Default: Empirical Evidence from 28 Provinces in China. Emerging Markets Finance and Trade. 2019; 56 (11):2443-2460.

Chicago/Turabian Style

Zhichong Zhao; Sisira Colombage; Guotai Chi. 2019. "Key Variables and Characteristics of Loan Loss Given Default: Empirical Evidence from 28 Provinces in China." Emerging Markets Finance and Trade 56, no. 11: 2443-2460.

Articles
Published: 19 September 2019 in Emerging Markets Finance and Trade
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We examined the impacts of venture capital investment (VCI) on the performance of online Peer-to-Peer (P2P) lending platforms. The research results show that: (1) Gaining the first round of VCI can increase the transaction scale and improve the compliance with regulatory requirement of the online P2P lending platforms; and (2) the platforms acquiring more rounds of VCI have greater turnover. The above research shows that under the background of information asymmetry, the certification function and monitoring mechanism of VCs can work in online P2P lending markets.

ACS Style

Hufeng Yang; Han Li; Zhen Hu; Guotai Chi. Impacts of Venture Capital on Online P2P Lending Platforms: Empirical Evidence from China. Emerging Markets Finance and Trade 2019, 56, 2039 -2054.

AMA Style

Hufeng Yang, Han Li, Zhen Hu, Guotai Chi. Impacts of Venture Capital on Online P2P Lending Platforms: Empirical Evidence from China. Emerging Markets Finance and Trade. 2019; 56 (9):2039-2054.

Chicago/Turabian Style

Hufeng Yang; Han Li; Zhen Hu; Guotai Chi. 2019. "Impacts of Venture Capital on Online P2P Lending Platforms: Empirical Evidence from China." Emerging Markets Finance and Trade 56, no. 9: 2039-2054.

Journal article
Published: 12 October 2017 in Sustainability
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A small enterprise’s credit rating is employed to measure its probability of defaulting on a debt, but, for small enterprises, financial data are insufficient or even unreliable. Thus, building a multi criteria credit rating model based on the qualitative and quantitative criteria is of importance to finance small enterprises’ activities. Till now, there has not been a multicriteria credit risk model based on the rank sum test and entropy weighting method. In this paper, we try to fill this gap by offering three innovative contributions. First, the rank sum test shows significant differences in the average ranks associated with index data for the default and entire sample, ensuring that an index makes an effective differentiation between the default and non-default sample. Second, the rating equation’s capacity is tested to identify the potential defaults by verifying a clear difference between the average ranks of samples with default ratings (i.e., not index values) and the entire sample. Third, in our nonparametric test, the rank sum test is used with rank correlation analysis made to screen for indices, thereby avoiding the assumption of normality associated with more common credit rating methods.

ACS Style

Guotai Chi; Zhipeng Zhang. Multi Criteria Credit Rating Model for Small Enterprise Using a Nonparametric Method. Sustainability 2017, 9, 1834 .

AMA Style

Guotai Chi, Zhipeng Zhang. Multi Criteria Credit Rating Model for Small Enterprise Using a Nonparametric Method. Sustainability. 2017; 9 (10):1834.

Chicago/Turabian Style

Guotai Chi; Zhipeng Zhang. 2017. "Multi Criteria Credit Rating Model for Small Enterprise Using a Nonparametric Method." Sustainability 9, no. 10: 1834.

Journal article
Published: 17 February 2017 in Risk Management
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Credit default prediction (CDP) modeling is a fundamental and critical issue for financial institutions. However, the previous studies indicate that the classifier’s performances in CDP analysis differ using different performance criterions on different databases under different circumstances. The performance assessment exercise under a set of criteria remains understudied in nature, on the one hand, and the real–scenario is not taken into account in that a single/very limited number of measure only are used, on the other hand. These problems affect the ability to make a consistent conclusion. Therefore, the aim of this study is to address this methodological issue by applying support vector machine (SVM)-based CDP algorithm by means of a set of representative performance criterions, with enclosing some novel performance measures, its performance compare with the results gained by statistical and intelligent approaches using six different types of databases from the credit prediction domains. Experimental results show that SVM model is marginally superior to CART with DA, being more robust than its other counterparts. In consequence, this study recommends that the supremacy of a classifier is linked to the way in which evaluations are measured.

ACS Style

Fahmida E. Moula; Chi Guotai; Mohammad Zoynul Abedin. Credit default prediction modeling: an application of support vector machine. Risk Management 2017, 19, 158 -187.

AMA Style

Fahmida E. Moula, Chi Guotai, Mohammad Zoynul Abedin. Credit default prediction modeling: an application of support vector machine. Risk Management. 2017; 19 (2):158-187.

Chicago/Turabian Style

Fahmida E. Moula; Chi Guotai; Mohammad Zoynul Abedin. 2017. "Credit default prediction modeling: an application of support vector machine." Risk Management 19, no. 2: 158-187.

Journal article
Published: 21 March 2016 in Applied Economics and Finance
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Standard risk management focuses on short-run risks rather than longer periods. We introduce an improved risk measure which can be used to estimate both short-and long-term structure of value at risk and the corresponding expected shortfall. The short- and long-term coherent measure of risk is specified and computed for both S&P 500, HSI and SHSZ 300. We also test long-term skewness and kurtosis from empirical analysis for S&P 500, HSI and SHSZ 300. We also show that our improved risk measure gives a better estimate of the value at risk for short horizons and never decreases to negative values like VaR for long-run horizons. Both long-term skewness and kurtosis for HSI and SHSZ 300 are analyzed empirically.

ACS Style

Weiping Li; Guotai Chi; Bin Meng. Short- And Long-Term Value-At-Risk, Skewness, Kurtosis and Coherent Risk Measure. Applied Economics and Finance 2016, 3, 65 -80.

AMA Style

Weiping Li, Guotai Chi, Bin Meng. Short- And Long-Term Value-At-Risk, Skewness, Kurtosis and Coherent Risk Measure. Applied Economics and Finance. 2016; 3 (3):65-80.

Chicago/Turabian Style

Weiping Li; Guotai Chi; Bin Meng. 2016. "Short- And Long-Term Value-At-Risk, Skewness, Kurtosis and Coherent Risk Measure." Applied Economics and Finance 3, no. 3: 65-80.

Research article
Published: 06 September 2015 in Mathematical Problems in Engineering
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This paper presents an approach for weighting indices in the comprehensive evaluation. In accordance with the principle that the entire difference of various evaluation objects is to be maximally differentiated, an adjusted weighting coefficient is introduced. Based on the idea of maximizing the difference between the adjusted evaluation scores of each evaluation object and their mean, an objective programming model is established with more obvious differentiation between evaluation scores and the combined weight coefficient determined, thereby avoiding contradictory and less distinguishable evaluation results of single weighting methods. The proposed model is demonstrated using 2,044 observations. The empirical results show that the combined weighting method has the least misjudgment probability, as well as the least error probability, when compared with four single weighting methods, namely, G1, G2, variation coefficient, and deviation methods.

ACS Style

Bin Meng; Guotai Chi. New Combined Weighting Model Based on Maximizing the Difference in Evaluation Results and Its Application. Mathematical Problems in Engineering 2015, 2015, 1 -9.

AMA Style

Bin Meng, Guotai Chi. New Combined Weighting Model Based on Maximizing the Difference in Evaluation Results and Its Application. Mathematical Problems in Engineering. 2015; 2015 ():1-9.

Chicago/Turabian Style

Bin Meng; Guotai Chi. 2015. "New Combined Weighting Model Based on Maximizing the Difference in Evaluation Results and Its Application." Mathematical Problems in Engineering 2015, no. : 1-9.

Journal article
Published: 30 April 2009 in Systems Engineering - Theory & Practice
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This paper establishes the non-linear functional relationship of risk overlap of incremental and existing portfolios. The decision-making model for incremental loan portfolio is based on how total loan portfolio optimization is built. The contributions and characteristics lie in three aspects. The first contribution is to put forward the scientific problem of controlling total portfolio risk based on risk overlapping of incremental and existing portfolios while putting out an incremental portfolio. The whole control of incremental and existing portfolios is the top issue, which not only exploits new idea about optimal allocation and control of financial assets but also alters traditional idea merely considering the risk of incremental portfolios. The second contribution is to establish the non-linear function relation of overlap between incremental and existing portfolios. Based on the theory of non-linear overlap of total portfolio, the function expression connecting total portfolio with incremental and existing portfolios is built. The third contribution is the controlling of total risk after overlapping between the existing and incremental portfolios risk. It solves the problem of how to control and optimize total portfolio risk while allocating an incremental portfolio.

ACS Style

Guo-Tai Chi; Feng Chi; Guang-Jun Zhao. Optimization Model of Incremental Loan Portfolio based on Risks Overlap of Incremental and Existing Portfolio. Systems Engineering - Theory & Practice 2009, 29, 1 -18.

AMA Style

Guo-Tai Chi, Feng Chi, Guang-Jun Zhao. Optimization Model of Incremental Loan Portfolio based on Risks Overlap of Incremental and Existing Portfolio. Systems Engineering - Theory & Practice. 2009; 29 (4):1-18.

Chicago/Turabian Style

Guo-Tai Chi; Feng Chi; Guang-Jun Zhao. 2009. "Optimization Model of Incremental Loan Portfolio based on Risks Overlap of Incremental and Existing Portfolio." Systems Engineering - Theory & Practice 29, no. 4: 1-18.

Journal article
Published: 28 February 2007 in Systems Engineering - Theory & Practice
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Based on the dynamic programming method, by the use of the constraints on VaR, laws, regulations, and operation, a multi-period dynamic portfolio optimal model for banks is successfully developed with the objective of maximizing the portfolio's yield. The characteristics and innovations of this paper are as follows. First, using the Backward Induction Method, the optimal portfolio in the current period is obtained on the basis of the optimal portfolio in the next period. It solves the problem of obtaining the portfolio's yield only in a single period and ignoring the interactions of various periods in the existing studies. Second, this paper considers that the credit rating migration in the previous year has a significant impact on the expected value of the loan's yield in the current year. Then both the loan's yields under different ratings and the one-year credit rating migration matrix are employed to calculate the corresponding annual expected values and standard deviations of the loan' s yields of various corporations. It can objectively reflect the real yield and risk. As a result the problem of simply seeking the expected value of each loan's yield or only considering it as a constant in the recent studies can be solved. Third, from the analysis result, it shows that the portfolio's yield in the near period is larger than that in the long period. And it also shows that the model gets a progressive optimal result and can well reflect the time value of assets. The paper solves the problem of neglect of the time value of assets in the recent studies. Finally, this paper uses VaR to control the multi-period portfolio risk and solves the problem of lack of consideration of the bank's risk tolerance ability and the requirement of capital supervision in recent multi-period studies.

ACS Style

Guo-Tai Chi; He-Chao Dong; Xiu-Yan Sun. Decision-making Model of Bank's Assets Portfolio based on Multi-period Dynamic Optimization. Systems Engineering - Theory & Practice 2007, 27, 1 -16.

AMA Style

Guo-Tai Chi, He-Chao Dong, Xiu-Yan Sun. Decision-making Model of Bank's Assets Portfolio based on Multi-period Dynamic Optimization. Systems Engineering - Theory & Practice. 2007; 27 (2):1-16.

Chicago/Turabian Style

Guo-Tai Chi; He-Chao Dong; Xiu-Yan Sun. 2007. "Decision-making Model of Bank's Assets Portfolio based on Multi-period Dynamic Optimization." Systems Engineering - Theory & Practice 27, no. 2: 1-16.