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1 – 10 of 159Bowen Jia, Jiaying Wu, Juan Du, Yun Ji and Lina Zhu
The purpose of this paper is to calculate the local guaranteed fiscal revenue with the local fiscal revenue of 31 provinces, and predict their guaranteed fiscal revenue in 2018…
Abstract
Purpose
The purpose of this paper is to calculate the local guaranteed fiscal revenue with the local fiscal revenue of 31 provinces, and predict their guaranteed fiscal revenue in 2018 with the artificial neural network (ANN).
Design/methodology/approach
The principal components analysis (PCA), particle swarm optimization (PSO) and extreme learning machine (ELM) model was designed to produce the inputs of KMV model. Then the KMV model was used for obtaining the default probabilities under different issuance scales. Data were collected from Wind Database. MATLAB 2018b and SPSS 22 were used in the field of modeling and results analysis.
Findings
This study’s findings show that PCA–PSO–ELM proposed in this research has the highest accuracy in terms of the prediction compared with ELM, back propagation neural network and auto regression. And PCA–PSO–ELM–KMV model can calculate the secure issuance scale of local government bonds effectively.
Practical implications
The sustainability forecast in this study can help local governments effectively control the scale of debt issuance, strengthen the budget management of local debt and establish the corresponding risk warning mechanism, which could make local governments maintain good credit ratings.
Originality/value
This study sheds new light on helping local governments avoid financial risks effectively, and it is conducive to establish a debt repayment reserve system for local governments and the proper arrangement for stock debt.
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Hsu-Che Wu and Yu-Ting Wu
An increasing number of investors have begun using financial data to develop optimal investment portfolios; therefore, the public financial data shared in the capital market plays…
Abstract
Purpose
An increasing number of investors have begun using financial data to develop optimal investment portfolios; therefore, the public financial data shared in the capital market plays a critical role in credit ratings. These data enable investors to understand the credit levels of debtors from a bank perspective; this facilitates predicting the debtor default rate to efficiently evaluate investment risks. The paper aims to discuss these issues.
Design/methodology/approach
A credit rating model can be developed to reduce the risk of adverse selection and moral hazard caused by information asymmetry in the loan market. In this study, a random forest (RF) was used to evaluate financial variables and construct credit rating prediction models. Data-mining techniques, including an RF, decision tree, neural networks, and support vector machine, were used to search for suitable credit rating forecasting methods. The distance to default from the KMV model was then incorporated into the credit rating model as a research variable to increase predictive power of various data-mining techniques. In addition, four-level and nine-level classification were set to investigate the accuracy rates of various models.
Findings
The experimental results indicated that applying the RF in the variable feature selection process and developing a forecasting model was the most effective method of predicting credit ratings; the four-level and nine-level feature-selection settings achieved 95.5 and 87.8 percent accuracy rates, respectively, indicating that RF demonstrated outstanding feature selection and forecasting capacity.
Research limitations/implications
The experimental cases were based on financial data from public companies in North America.
Practical implications
Practical implication of this study indicates the most effective financial variables were dividends common/ordinary, cash dividends, volatility assumption, and risk-free rate assumption.
Originality/value
The RF model can be used to perform feature selection and efficiently filter numerous financial variables to obtain crediting rating information instantly.
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Ahmed Imran Hunjra, Fazal Muhammad and Saber Sebai
Earnings management (EM) plays a vital role in risk management. This paper aims to investigate the impact of real earning management (REM) on credit risk.
Abstract
Purpose
Earnings management (EM) plays a vital role in risk management. This paper aims to investigate the impact of real earning management (REM) on credit risk.
Design/methodology/approach
This paper measures the credit risk by the expected default frequency of Kealhofer, McQuown and Vasicek model. This paper uses data from 2011 to 2020 of Pakistani manufacturing listed firms. This paper applies the fixed effect to analyze the results and generalized methods of moments to handle the heterogeneity issue.
Findings
This paper finds that the impact of REM on corporate credit risk is positive and significant and that of sales manipulation is negative and significant. This paper also reports similar outcomes of the robustness test using dynamic panel regression.
Originality/value
The findings of this study may help managers to modify the EM strategy to minimize corporate credit risk. Furthermore, the findings of this study are important for investors to enhance their understanding of firms’ accounting information, REM activities and cash flow patterns. It further suggests the manager should consider credit risk as an important factor while practicing REM.
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Mei‐Ying Wu, Yung‐Chien Weng and I‐Chiao Huang
The purpose of this paper is to use high‐tech companies in Taiwan as research subjects to verify the fit of the commitment‐trust theory and explore the supply chain relationships…
Abstract
Purpose
The purpose of this paper is to use high‐tech companies in Taiwan as research subjects to verify the fit of the commitment‐trust theory and explore the supply chain relationships among research variables.
Design/methodology/approach
The key mediating variables model (KMV) proposed by Morgan and Hunt is applied to construct the research structure, hypotheses, and questionnaire. The research hypotheses are validated through structural equation modelling and confirmatory factor analysis.
Findings
Research results show that for two parties of an exchange relationship, higher levels of trust can lead to better interactions and trust is an important factor affecting their supply chain partnerships. It helps increase interests of both parties, facilitate constant co‐operation and communication, and reduce uncertainties. Higher levels of commitment can also help increase value benefits, reduce a partner's propensity to leave, and enhance supply chain co‐operation efficiency.
Originality/value
Empirical results indicate that relationship marketing is a strategy that promotes trust and commitment of partners in high‐tech industries. While information sharing and communication can increase partners' intention of long‐term co‐operation, functional conflicts can facilitate positive interactions and reduce uncertainties. Through relationship marketing, high‐tech companies can create win‐win strategic alliances to develop their competitive advantages in the market.
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Shaun Shuxun Wang, Jing Rong Goh, Didier Sornette, He Wang and Esther Ying Yang
Many governments are taking measures in support of small and medium-sized enterprises (SMEs) to mitigate the economic impact of the COVID-19 outbreak. This paper presents a…
Abstract
Purpose
Many governments are taking measures in support of small and medium-sized enterprises (SMEs) to mitigate the economic impact of the COVID-19 outbreak. This paper presents a theoretical model for evaluating various government measures, including insurance for bank loans, interest rate subsidy, bridge loans and relief of tax burdens.
Design/methodology/approach
This paper distinguishes a firm's intrinsic value and book value, where a firm can lose its intrinsic value when it encounters cash-flow crunch. Wang transform is applied to (1) calculating the appropriate level of interest rate subsidy payable to incentivize banks to issue more loans to SMEs and to extend the loan maturity of current debt to the SMEs, (2) describing the frailty distribution for SMEs and (3) defining banks' underwriting capability and overlap index in risk selection.
Findings
Government support for SMEs can be in the form of an appropriate level of interest rate subsidy payable to incentivize banks to issue more loans to SMEs and to extend the loan maturity of current debt to the SMEs.
Research limitations/implications
More available data on bank loans would have helped strengthen the empirical studies.
Practical implications
This paper makes policy recommendations of establishing policy-oriented banks or investment funds dedicated to supporting SMEs, developing risk indices for SMEs to facilitate refined risk underwriting, providing SMEs with long-term tax relief and early-stage equity-type investments.
Social implications
The model highlights the importance of providing bridge loans to SMEs during the COVID-19 disruption to prevent massive business closures.
Originality/value
This paper provides an analytical framework using Wang transform for analyzing the most effective form of government support for SMEs.
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This study updates the literature review of Jones (1987) published in this journal. The study pays particular attention to two important themes that have shaped the field over the…
Abstract
Purpose
This study updates the literature review of Jones (1987) published in this journal. The study pays particular attention to two important themes that have shaped the field over the past 35 years: (1) the development of a range of innovative new statistical learning methods, particularly advanced machine learning methods such as stochastic gradient boosting, adaptive boosting, random forests and deep learning, and (2) the emergence of a wide variety of bankruptcy predictor variables extending beyond traditional financial ratios, including market-based variables, earnings management proxies, auditor going concern opinions (GCOs) and corporate governance attributes. Several directions for future research are discussed.
Design/methodology/approach
This study provides a systematic review of the corporate failure literature over the past 35 years with a particular focus on the emergence of new statistical learning methodologies and predictor variables. This synthesis of the literature evaluates the strength and limitations of different modelling approaches under different circumstances and provides an overall evaluation the relative contribution of alternative predictor variables. The study aims to provide a transparent, reproducible and interpretable review of the literature. The literature review also takes a theme-centric rather than author-centric approach and focuses on structured themes that have dominated the literature since 1987.
Findings
There are several major findings of this study. First, advanced machine learning methods appear to have the most promise for future firm failure research. Not only do these methods predict significantly better than conventional models, but they also possess many appealing statistical properties. Second, there are now a much wider range of variables being used to model and predict firm failure. However, the literature needs to be interpreted with some caution given the many mixed findings. Finally, there are still a number of unresolved methodological issues arising from the Jones (1987) study that still requiring research attention.
Originality/value
The study explains the connections and derivations between a wide range of firm failure models, from simpler linear models to advanced machine learning methods such as gradient boosting, random forests, adaptive boosting and deep learning. The paper highlights the most promising models for future research, particularly in terms of their predictive power, underlying statistical properties and issues of practical implementation. The study also draws together an extensive literature on alternative predictor variables and provides insights into the role and behaviour of alternative predictor variables in firm failure research.
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Jiajia Jin, Ziwen Yu and Chuanmin Mi
This paper attempts to analysis the credit risk at the angle of industrial and macroeconomic factor using grey incidence analysis method.
Abstract
Purpose
This paper attempts to analysis the credit risk at the angle of industrial and macroeconomic factor using grey incidence analysis method.
Design/methodology/approach
Credit asset quality problem is one of the obstacles limiting the further development of commercial banks; the research on credit risk becomes an important part of the implementation of a commercial bank's risk management. Different industries may have different effects on the credit risk of commercial bank. This paper proposes finding out the different incidences between industries and credit risk, as well as macroeconomics. Incidence identification method is established to investigate whether the industry and macroeconomic factor could affect an impaired loan ratio of a bank using the grey incidence analysis method.
Findings
The results indicate that the impaired loan ratio differs with diverse industry's influence and the macroeconomics also affect it. From the angle of the industry, the result can also determine the risk deviation scope in the grey risk control process which offers new content and ideas within the grey risk control.
Practical implications
Under the guidance of the principle of “differential treatment, differential control”, this research will help to strengthen the implementation of differentiated credit policy, focus on guiding and promoting the optimization of credit structure, so as to maintain a reasonable size of credit facilities and build a steady currency credit system.
Originality/value
The paper succeeds in finding the top five influent industries compared with others by using one of the newest developed theories: grey systems theory.
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