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Article
Publication date: 20 April 2020

Bowen 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…

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.

Details

Kybernetes, vol. 50 no. 5
Type: Research Article
ISSN: 0368-492X

Keywords

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Article
Publication date: 7 November 2016

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…

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.

Details

Kybernetes, vol. 45 no. 10
Type: Research Article
ISSN: 0368-492X

Keywords

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Abstract

Details

The Banking Sector Under Financial Stability
Type: Book
ISBN: 978-1-78769-681-5

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Book part
Publication date: 28 October 2019

Angelo Corelli

Abstract

Details

Understanding Financial Risk Management, Second Edition
Type: Book
ISBN: 978-1-78973-794-3

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Article
Publication date: 31 August 2012

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…

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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Article
Publication date: 26 July 2021

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…

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.

Details

China Finance Review International, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 2044-1398

Keywords

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Article
Publication date: 26 October 2012

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.

Details

Grey Systems: Theory and Application, vol. 2 no. 3
Type: Research Article
ISSN: 2043-9377

Keywords

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Book part
Publication date: 9 November 2009

Michael G. Papaioannou

The expanded sovereign bond portfolios from the sizeable public interventions in the financial sector during the current crisis need close monitoring and analysis of…

Abstract

The expanded sovereign bond portfolios from the sizeable public interventions in the financial sector during the current crisis need close monitoring and analysis of emerging vulnerabilities. This chapter presents some conventional and new measures of market, credit, and liquidity risks for government bond portfolios, considered from the perspective of a sovereign debt manager. In particular, it examines duration, convexity, and VaR statistics as measures of market exposure; the contingent-claims approach as the most promising measure of credit risk exposure; and a VaR statistic as a measure of liquidity risk.

Details

Credit, Currency, or Derivatives: Instruments of Global Financial Stability Or crisis?
Type: Book
ISBN: 978-1-84950-601-4

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Article
Publication date: 28 October 2014

Carlos Castro and Karen Garcia

Commodity price volatility and small variations in climate conditions may have an important impact on the creditworthiness of any agricultural project. The evolution of…

Abstract

Purpose

Commodity price volatility and small variations in climate conditions may have an important impact on the creditworthiness of any agricultural project. The evolution of such risk factors is vital for the credit risk analysis of a rural bank. The purpose of this paper is to determine the importance of price volatility and climate factors within a default risk model.

Design/methodology/approach

The authors estimate a generalized linear model (GLM) based on a structural default risk model. With the estimated factor loadings, the authors simulate the loss distribution of the portfolio and perform stress test to determine the impact of the relevant risk factors on economic capital.

Findings

The results indicate that both the price volatility and climate factors are statistically significant; however, their economic significance is smaller compare to other factors that the authors control for: macroeconomic conditions for the agricultural sector and intermediate input prices.

Research limitations/implications

The analysis of non-systemic risk factors such as price volatility and climate conditions requires statistical methods focussed on measuring causal effects at higher quantiles, not just at the conditional mean, this is, however, a current limitation of GLMs.

Practical implications

The authors provide a design of a portfolio credit risk model, that is more suited to the special characteristics of a rural bank, than commercial credit risk models.

Originality/value

The paper incorporates agricultural-specific risk factors in a default risk model and a portfolio credit risk model.

Details

Agricultural Finance Review, vol. 74 no. 4
Type: Research Article
ISSN: 0002-1466

Keywords

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Article
Publication date: 9 January 2007

Arindam Bandyopadhyay

The purpose of this paper is to develop a hybrid logistic model by using the inputs obtained from BSM equity‐based option model described in the companion paper, “Mapping…

Abstract

Purpose

The purpose of this paper is to develop a hybrid logistic model by using the inputs obtained from BSM equity‐based option model described in the companion paper, “Mapping corporate drift towards default – Part 1: a market‐based approach” that can more accurately predict corporate default.

Design/methodology/approach

In a set of logistic regressions, the ability of the market value of assets, asset volatility and firm's leverage structure measures to predict future default is investigated. Next, a check is made as to whether accounting variables and other firm specific characteristics can provide additional significant information in assessing the real world credit quality of a firm in a multifactor model

Findings

From analysis of 150 publicly‐traded Indian corporates over the year 1998 to 2005 it was found that in a volatile equity market like India, one needs to enhance the BSM model with other accounting information from financial statements and develop hybrid models. The results in this paper indicate that a mix of asset volatility, market value of asset and firm's leverage structure along with other financial and non financial factors can give us a more accurate prediction of corporate default than the ratio‐based reduced form model.

Originality/value

The hybrid model developed in this paper allows us to integrate information from the structural model as well as profitability of firms, liquidity risk, other firm specific supplementary information and macroeconomic factors to predict real world corporate distress potential through a multivariate analysis.

Details

The Journal of Risk Finance, vol. 8 no. 1
Type: Research Article
ISSN: 1526-5943

Keywords

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