The financing of innovative SMEs: A multicriteria credit rating model

Angilella, Silvia; Mazzù, Sebastiano

doi:10.1016/j.ejor.2015.01.033

Cited by 139 publications

(64 citation statements)

References 68 publications

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“…Hull and White (2012) concluded that risk measures (CRA) are not value measures, ratings (S&P and Fitch) create the illusion of a free lunch, and the securitization that was observed is consistent with structures exploiting the criteria used by the rating agencies, whereas Brookfield and Ormrod (2000) claimed that official recognition of the rating of bonds/securities has no market-based role. In addition, Angilella and Mazzu (2015) reiterated that a multiple-criteria decision aid is appropriate in situations in which there is a lack or absence of analytic quantitative techniques for constructing judgemental credit assessment models. In their book, Zopounidis and Doumpos (2017) emphasized the importance of taking a multiple-criteria decision-making approach to streamline the discussions from different experts, formulate creative solutions, and identify the preferred alternatives.…”

Section: Literature Reviewmentioning

confidence: 99%

The Impact of Risk Management in Credit Rating Agencies

Seetharaman¹,

Sahu²,

Saravanan

et al. 2017

Risks

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An empirical study was conducted to determine the impact of different types of risk on the performance management of credit rating agencies (CRAs). The different types of risks were classified as operational, market, business, financial, and credit. All these five variables were analysed to ascertain their impact on the performance of CRAs. In addition, apart from identifying the significant variables, the study focused on setting out a structured framework for future research. The five independent variables were tested statistically using structural equation modelling (SEM). The results indicated that market risk, financial risk, and credit risk have a significant impact on the performance of CRAs, whereas operational risk and business risk, though important, do not have a significant influence. This finding has a significant implication for the examination and inter-firm evaluation of CRAs.

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Section: Literature Reviewmentioning

confidence: 99%

The Impact of Risk Management in Credit Rating Agencies

Seetharaman¹,

Sahu²,

Saravanan

et al. 2017

Risks

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show abstract

“…This literature strand has been extended by various features such as the relationship between capital structure decisions and credit rating (Naeem 2012;Ntswane 2014;Kedia et al 2017), the relationship between ratings and the probability of default, rating transitions, bank internal credit rating systems, and numerous methodological approaches (Anjum 2012;Stepanyan 2014;Angilella and Mazzù 2015;Sanesh 2016).…”

Section: Introductionmentioning

confidence: 99%

Credit Rating as a Mechanism for Capital Structure Optimization: Empirical Evidence from Panel Data Analysis

Sajjad

Zakaria

2018

IJFS

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This paper empirically examines the significance of credit ratings for optimal capital structure decisions. Non-financial Asian listed companies, evaluated by Standard and Poor's, are selected from 2000 to 2016. Panel data analysis with pooled ordinary least square (OLS), fixed effect (FE), and generalized method of moment (GMM) estimation techniques are employed to test the effect of each credit rating scale on capital structure choices. For the problem of heteroskedasticity in OLS, the heteroskedastic white consistent variance is used for the best fit of the model. Findings of all estimation techniques show that the relationship between credit rating scales and leverage ratio is a non-linear inverted U shape. High-and low-rated companies have a low level of leverage, whereas mid-rated companies have a high level of leverage. It is evident that costs and benefits of each rating scale have a substantial effect on the behavior of a company's choices for optimal capital structure. The study suggests that policymakers, investors, and financial officers should consider credit rating as an important measure of financing decisions.

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“…Small and medium size enterprises also face many obstacles in their attempts to access the credit market and according to Angilella and Mazzù (2015), these obstacles increase greatly if the SME is an innovative one. A study of Liang, McLean and Zhao (2013) found a clear, positive link between the financial system and meaningful innovation.…”

Section: Financial Constraintsmentioning

confidence: 99%

Why say no to innovation? Evidence from industrial SMEs in European Union

Carvalho

Yordanova

2018

Journal of Technology Management & Innovation

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This paper aims at presenting the results from a research on the reasons why more than 50% of the small and medium size enterprises (SMEs) in European Union (EU) are non-innovative. The paper intends to find out which are the specific factors that influence the percentage of non-innovative industrial SMEs, considering that SMEs are the main stakeholders and target group of EU funds and policies, and industry is the main direction for economic development.The study employs a cross-sectional study and linear regressions. The findings after applying the research methodology show some already known factors that obstruct SMEs innovation performance like lack of internal finance and little market competition. However, the main contribution of the research is the finding that a low market demand does not contribute to an increase in the percentage of non-innovative enterprises.As a conclusion, all the measures and EU programs for boosting market demand for the purposes of increasing the number of innovative enterprises would be useless.Recently and during the last 20 years, EU has implied many mechanisms, policies, funding opportunities and monitoring so to achieve an increase in enterprises' innovativeness. European Union has spent a lot of money and efforts on that initiative (Chobanova 2016) and has transformed its identity by pursuing that goal and the metrics for innovation development that it sets all the time (Fougère, Segercrantz, and Seeck 2017).At the end, between 2014 and 2020, more than 190 billion euros will have been spent for enterprises' innovations via Horizon 2020 and European Structural and Investment Funds, but still almost 50% of EU enterprises are non-innovative (Eurostat 2017).

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The financing of innovative SMEs: A multicriteria credit rating model

Cited by 139 publications

References 68 publications

The Impact of Risk Management in Credit Rating Agencies

The Impact of Risk Management in Credit Rating Agencies

Credit Rating as a Mechanism for Capital Structure Optimization: Empirical Evidence from Panel Data Analysis

Why say no to innovation? Evidence from industrial SMEs in European Union

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