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1 – 10 of over 107000Abdelhakim Ben Ali and Jamel Chouaibi
This study aims to investigate whether integrating environmental, social and governance (ESG) practices mediates the relationship between executive incentive compensation and the…
Abstract
Purpose
This study aims to investigate whether integrating environmental, social and governance (ESG) practices mediates the relationship between executive incentive compensation and the financial performance of Islamic and conventional banks in the Middle East and North Africa (MENA) region.
Design/methodology/approach
This study used multiple regression models to analyze the effectiveness of ESG practices as a mediating variable in explaining the relationship between executive incentive compensation and banks’ financial performance between 2015 and 2021. The sample consisted of 57 Islamic and conventional banks operating in the MENA region, and the data were collected from the Thomson Reuters database (Data Stream).
Findings
This research paper showed the positive and significant mediating effect of the ESG practice on Banks’ financial performance. Thus, banks’ financial and stock market profitability is influenced by ESG information disclosure. This finding shows that taking ESG into account improves the relationship between executive incentive compensation and banks’ financial performance.
Practical implications
The results may interest academic researchers, regulators and policymakers and would support stakeholders and decision-makers who wish to discover how executive incentive compensation affects financial performance in banks.
Originality/value
This study contributes to previous literature by studying the mediating effect of ESG practices on the relationship between executive incentive compensation and banks’ financial performance. Indeed, the originality of this research paper is justified by the scarcity of studies and, to the best of the authors’ knowledge, constitutes one of the first attempts to examine this relationship via a mediating variable, i.e. ESG.
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Sihem Khemakhem and Younes Boujelbene
Data mining for predicting credit risk is a beneficial tool for financial institutions to evaluate the financial health of companies. However, the ubiquity of selecting parameters…
Abstract
Purpose
Data mining for predicting credit risk is a beneficial tool for financial institutions to evaluate the financial health of companies. However, the ubiquity of selecting parameters and the presence of unbalanced data sets is a very typical problem of this technique. This study aims to provide a new method for evaluating credit risk, taking into account not only financial and non-financial variables, but also the class imbalance.
Design/methodology/approach
The most significant financial and non-financial variables were determined to build a credit scoring model and identify the creditworthiness of companies. Moreover, the Synthetic Minority Oversampling Technique was used to solve the problem of class imbalance and improve the performance of the classifier. The artificial neural networks and decision trees were designed to predict default risk.
Findings
Results showed that profitability ratios, repayment capacity, solvency, duration of a credit report, guarantees, size of the company, loan number, ownership structure and the corporate banking relationship duration turned out to be the key factors in predicting default. Also, both algorithms were found to be highly sensitive to class imbalance. However, with balanced data, the decision trees displayed higher predictive accuracy for the assessment of credit risk than artificial neural networks.
Originality/value
Classification results depend on the appropriateness of data characteristics and the appropriate analysis algorithm for data sets. The selection of financial and non-financial variables, as well as the resolution of class imbalance allows companies to assess their credit risk successfully.
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Jaruwan Songsang, Kamonchanok Suthiwartnarueput and Pongsa Pornchaiwiseskul
The purposes of this paper are 1) to develop model of long term financial health for logistics companies in Thailand 2) to identify factors that determine long term financial…
Abstract
The purposes of this paper are 1) to develop model of long term financial health for logistics companies in Thailand 2) to identify factors that determine long term financial stability. Many researchers currently provide factors affecting financial health. Most factors refer to financial ratios, not many non-financial ratios such as age and size have been mentioned. This paper considers both financial and non-financial ratios that affect financial performance of Logistics companies in Thailand. The study has covered some interesting non-financial ratios such as Nationality of Shareholders, type of network in Logistics Company, growth rate (consisted of sales growth rate/profit growth rate/asset growth rate / Liability growth rate) and variable of growth rates. The target group is 110 logistics companies in Thailand enlisted from Department of International Trade Promotion Ministry of Commerce, Royal Thai Government. The group is divided into three categories according to financial health of company; Healthy financial, Unhealthy (Distress) and normal situation. The Multidiscriminant Analysis (MDA) is applied to analyze the differentiations among the three categories. Significant variables from MDA will be used as the independent variables for Multimonial Logistic Regression Analysis (MLRA) to identify factors that determine long terms financial stability. This paper find CF/D, RE/TA, BE/TL, Size, Age, Type of network, Nationality of Shareholders and Number of Shareholders are significant factors determine long term financial stability of Logistics company in Thailand.
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Yasmine M. Ragab and Mohamed A. Saleh
This study examines the effect of non-financial variables related to governance on the accuracy of financial distress prediction among Egyptian listed small and medium-sized…
Abstract
Purpose
This study examines the effect of non-financial variables related to governance on the accuracy of financial distress prediction among Egyptian listed small and medium-sized enterprises (SMEs), by using the logistic regression technique.
Design/methodology/approach
This study used a sample of 24 Egyptian-listed SMEs in each year, totaling 120 firm observations, of which 25 were classified distressed and 95 of them non-distressed between 2014 and 2018. The variables for the study included five financial variables and thirteen non-financial variables related to governance. The models were developed using financial variables alone as well as combining financial and non-financial variables related to governance.
Findings
The results showed that the model with financial variables had a prediction accuracy of 91.7% , whereas models with a combination of financial and non-financial variables related to governance predict with comparatively better accuracy of 92.7 and 93.6% .
Research limitations/implications
Although the results seem to be conclusive, it could be noted that the non-distressed sample was not paired with the distressed sample. Other studies showed that paired samples increase the financial distress prediction rate. Furthermore, due to the small sample size, this study was unable to create a hold-out sub-sample for the accuracy test.
Practical implications
The proposed distress prediction model for SMEs is effective for stakeholders, including banks and other financial institutions, in the assessment of the credit risk of SMEs. Using such a model, they could better identify SMEs with a higher risk of failure in their lending decisions. Moreover, SME managers' could be interested in using such models as a tool for planning corrective action, in addition to planning and controlling current operations to avoid financial failure in the future.
Originality/value
This study contributes to financial distress prediction literature in different ways. First, few studies were conducted in the area of financial distress among SMEs. Second, neither of these studies was conducted within the Egyptian context, nor any of them had used non-financial variables related to governance in the prediction of financial distress among SMEs.
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Tatiana Albanez and Gerlando Augusto Sampaio Franco de Lima
According to the market timing theory, firms try to take advantage of windows of opportunity to raise capital by exploiting temporary cost fluctuations of alternative financing…
Abstract
Purpose
According to the market timing theory, firms try to take advantage of windows of opportunity to raise capital by exploiting temporary cost fluctuations of alternative financing sources. In this context, the main objective of this paper is to examine the influence and persistence of market timing in the financing decisions of Brazilian firms that launched IPOs in the period from 2001 to 2011.
Methodology/approach
We analyze the influence of past market values on the capital structure of these firms, based on the main models proposed by Baker and Wurgler (2002), adapted to reflect the characteristics of Brazilian firms’ financial statements.
Findings
We find evidence of market timing, but this behavior is not sufficiently persistent in the period studied to the point of determining these firms’ capital structure. We believe the fact that Brazilian companies rarely carried out follow-on primary equity issues after floating their capital in the period analyzed, due to the presence of more advantageous financing sources (particularly from the national development bank, BNDES), explains the results. Therefore, Brazilian firms appear to be pay heed to different funding sources, in search of windows of opportunity, to guide their financing decisions and determine their capital structures.
Originality/value
The Brazilian capital market has been developing intensely in recent years, making it increasingly relevant to analyze the financing and investment decisions of the country’s listed companies. The Brazilian literature on capital structure is extensive, but few works have addressed the issue of market timing.
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The purpose of the research is to analyse the ability of nonfinancial factors to predict value creation in Finnish technology firms. Nonfinancial factors are defined in terms of a…
Abstract
The purpose of the research is to analyse the ability of nonfinancial factors to predict value creation in Finnish technology firms. Nonfinancial factors are defined in terms of a large set of variables on organizational characteristics, strategy, competitive stance, consistency of performance measurement, management control systems (MCSs), and quality of MCSs. Financial ratios are used as a benchmark. The hypotheses are that, firstly, nonfinancial factors include important information for prediction and, secondly, that they provide incremental information over financial ratios. The nonfinancial variables are drawn from a postal survey carried out in 1999. Financial variables for 1998–2001 are obtained for 40 private firms of the 110 firms responding to the survey. Shareholder value is estimated on the basis of the four‐year financial data for 2001. This value divided by the shareholder book value (estimated‐to‐book value ratio, EBV) as well as its drivers are predicted by past non‐financial and financial data. Partial Least Squares (PLS) method is used to analyse the importance of information in prediction. The results give support to the hypotheses. Moreover, the results show that nonfinancial factors yield important incremental information over financial ratios when predicting value drivers, that is, growth, profitability, and risk. Especially, financial ratios are weak in predicting growth.
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Senthil Arasu Balasubramanian, Radhakrishna G.S., Sridevi P. and Thamaraiselvan Natarajan
This paper aims to develop a corporate financial distress model for Indian listed companies using financial and non-financial parameters by using a conditional logit regression…
Abstract
Purpose
This paper aims to develop a corporate financial distress model for Indian listed companies using financial and non-financial parameters by using a conditional logit regression technique.
Design/methodology/approach
This study used a sample of 96 companies, of which 48 were declared sick between 2014 and 2016. The sample was divided into a training sample and a testing sample. The variables for the study included nine financial variables and four non-financial variables. The models were developed using financial variables alone as well as combining financial and non-financial variables. The performance of the test sample was measured with confusion matrix, sensitivity, specificity, precision, F-measure, Types 1 and 2 error.
Findings
The results show that models with financial variables had a prediction accuracy of 85.19 and 86.11 per cent, whereas models with a combination of financial and non-financial variables predict with comparatively better accuracy of 89.81 and 91.67 per cent. Net asset value, long-term debt–equity ratio, return on investment, retention ratio, age, promoters holdings pledged and institutional holdings are the critical financial and non-financial predictors of financial distress.
Originality/value
This study contributes to the financial distress prediction literature in different ways. First, there have been, until now, few studies in the area of financial distress prediction in the Indian context. Second, business failure studies in the past have used only financial variables. The authors have combined financial and non-financial variables in their model to increase predictive ability. Thirdly, in most earlier studies, variable institutional holdings were found to affect financial distress negatively. In contrast, the authors found this parameter to be positively significant to the financial distress of the company. Finally, there have hitherto been few studies that have used promoter holdings pledged (PHP) or pledge ratio. The authors found this variable to influence business failure positively.
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This study examines dynamics of global and regional financial market efficiency; and how specific features of the market and other conditions influence variability in such…
Abstract
Purpose
This study examines dynamics of global and regional financial market efficiency; and how specific features of the market and other conditions influence variability in such efficiency.
Design/methodology/approach
The study employs fixed effects statistical approach in its examination of how specific features of financial markets influence variability in its efficiency.
Findings
This study finds that individual IMF defined economic regions tend to exhibits significantly different financial market efficiency characteristics given specific market features and conditions. In regional level comparative analysis (e.g. Europe, Africa, Asia–Pacific etc.) this study finds that incidence of financial market uncertainty is the dominant condition with significant effect on financial market efficiency across all the IMF regions. In the global level analysis, empirical estimates presented suggest that financial market uncertainty, financial institutional depth and financial institutional efficiency tend to have significant positive influence on global financial market efficiency all things being equal. In the same analysis however, this study finds that financial market and financial institutional access growth has significant negative impact on financial market efficiency.
Originality/value
The uniqueness of this study compared to related ones found in the literature stems from its focus on financial market efficiency at the global, and IMF defined regional block level instead of on a specific economy as often found in the literature. Additionally, in contrast to other related studies, this study further examines the role of global financial market uncertainty in its financial market efficiency analysis. Financial market uncertainty variable may be unique to this study because the variable is derived through an econometric process from a base variable.
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To determine the relationship among covariates used in financial distress studies.
Abstract
Purpose
To determine the relationship among covariates used in financial distress studies.
Design/methodology/approach
The study selects four specific bankruptcy studies and employs canonical correlation analysis to determine the relationship among the different variable sets that these studies used as predictors of financial distress. Canonical correlation analysis identifies the relationship and provides an indication of the amount of redundancy that exists between two variable sets. The four studies are representative of the genre, similar as to choice of statistical technique, and frequently cited by researchers.
Findings
The research findings indicate that the relationships between the alternative variable sets are very weak and alternative variable sets do not represent similar financial relationships. Redundancy coefficients suggest that, if one variable set is redundant to another variable set, it is because the redundant variable set, is much smaller than the predictor variable set.
Research limitations/implications
The results suggest that there is not much similarity among the variable sets used in financial distress studies; to the extent that there is any similarity, it is due to variables common to each set or one variable set being larger than the other variable set. Ad hoc variable selection in financial distress studies results in the use of alternative variable sets containing heterogeneous variables unrelated to one another.
Originality/value
A common criticism of financial distress research is that a theory of corporate failure does not exist. Variable selection is not prompted by economic theory but is based upon suggestions in the literature, the success of variables in earlier studies, or the selection of a large set of variables with an accompanying data reduction procedure. Despite nearly 30 years of research in the area, the absence of an inter‐correlational structure among alternative variable sets highlights the atheoretical nature of financial distress research.
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A major lesson of the European Monetary Union crisis is that serious disequilibria in a monetary union result from arrangements not designed to be robust to a variety of shocks…
Abstract
Purpose
A major lesson of the European Monetary Union crisis is that serious disequilibria in a monetary union result from arrangements not designed to be robust to a variety of shocks. With the specter of this crisis looming substantially and scarring existing monetary zones, the purpose of this paper is to complement existing literature by analyzing the effects of monetary policy on economic activity (output and prices) in the CEMAC and UEMOA CFA franc zones.
Design/methodology/approach
VARs within the frameworks of Vector Error-Correction Models and Granger causality models are used to estimate the long- and short-run effects, respectively. Impulse response functions are further used to assess the tendencies of significant Granger causality findings. A battery of robustness checks are also employed to ensure consistency in the specifications and results.
Findings
–H1. monetary policy variables affect prices in the long-run but not in the short-run in the CFA zones (broadly untrue). This invalidity is more pronounced in CEMAC (relative to all monetary policy variables) than in UEMOA (with regard to financial dynamics of activity and size). H2. monetary policy variables influence output in the short-term but not in the long-run in the CFA zones. First, the absence of cointegration among real output and the monetary policy variables in both zones confirm the neutrality of money in the long term. With the exception of overall money supply, the significant effect of money on output in the short-run is more relevant in the UEMOA zone, than in the CEMAC zone in which only financial system efficiency and financial activity are significant.
Practical implications
First, compared to the CEMAC region, the UEMOA zone’s monetary authority has more policy instruments for offsetting output shocks but fewer instruments for the management of short-run inflation. Second, the CEMAC region is more inclined to non-traditional policy regimes while the UEMOA zone dances more to the tune of traditional discretionary monetary policy arrangements. A wide range of policy implications are discussed. Inter alia: implications for the long-run neutrality of money and business cycles; implications for credit expansions and inflationary tendencies; implications of the findings to the ongoing debate; country-specific implications and measures of fighting surplus liquidity.
Originality/value
The paper’s originality is reflected by the use of monetary policy variables, notably money supply, bank and financial credits, which have not been previously used, to investigate their impact on the outputs of economic activities, namely, real GDP output and inflation, in developing country monetary unions.
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