Search results

This paper aims to verify whether the integration of sustainability in executive compensation positively affects firms’ non-financial performance and whether corporate governance characteristics enhance the relationship between sustainability compensation and firms’ non-financial performance and to expand the domain of the impact of sustainability on non-financial performance.

This analysis is based on a sample of companies listed on the Milan Italian Stock Exchange from the Financial Times Milan Stock Exchange Index over the 2016–2020 period. Regression analysis was used by using data retrieved from the Refinitiv Eikon database and the sample firms’ remuneration reports.

The findings of this paper show that embedding sustainability in executive compensation positively affects firms’ non-financial performance. The results of this paper also reveal that specific corporate governance features can improve the impact of sustainability on non-financial performance.

This analysis is limited to Italian firms included in the Financial Times Milan Stock Exchange Index; however, the findings are highly significant.

The findings provide regulators with useful insights for considering the integration of sustainability goals into executive remuneration. Another implication is that policymakers should require – at least – listed firms to fulfil specific corporate governance structural requirements. Finally, the findings can provide investors and financial analysts with a greater awareness of the role played by executive remuneration in the long-term value-creation process.

This paper contributes to addressing the relationship among sustainability, remuneration and non-financial disclosure, drawing on the stakeholder–agency theoretical framework and focusing on Italian firms. This issue has received limited attention with controversial results in the literature.

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.

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.

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.

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.

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.

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.

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% .

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.

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.

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.

This study aims to compare the impact of board characteristics on the performance of listed non-financial firms to the impact of board characteristics on the performance of listed financial firms (commercial banks) in Ghana.

The fixed and random effects models with generalized least square specifications are used in estimating regressions to correct for heteroscedasticity and serial correlation. Additionally, this study uses lagged models of the board variables to address the possibility of the presence of endogeneity and to generate robust estimates.

The empirical results show some similarities and differences on the impact of board characteristics on the performance of listed non-financial firms and banks. On similarities, for both non-financial firms and banks, board size is seen to have a significant non-linear impact on Tobin’s q. Also, the proportion of foreign board members shows a positively significant relationship with firm performance for both listed non-financial firms and banks. The effect of the proportion of board members with higher educational qualifications on firm performance appears to be negative and statistically significant for both sample of firms. On the other hand, the impact of board composition and board gender diversity on firm performance differs from listed banks and non-financial firms.

The panel regressions for the listed banks were run on 63 observations because of the small sample size for the listed banks. Though enough for estimation purposes, inferences from results should be made with caution.

This paper, unlike most corporate governance – firm performance studies, focuses not only on listed non-financial firms but also on listed banks. From a multi-theoretical perspective, this paper provides a comparative analysis on the impact of board characteristics on financial performance of listed non-financial firms and banks.

This study attempts to identify fundamental determinants of bond ratings for non-financial and financial firms. Further the study aims to develop a parsimonious bond rating model and compare its efficacy across statistical and range of machine learning methods in the Indian context. The study is motivated by the insufficiency of prior work in the Indian context.

The authors identify the critical determinants of non-financial and financial firms using multinomial logistic regression. Various machine learning and statistical methods are employed to identify the optimal bond rating prediction model. The data cover 8,346 bond issues from 2009 to 2019.

The authors find that industry concentration, sales, operating leverage, operating efficiency, profitability, solvency, strategic ownership, age, firm size and firm value play an important role in rating non-financial firms. Operating efficiency, profitability, strategic ownership and size are also relevant for financial firms besides additional determinants related to the capital adequacy, asset quality, management efficiency, earnings quality and liquidity (CAMEL) approach. The authors find that random forest outperforms logit and other machine learning methods with an accuracy rate of 92 and 91% for non-financial and financial firms.

The study identifies important determinants of bond ratings for both non-financial and financial firms. The study interalia finds that the random forest technique is the most appropriate method for bond ratings predictions in India.

Better bond ratings may mitigate corporate defaults.

Unlike prior literature, the study identifies determinants of bond ratings for both non-financial and financial firms. The study also experiments with modern machine learning techniques besides the traditional statistical approach for model building in case of relatively under researched market.

The purpose of the paper is to investigate the extent to which a model based on financial and non-financial variables predicts auditors' decisions to issue qualified audit reports in the case of companies listed on the Tehran Stock Exchange (TSE).

The authors utilized data from the financial statements of 96 Iranian firms as the sample over a period of five years (2012–2016). A total of 480 observations were analysed using a probit model through 11 primary financial ratios accompanying non-financial variables, including the type of audit firm, auditor turnover and corporate performance, which affect the issuance of audit reports.

The results demonstrated high explanatory power of financial ratios and type of audit firm (the national audit organization vs other local audit firms) in explaining qualifications through audit reports. The predictive accuracy of the estimated model is evaluated using a regression model for the probabilities of qualified and clean opinions. The model is reliable, with 72.9% accuracy in classifying the total sample correctly to explain changes in the auditor's opinion.

This study contains some limitations. First, it is likely that similar researches in developed countries set a large sample (e.g. over 1,000 firms) including more years, but the authors cannot follow such a trend due to data access restrictions. Second, banks and financial institutions, investment and holding firms are removed from the sample, because their financial structure is diverse. The third limitation of the study represents the different economic and cultural conditions of Iran compared to other countries. Future studies could focus on internal control material weaknesses or earnings management to predict audit opinion in emerging economies including Iran.

The paper has practical implications and can assist auditors in identifying factors motivating audit report qualifications, mainly in emerging economies.

The paper contributes to auditing research, since very little is known about the determinants of audit opinion in emerging markets including Iran; it also constitutes an addition to previous knowledge about audit opinion in the context of TSE. The paper is one of the rare studies predicting auditor opinions using both financial variables and non-financial metrics.

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.

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.

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.

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.

The purpose of this paper is to focus on the development of a business failure prediction model on a sample of small and medium‐sized firms with head offices located in the region of Castilla y León (Spain), in order to prove the significance of non‐financial information on the prediction of business failure.

In order to reach the authors' aim, one of the most used predictive statistical methods in this field (logistic regression) is applied, in which the authors consider financial ratios and non‐financial information as potential variables to predict failure. But before developing the respective models, in order to reduce the number of variables, a principal components analysis (PCA) is first applied. Then, the achieved results with this analysis are used in the prediction step, so as to estimate the models.

The results of the predictive method show that non‐financial information, which becomes significant in the developed models, helps financial ratios to improve the ability to predict failure, so any business failure model should also consider both types of information to be accurate.

Most of the developed business failure prediction models have used a paired sample with the same number of failed and non‐failed firms, which has the drawback of not being representative of the population from which it is chosen. In order to obtain a representative sample, a random sampling method is applied, on the basis of the population size and composition. The selected sample assures that parameter estimates are not inconsistent and biased, as the statistical methods assume.

This paper aims to investigate the effect of voluntary non-financial reporting on the evaluation of audit risk from the auditors’ viewpoint in a post-crisis period. Furthermore, this paper analyses whether auditors perceive that voluntary non-financial reporting impacts audit risk differently for old clients as compared with new clients.

This study is conducted on a sample of Italian audit firms through a paper-based questionnaire. Both Big4 and non-Big4 audit firms have been included in the sample.

Results show that integrated reporting is perceived to be the most relevant reporting method and intellectual capital statement the least relevant. Surprisingly, empirical findings over the sample period show that auditors do not perceive statistically significant differences between old and new clients.

Auditors can identify opportunities to adapt their assessment model to include voluntary non-financial report information. Moreover, they can use different assessment models regarding the research variables in the case of new and old clients.

Empirical findings highlight the growing role of voluntary non-financial reporting in the auditors’ perception of their client’s audit risk. All the observed voluntary non-financial reporting forms, except for intellectual capital, are considered as relevant by auditors in the evaluation of their client’s audit risk when compared to an indifference point. In addition, findings reveal that female auditors perceive a reduced gap in the relevance between integrated reports and intellectual capital reports compared to their counterparts.

This paper aims to explore the relationship of various financial and non‐financial factors to corporate value and how these factors can be used for the purpose of firm valuation. The focus is placed on a developing high‐tech industry.

The authors collect and compare data from companies within the time window of 1997 through 2010. The techniques of stepwise regression and back‐propagation neural network (BPNN) are applied to analyze this data, where the variables of operating profit margin, ROE, ROA, net income ratio, Tobin's Q and stock price are chosen to indicate firm value.

Each firm value variable appears to have a different set of estimator variables consisting of financial and non‐financial factors. The estimator variable in the set that has a high influence relative to the others tends to be financial factor. However, certain non‐financial factors appear to be considered as an estimator variable for different firm value variables more often than financial factors such as employee productivity, wealth created per employee, revenue growth rate, management expense per employee, R&D expense to management expense ratio, and R&D expenditure to total assets ratio. Further, the incorporation of BPNN shows an improvement of the result of the regression method in terms of overall estimation error, especially for operating profit margin.

The authors' investigation highlights the importance of the use of non‐financial factors for firm valuation in developing biotech industries. The result can be helpful for investors who seek to examine information variables and indicators for the opportunity presented by the above industries. In addition, the significant estimation improvement by incorporating the BNPP method into the commonly used regression method suggests the beneficial use of BPNN in refining the traditional methods in the field.