This paper aims to investigate how financial variables and exogenous crises influence firms’ financial performance, and how these factors may help managers in decision-making to increase their firm’s wealth. The dynamic interactions among variables were studied by applying a panel vector autoregressive model using annual data for a sample of non-financial firms from European countries. Results indicate that liquidity, leverage and productivity positively affect firm performance, while solvency and asset turnover are positive and statistically significant only in the case of return on equity. Labour productivity induces that firms tend to display larger efforts to keep financial performance in face of a crisis, considering that the crisis reveals a negative statistical impact over return on assets.

Assessment and estimation of bankruptcy risk is important for managers in decision making for improving a firm’s financial performance, but also important for investors that consider it prior to making investment decision in equity or bonds, creditors and company itself. The aim of this paper is to improve the knowledge of bankruptcy prediction of companies and to analyse the predictive capacity of factor analysis using as basis the discriminant analysis and the following five models for assessing bankruptcy risk: Altman, Conan and Holder, Tafler, Springate and Zmijewski. Stata software was used for studying the effect of performance over risk and bankruptcy scores were obtained by year of analysis and country. Data used for non-financial large companies from European Union were provided by Amadeus database for the period 2006–2015. In order to analyse the effects of risk score over firm performance, we have applied a dynamic panel-data estimation model, with Generalized Method of Moments (GMM) estimators to regress firm performance indicator over risk by year and we have used Tobit models to infer about the influence of company performance measures over general bankruptcy risk scores. The results show that the Principal Component Analysis (PCA) used to build a bankruptcy risk scored based on discriminant analysis indices is effective for determining the influence of corporate performance over risk.

Trade credit is one of the most flexible short-term funding sources for companies and covers a significant part of the financial resources used by firms. Trade credit received makes available financial resources to achieve other economic objectives of the company. The managerial decision to increase or reduce trade credit offered and received or the collection and credit period is influenced by the company's financial performance. The aim of this paper is to analyse the correlation between trade credit receivable/payable and collection/credit period and six measures of financial performance and find if these variables have any impact on the decision to use trade credit. To achieve this aim, we used a sample of 958 European firms from the construction sector which were analysed using correlation and OLS regression, separately on developed and emerging countries. The main results found are: trade credit offered/received is directly correlated with return on equity and firm size, and inversely correlated with return on assets; trade credit offered is directly correlated with current liquidity and long-term banking loans; trade credit received is directly correlated with liquidity ratio and inversely correlated with current liquidity and long-term banking loans; and trade credit offered is inversely correlated with liquidity ratio.

The banking sector plays an important role in the development of any economy. The performance of the loans in bank portfolios is a critical issue for the banking sector. The increased number of nonperforming loans (NPLs) after the financial crisis of 2008 has questioned the robustness of many banks and the stability of the entire sector. Our study aims to present the most important aspects related to NPLs and to investigate some macroeconomic determinant factors affecting the rate of NPLs in Romania. Based on a set of data for the period 2009–2019, the analysis of NPLs was made using linear regression. The results showed that all selected independent variables (exchange rates of the most used currencies (EUR, USD and CHF), unemployment rate, and inflation rate) have a significant impact on the dependent variable NPL. The study reveals strong correlations between NPLs and the macroeconomic factors studied and that the Romanian economy is clearly connected to the quality of the loan portfolios. Additionally, an econometric analysis of the empirical causes of NPLs shows that the RON–CHF exchange rate has been the main factor in increasing the NPL ratio in the last 5 years in Romania.

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