Companies operating in the energy sector are under pressure to boost the level of green energy production. The use of renewable energy sources will have a positive impact on the environment, but the basic question is whether power plants that produce electricity using renewable energy sources are in better financial condition than those that use only traditional energy sources. We address the latter using a new hybrid approach that extends prior research by combining three analyses: ratio analysis with a large set of indicators, the Altman model and cluster analysis. To test the statistical significance of differences between groups, Student’s t-test is applied. The sample concerns companies from the Baltic States and Central Europe in the years 2008–2017. The results indicate that in most cases there is no statistical difference in the financial standing of companies that use renewable energy sources and those that generate only fossil fuel-based energy.

The changes that have been triggered in market economies by COVID-19 have increased the importance of assessing the financial standing of companies and sectors. It is essential for managers, lenders, and investors to properly evaluate the financial condition of companies. Therefore, it is crucial to select indicators that show the differences in the values of market sectors before, and during, the COVID-19 pandemic (checking the stability of ratios over time). We used parametric and nonparametric analyses of variance (ANOVA) to single out indicators. The sample consists of listed companies in six sectors from the Visegrad group: manufacturing, construction, retail, wholesale trade, transportation and warehousing, and energy. We applied yearly and quarterly analyses in the periods from Q1 2017–Q1 2021. The analyses take into account 82 indicators. The results of the parametric ANOVA indicate that only the ratio of the company size shows the differences between the sectors in most of the periods of quarterly analysis. In comparison, the results of the nonparametric ANOVA demonstrate that five ratios show differences between the sectors in the quarterly analysis, and nine show differences in the yearly analysis. On the basis of the results, the construction and energy sectors are the least effective in managing their assets.

In an era of increasing energy production from renewable sources, the demand for components for renewable energy systems has dramatically increased. Consequently, managers and investors are interested in knowing whether a company associated with the semiconductor and related device manufacturing sector, especially the photovoltaic (PV) systems manufacturers, is a money-making business. We apply a new approach that extends prior research by applying decision trees (DTs) to identify ratios (i.e., indicators), which discriminate between companies within the sector that do (designated as “green”) and do not (“red”) produce elements of PV systems. Our results indicate that on the basis of selected ratios, green companies can be distinguished from the red companies without an in-depth analysis of the product portfolio. We also find that green companies, especially operating in China are characterized by lower financial performance, thus providing a negative (and unexpected) answer to the question posed in the title.

The post-Altman models suffer from moral amortization. This paper asks whether models developed in one country can be applied in other economies. One of the characteristics of the prediction model is that a date drives the estimation. Thus, the estimated model based on one economy is not necessarily applicable to other economies. To verify such a statement, we carried out a literature review to identify the manufacturing models constructed during the last 30 years that were reported in reputable scientific journals. Our literature comprised 75 papers, and with the application of the citation count and citation mining, we selected a sample and traced the selected papers to the cross-country application. Our results indicated an existing gap in the cross-economy validation of existing manufacturing models. Our study has implications for policy, as the application of the prediction models to cross-economies’ consolidated financial statements is biased.