Corporate failure prediction in the European energy sector: A multicriteria approach and the effect of country characteristics

Doumpos, Michael; Andriosopoulos, Kostas; Galariotis, Emilios C.; Makridou, Georgia; Zopounidis, Constantin

doi:10.1016/j.ejor.2017.04.024

Cited by 44 publications

(30 citation statements)

References 49 publications

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“…We define six samples, both balanced and unbalanced samples and comprising large and small samples of companies and 25-fold cross-validation, which does not significantly improve the results. The literature reports on RES companies, with most of the existing literature related to power plants [21,22], electric utilities [24] or energy companies [30]. Our results are similar to other studies of RES companies.…”

Section: Discussionsupporting

confidence: 87%

“…To answer to this question, we apply a decision tree based analysis. We frame our analysis within the theoretical basis provided by literature on corporate failure and financial distress, which relies upon the classification of firms, as we do in this study [30,31]. The genesis of bankruptcy prediction models, is attributed to Beaver (1966) who uses 30 grouped ratios and information for failed and non-failed industrial firms to identify five ratios predictive of bankruptcy.…”

Section: Introductionmentioning

confidence: 99%

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Is Investing in Companies Manufacturing Solar Components a Lucrative Business? A Decision Tree Based Analysis

2020

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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.

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Section: Discussionsupporting

confidence: 87%

Section: Introductionmentioning

confidence: 99%

Is Investing in Companies Manufacturing Solar Components a Lucrative Business? A Decision Tree Based Analysis

2020

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show abstract

“…Nevertheless, businesses operating in the energy sector have recently been under serious financial threat as a result of the plunge in oil prices, which has led to the calling off or delay of planned infrastructure and exploration tasks, while shrinking the prospects of investments already under implementation. Consequently, the viability of 2 of 20 power plants is a timely topic, notably in a global context, bearing in mind the developments in the economic/business environment and the energy sector itself [2].…”

Section: Introductionmentioning

confidence: 99%

Comparison of the Financial Standing of Companies Generating Electricity from Renewable Sources and Fossil Fuels: A New Hybrid Approach

Tomczak

2019

Energies

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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.

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“…This trained model achieves much higher prediction ability compared to models created by other methods (decision trees, support vector machines, etc.). The study of Doumpos et al (2017) examines the development of corporate failure prediction models for European firms in the energy sector, using a large dataset from 18 countries. The results indicate that country-level data related to the economic and business environment, as well as data about the energy efficiency policies of the countries and the characteristics of their energy markets and networks, have high discriminating power and add valuable information compared to the traditional financial variables.…”

Section: Literature Reviewmentioning

confidence: 99%

Business failure prediction using cart-based model: A case of Slovak companies

Ďurica¹,

Podhorská²,

Ďurana³

2019

EMS

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Over the last decades, many financial distress prediction models have been created all over the world. Usually, these models are not very useful for Slovak companies because of their lower accuracy. The main aim of the paper is to create a business failure prediction model for the companies in Slovakia. To achieve it, we use the financial data of more than 100,000 real companies, covering the year 2016 and 2017, operating in the Slovak national economic conditions. Realizing the CART algorithm, the most significant predictors of the company prosperity are identified and the final model is formed. This model achieves 92% overall classification ability. Obtained results are important for companies themselves, but also for their business partners, to eliminate financial and other corporate risks related to the business failure situation of the companies.

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Corporate failure prediction in the European energy sector: A multicriteria approach and the effect of country characteristics

Cited by 44 publications

References 49 publications

Is Investing in Companies Manufacturing Solar Components a Lucrative Business? A Decision Tree Based Analysis

Is Investing in Companies Manufacturing Solar Components a Lucrative Business? A Decision Tree Based Analysis

Comparison of the Financial Standing of Companies Generating Electricity from Renewable Sources and Fossil Fuels: A New Hybrid Approach

Business failure prediction using cart-based model: A case of Slovak companies

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