مقاله انگلیسی رایگان در مورد مجموعه داده های ورشکستگی شرکت های کوچک و متوسط – الزویر 2019

1- Data

2- Experimental design, materials, and methods

References

Abstract

Bankruptcy prediction is a long-standing issue that receives significant attention of academic researchers and industry practitioners. Most of the papers on bankruptcy prediction focus on companies that are listed on the stock market, and there are only limited data for the rest of the companies. These companies, not indexed at any stock market, represent a significant part of the economy. The presented dataset consists of financial ratios of Slovak companies. There are 21 distinctive financial ratios which are available for three consecutive years prior to evaluation year in which companies may have filed for bankruptcy or not. The companies come from four different industries – agriculture, construction, manufacture, retail. We provide data for four consecutive years 2013–2016 for each industry. All companies are categorized as small-medium enterprises according to EU classification. Prediction performance results on this dataset are published in the research paper “Bankruptcy prediction for small- and medium-sized companies using severely imbalanced datasets” (Zoričák et al., 2019).

Data

The dataset is accessible on Data Mendeley [2] and provides financial ratios of limited liability companies. There are three possible views on the data as depicted in Table 1: 1) Companies are divided into four different industries: agriculture, construction, manufacture, and retail, 2) Four different evaluation years are considered: 2013, 2014, 2015 and 2016 3) Two classes are defined (bankrupt (B) and non-bankrupt (NB)) for each evaluation year and industry Each company is characterized by 21 financial ratios listed in Table 2. These are provided for three consecutive years prior to the evaluation year. The distribution of missing values is visualized in a heatmap in the Fig. 1. Missing values for individual datasets are displayed one, two and three years prior to evaluation. The most missing values are for the financial variable Labor Productivity (LP). From the industry perspective, the most missing values are for retail with the year of evaluation 2016.