Predicting Firm-Level Bankruptcy in the Spanish Economy Using Extreme Gradient Boosting

Smith, Matthew; Álvarez, Francisco

doi:10.1007/s10614-020-10078-2

Cited by 29 publications

(20 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Nevertheless, H5 is accepted for all company sizes. The analysis suggests that XGBoost provides more accurate results than logistic regression, which is in line with Yousaf et al (2021), Yao et al (2022) and Smith and Alvarez (2022). Therefore, a feature explanation based on XGBoost models better describes the impact of GD on the likelihood of bankruptcy.…”

Section: Model Performancesupporting

confidence: 53%

“…In the application of ML, the dominance of boosting algorithms such as Categorical boosting (CatBoost), Light gradient boosting machine (LightGBM) or Extreme gradient boosting (XGBoost) is observed. XGBoost is one of the most frequently tested methods that dominate other methods, such as neural network, support vector machine, decision trees, random forest or logistic regression (Carmona et al, 2019;Son et al, 2019;Yousaf et al, 2021;Yao et al, 2022;Smith and Alvarez, 2022). However, a direct comparison of regression models with ML models using GD features has not been performed yet, and H5 was defined as follows:…”

Section: Literature Reviewmentioning

confidence: 99%

“…Other control features were related to company characteristics such as total assets, number of employees, sales growth, debt, tangible assets and the company age. Several authors agree (Kim et al , 2008; Gupta et al , 2018; Smith and Alvarez, 2022) that larger or longer-established companies have a lower chance of bankruptcy because they have enough resources or more accessible access to them. However, older and larger companies may face fewer bureaucratic issues, reflecting weaker performance (Martín-Ugedo and Antonio, 2014; Shehata et al , 2017).…”

Section: Data and Samplementioning

confidence: 99%

See 2 more Smart Citations

Gender diversity of board of directors and shareholders: Machine learning exploration during COVID-19

Papíková,

Papík

2023

View full text Add to dashboard Cite

Purpose European Parliament adopted a new directive on gender balance in corporate boards when by 2026, companies must employ 40% of the underrepresented sex into non-executive directors or 33% among all directors. Therefore, this study aims to analyze the impact of gender diversity (GD) on board of directors and the shareholders’ structure and their impact on the likelihood of company bankruptcy during the COVID-19 pandemic. Design/methodology/approach The data sample consists of 1,351 companies for 2019 and 2020, of which 173 were large, 351 medium-sized companies and 827 small companies. Three bankruptcy indicators were tested for each company size, and extreme gradient boosting (XGBoost) and logistic regression models were developed. These models were then cross-validated by a 10-fold approach. Findings XGBoost models achieved area under curve (AUC) over 98%, which is 25% higher than AUC achieved by logistic regression. Prediction models with GD features performed slightly better than those without them. Furthermore, this study indicates the existence of critical mass between 30% and 50%, which decreases the probability of bankruptcy for small and medium companies. Furthermore, the representation of women in ownership structures above 50% decreases bankruptcy likelihood. Originality/value This is a pioneering study to explore GD topics by application of ensembled machine learning methods. Moreover, the study does analyze not only the GD of boards but also shareholders. A highly innovative approach is GD analysis based on company size performed in one study considering the COVID-19 pandemic perspective.

show abstract

Section: Model Performancesupporting

confidence: 53%

Section: Literature Reviewmentioning

confidence: 99%

Section: Data and Samplementioning

confidence: 99%

See 1 more Smart Citation

Gender diversity of board of directors and shareholders: Machine learning exploration during COVID-19

Papíková,

Papík

2023

View full text Add to dashboard Cite

show abstract

“…Methods such as Neural Networks, Support Vector Machine and Ensemble Methods were used extensively. Some of the studies include Martín-del-Brío & Serrano-Cinca (1993), Min & Lee (2005, Zoričák et al (2020), Tsai et al (2014), Kim et al (2021 or M. Smith & Alvarez (2021).…”

Section: Literature Reviewmentioning

confidence: 99%

Balanced Bagging With Expectation Maximization Imputation in Bankruptcy Prediction – Application on Romanian Companies

CLEMENT¹

2022

REVECO

View full text Add to dashboard Cite

Bankruptcy prediction models are widely used by lending institutions, policy makers or investors. Despite the large volume of international research, limited studies have addressed the particularities of Romanian companies. Balanced Bagging is an Ensemble Method that uses a voting mechanism for a classification task. Expectation Maximization Imputation helps replacing the missing data. In this study we report a promising accuracy performance of 90.03% for the model of Balanced Bagging with Expectation Maximization Imputation on a dataset of more than 20,000 Romanian companies.

show abstract

“…The code, plots and interpretation can be applied to any binary classification problem. [14] applied Machine Learning to the classification of bankrupt versus non-bankrupt firms with interpretation on case study levels. [15] applied Shapley values to credit risk management using a number of financial ratios to show the contribution each variable made to the models prediction.…”

Section: Shapley Valuesmentioning

confidence: 99%

A machine learning research template for binary classification problems and shapley values integration

Smith

Álvarez

2021

Software Impacts

Self Cite

View full text Add to dashboard Cite

Predicting Firm-Level Bankruptcy in the Spanish Economy Using Extreme Gradient Boosting

Cited by 29 publications

References 32 publications

Gender diversity of board of directors and shareholders: Machine learning exploration during COVID-19

Gender diversity of board of directors and shareholders: Machine learning exploration during COVID-19

Balanced Bagging With Expectation Maximization Imputation in Bankruptcy Prediction – Application on Romanian Companies

A machine learning research template for binary classification problems and shapley values integration

Contact Info

Product

Resources

About