Looking Beyond the Financial Numbers: The Relationship Between Macroeconomic Indicators and the Likelihood of Financial Distress

Hafeez, Ammar; Kar, Sujata

doi:10.1177/0972150918811716

Cited by 3 publications

(2 citation statements)

References 41 publications

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“…The managerial implications of our study deal with the necessity to consider systemic risk in the corporate governance of banks in order to give contributions to its decline. The increasing role of macroeconomic factors for bank riskiness is a problem that deals with both developed and emerging countries (Hafeez & Kar, 2018). In Europe, this issue cannot be disentangled from the Basel III framework.…”

Section: Managerial Implicationsmentioning

confidence: 99%

Eurozone Crisis and Banks’ Creditworthiness: What is New for Credit Default Swap Spread Determinants?

Ortolano

Angelini

2020

Global Business Review

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The article analyses banks’ credit default swap (CDS) spread determinants, in light of the Eurozone debt crisis. The attention to this aspect is due to the very linkage between banking and sovereign sectors particularly evident during the aforementioned crisis. The study is conducted on a sample of Eurozone banks over the period 2009–2014 through a feasible generalized least squares (FGLS) linear panel data regression. The variables adopted are both balance sheet ratios and macroeconomic factors. The main results confirm the attention pointed at the influence of public conditions to the banking sector, as proved by the significance of variables like the 10-year bond yields or the long-term sovereign rating. It is also interesting to observe the output dealing with public debt, which may suggest opportunities not only of investment but also of speculation for banks on the debt itself. Balance sheet ratios, instead, are not significant. Our study is an additional contribution to the strand of literature that analyses the strong interconnections between the riskiness of banks and the public sector and it is a suggestion to proceed the research with a deeper analysis on systemic risk and its impact on banking CDS spread.

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Section: Managerial Implicationsmentioning

confidence: 99%

Eurozone Crisis and Banks’ Creditworthiness: What is New for Credit Default Swap Spread Determinants?

Ortolano

Angelini

2020

Global Business Review

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“…EBIT/interest expense < 0.7 and Fixed assets decreases and Share capital decreases for current year or for 2 consecutive years - [31] 11.…”

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confidence: 99%

Balancing Techniques for Advanced Financial Distress Detection Using Artificial Intelligence

Kuizinienė,

Krilavičius

2024

Electronics

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Imbalanced datasets are one of the main issues encountered by artificial intelligence researchers, as machine learning (ML) algorithms can become biased toward the majority class and perform insufficiently on the minority classes. Financial distress (FD) is one of the numerous real-world applications of ML, struggling with this issue. Furthermore, the topic of financial distress holds considerable interest for both academics and practitioners due to the non-determined indicators of condition states. This research focuses on the involvement of balancing techniques according to different FD condition states. Moreover, this research was expanded by implementing ML models and dimensionality reduction techniques. During the course of this study, a Combined FD was constructed using five distinct conditions, ten distinct class balancing techniques, five distinct dimensionality reduction techniques, two features selection strategies, eleven machine learning models, and twelve weighted majority algorithms (WMAs). Results revealed that the highest area under the receiver operating characteristic (ROC) curve (AUC) score was achieved when using the extreme gradient boosting machine (XGBoost) feature selection technique, the experimental max number strategy, the undersampling methods, and the WMA 3.1 weighted majority algorithm (i.e., with categorical boosting (CatBoost), XGBoost, and random forest (RF) having equal voting weights). Moreover, this research has introduced a novel approach for setting the condition states of financial distress, including perspectives from debt and change in employment. These outcomes have been achieved utilizing authentic enterprise data from small and medium Lithuanian enterprises.

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A comparative study of feature selection and feature extraction methods for financial distress identification

Kuizinienė,

Savickas,

Kunickaitė

et al. 2024

PeerJ Computer Science

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Financial distress identification remains an essential topic in the scientific literature due to its importance for society and the economy. The advancements in information technology and the escalating volume of stored data have led to the emergence of financial distress that transcends the realm of financial statements and its’ indicators (ratios). The feature space could be expanded by incorporating new perspectives on feature data categories such as macroeconomics, sectors, social, board, management, judicial incident, etc. However, the increased dimensionality results in sparse data and overfitted models. This study proposes a new approach for efficient financial distress classification assessment by combining dimensionality reduction and machine learning techniques. The proposed framework aims to identify a subset of features leading to the minimization of the loss function describing the financial distress in an enterprise. During the study, 15 dimensionality reduction techniques with different numbers of features and 17 machine-learning models were compared. Overall, 1,432 experiments were performed using Lithuanian enterprise data covering the period from 2015 to 2022. Results revealed that the artificial neural network (ANN) model with 30 ranked features identified using the Random Forest mean decreasing Gini (RF_MDG) feature selection technique provided the highest AUC score. Moreover, this study has introduced a novel approach for feature extraction, which could improve financial distress classification models.

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Looking Beyond the Financial Numbers: The Relationship Between Macroeconomic Indicators and the Likelihood of Financial Distress

Cited by 3 publications

References 41 publications

Eurozone Crisis and Banks’ Creditworthiness: What is New for Credit Default Swap Spread Determinants?

Eurozone Crisis and Banks’ Creditworthiness: What is New for Credit Default Swap Spread Determinants?

Balancing Techniques for Advanced Financial Distress Detection Using Artificial Intelligence

A comparative study of feature selection and feature extraction methods for financial distress identification

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