Financial ratios, corporate governance and bank-firm information: a Bayesian approach to predict SMEs’ default

Gallucci, Carmen; Santulli, Rosalia; Modina, Michele; Formisano, Vincenzo

doi:10.1007/s10997-021-09614-5

Cited by 10 publications

(3 citation statements)

References 70 publications

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“…Nevertheless, findings from empirical research on bank performance are more contradicting (Terjesen et al, 2015). Previous empirical studies investigating the influence of board gender diversity on the bank performance (Gallucci et al, 2022;Grove et al, 2011) suggest that more women are better able to lower operating costs (Chakrabarty and Bass, 2014) and improve financial performance (Strøm et al, 2014) especially when they reach a critical mass of at least three (Liu et al, 2017). However, the positive effect of gender lessens during the crisis periods (Pathan and Faff, 2013).…”

Section: The Board Monitoring In Banksmentioning

confidence: 99%

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Governing FinTech for performance: the monitoring role of female independent directors

Arena

Catuogno

Naciti

2023

EJIM

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PurposeThe use of digital technologies in the financial service industry has brought new complexities to the corporate governance in banks. Relying on the agency perspective of the shareholder, debtholder and societal governance in banks, this research examines the impact of financial technology innovation (FinTech) on banks' performance by enlightening the monitoring role of female independent directors.Design/methodology/approachRelying on a sample of Italian banks observed during the period 2016–2020, the authors hand-collected data on the use of FinTech by considering (1) the in-house provisions of FinTech solutions, (2) the collaboration with external FinTech firms and (3) a combination of both measures. The authors run a panel data regression analysis with fixed effects, measuring bank performance through bank competitiveness and bank riskiness.FindingsThe authors find that FinTech increases bank competitiveness in gathering money from depositors and that independent women on board mitigate the negative relationship between FinTech and the riskiness of banks' assets, ameliorating the conflicting interests among shareholders, debtholder and societal governance.Originality/valueThis study emphasizes the complexities of bank governance when dealing with FinTech in the wider perspective of equity governance, debt governance and the societal governance spotlighting the importance of appointing female directors in independent positions to enhance the bright sides of financial innovation. The authors enrich the literature on FinTech with a finer understanding of the drivers and implications of in-house provisions of FinTech solutions versus the collaboration with external FinTech firms.

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Section: The Board Monitoring In Banksmentioning

confidence: 99%

“…, 2015). Previous empirical studies investigating the influence of board gender diversity on the bank performance (Gallucci et al. , 2022; Grove et al.…”

Section: Theory and Hypothesismentioning

confidence: 99%

Governing FinTech for performance: the monitoring role of female independent directors

Arena

Catuogno

Naciti

2023

EJIM

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“…The increase in training time and computational resources SmoteBoost, RusBoost, etc. [51,57] The problem of class imbalance in financial distress research is solved in three ways: without applying class imbalance techniques [15,20,32,41,68,94,102].…”

Section: Hybrid IImentioning

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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Using Decision Trees to Predict Insolvency in Spanish SMEs: Is Early Warning Possible?

Navarro-Galera,

Lara-Rubio,

Novoa-Hernández

et al. 2024

Comput Econ

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Financial ratios, corporate governance and bank-firm information: a Bayesian approach to predict SMEs’ default

Cited by 10 publications

References 70 publications

Governing FinTech for performance: the monitoring role of female independent directors

Governing FinTech for performance: the monitoring role of female independent directors

Balancing Techniques for Advanced Financial Distress Detection Using Artificial Intelligence

Using Decision Trees to Predict Insolvency in Spanish SMEs: Is Early Warning Possible?

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