Criminals, bankruptcy, and cost of debt

Regenburg, Kasper; Seitz, Morten Nicklas Bigler

doi:10.1007/s11142-021-09608-6

Cited by 11 publications

(12 citation statements)

References 76 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Moreover, Regenburg and Seitz (2021) use the quartiles based on the employees' salaries to identify their authority within the decision‐making process because they expect that the most‐paid employees will have more power (e.g., authority) to influence the decision‐making process. Consistently, we consider the percentage of women in each quartile of the firm's pay distribution and employ a dummy variable that captures the female participation in each pay quartile.…”

Section: Additional Analysesmentioning

confidence: 99%

Do women empower other women? Empirical evidence of the effect of female pervasiveness on firm risk‐taking

Ciappei

Terzani

Bafundi

et al. 2023

Accounting & Finance

View full text Add to dashboard Cite

This study examines the relationship between female pervasiveness within the entire company and firm‐risk taking. We exploit The UK Equality Act (2010), further enforced in 2017, which was made mandatory for firms operating in UK to disclose their gender pay gap. We use this measure to proxy female pervasiveness and we find it to be negatively associated with firm risk‐taking. These results are robust to several tests using female participation in each pay quartile and the difference in bonus payments between men and women. Our findings provide insights into the role played by women consistent with tokenism theory predictions.

show abstract

Section: Additional Analysesmentioning

confidence: 99%

Do women empower other women? Empirical evidence of the effect of female pervasiveness on firm risk‐taking

Ciappei

Terzani

Bafundi

et al. 2023

Accounting & Finance

View full text Add to dashboard Cite

show abstract

“…Timmermans (2010) provides an excellent description of the database targeted an English speaking audience. Several papers published in prestigious journals within accounting, finance, management and innovation rely on this dataset (e.g., Bennedsen et al., 2019; Dahl, 2011; Dahl et al., 2012; Jensen et al., 2022; Regenburg & Seitz, 2021).…”

Section: Data Sample Selection and Descriptive Statisticsmentioning

confidence: 99%

“…Panel C presents descriptive statistics for the variables used for the interest rate estimations. This sample size is smaller because we truncate the interest rate variable, as is standard in the literature (e.g., Gassen & Fülbier, 2015; Minnis, 2011; Regenburg & Seitz, 2021; vander Bauwhede et al., 2015). Section 3.2 elaborates on this.…”

Section: Data Sample Selection and Descriptive Statisticsmentioning

confidence: 99%

“…The IDAN database is used by a range of papers published in prestigious journals on employee-related issues within accounting, finance, management and innovation (e.g.,Dahl, 2011;Dahl et al, 2012;Bennedsen et al, 2019;Regenburg and Seitz, 2021;Jensen et al, 2022).3 Most research on employee flows and firm performance relies on surveys, which lack panel data, are subject to low response rates and cover small samples(Li et al, 2022).…”

mentioning

confidence: 99%

See 1 more Smart Citation

Accounting for employee flows

Christoffersen

Plenborg

Seitz

2022

Business Fin & Account

View full text Add to dashboard Cite

This paper examines employee flows and the association with firm earnings and interest rates. We use administrative employer–employee matched panel data from Denmark spanning 17 years and hence exploit actual data on employee arrivals (labor inflows) and departures (labor outflows). Three main findings emerge. First, we condition by firms’ economic conditions. Departures predict earnings increases for prior‐year loss firms, while they predict earnings decreases for prior‐year profit firms, suggesting that this conditioning can help explain the mixed results in the literature. Arrivals predict earnings increases, though only for prior‐year profit firms. These effects are stronger for high‐paid employees than for low‐paid ones. Second, the effects of departures are generally larger than the effects of arrivals, consistent with departures disrupting operations. Third, we find that lenders price employee flow information but only for departures of high‐paid employees, despite the predictive ability of the flow of other employees for future earnings. Overall our results suggest that employee flows predict firm financial performance but are only partially priced by lenders.

show abstract

“…The following Table 2 provides only direct indicators, not derived ones. For example, CEO age, not the log of CEO ages [75], or tenure, expertise, and education diversity, not its sum named as cognitive diversity [23], etc. In addition, these indicators can be used to create graphs, such as shareholder or manager connection graphs [76].…”

mentioning

confidence: 99%

Balancing Techniques for Advanced Financial Distress Detection Using Artificial Intelligence

Kuizinienė,

Krilavičius

2024

Electronics

View full text Add to dashboard Cite

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.

show abstract

Criminals, bankruptcy, and cost of debt

Cited by 11 publications

References 76 publications

Do women empower other women? Empirical evidence of the effect of female pervasiveness on firm risk‐taking

Do women empower other women? Empirical evidence of the effect of female pervasiveness on firm risk‐taking

Accounting for employee flows

Balancing Techniques for Advanced Financial Distress Detection Using Artificial Intelligence

Contact Info

Product

Resources

About