Discussion of “The Asymmetric Impact of COVID-19: A Novel Approach to Quantifying Financial Distress across Industries”

Wang, Xuan

doi:10.1016/j.euroecorev.2023.104501

Cited by 2 publications

(1 citation statement)

References 9 publications

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“…The liquidity of the company is a precondition for its financial stability (Sliwinski, 2023), while if the liquidity is impacted, it might indicate corporate insolvency (Karas & Reznakova, 2023), which is directly related to indebtedness (Abinizano et al, 2023). Corporate indebtedness is the use of funds borrowed by a firm to finance its activities and assets (Wang, 2023). According to Mazanec (2023), a high debt ratio indicates that the firm significantly depends on debt to pay its longterm needs.…”

Section: Theoretical Backgroundmentioning

confidence: 99%

Corporate Debt Dynamics: Sectoral Clustering Analysis Using NACE Classification in Slovakia

Gajdosikova,

Valaskova,

Lopatka

et al. 2024

JOBS Journal

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Research background: Many authors are currently exploring the impact of the industry on the financial structure of enterprises since there are statistically significant differences across various sectors, exposing the industry as a critical factor influencing corporate indebtedness. Clusters of sectors with homogeneous patterns of indebtedness and comparable levels of debt in various economic conditions may be determined, and, therefore, firms and their debt levels should be systematically examined and evaluated. Purpose of the article: The main aim of this paper is to evaluate the level of debt in the Slovak environment on a sample of 19,480 firms from various sectors and to identify the relationships among them to identify sectors with homogeneous patterns of indebtedness and, consequently, comprehend which sectors are the most stable and independent. Methods: Because NACE classification provides a framework for gathering and presenting statistical data based on many economic sectors, the number of input data was reduced based on cluster analysis. Using Ward's hierarchical clustering method using squared Euclidean distance, selected indebtedness ratios were used to define the sectors with comparable debt levels. To determine if there were statistically significant differences between the calculated debt ratios related to the economic sector, the Kruskal-Wallis test was performed. Subsequently, as the results indicated significant differences across various indebtedness ratios, a post hoc analysis was performed. Findings & Value added: A group of NACE activities in which enterprises are sufficiently comparable that it is appropriate to evaluate their debt levels using the chosen indebtedness ratios are considered to be in sectors C, F, G and H, which included to tertiary sector, while the sectors K, R and S may also be grouped in one cluster, while form the secondary sector. The key relevance of our findings is the benchmarking of selected sectors about indebtedness, which may be used to further examine their growth in each of the V4 nations, which is an essential area for the evolution of the European economy as a whole. Studies considering a relatively significant amount of capital structure determinants may be beneficial to owners and managers, regulators, and financial institutions since debt policy affects firm performance, value, and survival.

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Section: Theoretical Backgroundmentioning

confidence: 99%

Corporate Debt Dynamics: Sectoral Clustering Analysis Using NACE Classification in Slovakia

Gajdosikova,

Valaskova,

Lopatka

et al. 2024

JOBS Journal

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A Cautionary Note on the Use of Accounting Semi-Identity-Based Models

Sánchez-Vidal

2023

JRFM

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This study employs a Monte Carlo simulation to see whether accounting identity problems are present in the Fazzari, Hubbard, and Petersen model (1988). The Monte Carlo simulation generates 50,000 random cash flows, Tobin’s Q, and error term variables, which shape an investment variable that is dependent on them. Cash flows and investments are linked by a partial accounting identity, also known as an accounting semi-identity (ASI). An accounting identity is, for example, an equality between the left and right sides of a balance sheet. An ASI is not a complete one since one or more components of the accounting identity are missing. The estimated coefficients of an ASI do not represent reality, according to the OLS estimations. The regression tells us less about causality the closer the data are to the accounting identity. This is the first time that the biases of OLS estimations in an ASI-based model have been demonstrated.

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Discussion of “The Asymmetric Impact of COVID-19: A Novel Approach to Quantifying Financial Distress across Industries”

Cited by 2 publications

References 9 publications

Corporate Debt Dynamics: Sectoral Clustering Analysis Using NACE Classification in Slovakia

Corporate Debt Dynamics: Sectoral Clustering Analysis Using NACE Classification in Slovakia

A Cautionary Note on the Use of Accounting Semi-Identity-Based Models

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