A search for macroeconomic determinants of corporate financial distress

Sehgal, Sanjay; Mishra, R. K.; Jaisawal, Ajay

doi:10.1007/s41775-021-00119-4

Cited by 7 publications

(4 citation statements)

References 55 publications

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“…However, there are studies that highlight the importance of studying macroeconomic factors for corporate liquidity [18][19][20]. Moreover, the literature on corporate financial distress explicitly states that the addition of macroeconomic variables significantly improves the explained relationships relative to when only firm-specific variables are considered [52][53][54].…”

Section: Literature Review and Research Questionsmentioning

confidence: 99%

Impact of Macroeconomic Factors on Financial Liquidity of Companies: A Moderation Analysis

Nowicki,

Ratajczak,

Szutowski

2024

Sustainability

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The objective of this study was to examine the potential moderating effects of the relationship between macroeconomic variables and the financial liquidity of enterprises. Given the significance of liquidity for companies and the profound impact of the macroeconomic environment, a research gap was identified in relation to the limited number of studies investigating the influence of macroeconomic factors on corporate liquidity. Additionally, the limited scope of companies surveyed in this area, in terms of sector, size, capital market presence, and the limited range of macroeconomic variables examined were notable. Most importantly, the absence of studies examining moderators of the relationship between macroeconomic factors and liquidity was a significant concern. To this end, two main research questions were formulated. First, what factors moderate the relationship between macroeconomic variables and the financial liquidity of companies? Second, what is the nature of the moderating effects on the relationship between macroeconomic variables and corporate financial liquidity? This research employed panel data analysis on an unbalanced panel comprising 5327 Polish enterprises spanning from 2003 to 2021. The primary analytical technique utilised was linear regression (pooled OLS) with robust standard errors clustered at the firm level. The main results of this study indicate that: (1) debt level, profitability, and the fixed assets to total assets ratio are significant moderators of some of the relationships between macroeconomic variables and corporate liquidity; (2) debt level moderates the relationship between the ratio of internal expenditures on research and development to GDP and financial liquidity, as well as the relationship between inflation rate and liquidity; the relationship is statistically significant and positive only for those enterprises with above-median debt levels; (3) profitability moderates the relationship between the employment coefficient and financial liquidity, as well as the relationship between the inflation rate and liquidity; in the high-profitability group, those relationships are positive, whereas in the low-profitability group, they are negative; (4) the ratio of fixed assets to total assets moderates the relationship between the money supply and corporate financial liquidity; for enterprises with low asset flexibility, there is a negative relationship between the money supply and financial liquidity; conversely, for enterprises with high asset flexibility, there is a positive relationship between the money supply and financial liquidity; (5) the rationale behind these findings can be derived from capital structure theory and financial analysis theory. The results of this study represent a step towards a more comprehensive understanding of the relationship between the macro environment and corporate liquidity, as well as the factors that moderate this relationship from both a microeconomic and a macroeconomic perspective. The findings of this study may also inform policy decisions governing the corporate sector due to a more nuanced understanding of the relationships between macroeconomic factors and corporate liquidity.

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Section: Literature Review and Research Questionsmentioning

confidence: 99%

Impact of Macroeconomic Factors on Financial Liquidity of Companies: A Moderation Analysis

Nowicki,

Ratajczak,

Szutowski

2024

Sustainability

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“…Macroeconomic variables are quantitative indicators that represent various aspects of the economy, including price levels, financial stability, and economic growth (Foroni & Ravazzolo, 2019). Examples of macroeconomic variables include exchange rates, inflation rates, interest rates, stock market indices, and GDP growth rates, which are crucial for understanding and assessing the overall state and performance of an economy (Sehgal et al, 2021).…”

Section: Literature Review Macroeconomic Factors and Financial Perfor...mentioning

confidence: 99%

From Crisis to Stability: How Macroeconomics Shapes Islamic Banking and MSME Futures?

Nasution,

Fakhri

2024

SJEKI

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This study examines the impact of the COVID-19 pandemic on the nexus between macroeconomic conditions, Islamic banking performance, and MSME financing in Indonesia. Utilizing Partial Least Squares Structural Equation Modeling (PLS-SEM) and secondary data spanning January 2020 to June 2023, the research analyzes the direct effects of macroeconomic factors on both the financial performance of Islamic banks and the provision of financing to MSMEs. The findings indicate a statistically significant positive relationship between macroeconomic stability and both Islamic banking financial performance and MSME financing. Conversely, the study reveals no statistically significant relationship between the financial performance of Islamic banks and the scale of MSME financing during the period. These results underscore the critical role of macroeconomic stability as a primary determinant of financial performance for both Islamic banks and MSMEs, particularly during periods of economic crisis.========================================================================================================ABSTRAK – Dari Krisis ke Stabilitas: Bagaimana Makroekonomi Membentuk Masa Depan Perbankan Syariah dan UMKM? Penelitian ini mengkaji dampak pandemi COVID-19 terhadap keterkaitan antara kondisi makroekonomi, kinerja perbankan syariah, dan pembiayaan UMKM di Indonesia. Menggunakan metode Partial Least Squares Structural Equation Modeling (PLS-SEM) dan data sekunder yang mencakup periode Januari 2020 - Juni 2023, penelitian ini menganalisis pengaruh langsung faktor-faktor makroekonomi terhadap kinerja keuangan bank syariah dan penyediaan pembiayaan kepada UMKM. Hasil penelitian mengindikasikan adanya hubungan positif yang signifikan secara statistik antara stabilitas makroekonomi dengan kinerja keuangan perbankan syariah dan pembiayaan UMKM. Sebaliknya, studi ini tidak menemukan hubungan yang signifikan secara statistik antara kinerja keuangan perbankan syariah dengan skala pembiayaan UMKM periode tersebut. Temuan ini menekankan pentingnya stabilitas makroekonomi sebagai penentu utama kinerja keuangan, baik bagi bank syariah maupun UMKM, terutama dalam masa-masa krisis.

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“…e artificial neural network is an information processing system formed by theoretical abstraction and simplification by simulating the structure and function of the human brain neural network in physiology and its basic characteristics [15].…”

Section: Bp Artificial Neural Networkmentioning

confidence: 99%

Design and Application of a Financial Distress Early Warning Model Based on Data Reasoning and Pattern Recognition

2022

Advances in Multimedia

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Since the 1990s, emerging market financial crises have occurred frequently, causing huge damage to the real economy, and if we cannot find effective means of early warning and prevention of financial crises, the entire international economy and society will bear the high costs of crisis management. Difference nonparametric test and Spearman nonparametric correlation analysis were carried out with cash flow financial data, and 14 financial indicators with strong discriminant ability were selected from 28 financial indicators as the input variables of the model. Due to the limitations of traditional statistical methods, a BP neural network financial distress early warning model is established. Finally, a particle swarm optimization BP neural network financial early warning model is established for the shortcomings of the BP network. These 14 indicators can have strong information timeliness. The prediction accuracy rates of the two early warning models for the test samples are 80% and 85%, respectively. The empirical results show that the two models have good prediction effects. The prediction effect of the swarm optimization BP neural network model is better than that of the BP neural network model. Therefore, the particle swarm optimization BP neural network model proposed in this paper is suitable for solving the problem of discrimination and prediction of the financial distress of enterprises. The company’s financial distress early warning has good application prospects and application value. Therefore, it has very important research significance for the early warning of the corporate financial crisis.

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A search for macroeconomic determinants of corporate financial distress

Cited by 7 publications

References 55 publications

Impact of Macroeconomic Factors on Financial Liquidity of Companies: A Moderation Analysis

Impact of Macroeconomic Factors on Financial Liquidity of Companies: A Moderation Analysis

From Crisis to Stability: How Macroeconomics Shapes Islamic Banking and MSME Futures?

Design and Application of a Financial Distress Early Warning Model Based on Data Reasoning and Pattern Recognition

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