Determinants of financial distress in listed firms in vietnam

Figure 1: Vietnam GDP Growth % Evaluating and identifying determinants that impact financial distress will help businesses self-assess their situation to make appropriate adjustments, in

Motivation

Vietnam, recognized by the World Bank for its rapid economic growth, consistently achieves a GDP growth rate exceeding 5% during stable conditions and over 2% even amidst the challenges of the COVID pandemic This impressive growth has attracted substantial global investments, yet it also presents a dynamic market landscape, compelling companies to be prepared for potential financial challenges in any situation.

In 2023, Vietnam is witnessing a troubling rise in financial difficulties among businesses, with 88,000 companies exiting the market in the first five months, marking a 22.6% increase compared to the previous year (General Statistics Office, 2023) This alarming trend contrasts sharply with the mere 61,900 new businesses established during the same timeframe Research indicates that approximately 17,600 businesses are closing each month, alongside 55,200 temporarily suspending operations Contributing factors include market instability post-COVID-19 and challenges in revenue growth and accessing state subsidies.

For that reason, research that involve assessing and predicting financial distress is critical

Assessing the factors that contribute to financial distress enables businesses to self-evaluate and implement necessary changes, provides investors with a valuable metric for assessing business potential, and offers policymakers a framework to enhance market adjustments for economic growth.

Research on financial distress forecasting in Vietnam is increasingly urgent, as it remains a relatively new area of study A recent study by Manh & Nguyen (2024) evaluated the Z-Score model, but its findings were limited due to a small data sample that lacked variations reflecting recent macroeconomic influences Similarly, Hoang, Pham & To (2023) explored a related topic but focused solely on construction enterprises, indicating potential for further improvement in their research scope.

In 2021, a study was conducted to evaluate the risk of financial distress; however, the data used was limited to a relatively short timeframe and was outdated Consequently, it is evident that prior research on this topic exists.

Despite the existence of several studies on the topic in Vietnam, none have offered a comprehensive overview of the market Incorporating immediate factors like the financial crisis could enhance the evaluation model, leading to more accurate and meaningful results.

Given the existing factors and research gaps, there is an urgent need for a study to evaluate the current market in Vietnam This research should identify key factors that significantly influence the risk of financial distress By doing so, it will enable businesses, creditors, investors, and policymakers to make informed decisions aimed at minimizing risks and fostering growth in the Vietnamese economic market.

Objectives

The objectives of the study include the following criteria:

- Identify the determinants leading to financial distress for businesses in the Vietnamese market

- Evaluate and compare the level of influence of these determinants with each other and the interaction relationship of the variables on the dependent variable, which is financial distress

- Evaluate and explain if there are determinant variables that do not have a significant impact on the dependent variable in the Vietnamese market

Scope

The study will analyze financial statements from companies listed on the Vietnam Stock Exchange (HOSE) between 2013 and 2023, specifically excluding those in the Banking and Financial Institutions sector.

Methodology

This study will employ various research methods to address the proposed research questions, with a focus on Multiple Binary Logistic Regression This statistical technique will be utilized to assess the influence of key determinants on the likelihood of financial distress among enterprises in Vietnam.

Structure

In addition to the Introduction, table of contents, list of charts and tables, the study will be presented in the following order:

THEORETICAL BASIS ON THE DETERMINANTS OF

Financial distress

Financial distress is a multifaceted concept in corporate finance, characterized by a firm's inability to meet its current liabilities (Baldwin & Scott, 1983) and a lack of financial resources to fulfill debt obligations (Whitaker, 1999) It is also defined by Shumway (2001) as the failure to meet these obligations, while Wesa and Otinga (2018) identify cash shortages on the asset side or excessive liabilities as key indicators Purnanandam (2008) adds that low earnings can lead to an inability to meet financial commitments However, the most comprehensive definition comes from the European Central Bank (2005), which states that financial distress occurs when a firm's earnings before interest, taxes, depreciation, and amortization (EBITDA) cannot cover interest expenses, referred to as the firm's risk buffer.

Financial distress has been a subject of extensive research over the years, influenced by its complex nature and the diverse factors such as time period and location Before delving into a detailed review of previous studies in the upcoming sections, it is essential to reflect on key aspects of this topic.

7 main theories that set the base for those research and change the way people examine the financial distress

Since the early years of research leading up to 2000, the accounting-based method has been a foundational approach in financial analysis, appreciated for its simplicity and ease of data collection However, this method primarily relies on historical data, which may not accurately predict future outcomes Beaver (1966) was a pioneer in this field, introducing a model that utilized 30 financial ratios to assess financial distress Nevertheless, the most prominent model is Altman's (1968) approach, which employs multiple discriminant analysis (MDA) to provide a more refined evaluation of financial health.

C = Earnings before interest and tax / total assets

D = Market value of equity / Total Liabilities

Z-Score > 2.67 implies that the firm are in a good position, while under 1.81 is a bad signal that the firm can be in a financial distress

In 1980, Ohlson developed the O-Score, an accounting-based model that assesses the probability of a company's failure by utilizing a nine-factor coefficient The O-Score calculation incorporates various financial metrics to provide insights into a firm's financial health and risk of insolvency.

And J = 1 if total liabilities is greater than total assets

After get the O-Score, we will take the exp⁡(𝑂−𝑆𝑐𝑜𝑟𝑒)

1+exp⁡(𝑂−𝑆𝑐𝑜𝑟𝑒 and compare to 0.5, if it is higher than 0.5 then the firm have a high chance of default

Researchers after that still developed another method using financial report factors which is the Zmijewski’s model (1984) which is:

Zmijewski’s Model assess Zm value to see whether it is higher than lower than 0.5

If Zm > 0.5, the firm is classified as bankrupt

Despite the widespread use of accounting-based methods, they often rely on historical data, leading to inaccurate predictions of future performance To address this issue, researchers have developed market value-based models, with Metron's Distance-to-Default (DTD) model in 1974 being a pioneering example This model values a firm's equity as a call option on its assets, offering a more forward-looking approach to assessing financial health.

With 𝑑 2 is the Distance to default, A is the market value of assets, and 𝜇 is the expected annual risk-free rate of return and E is the equity value

In addition to Metron, the application of Black and Scholes' option pricing theory (1973) has emerged as a prominent method in financial analysis A notable example is the BSM-Prob formula developed by Hillegeist et al (2004), which has gained significant popularity in the field.

Where : P is the probability of bankruptcy, 𝑉 𝐴 is the current market value of assets,

X is the face value of debt maturing at time T, 𝜇 is compounded expected return, 𝛿 is the continuous dividend rate of 𝑉 𝐴

Hillegeist and colleagues proposed that as the BSM-Prob increases, the decline in asset equity value becomes more pronounced Consequently, companies experiencing higher BSM-Prob are likely to see a more significant reduction in their asset values.

10 a high BSM-Prob will have a high chance sustain a negative returns and cause a default

Despite the precision of sophisticated market-based approaches in predicting financial distress, researchers often rely on accounting-based methods due to the ease of collecting financial statements, which provide a snapshot of a firm's current situation In contrast, market-based data is more challenging to gather and requires complex technology for real-time analysis To address the limitations of both methods, this research will utilize financial indicators from financial statements as independent variables while incorporating market-based variables over time to more accurately reflect changes in the firm's and market's conditions Further details of the model will be discussed in the upcoming section.

Recent research has extensively examined the effects of financial distress, which can be categorized into three main components: its influence on financial reporting, auditing, and operational activities at the firm level; its effects on the capital market; and its implications for corporate governance.

The impact of the financial distress on the financial reporting quality is the first thing that is easily to spotted out Manipulation of accounting is an understandable

Firms facing financial distress often resort to manipulating business results to appease investors and customers while seeking real solutions Key indicators of such distress include reduced dividends and three consecutive years of negative income, prompting firms to alter income statements to renegotiate debts with lenders, unions, and the government (DeAngelo et al., 1994) Common manipulation tactics include income-increasing strategies and adjustments to cash flow, as managers aim to present a more favorable financial position (Watts and Zimmerman, 1986) Additionally, a history of unqualified audit opinions can motivate firms to inflate income figures, as these opinions suggest financial health to stakeholders (Rosner, 2003) Distressed firms may also fabricate revenue increases to avoid delisting threats (Chen et al., 2010) and shift expenses to reduce reported revenues, thereby altering cash flow perceptions (Nagar and Sen, 2017) For instance, distressed firms have been known to reclassify $2.5 million from operating expenses to income-decreasing special items to meet earnings targets Furthermore, financial manipulation can extend to cash flow statements, with firms adjusting operating cash flow figures to present a more favorable outlook (Lee, 2012).

Managers of distressed firms often manipulate earnings a year before bankruptcy to create a façade of financial stability, aiming to secure positive audit responses and raise funds to address looming debts This practice can lead to significant consequences at the firm level, including increased tax avoidance Research by Richardson et al (2015) indicates that distressed firms engage in more tax avoidance strategies compared to healthier firms, as they seek to reduce costs and meet their financial obligations.

The capital market is significantly affected by financial distress, with three key impacts: dividend adjustments, reorganization strategies, and market anomalies When a firm faces financial difficulties, managing debt obligations becomes critical, leading companies to modify their dividend payouts to address impending payments This phenomenon is supported by research from DeAngelo and DeAngelo (1990).

Eighty distressed firms listed on the NYSE have experienced rapid dividend reductions due to three consecutive years of losses, necessitating these cuts to meet debt obligations Research by Sundarsanam and Lai (2001) on 166 UK firms supports this finding, indicating that firms unable to recover from distress often eliminate dividends However, in recent years, financially distressed companies have increasingly opted for restructuring instead of dividend cuts One common form of restructuring is leveraged buyouts, as distressed firms can present significant value to investors, allowing them to be acquired at higher prices (Lasfer et al., 1996) Another approach is debt restructuring, where distressed firms negotiate with debt holders to secure additional funding.

Financial distress significantly influences capital markets, as evidenced by the shift in ownership dynamics in German distressed firms, where private investors' shares diminished while bank and external investors' stakes increased (Jostarndt and Sautner, 2008) Distressed firms often face challenges in securing external capital, leading them to rely on internal cash flow and potentially manipulate earnings to obscure negative news, which can temporarily inflate stock prices and mislead the market (Jin and Myers, 2006) Additionally, research indicates that financial distress contributes to market anomalies; for example, Su (2016) found that distressed firms with lower returns sometimes exhibited higher capital expenditures (CAPEX), suggesting that increased CAPEX may signal a lower risk of default and justify a higher expected return.

Corporate governance is significantly affected by financial distress, as the board of directors plays a crucial role in firm oversight Research by Chou et al (2010) indicates a strong negative correlation between directors' meeting attendance and financial distress Additionally, the labor market sees a notable rise in managerial turnover during such distress, with Gilson (1989) reporting that 52% of senior management in distressed firms in the U.S experienced turnover This finding is further supported by DeAngelo et al (1994), who calculated that 46% of senior management layoffs occur in distressed firms These trends suggest that firms often require new management teams to navigate challenging financial situations.

Determinants of financial distress

This section explores the key determinants of financial distress, which can be categorized into three main groups: firm-level fundamentals, macroeconomic factors, and corporate governance elements These categories are interconnected and provide a comprehensive understanding of the various influences on financial distress.

Financial distress in firms is often indicated by various signals, including the manipulation of book values to conceal negative news This practice creates a significant discrepancy between book income and taxable income, known as book-tax differences (BTD) Research by Lev and Nissim (2004), as well as subsequent studies by Noga and Schnader, supports this theory.

Research from 2013 indicates that companies exhibiting a significant book-tax difference are more likely to experience poor financial health and low future income, which increases their risk of financial distress.

Zhang (2015) discovered that firms with substantial R&D investments may face a higher risk of financial distress due to accumulating costs and resulting uncertainties To effectively identify companies in financial distress, Zhang utilized the Z-Score and delisting status as proxies, while also incorporating state R&D tax credit rates to mitigate bias in the analysis.

Investing in risky research and development (R&D) for distressed firms can lead to more reliable outcomes This relationship becomes even more pronounced during market downturns, as declining revenues make it increasingly difficult for companies to manage their costs effectively.

Employee relations significantly impact financial distress, as demonstrated by Kane et al (2005), who found that firms with strong employee relations typically experience lower distress risk In this context, employee relations refer to the economic goodwill that fosters future benefits for the organization When employees feel a sense of alignment with the company's goals, they perceive the firm's success as intertwined with their own Strong employee relations also enhance budget management flexibility, particularly during economic downturns, as both parties can negotiate to avoid overspending and subsequent financial distress Additionally, investing in employee relations through training and skill development not only boosts revenue but also mitigates the likelihood of financial difficulties.

Corporate Social Responsibility (CSR) has emerged as a crucial factor influencing financial stability, with research indicating that companies demonstrating strong CSR performance are less likely to experience financial distress (Al-hadi et al., 2017) Supporting this notion, Hasan and Habib (2017) discovered that larger and more mature companies possess greater resources to invest in CSR initiatives, thereby mitigating financial distress risks Conversely, a study by Chang et al (2013) revealed that companies in Taiwan facing financial difficulties tend to scale back their CSR activities Overall, CSR represents a strategic approach that has become increasingly significant in the corporate landscape.

Mature firms, typically in operation for 16 years or more, often face significant costs associated with corporate social responsibility (CSR) initiatives These established companies are more likely to attract experienced leaders as CEOs, fostering effective management that recognizes the value of CSR Consequently, there is ongoing debate regarding whether CSR directly influences the success of mature firms or if their strong management practices, which prioritize CSR and mitigate financial distress, are the primary factors behind their performance.

According to Ting et al (2008), a qualified audit opinion significantly increases the likelihood of a firm facing future financial distress, as it serves as an early warning sign of poor performance Investors and shareholders prioritize audit opinions when evaluating a firm's performance over time Additionally, a low audit opinion indicates not only poor income performance but also underlying issues within the firm that may lead to inaccuracies in financial statements, thereby heightening the risk of imminent financial distress.

Financial statements and ratios play a crucial role in determining a firm's risk of financial distress, as they provide a comprehensive overview of its current status Korobow et al (1976) found that 87% of US banks with low ratings were accurately identified as distressed based on six key ratios Additionally, Chiaramonte and Casu (2017) demonstrated that a decline in liquidity significantly heightens the likelihood of financial distress Furthermore, Desai et al (2016) indicated that financial statements can partially reveal a firm's financial health.

Research indicates that a firm’s financial performance, as reflected in its financial statements, is a key determinant of financial distress This performance provides insights into the health of the firm's income and expenses over a recent period, highlighting potential risks associated with financial instability.

Macroeconomic factors significantly contribute to financial distress, particularly during financial crises or economic recessions Companies often experience declines in revenue, cash flow, and profitability during such downturns, making survival increasingly challenging Additionally, economic conditions can affect inflation rates, interest rates, and national policies, all of which can elevate the risk of financial distress.

Macroeconomic factors have been shown to enhance the accuracy of firm distress prediction (Tinoco and Wilson, 2013) However, existing research often assumes that firms will act rationally in line with economic conditions, such as reducing activity during recessions and increasing investment in better scenarios This assumption is flawed, as even healthy firms can make poor decisions, such as investing heavily during a recession, leading to financial distress Conversely, distressed firms may continue to invest in unfavorable conditions, risking bankruptcy Therefore, it is crucial to consider these behaviors when evaluating macroeconomic determinants.

Corporate governance is believed to better explain the financial distress than macroeconomics factors (Johnson et al, 2000), and thus is worth considering when

The determinants of financial distress are influenced significantly by the role of boards of directors Research by Fich and Slezak (2008) indicates a positive correlation between board size and company failure, while Elloumi and Gueyie (2001) found no such relationship, suggesting that the impact of board size may vary based on individual firm operations A larger board may attract bigger investors, potentially increasing survival chances during financial distress, but can also complicate management and control Therefore, it is essential to analyze board size in conjunction with other factors, such as board capital, to accurately assess its effect on a firm's likelihood of financial distress.

The independence of a director board plays a crucial role in mitigating financial distress risks, as independent boards are more likely to make unbiased decisions (Wang and Deng, 2006) Additionally, Hsu and Wu (2014) highlight that boards with grey directors, who possess specialized knowledge, also experience lower financial distress risk This finding is supported by Darrat et al (2016), which indicates that firms with insightful directors are less prone to financial difficulties The underlying reason for this trend is that distressed firms often struggle to attract top talent due to budget constraints and reputational issues, resulting in poor decision-making and an increased likelihood of bankruptcy.

Literature Review and Research gap

• Research in other countries for emerging markets

Saif H Al Zaabi (2011) conducted a study on business failure and financial performance measurement of large Islamic banks in the UAE, employing the Z-score model tailored for emerging markets Key determinants analyzed included working capital to total assets (WCTA), retained earnings to total assets (RETA), earnings before interest and taxes per total assets (EBITTA), and market value of equity to book value of liabilities (MVETL) Utilizing MDA methods, the research compared financial ratios of non-failed banks against failed ones, demonstrating the effectiveness of these ratios in assessing financial distress risk The findings indicated that the Altman model is applicable to Islamic banks, with its factors capable of predicting financial distress up to five years in advance.

In their 2018 study, Januri et al investigated the prediction of financial distress in cement companies listed on the Indonesia stock market, utilizing the Z-Score, Springate, and Zmijewski models The research analyzed data from three companies over a five-year period, from 2011 to 2015 Notably, the Altman Z-Score model applied in this study aligns with the Z-Score model tailored for emerging markets, as referenced in Saif's work.

H Al Zaabi (2011) research The Springate model also included four factors: working capital to total assets, earning before interest and taxes to total assets, earning before tax to total assets, and revenue to total assets On the other hand, Zmijewski model will include of three factors: assets turnover ratio (ROA), financial leverage, and short-term solvency ratio (current asset to current liabilities

The research findings indicate that only two factors negatively influence financial distress: Return on Assets (ROA) and the Current Assets to Current Liabilities (CACL) ratio as per the Zmijewski model In contrast, other factors and models do not demonstrate a similar impact.

The Z-Score model is widely recognized for its accuracy in assessing financial distress in firms, as demonstrated by recent research highlighting its positive impact.

Nanayakkara and Azeez (2014) introduced a model to forecast financial distress in Sri Lankan companies by utilizing the Z-Score model alongside MDA analysis Their research analyzed 134 firms, encompassing both distressed and non-distressed entities, over a specified period.

Between 2002 and 2011, a revised Z-Score model demonstrated an impressive accuracy rate of 76.9% in predicting financial distress one year in advance Further analysis revealed that the model maintained a promising accuracy of 74.6% and 67.2% for predicting distress in the second and third years, respectively Consequently, this model serves as a valuable tool for investors, creditors, managers, auditors, and government policymakers to inform their decisions and strategies.

In a study conducted by Lord et al (2020), the Z-Score model was utilized to assess financial distress in nursing homes, analyzing data from 167,268 facilities between 2000 and 2015 The findings revealed that out of the four variables in the Z-Score model, only three significantly influenced the likelihood of financial distress, while the variable of working capital to total assets was found to have no effect This lack of impact is attributed to the unique characteristics of nursing homes, where short-term net assets constitute a very small portion of total assets, thereby diminishing their influence on financial distress.

AlAli (2018) did a research to determine the financial distress of the healthcare companies listed on the Kuwait Stock Exchange (KSE) from 2013 to 2016 The

The Z-Score model remains the primary research framework, incorporating four key factors that significantly influence a firm's likelihood of financial distress in the stock market Recent findings indicate that all four factors positively affect the potential for financial distress, underscoring their importance in assessing a company's financial health.

In 2017, Babatunde, Akeju, and Malomo conducted a study on financial distress among 10 firms listed on the Nigerian Stock Exchange (NSE) in 2015 Their research employed a five-factor model, which included working capital to total assets, retained earnings to total assets, EBIT to total assets, market value of equity to liabilities, and revenue to total assets The findings indicated that the Z-Score five-factor model was highly effective in identifying non-performing firms within Nigeria's manufacturing sector.

Manaseer and Al-Oshaibat (2018) employed the Z-score factors model to assess financial risk among non-manufacturing firms listed on the Amman Stock Exchange (ASE) in Jordan from 2011 to 2016, specifically analyzing 21 insurance companies Their findings indicated that all four Z-score factors significantly influence the financial distress of these firms, demonstrating positive correlations among them The study concluded that the model is a valuable tool for Jordanian policymakers to implement effective financial strategies.

Hayes et al (2010) conducted a study on the Altman’s Z-score model, assessing its effectiveness in predicting bankruptcy among US retailers The findings revealed a strong positive correlation, with the Z-score model accurately predicting 94% of companies that declared bankruptcy and 90% of firms facing financial distress.

Alkhatib and Al Bzour (2011) conducted a study to evaluate how financial ratios influence bankruptcy predictions for publicly listed companies in Jordan The research employed two analytical frameworks: the Alman model and the Kida model.

Bzour analyzed data from 16 industrial and non-financial service companies over a 15-year period, from 1990 to 2006 The findings revealed that Altman's Z-Score model remains highly effective in predicting bankruptcy, achieving a remarkable 93.8% accuracy rate.

5 year prior period This number for Kida model is only 69% which is relatively lower than the Altman model

Manh & Nguyen (2024) utilized the Z-Score model to assess financial distress among listed firms in the Vietnam Stock Market, analyzing data from 30 delisted companies over a five-year period from 2018 to 2022 They selected Altman's Z’’ model, which incorporates four key determinants: Working Capital/Total Assets, Retained Earnings/Total Assets, Earnings Before Interest and Taxes/Total Assets, and Market Value of Equity/Book Value of Liabilities, making it suitable for emerging markets like Vietnam The study confirmed the Z-Score model's effectiveness in distinguishing between distressed and healthy firms based on these variables However, the limited sample size and short timeframe raise concerns about potential model bias and prediction inaccuracies Additionally, while the COVID-19 pandemic motivated the research, its economic impact was not integrated into the model, suggesting a need for further exploration in future studies.

Hoang, Pham & To (2023) had also used the Z-Score model to assessed the financial situation of militaty construction enterprise in Vietnam The research consisted of

DATA AND METHODOLOGY

Methodology

This research will detail the variables used, referencing them in Table 1 Financial distress is defined in Chapter 1, and the study will utilize the total capital risk buffer as a proxy for financial distress (European Central Bank, 2005) A higher risk buffer indicates that a firm is better equipped to manage its financial obligations, thereby reducing the risk of financial distress Conversely, a negative risk buffer signifies difficulties in covering financial costs Therefore, firms with a risk buffer of zero or greater will be classified as non-financially distressed, while those with a risk buffer below zero will be identified as financially distressed.

To address the research gap identified in previous studies, this analysis incorporates a diverse array of economic characteristics as independent variables Key variables include a macroeconomic dummy variable indicating financial crises, alongside firm-specific factors such as performance, capital structure, and size Detailed information is presented in the table below.

Table 1: Definitions and References of model variables

It is a dummy variable for risk buffer of total capital: EBITDA – interest expenses (value of zero or

27 positive will be 0, value of negative risk buffer will be 1)

LQ – Liquidity Working Capital / Total Assets Altman (1968)

Net income / Total Sales Altman (1968)

SV – Solvency EBIT / Total Assets Altman (1968)

Total Equity / Total Liabilities Altman (1968)

SZ – Firm Size Natural lograrithm of turnover Rianti and

Dummy Variable reflect the COVID-19 impact year ( For year

Socio-economic variables are typically categorical rather than numerical, which complicates the use of basic multiple linear regression due to unmet assumptions (Trammer & Elliot, 2008) In such cases, Multiple Binary Logistic Regression is a more suitable analytical approach.

Binary logistic regression is a statistical method used to analyze data with categorical variables, particularly when the dependent variable has two or very few possible outcomes, such as employed versus unemployed or yes versus no.

This regression will quite different from the normal distribution, since it violated some of the assumptions:

- Linearity: The function of logistic will follow a S-curve rather than a linear prediction

- Normality of Residual: Linear regression assumes that the residuals (error) will be normally distributed However, since that value of dependent variable is only 0 or

1, the distribution of residual will follow a Bernoulli distribution rather than a normal distribution

Homoscedasticity refers to the assumption in linear regression that the variance of the residuals remains constant across all levels of the independent variable However, this assumption may not hold true due to the inherent characteristics of the data, leading to a phenomenon known as heteroscedasticity.

Linear regression typically predicts values along a continuous line, which can result in outputs that exceed the bounds of 0 and 1 This limitation creates a mismatch when applying the linear model to scenarios where the dependent variable is restricted to only two values.

For that reason, instead of using normal Y as dependent, we must transform the value to a logit (or logistic) form as follows:

1−𝑌 is called the odd term, this transformation will make sure the value of the model will always in 0 or 1 and suitable for the dataset

Overall, the model of a multiple binary logistic regression can be written as follows:

𝑋 𝑖 = the independent variable number i e = residual error

To evaluate the appropriateness of multiple binary logistic regression, we will conduct tests that differ significantly from those used in linear regression, owing to the distinct assumptions underlying each method.

To evaluate model fit in binary logistic regression, the Chi-Square Test is employed instead of the F-Test This test compares the -2 Log-Likelihood (-2LL) values of the baseline model (block 0) and the proposed model A lower -2LL value in the recommended model indicates a better fit, confirming its suitability for use.

In binary regression analysis, traditional R-Square values are not applicable due to the non-linear nature of the regression Instead, the Pseudo R Square is utilized, with the Cox & Snell method (1989) being one of the two suitable options for this type of regression.

R Square and the Nagelkerke (1991) The higher the R Square values, the better the model in explaining the dependent variable

The regression coefficient will be evaluated using the Wald Test, which accepts the coefficient if the significance value is below 5% Additionally, the binary logistic regression coefficient can only indicate a positive or negative effect on the dependent variable.

This research employs multiple binary logistic regression analysis to examine financial distress, treating it as a binary dependent variable Unlike ordinary least squares (OLS) models, this approach imposes fewer assumptions regarding normal distribution of observations, homogeneity of variance, and linearity By coding the dependent variable as 0 and 1, the model effectively minimizes bias and reduces the impact of outliers on the final outcomes.

𝑙𝑜𝑔𝑖𝑡(𝐹𝐷 𝑖 ) = FD is Financial distress, which is the risk buffer of total capital (EBITDA – interest expenses), with financial distress firm = 1, and non financial distress firm = 0 Where 𝑙𝑜𝑔𝑖𝑡(𝐹𝐷) = ln ( 𝐹𝐷

1−𝐹𝐷) is the inverse of the standard logistic function

𝐿𝑄 = The liquidity performance of the firm, that is, Working capital / Total assets

𝑃𝐹 = The profitability status of the firm, that is, Net income / Total sales

𝑆𝑉 = The solvency status of the firm, that is, EBIT / Total assets

𝐶𝑆 = The capital structure of the firm, that is, Total equity / Total liabilities

𝑆𝑍 = The size estimation of the firm, that is, the natural logarithm of sales turnover

FC = The Financial crisis, which is a dummy variable, for the report year in 2020 to 2022, the value = 1, while other years will be 0

Research Hypothesis

With the goal to identify the determinants of financial distress in Vietnam, the research will test and answer these following hypothesis:

H1: Firm financial distress in Vietnam is expected to to increase as firm liquidity performance decreases

Liquidity ratios are essential indicators of a firm's ability to meet short-term obligations and have been identified as significant factors in corporate financial distress Early research by Altman (1968) highlighted the negative relationship between liquidity and financial distress Supporting this, Manh & Nguyen (2024) found that improved liquidity in Vietnamese firms correlates with a reduced risk of financial distress Conversely, studies by Gathecha (2016) and Kristani et al (2016) suggested a positive correlation between liquidity and financial distress, indicating a more complex relationship.

H2: Firm financial distress in Vietnam is expected to to increase as firm profitability performance decreases

Profitability ratios assess a company's effectiveness in generating profits relative to its sales and capital assets, while also evaluating its capacity to generate revenue exceeding expenses Research by Chang-e (2006) indicates that financially distressed firms tend to exhibit lower profitability levels Additionally, Campbell et al (2005) conducted studies that further explore these financial dynamics.

32 that lower profitability will lead to a higher chance that the firm will fall into bankruptcy Thus, implies that there is a negative relationship between profitability and financial distress

H3: Firm financial distress in Vietnam is expected to to increase as firm solvency performance decreases

Firm solvency ratios measure a company's ability to meet its financial obligations A low solvency ratio indicates a higher risk of financial distress, as the firm struggles to manage its debt burden This relationship has been validated by Altman's research.

(1968), and then later was proved again by many other research including: Hayes et al (2010), Alkhatib and Al Bzour (2011), and the recent research of Manh & Nguyen (2024) in Vietnam

H4: Firm financial distress in Vietnam is expected to to increase as firm capital structure decreases

A firm's capital structure reflects the proportion of equity relative to its liabilities, which is crucial for financial health Companies facing financial distress typically grapple with substantial debt obligations and elevated interest expenses Research by Gathecha (2016) and Chancharat (2008) confirms that a decline in capital structure correlates with an increased likelihood of financial distress.

H5: Firm financial distress in Vietnam is expected to to increase as firm size decreases

Many researches have shown the evidence that firm size is one of the main determinants of corporate financial distress with an inverse relationship Honjo

(2000) shown that firm with small size have the likelihood to fail than big firms due

Limited market experience, restricted connections, and insufficient financial resources contribute to a higher risk of bankruptcy for firms This finding aligns with the research conducted by Freixas et al (2000), which indicates that smaller firm size negatively affects the likelihood of a company facing bankruptcy.

H6: Firm financial distress in Vietnam is expected to to increase during financial crisis

During a financial crisis, firms encounter significant challenges, including reduced sales and increased costs due to the necessity of taking on additional loans to meet financial obligations Research by Tinoco and Wilson (2013) indicates that financial crises elevate environmental uncertainty and decrease demand, which in turn heightens the risk of financial distress These findings are further supported by subsequent studies conducted by Quintilani (2017) and Kim and Upneja (2014).

EMPIRICAL ANALYSIS

Data

This study analyzes financial data from Vietnamese firms listed on the Ho Chi Minh Stock Exchange (HOSE) from 2013 to 2023, utilizing information sourced from the HOSE public database To ensure accuracy, records from the Banking and Financial Institutions sectors were excluded due to differing financial reporting practices, as well as any entries lacking essential variables such as Total Sales, EBITDA, Interest Expenses, Net Income, Total Assets, Total Liabilities, and Total Equity The research emphasizes the use of financial ratios to mitigate size-related biases, resulting in the removal of records that produced erroneous ratios Ultimately, the final dataset comprises 296 companies and a total of 3,129 observations.

Descriptive Analysis

The results of the descriptive analysis are summarized in Table 2 Overall, over

The analysis of 3,129 records reveals a mean financial distress score of 0.93, indicating that most firms are non-distressed Profitability and capital structure exhibit the highest standard deviations, reflecting significant variability In Vietnam's market, profitability ratios range dramatically from -49.95 to 141.97, highlighting stark differences in performance among listed companies, with some achieving substantial profits while others struggle to break even.

Variables N Min Max Mean Std Deviation

The capital structure (CS) exhibits a maximum value of 1464, yet quartile analysis reveals that 75% of CS values fall below 2.3, suggesting a significant disparity in equity and liabilities among a few companies, while the majority show minimal variation In contrast, the liquidity (LQ) and solvency (SV) variables, expressed as ratios, yield relatively low values Notably, the solvency variable has a maximum value of 0.83, but the mean is only 0.08, indicating that most companies struggle to achieve high solvency levels.

Percentiles FS LQ PF SV CS SZ FC

Pearson Correlation Analysis

Before analyzing the regression results, Pearson analysis was conducted to explore the bivariate relationships between variables, providing insight into their correlations Although binary logistic regression does not fully capture linear relationships, it remains essential for understanding variable connections The Financial Distress (FD) variable exhibits a strong positive correlation with Liquidity Performance (LQ), Solvency Performance (SV), and Firm Size (SZ) Despite the significance values exceeding 0.5, indicating a lack of strong linear connections, the binary logistic regression approach minimizes the impact of this finding, warranting further testing Additionally, Pearson’s correlation revealed moderate relationships among independent variables, with none exceeding a correlation coefficient of 0.7, suggesting that no two variables share an excessively strong connection.

FD LQ PF SV CS SZ FC

** Correlation is significant at the 0.01 level (2-tailed)

* Correlation is significant at the 0.05 level (2-tailed).

Regression Result

To test the research hypotheses, a multiple binary logistic regression analysis was conducted The results, presented in Table 5, indicate that at a 95% confidence level, three key determinants—Solvency (SV), Capital Structure (CS), and Financial Crisis (FC)—are significant factors in explaining financial crises.

The study on financial distress in Vietnam found that solvency, capital structure, and financial crisis positively influence the situation, supporting hypotheses H3, H4, and H6 However, liquidity, profitability, and firm size were determined to have no significant impact on financial distress, leading to the rejection of hypotheses H1, H2, and H5 This suggests that these factors are not key determinants of financial distress in the sample analyzed.

Table 5: Binary logistic regression result

From the regression table, the final regression model is as follows:

𝑙𝑜𝑔𝑖𝑡(𝐹𝐷 𝑖 ) = ⁡0.525 + ⁡244.964 ∗ 𝑆𝑉⁡ + ⁡4.403 ∗ 𝐶𝑆⁡ + ⁡5.373 ∗ 𝐹𝐶⁡ + 𝑒 For the coefficient, the binary logistic regression will use the Wald test value instead of the T-Test one, as the coefficient indicate the impact of independent variable to

40 the dependent one All these variables SV, CS and FC all have a positive correlation with the dependent variable FD, in which:

Solvency plays a crucial role in determining a firm's financial stability, as it has the highest coefficient impacting financial distress A low solvency ratio significantly increases the likelihood of a company facing a financial crisis.

- Financial Crisis is the second highest impact on the financial distress The result shown that the financial distress will be likely to happen in the financial crisis period

The capital structure of a firm has the least impact on financial distress, with findings showing that companies exhibiting a small gap between equity and liabilities are more likely to experience financial difficulties.

We will analyze various diagnostic tests to assess the suitability and robustness of the model The Omnibus test reveals a significance value of 0.000, which is less than the 0.05 threshold, indicating that the model is appropriate for interpretation Further details are provided in Table 6.

Table 6: The Omnibus test result

Next, The Cox and Snell and the Nagelkerke R 2 test will measure the overall explanatory power of the model, indicating that the research multiple logistic

41 regression explains approximately 28% and 68%, respectively, of the change in the financial distress among sample companies

Finally, based on the classification table results, the model can accurately predict 95.8 percent of all the financial distress value, adding an higher layer to the output of the research

Result Discussion

The regression results answered the research question and tested all 6 hypotheses of the study This following section will discuss the results for each variable:

The Wald Test indicates a Sig value of 0.4, which is greater than 0.05, leading us to conclude that liquidity does not significantly affect financial distress among the sampled firms in Vietnam This finding contradicts earlier studies, including those by Altman (1986) and Manh & Nguyen (2024), which suggest a negative relationship between liquidity ratios and financial distress, as well as research by Gathecha (2016) that supports a positive correlation between these two variables.

The limited impact of liquidity on financial distress in Vietnam can be attributed to the country's predominant focus on manufacturing and agriculture, which involves long-term assets that buffer businesses from immediate liquidity issues Additionally, the State Bank's control over lending policies has resulted in supportive interest rates, particularly during challenging periods like the COVID-19 pandemic, when rates fell to encourage business growth This environment has facilitated easier access to external capital for Vietnamese businesses, leading to the conclusion that liquidity does not significantly affect financial distress in Vietnam, prompting a rejection of the H1 hypothesis.

Figure 2: Vietnam lending interest rate (%) (World Bank)

The Wald Test for Profitability in Vietnam's listed firms shows a Sig value of 0.19, indicating a rejection of the hypothesis that profitability positively impacts financial distress This finding contrasts with previous studies by Chang-e (2006) and Campbell et al (2005), which established a negative relationship between profitability and the probability of financial distress.

Vietnam's status as an emerging market is characterized by a plethora of young businesses with significant potential However, these companies may experience temporary losses and may not achieve promising revenue levels in the near future Consequently, when evaluating profitability through the net income-to-total sales ratio, many Vietnamese businesses that are financially stable and possess strong growth prospects may appear less profitable than they truly are.

Despite having a relatively low ratio, large businesses often secure substantial loans for investment and development, demonstrating strong growth rates.

The Wald coefficient value of 244.964 demonstrates a significant correlation between the Solvency ratio and financial distress, indicating that a higher Solvency ratio reduces the risk of financial distress for firms This finding is consistent with previous studies, including those by Altman (1968), Hayes et al (2010), and Manh & Nguyen (2024).

A high solvency level enables businesses to meet their debt obligations more easily, reducing the risk of liquidity issues and bankruptcy This principle applies universally, including in Vietnam, where maintaining strong solvency and profit surpluses is crucial for avoiding financial distress The strong correlation between solvency and financial stability underscores the importance of prioritizing these factors in business operations.

The solvency ratio, calculated using the formula EBIT / Total Assets, indicates the extent to which an enterprise's remaining profit after expenses can cover its asset value This metric also reflects the effectiveness of asset optimization in generating new profits The regression results suggest that if a company's profit level is insufficient to cover its assets, it signifies inefficient asset utilization.

45 properly, or is not doing business well enough and is very likely to face the risk of not being able to maintain business operations leading to financial distress

The Wald Coefficient for Capital Structure (CS) is 4.403, suggesting that a higher capital structure correlates with a reduced risk of financial distress This finding aligns with previous studies by Gathecha (2016) and Chancharat (2008) Consequently, we reject hypothesis H4, indicating that capital structure does not significantly impact financial distress in Vietnam.

The study reveals that businesses with a low capital structure may face challenges due to excessive debt or a low equity ratio This situation results in a reliance on borrowed capital accompanied by high debt obligations, increasing the risk of financial distress and the inability to meet expenses.

The current formula for calculating the equity to liabilities ratio is total equity divided by total liabilities A decrease in this ratio indicates potential financial distress for the business, as it suggests a higher reliance on borrowed capital compared to equity capital This shift results in increased periodic debt obligations and a heightened risk of default While the impact of this ratio on business health is significant, it has a smaller coefficient compared to the primary variable of solvency This is partly because the ratio can still account for irregular situations, such as business restructuring or debt repayment.

46 short time or offering new shares However, we still cannot deny the impact of having a good capital structure on the business avoiding financial distress

The Sig value of 0.2800, which exceeds 0.05, suggests that firm size does not significantly influence financial distress in Vietnamese firms This finding contradicts earlier studies by Honjo (2000) and Freixas et al (2000), which indicated that larger firms tend to experience lower financial risk.

The results indicate that Vietnam's business landscape is characterized by a significant number of potential young enterprises, which, despite having substantial investment capital and effective operational levels, still generate relatively low revenue Conversely, there are larger companies facing high operational costs, which increases their risk of financial distress Additionally, in a new and volatile market like Vietnam, businesses across all segments share a similar level of financial distress risk.

The analysis reveals that the financial crisis significantly increases the likelihood of a firm facing financial distress, with a coefficient value of 5.373, particularly evident during the COVID-19 pandemic This finding aligns with previous studies by Tinoco and Wilson (2013), Quintilani (2017), and Kim and Upneja (2014).

During challenging economic times, companies often experience a drop in revenue while fixed costs remain unchanged, resulting in a notable reduction in profit margins When expenses surpass a firm's capacity to cover them, financial distress becomes unavoidable Additionally, these firms face challenges in securing investment capital, with borrowing costs rising, which further exacerbates cash flow issues essential for maintaining operations.

Summary

The regression analysis indicates that the initial model partially accounts for the likelihood of financial distress in enterprises Key variables such as Solvency, Capital Structure, and Financial Crisis significantly influence financial distress, serving as reliable determinants In contrast, Liquidity, Profitability, and Firm Size demonstrate an inadequate impact on the potential for financial distress within this sample.

The regression model indicates that businesses must effectively manage their revenue relative to their asset levels while also preparing for potential financial crises This risk is heightened across both small and large enterprises, suggesting that the likelihood of financial distress remains comparable regardless of company size.

The R2 value indicates that the model accounts for 67.8% of the factors influencing the likelihood of financial distress among enterprises in Vietnam, demonstrating its effectiveness for future forecasting studies with an impressive accuracy rate of nearly 96% across all scenarios.

CONCLUSIONS AND RECOMMENDATIONS

Introduction

This study investigates the determinants of financial distress among enterprises listed on the Vietnam financial market, utilizing a decade's worth of financial statement data from 2013 to 2023 By evaluating both macroeconomic and firm-specific factors through analysis, testing, and regression, the research identifies key indicators for assessing financial distress in Vietnam's volatile economic landscape This advancement is crucial for enterprises aiming to mitigate business risks and provides investors with valuable insights into the growth potential of companies before making investment decisions.

4.2 Achievement of the research objectives

Overall, the research results have answered the initial questions and objectives of the research, including:

This study identifies key determinants of financial distress in the Vietnamese market, focusing on solvency performance, capital structure, and financial crises, while supporting hypotheses H3, H4, and H6 Conversely, it rejects hypotheses H1, H2, and H5, indicating that liquidity and profitability do not significantly affect the likelihood of businesses facing financial distress Additionally, the research highlights that in Vietnam's emerging market, the risk of financial distress is uniformly distributed across various business sizes.

To ensure financial stability, businesses must assess the impact and prioritize actions based on key determinants A critical focus should be on achieving a healthy solvency level, as it significantly influences a company's ability to navigate financial distress Additionally, maintaining an optimal debt-to-equity ratio is essential to prevent difficulties in meeting debt obligations Finally, companies should proactively prepare for potential financial crises to mitigate adverse effects on their performance.

This study, like all other studies, has certain limitations and these are also the starting points for further studies

The primary limitation of current research lies in its reliance on financial ratios derived from Altman's Z-Score to evaluate financial distress While these ratios have undergone extensive analysis, there remains the potential for discovering alternative ratios that may more accurately predict financial distress Consequently, future studies should focus on developing a more precise set of financial indicators.

The study's sample size presents a limitation, as it focuses solely on businesses listed on the HOSE stock exchange in Vietnam While HOSE encompasses many of the country's major enterprises, expanding the research to include companies from other exchanges, such as the Hanoi Exchange (HNX) and UPCOM, would enhance the comprehensiveness and accuracy of the findings.

One significant limitation is the presence of missing data among the collected enterprises, which may persist for several years This issue is largely influenced by the quality of the data sources used Future research could focus on identifying more comprehensive data sources or exploring alternative methodologies to mitigate the impact of incomplete financial statements from these enterprises.

Recent studies on financial distress, particularly in Vietnam, have predominantly utilized the Z-Score model This article proposes a novel approach to identify the determinants of financial distress among businesses listed on Vietnam's stock exchange While maintaining the use of established financial indicators from Altman's model, this study introduces a new variable—risk buffer (EBITDA minus interest expenses)—to assess financial distress Additionally, it incorporates a macroeconomic factor, financial crises, to evaluate their impact on financial distress during the COVID-19 pandemic Furthermore, this research adopts a binary logistic regression model, shifting from the linear regression approach used in prior studies.

The study's regression analysis revealed that in Vietnam, Liquidity, Profitability, and Firm Size do not significantly influence the likelihood of financial distress in businesses In contrast, Solvency emerged as the most critical factor affecting the determination of financial stability.

Businesses with low solvency levels are more susceptible to financial distress, particularly during market crises Additionally, the organizational structure of a business plays a significant role in determining its vulnerability to financial challenges.

The regression model exhibits a relatively low explanatory power, with an R² value of 28% according to Cox & Snell and 68% based on Nagelkerke This indicates significant potential for enhancing the model's performance in future analyses.

The study evaluates the Vietnamese market, identifying key determinants that contribute to financial distress among businesses It highlights that the factors influencing financial distress vary from those in previous research and are influenced by the specific market characteristics and the timing of the study.

APPENDIX CLEAN DATA FOR REGRESSION

Finance Dissertation Data _ UWE7_ 23081333_Le Tuan Anh_08.09.24.xlsx

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Limitations and Recommendations

This study, like all other studies, has certain limitations and these are also the starting points for further studies

The primary limitation of current research is its reliance on financial ratios derived from Altman's Z-Score study to evaluate financial distress Despite extensive evaluation of these ratios, there remains a potential for discovering alternative ratios that may more accurately predict financial distress Consequently, future studies should focus on developing a new set of financial indicators to enhance predictive accuracy.

One limitation of the study is its sample size, which currently focuses solely on businesses listed on the HOSE stock exchange in Vietnam While the HOSE includes many major enterprises, incorporating companies from other exchanges, such as the Hanoi Exchange (HNX) and UPCOM, could enhance the research sample Expanding the sample would lead to more comprehensive and accurate findings in the study's model.

A significant limitation is that the data from the collected enterprises may still include gaps for several years This issue is largely influenced by the quality of the data sources used Future research should aim to identify more comprehensive data sources or explore alternative methods to mitigate the impact of incomplete financial statements from the enterprises.

Conclusion

Recent studies on financial distress, both in Vietnam and globally, have predominantly utilized the Z-Score model This research proposes a novel approach to identify the determinants of financial distress for businesses listed on Vietnam's stock exchange While retaining proven financial indicators from Altman's model, this study introduces a new variable, the risk buffer (EBITDA - interest expenses), to assess whether a business is experiencing financial distress Additionally, it incorporates a macroeconomic variable, the financial crisis, to evaluate its impact on financial distress during the COVID-19 pandemic Furthermore, this study adopts a binary logistic regression model, diverging from the linear regression approach used in prior research.

The study revealed that in Vietnam, Liquidity, Profitability, and Firm Size do not significantly influence the likelihood of businesses experiencing financial distress In contrast, Solvency emerged as the most critical factor affecting the determination of financial stability in these enterprises.

Businesses with low solvency levels are at a higher risk of experiencing financial distress Additionally, the organizational structure of a business can influence its vulnerability to financial challenges During market crises, the likelihood of financial distress increases for many companies.

The regression model demonstrates a modest explanatory power, with a dependent variable explanation level of 28% according to Cox & Snell R² and 68% based on Nagelkerke R² This indicates significant potential for future enhancements in the model's performance.

The study assessed the Vietnamese market and identified key determinants contributing to the financial distress of businesses These influencing factors vary from those found in earlier research and are shaped by the unique characteristics of the market and the specific time frame of the analysis.

APPENDIX CLEAN DATA FOR REGRESSION

Finance Dissertation Data _ UWE7_ 23081333_Le Tuan Anh_08.09.24.xlsx

Al-Hadi, A., Chatterjee, B., Yaftian, A., Taylor, G., and Hasan, M.M (2017) Corporate social responsibility performance, financial distress and firm life cycle: evidence from Australia Accounting and Finance

Alkhatib, K., & Al Bzour, A E (2011) Predicting corporate bankruptcy of Jordanian listed companies:Using Altman and Kida models International Journal of Business and Management 6(3), pp 208

Altman, E (2000) Predicting Financial Distress of Companies: Revisiting the Z-

Score and Zeta R-Models Stern School, New York University, New York, NY, July

Altman, E (2002), Revisiting Credit Scoring Models in a Basel 2 Environment

Stern School, New York University, May

Altman, E.I (1968) Financial ratios, discriminant analysis and the prediction of corporate bankruptcy The Journal of Finance 23, pp 589–609

Altman, E.I., and Hotchkiss, E (2010) Corporate Financial Distress and Bankruptcy:Predict and Avoid Bankruptcy, Analyze and Invest in Distressed Debt

John Wiley & Sons, Hoboken, NJ

Babatunde, A.A., Akeju, J.B., & Malomo, E (2017) The effectiveness of Altman’s Z-Score in predicting bankruptcy of quoted manufacturing companies in Nigeria

European Journal of Business, Economics and Accountancy 5(5), pp 74-83

Baldwin, C and Scott, M (1983) The resolution of claims in financial distress: the case of Massey Ferguson The Journal of Finance 38 (2), pp 505-516

Beaver, W.H (1968) Market prices, financial ratios, and the prediction of failure

Black, F., and Scholes, M.(1973) The pricing of options and corporate liabilities

Journal of Political Economy 81, pp 637–654

Campbell, J.Y., & Viceira, L.M (2005) The term structure of the risk–return tradeoff Financial Analysts Journal 61(1), pp 34–44

Chancharat, N (2008) An empirical analysis of financially distressed Australian companies: The application of survival analysis

Chang, T.C., Yan, Y.C., and Chou, L.C (2013) Is default probability associated with corporate social responsibility? Asia-Pacific Journal of Accounting and Economics 20, pp 457–472

Chang-e, S (2006) The causes and salvation ways of financial distress companies:

An empirical research on the listed companies in China

Charitou, A., Lambertides, N., and Trigeorgis, L (2007) Managerial discretion in distressed firms The British Accounting Review 39, pp 323–346

Chen, Y., Chien, C., and Huang, S (2010) An appraisal of financially distressed companies’ earnings management: evidence from listed companies in China

Chiaramonte, L., and Casu, B (2017) Capital and liquidity ratios and financial distress Evidence from the European banking industry The British Accounting Review 49, pp 138–161

Chou, H.I., Li, H., and Yin, X (2010) examined the impact of financial distress and capital structure on the work effort of outside directors, as published in the Journal of Empirical Finance, volume 17, pages 300-312 Their study utilized accounting, market, and macroeconomic variables to analyze how these factors influence director performance in companies.

Cox, D.R & Snell, E.J (1989) The Analysis of Binary Data 2nd ed London:

Darrat, A.F., Gray, S., Park, J.C., and Wu, Y (2016) Corporate governance and bankruptcy risk, Journal of Accounting Auditing and Finance 31, pp 163–202

DeAngelo, H., and DeAngelo, L (1990) Dividend policy and financial distress: an empirical investigation of troubled NYSE firms The Journal of Finance 45, pp

In their 1994 study published in the Journal of Accounting and Economics, DeAngelo, H., DeAngelo, L., and Skinner, D.J explore the accounting choices made by troubled companies The authors analyze how financial distress influences these companies' accounting decisions, highlighting the implications for financial reporting and stakeholder perception Their findings contribute to a deeper understanding of the relationship between accounting practices and corporate financial health, offering valuable insights for investors and regulators alike.