Introduction of the study
Rationale of the study
The stock market is a crucial component of the modern economy, serving as the platform for trading medium and long-term securities Participants, including individual and institutional investors like mutual funds and insurance companies, engage in buying and selling stocks for various reasons, such as generating profits or gaining control of companies One of the primary advantages of the stock market is its ability to provide a flexible source of capital for business operations This has led to the establishment of numerous stock markets worldwide, following the inception of the New York Stock Exchange (NYSE), significantly contributing to national economies.
The Ho Chi Minh City Stock Exchange (HOSE), established on July 11, 1998, and operational since July 28, 2000, plays a crucial role in the stock market Initially starting with just two listed companies, HOSE has achieved significant growth, reaching approximately 507 listed companies and a total market value of 365 trillion VND by December 31, 2007 Additionally, it includes three securities investment funds and 366 different types of bonds, with nearly 298,000 investor accounts opened at various securities companies.
In recent times, Vietnam has attracted 7,000 foreign investors, reflecting a significant surge in interest in its financial markets The Vietnam Index, which tracks the performance of companies on the Ho Chi Minh Stock Exchange, has shown consistent growth during this period This marks a remarkable phase in the evolution of the Vietnam Stock Exchange, highlighting its increasing prominence in the global investment landscape.
However, after a rapid and hot growth, the Vietnamese Stock Market in
The year 2008 concluded with a significant downturn, leading numerous publicly traded companies to encounter severe financial challenges The economic crisis profoundly affected the market, resulting in an overall profit reduction of 30% among all listed firms By the end of 2009, 23 companies were still struggling to recover.
Financial distress in companies can significantly impact market participants, including shareholders, creditors, managers, and individual investors To mitigate potential losses, investors must identify key characteristics of financially distressed firms, such as high leverage and volatile stock returns Recognizing these warning signs is crucial for both investors and operating enterprises Consequently, numerous qualitative and quantitative studies have been conducted to pinpoint failing firms and enhance investor protection.
The two most widely used methods for financial analysis are Multiple Discriminant Analysis and Logistic Regression When applied alongside various financial ratios, these techniques give rise to two renowned models: Altman’s Z-score model (1968) and Ohlson’s model (1980).
Beaver (1966) and Altman (1968) highlight the critical issue of the predictability of models derived from financial ratios Building on this foundational research, the Z-score model has been meticulously developed through subsequent studies by researchers such as Deakin (1972) and Taffler (1985).
Goudie (1987), Grice and Ingram (2001), Agarwal and Taffler (2007), and
Similarly, other studies also have been performed relating to the Ohlson, including Lau (1987), Fauzias and Chin (2002), Boritz, Kennedy, and Sun (2007), Muller, Steyn-Bruwer, and Hamman (2009)
In today's financial landscape, techniques for assessing financial distress are crucial for credit analysis within financial institutions When customers seek loans, these institutions evaluate their creditworthiness to identify any potential risks of financial distress If a risk is detected, preventive measures may be taken, such as rejecting loan applications or requiring borrowers to undertake additional steps to mitigate the risk of default before funds are disbursed.
Determining the most effective technique is a complex question, as each method has its unique advantages and disadvantages The effectiveness of these approaches can vary significantly depending on the context and timing, largely because individual reactions to information are often unpredictable.
In a person’s behaviors, some basic trends and random elements stay together
Predicting the behaviors of millions of investors is inherently less precise than forecasting the actions of an individual Additionally, research outcomes are influenced by various factors, including the unique characteristics of each stock market and the duration of data collection.
Despite the limited application of financial distress prediction techniques in the Vietnam Stock Exchange, many publicly listed companies have encountered this issue Therefore, it is essential to conduct research that explores the significance of financial ratios in forecasting financial distress This research holds considerable relevance and utility for both private entities and governmental institutions in evaluating the financial health of firms.
Research objectives and questions
This thesis aims to elucidate the connection between financial ratios and financial distress among publicly listed companies in Vietnam, building on previous research findings.
- Which is the suitable method to evaluate the impact of financial ratios on the probability of failure ?
- How is the relationship between the financial ratios and the financial distress state of companies?
Structure of the study
This research comprises four chapters: Chapter 1 introduces the research problem, questions, and objectives; Chapter 2 provides a literature review on financial distress prediction; Chapter 3 analyzes the collected data and presents the research findings; and Chapter 4 discusses the conclusions and implications derived from the study.
Literature Review
Definition of financial distress
Financial distress is commonly defined in various research studies, with Dun and Bradstreet (1985) describing it as the interruption of business operations According to Foster (1986), a company experiences financial distress when it faces significant liquidity issues that necessitate critical operational changes Liquidity problems arise when a company's current obligations cannot be met or enforced effectively.
There also have been definitions of financial distress like reduction of dividend and defaults on debt
Research has identified various stages of financial difficulty, including Guthmann and Dougall's (1952) three stages: technical insolvency, unsupportable debt burden, and reorganization Newton (1975) expanded this concept into four stages of deterioration: incubation, cash shortage, financial insolvency, and total insolvency Additionally, Lau (1987) utilized a five-state model to analyze financial distress, while Somerville (1989) opted for a three-state model.
In general, “financial distress” is a term indicated a condition when commitments to creditors of a company are broken or in difficulty
Financial distress can lead to insolvency, prompting governments worldwide to establish regulations for managing corporate financial issues This has sparked extensive discussions on legally defining corporate failure, which offers a clear criterion for researchers to differentiate between distressed and non-distressed firms For example, in the context of the Malaysia Stock Exchange, financial distress is identified through several measures: a) closure under the Companies Act 1965, b) commitment to a Scheme of Arrangement and Reconstruction, c) debt restructuring via the Corporate Debt Restructuring Committee, d) selling off the firm’s loans, and e) restructuring for small borrowers.
A study conducted in the United Kingdom identifies failed firms based on criteria set forth in the Insolvency Act of 1986, outlining five primary courses of action: administration, company voluntary arrangement, receivership, liquidation, and dissolution.
In Vietnam, the Law on Bankruptcy, enacted by the National Assembly on June 15, 2004, states that a company can be declared bankrupt if it fails to meet its debt obligations when due Nevertheless, there remains an opportunity for the company to revive its operations before the bankruptcy is finalized.
The High Court has declared bankruptcy, prompting all creditors to convene a conference aimed at evaluating and adjusting the company's rehabilitation plan for production and payment This comprehensive strategy includes several key measures: mobilizing new capital, altering production commodities, implementing technological innovations, reorganizing the management system, merging or splitting production departments to enhance productivity and quality, selling shares to creditors, and selling or leasing non-essential assets.
It is uncommon for Vietnamese firms to declare bankruptcy in the High Court due to the complex administrative procedures involved Gathering information about these companies is also challenging Despite some publicly listed companies on the Ho Chi Minh Stock Exchange facing dire financial conditions, they have not been required to file for bankruptcy in over fifteen years of operation.
To enhance the efficiency and credibility of the stock market while safeguarding the interests of directors, intermediaries, and shareholders, the Ho Chi Minh Stock Exchange implemented Decree 04/QD-SGDHCM on April 17, 2009, which amends and adds to several articles of the listing regulations In a similar effort, the Ha Noi Stock Exchange introduced Decree 324/QD-SGDHN on June 4, 2009.
In 2010, regulations were established for the listing of securities, categorizing those with unsatisfactory conditions into warnings, controls, trading halts, and delisting Stock Exchanges implement warning signs and ensure full market disclosure for these securities Warning signs may be removed if listed companies address and rectify the issues that led to the warnings, controls, trading halts, or delisting.
Some underlying conditions of classification pursuant to the law for each case imply the finance nature such as: a) The case for warned stocks:
There is a one-year-overdue debt or a rate of overdue debt higher than 10% equity
There are not enough 100 shareholders holding at least 20% shares of the company
The earning at the same year is negative
The company’s operation is stopped
Listed companies continue to violate the regulations in relation to disclosing the information although being warned
Shares do not trade within 90 days
It is deemed necessary to protect the benefit of investors b) The case for stocks put under control:
Listed companies have not improved situations leading them to being warned
Listed companies violate regulations involved in stocks and the stock market seriously c) The case for delisted shares:
The charter capital decreases to below 80 billion VND
Listed organization’s certificates of business registration or certificates in specialized business are revoked
Shares have not traded in 12 months
Audition organizations have disapproved of or refuse to give idea of listed firms’ latest financial statement
A company listed on the Ho Chi Minh Stock Exchange faces delisting if it reports negative earnings after tax for three consecutive years, leading to total accumulated losses that surpass its equity in the latest financial statement.
Stock exchange – the action of delisting The listed firms stop trading when their earnings for two consecutive years are negative
Finding annual financial statements for listed companies can be challenging Financial distress firms are defined as those whose shares have been placed under control or delisted, in accordance with Decree 04/QD-SGDHCM issued on April 17, 2009, and Decree 324/QD-SGDHN dated June 4, 2010, which amend and add to the regulations for listing.
Ratios in designing models
Foster (1986) suggests that utilizing financial ratios is the most effective method for identifying companies facing financial difficulties, as these ratios reveal consistent patterns linked to specific events These ratio models, based on financial statements, highlight differences between stable and unstable firms However, careful consideration is necessary when analyzing financial ratios due to the varying interpretations of accounting standards that underpin financial reporting.
Collecting financial ratios as predictor variables relies on their popularity and predictive power established in prior studies This reliance stems from a lack of theoretical support for the causal relationship between financial ratios and bankruptcy Most evidence is empirical, as demonstrated by researchers like Jones (1987) and others, including Karels & Prakash (1987) and Lam (1994).
Wilson and Sharda (1994) emphasize that the advancement of bankruptcy models is closely linked to the selection of economic variables that enhance predictive accuracy Jones (1987) highlights the significance of this, noting that numerous studies employing different methodologies have yielded consistent ratios Pinches, Mingo, and Caruthers also contribute to this discourse.
In 1973, key financial ratios were identified, including return on investment, capital turnover, financial leverage, short-term liquidity, cash position, inventory turnover, and receivables turnover According to Jones (1987), these factors are crucial for making economic interpretations.
On the other hand, a large number of researchers (Altman, Haldeman &
Narayanan, 1977; Marais, Patell & Wolfson (1984); Foster, 1986) have introduced ratios concerning the financial market with the reason that they contain essential information not derived from in financial statements
Zavgren (1983) argues that using an excessive number of ratios in research can result in overfitting issues However, Wilson and Sharda (1994) contend that the Neural Network method yields superior analysis results when more ratios are included, in contrast to Multivariate Discriminant Analysis.
Wilson & Sharda (1994), Udo (1993) find that a significant breakthrough in computer today is of great advantage to model using many information
Karels & Prakash (1987) believe that selecting ratios in the aforementioned researches have not included the assumptions of Multivariate
Multivariate Discriminant Analysis (MDA) was employed to evaluate the ratios against specific assumptions, revealing that while the selected ratios do not fully meet the joint normality criteria, their deviations differ from those observed in previous studies Notably, when compared to Altman's 1968 research, the ratios analyzed demonstrate an enhanced predictive capability.
Karels & Prakash (1987) identified key financial ratios that align with the seven categories established by Pinches et al (1973), which are based on experimental foundations These essential ratios include the working capital ratio, gross profit margin, earnings per share, total debt to total assets, cash flow per share, market value of common stock, asset turnover, sales per cash, and sales per receivables.
One of the important characteristic in relation to the life or the firm is profitability (Lam 1994) The lower these ratios are, the higher the probability of financial distress is
Liquidity ratios are crucial as they reflect a company's ability to meet its obligations without disrupting operations Insufficient liquidity can lead to missed debt payments (Lam, 1994) Specifically, the working capital ratio tends to decline when companies face financial difficulties.
High total debt to total assets ratios indicate financial distress in companies (Somerville, 1989) Economic factors such as financial crises, intense competition, and significant interest rate fluctuations greatly impact a company's ability to meet its payment obligations.
Hence, levels of leverage are one of indispensable factors in model
Karels and Prakash (1987) highlight that unstable financial conditions in a firm lead to cash flow challenges Cash flow ratios serve as indicators of a company's potential to generate future cash flows Furthermore, there exists a notable indirect relationship between cash flow and the long-term sustainability of dividend payouts A common signal that management lacks confidence in future cash flows supporting dividends is the decision to cut dividends (Somerville, 1989; Lau, 1987).
Activity ratios, including asset turnover, sales per cash, and sales per receivables, are crucial for researchers analyzing company performance (Zavgren, 1985; Somerville, 1989) These ratios tend to be lower for companies in distress, indicating long-term financial health Specifically, sales per receivables serve as an important metric for assessing the likelihood of a company recovering its debts.
Foster (1986) identifies the market price of shares as a crucial indicator of bankruptcy, despite it not being a traditional financial ratio This preference arises from the observation that financial statements may lack essential information when compared to market data Similarly, Karels & Prakash (1987) conclude that financial statements are less effective in predicting bankruptcy probabilities than market indicators.
Techniques used in financial distress predictions
A variety of estimation techniques have performed in the academic literature to build the prediction model One of the pioneering researches is
Beaver (1966) utilized a univariate method to address the complexities of business; however, this approach has been criticized for its inadequacy in accurately measuring a company's financial condition due to its overly simplistic nature (Foster, 1986).
Jones, 1987; Lam, 1994) In spite of this, his study becomes a source of inspiration for later researches
Altman (1968) expanded on Beaver's (1966) work by employing a discriminant function within a multivariate analysis framework This approach, known as Multiple Discriminant Analysis (MDA), has become a widely used method for predicting financial distress, as it overcomes the limitations of univariate analysis Unlike univariate methods, MDA captures the multidimensional aspects of a company, providing clearer insights without generating conflicting signals.
Despite its utility, MDA is based on two key assumptions that are frequently violated: the requirement for multivariate normal distribution of variables and the necessity for consistent covariance matrices among predictors across companies (Foster, 1986; Jones, 1987; Lam, 1994) To address these issues, Jones (1987) employed techniques such as log transformation, square root transformation, and outlier removal to enhance the first assumption Subsequently, he utilized quadratic discriminant analysis to tackle the second assumption.
There is a contrasting perspective on the analysis, as noted by Altman, Haldeman, and Narayanan (1977), who argue that the quadratic model exhibits heightened sensitivity to the derivation sample, leading to poor classification performance in holdout samples.
The validity test results do not align with theoretical expectations, despite the quadratic structure being deemed suitable according to statistical data (Jones, 1987) Jones argues that minor adjustments to the MDA technique's theory do not enhance its classification accuracy.
Udo (1993) highlights several issues with MDA, including the impact of autocorrelation and the omission of error handling in the data, as well as the lack of strategies for addressing missing values.
Heine (1995) declares that the accuracy rate of model using MDA from
1968 to 1995, specifically Altman (1968) Z score, achieves not less than eighty to ninety percent However, Ohlson (1980) and Karels & Prakash
(1987) add that the prediction probabilities are reliable only if statistical assumptions are not complied in any case
Logit analysis, derived from the logistic cumulative probability function, is a statistical method used to assess classification and prediction accuracy This model incorporates a critical probability threshold; if a company's value exceeds this threshold, it indicates a higher likelihood of insolvency.
This method avoids the restrictive assumptions of MDA while assuming that the costs associated with type I errors (misclassifying a bankrupt company as non-bankrupt) and type II errors (misclassifying a non-bankrupt company as bankrupt) are equal Furthermore, it posits that changes in the independent variables have immediate effects on the outcomes.
Being different from MDA, logit analysis is not easy to correct for prior probabilities A technique, called the Weighted Exogenous Sample Maximum
Likelihood (WESML), is applied for the purpose of correcting it
According to Zmijewski (1984), using the WESML method helps eliminate biases that arise from the assumption that type I and type II errors are equal Without proper adjustments, these methods may yield inaccurate probabilities, particularly if the proportions of failing versus stable firms differ between the entire population and the sample (Jones, 1987) To address the costs associated with misclassification and to clarify the differences between type I and type II errors, adjusting the cutoff score is essential (Jones, 1987).
Comparing two techniques reveals that neither MDA nor Logit analysis provides substantially better results (Wilson & Sharda, 1994) Tam & Kiang
Research has shown varying results regarding the effectiveness of Logit Analysis compared to MDA While Somerville (1989) suggests that Logit Analysis yields superior outcomes, Hamer (1983) also finds that Logit Analysis may provide slightly more accurate results than MDA.
Ohlson (1980) highlights the effectiveness of the logit model in overcoming the limitations of multiple discriminant analysis (MDA) in predicting corporate failure By utilizing nine theoretically selected independent variables, Ohlson calculates the probability of failure for industrial firms that were traded between 1970 and 1976.
Over a three-year period, the US stock exchange has seen 105 firms fail among a total of 2,000 firms To analyze this, three predictive models have been developed: the first forecasts failures within one year, the second within two years, and the third assesses the likelihood of failure within either one or two years The probability of failure for each firm is determined using a logistic function in each model.
Jones (1987) highlights that Logit analysis models are preferred over MDA due to their strong theoretical foundation for evaluating results Additionally, Harrell and Lee (1985) assert that Logit models remain effective even in the presence of MDA's assumptions.
Artificial Neural Networks (ANN) have been shown through various studies to be superior in predicting financial distress compared to other methods (Charitou and Kaourou, 2000; Tan and Dihardjo, 2001) However, a significant limitation of ANN is its "black box" nature, which obscures how it differentiates between failing and non-failing companies (Hawley, Johnson, and Raina, 1990) Additionally, ANN does not provide insight into the importance of individual variables in the final classification, hindering the understanding of each variable's contribution to the results.
Hypotheses
This thesis aims to clarify the connection between financial ratios and the financial distress of publicly listed companies in Vietnam, a market that has been established for just over 12 years.
It has been considered as a “young market” One more problem is considered whether these connections are consistent with earlier researches
Thus, some hypotheses to be tested will be introduced in this thesis:
- H1: Earnings per share impacts negatively on the probability of failure
- H2: Cash flow per share impacts negatively on the probability of failure
- H3: Asset turnover impacts negatively on the probability of failure
- H4: Sales per receivables is negatively impacting on the probability of failure
- H5: Working capital is negatively related to the probability of failure
- H6: Sales per cash is negatively related to the probability of failure
- H7: Gross profit margin is negatively impacting on the probability of failure
- H8: Total debt to total assets is positively related to the probability of failure.
Conclusions
Numerous models have been utilized in academic research, including multiple discriminant analysis (MDA), logit, and neural networks While there is a diverse range of models, many are fundamentally rooted in the pioneering work of Altman (1968) and Ohlson (1980) (Boritz et al 2007).
However, logistic regression analysis is more and more popular in the vast majority of international failure prediction studies (Barniv, 2002; Charitou,
By means of the logistic regression technique’s advantage, this study aims to research the financial ratios’ impact on the financial distress among listed companies in Viet Nam.
Research Methods
The model
Logit analysis is a statistical method that extends the Linear Regression model by incorporating one dependent variable and multiple independent variables While it shares some similarities with the cumulative normal function, Logit analysis is generally more user-friendly in terms of computation This ease of use is a key reason why Logit analysis is frequently employed as an alternative to the probit model.
Where Y is the state of the company (0 = financial distress and 1= non- financial distress), β is the coefficient and X are the financial ratios calculated, k is the number of explanatory variables
In binary dependent variable analysis, issues like heteroskedasticity and boundary constraints arise, as the variable Y is limited to values between 0 and 1 while the independent variables can extend infinitely To address these challenges, the logit function is employed, providing a suitable framework for modeling such data effectively.
1 – Pi The dependent variable (Y) is the logarithm of the odds that is the probability of the event divided by the probability of an event not occurring
The Logit model predicts the likelihood of an event occurring within a specific range rather than estimating probabilities between 0 and 1 At the midpoint of the logistic distribution, where Pi = ẵ, the slope is steepest, indicating that changes in independent variables have the most significant impact on the probability of selecting a particular option Conversely, near the endpoints, minor fluctuations in independent variables result in minimal changes in probability (Pindyck & Rubinfeld, 1991) The model relies on the cumulative logistic probability function for its calculations.
Pi represents the probability of companies being in one of two states (Y = 1 or 0) based on eleven financial ratio explanatory variables The model predicts one state, with the alternative state represented as 1-Pi To calculate the βi and Xi in the logit analysis model, an empirical dataset containing actual final states and financial ratio values is utilized The model's predictive accuracy hinges on the alignment between the predicted state (Pi) and the actual state Maximum likelihood estimation is employed to determine the model parameters, demonstrating that the influence of variables on a company's state and the sign of each coefficient in the logit function are interdependent For example, the working capital ratio illustrates this relationship effectively.
(1987) shows a result that a higher working capital ratio leads a higher probability of entering the financially stable state and a lower probability of entering bankrupt state.
Selection of predictor variables
Previous research highlights the absence of a theoretical model for predicting financial distress, leading to a reliance on empirical processes for selecting financial ratios Most studies have derived these ratios without a standardized framework, emphasizing the need for a more structured approach in future analyses.
This study adopts eight of the nine independent variables from Karels and Prakash's research (1987), due to the lack of a suitable set of ratios and challenges in collecting financial statements The selected ratios were tested for univariate normality, multivariate normality, and lognormality, revealing some deviations from normality However, the extent of these deviations was found to be relatively lower compared to other studies Notably, the results were also compared to Altman's research, providing a basis for further analysis.
& Prakash conclude that their ratios enhance the predictive ability
These financial ratios are in five main categories of firms included profitability, liquidity, leverage, cashflow and activity ratios in the model The independent variables are:
1 Working capital is computed by the difference of Current assets and Current liabilities by Total assets (WOCA)
2 Gross profit margin is defined by the difference of Net sales and Cost of Goods sold divided by Net sales (GROPROM)
3 Earnings per share is computed by dividing Net income by number of shares outstanding (EPS)
4 Total debt to total assets is computed by the sum of Current Liabilities and Long Term Debt divided by Total Assets (DEBTTOTAL)
5 Cash flow per share is defined by dividing the sum of Net Income and Depreciation by number of shares outstanding (CASPSHARE)
6 Asset turnover is computed by dividing Sales by Total Assets (ATURNOVER)
7 Sales per cash is computed by dividing Sales by Cash (SALEPERCA)
8 Sales per receivables is computed by dividing Sales by Receivables (SALEPERRE)
The discriminating model utilizes independent variables to predict a company's status, with the importance of each variable determined through logistic regression analysis.
The dependent variable in this study is a company's financial condition, categorized as dichotomous variables: state 1 represents non-financial distress companies, while state 2 denotes financially distressed companies A company classified as state 1 is deemed healthy and compliant with listing requirements, whereas state 2 refers to companies experiencing financial distress, as outlined in section 2.1 of the literature review.
With the selection of financial ratios as mentioned above, the model’s prediction equation is:
Data set
Financial statements are collected annually on December 31 from two finance websites, www.cafef.vn and www.cophieu68.com The financial ratios used in the model are derived from this data, focusing on companies listed on both the Ho Chi Minh Stock Exchange and the Hanoi Stock Exchange.
Noi Stock Exchange with two groups from 2007 - 2011
Financially distressed firms are defined as companies facing significant financial challenges, and researching this issue has encountered limitations, particularly regarding the availability of data on affected companies This has implications for the selection of the research period The study focuses on a group of 28 listed companies classified as financially distressed, drawn from nine different sectors.
6 Technology hardware and equipment (1 company)
The second group with the non – financial distress companies that met the following criteria:
- They have not violated the listing requirement that may be lead to situations being warned, put under control, stopped trading and delisted during the research time
- The listed securities were trade on two Stock Exchanges before 2008
- They have enough financial statements during the research time
In each year of the study, listed companies were classified based on their financial status Due to the delisting of certain firms, some lacked the necessary four consecutive years of financial data Consequently, the final sample comprised 982 non-financially distressed companies and 36 financially distressed companies, forming a comprehensive database for analysis.
Using Eview software, financial ratios were analyzed through a Binary model to determine their relationship with the future performance of companies.
Data analysis and Findings
Descriptive Statistics
A fundamental analysis of the data involves distinguishing between failed and non-failed companies Table 1 presents the arithmetic mean and standard deviation of eight independent variables for both groups, highlighting key differences in their performance metrics.
One year before financial failure, six out of eight financial ratios for non-failing companies were significantly higher than those for failing firms Specifically, metrics such as working capital (WOCA), gross profit margin (GROPROM), and cash flow per share (CASPSHARE) showed a decline in companies facing financial distress as they approached failure This trend aligns with previous findings that indicate a negative correlation between cash flow and profitability ratios and the likelihood of financial failure.
During the failure period, the mean of total debt to total assets (DEBTTOTAL) exhibited an increasing trend, contrasting with lower levels observed in healthy firms This finding clearly illustrates the positive relationship between financial leverage and the likelihood of failure.
Surprisingly, the SALEPERCA (sales per cash) metric was found to be higher for companies in distress compared to those that were not Specifically, the average SALEPERCA for the failure group was double that of the non-failure group.
Variables Non - failure group Failure group
Mean Std Dev Mean Std Dev
Variables Non - failure group Failure group
Mean Std Dev Mean Std Dev
Correlations
While the regression analysis was performed, one of noticeable problem was the multicolineality regarding the high correlations between the independent variables Table 2 introduces the correlations of the full sample
The highest correlation was between WOCA and DEBTTOTAL although the calculation methods were quite different It suggests that the level of DEBTTOTAL increases as the WOCA decreases
The next two highest correlations of 0.56 and 0.51 were between Sale per receivables and Asset turnover, EPS and Cash per share, respectively
These correlations were also strong ones and it may be explained through some similarities in using financial ratios for computing them
Independent variables serve as key indicators of distress and significantly contribute to predicting the likelihood of failure However, a model that integrates these variables provides a more accurate assessment of distress The following section will explore the optimal combination of various explanatory variables into a unified model.
Regression model
In order to investigate the relationship between the financial ratios and the probability of failure, the next analysis was to run a binominal logistic regression
To prevent multicollinearity, it is essential to omit one of the highly correlated independent variables Failing to do so may render one or both variables insignificant, leading to inconclusive test results.
After analyzing the correlation among eight explanatory variables, we identified eight potential models, each comprising five independent variables, following the exclusion of highly correlated variables For instance, in the model that includes Earnings Per Share (EPS), Cash per Share is omitted The summary below presents the outcomes of the logistic regression conducted on these eight models.
Table 3 The performance of logistic regression for 8 models
Mo del Variables in model McFadden
SALEPERCA, GROPROM 29.25% 0.2346 EPS, SALEPERRE WOCA
The analysis from Table 3 and Appendices indicates some following results:
Financial ratios such as EPS, ATURNOVER, SALEPERRE, and CASPSHARE negatively influence the likelihood of financial failure; lower values of these ratios correlate with an increased probability of financial distress Additionally, z-tests indicate that the effects of these independent variables are statistically significant.
They had statistically significant relationship at the 5% level Thus, these hypotheses such as H1, H2, H3, and H4 are accepted
The findings align with previous research, highlighting that the asset turnover ratio (ATURNOVER) reflects a firm's efficiency in utilizing its assets to generate sales or revenue A company that effectively produces sales will experience increased cash inflows, thereby reducing the risk of financial distress This performance correlation is supported by the study conducted by Altman and Lavallee (1981).
Sales per receivables (SALEPERRE) measures a firm's likelihood of collecting revenue after a sale Efficient revenue collection enables quicker debt settlement, enhancing financial stability.
Cash flow per share (CASPSHARE) is inversely related to the likelihood of a firm experiencing financial distress, as established by Westgaard and Van der Wijst (2001) This indicates that higher earnings reduce the probability of a company encountering financial difficulties.
A decrease in Working Capital (WOCA) is linked to an increased likelihood of financial distress for companies, indicating a negative association with the probability of failure This relationship is statistically significant at the 0.05 level in both Model 5 and Model 6, leading us to accept the hypothesis H5.
SALEPERCA, which measures the frequency of cash turnover annually, tends to be higher in companies experiencing financial distress In Models 5, 6, and 7, a significant positive correlation was found between SALEPERCA and the probability of failure at the 5% level, leading to the rejection of H6 This suggests that a high sales-to-cash ratio may indicate insufficient cash reserves, potentially resulting in financial difficulties if further financing cannot be secured at reasonable rates.
In our analysis of Model 5 and Model 8, we found that the H7 hypothesis remains valid The results demonstrated that GROPROM has a negative effect on the probability of failure, achieving significance at the 5% level This indicates that increased levels of GROPROM correlate with a reduced likelihood of failure.
GROPROM is, the lower the probability of failure is
The DEBTTOTAL ratio, analyzed through Model 7 and Model 8, supports H8 at the 5% significance level, indicating that the total liabilities to total assets ratio significantly correlates with a firm's likelihood of experiencing financial distress This finding aligns with previous research conducted by Mohamed et al (2001) and Nur, reinforcing the importance of this financial metric in assessing corporate financial health.
Table 3 presents the McFadden R-squared values for each model, which serve as a coefficient of determination This metric assesses the percentage of variance in the dependent variable that can be accounted for by the independent variables within the model.
In comparison to model 5, where the EPS and ATURNOVER variables were substituted with CASPSHARE and SALEPERRE, model 1 exhibited an improvement in McFadden R-squared, rising from 21.74% to 29.89% Additionally, the Akaike information criterion for model 1 decreased to 0.2325, indicating a better fit than that of model 5.
The analysis of models 3 and 5 revealed a decrease in McFadden R-squared from 29.25% to 21.74% upon substituting EPS with CASPSHARE, indicating that EPS has a more significant impact on the probability of failure than CASPSHARE Furthermore, the evaluation of models 7 and 8 demonstrated that ATURNOVER exerts a stronger influence on the likelihood of failure compared to SALEPERRE.
Moreover, the result of logistic regression also demonstrated that the impact of CASPSHARE on the probability of failure seemed to be higher compared to ATURNOVER
The analysis indicates that earnings per share (EPS), cash per share, and asset turnover significantly influence the likelihood of company failure.
Conclusions
Summary
This study investigates the relationship between a set of financial ratios and the likelihood of financial distress among companies listed on the Vietnam Stock Exchange, particularly focusing on the year leading up to their potential failure.
Logistic regression analysis reveals that six out of eight financial ratios, including Earnings per Share, Asset Turnover, Sales per Receivables, Cash per Share, Working Capital, and Gross Profit Margin, are negatively correlated with the likelihood of business failure Additionally, Earnings per Share is highlighted as a significant factor in this analysis.
Asset turnover and cash per share are critical financial ratios that significantly impact a firm's health Additionally, total debt to total assets and sales per cash are positively correlated with a firm's likelihood of experiencing financial distress This study's findings on profit and activity ratios align with previous research, including Altman's Z-Score model from 1978.
The logistic regression analysis reveals that financial distress among listed companies on the Vietnam Stock Exchange is primarily caused by inefficient operational activities, resulting in losses reflected in earnings per share (EPS), as well as inadequate asset management that hinders revenue generation, indicated by low asset turnover.
Understanding the connection between financial ratios and a company's condition is crucial for investors in Vietnam By analyzing these ratios, investors can assess a company's financial health, enabling them to pinpoint firms facing financial difficulties This knowledge helps mitigate risks when investing in potentially troubled stocks.
Limitation of the research study
One limitation of the study is that the independent variables do not sufficiently explain financial distress, as indicated by a McFadden R-squared value around 30% This issue arises from the data collection period of 2007 to 2011, during which the number of listed companies experiencing financial distress was relatively low Although the number of distressed firms increased in 2012, gathering financial ratios became unfeasible due to the unavailability of financial statements from that period Consequently, the limited data restricts the identification of additional ratios, such as market ratios, that could influence the likelihood of financial failure.
Financial ratios, derived from financial statements, face challenges due to the inherent complexities in interpreting accounting standards Additionally, the validity of the thesis results is significantly influenced by the reliability of these financial statements, highlighting another limitation in the analysis.
The identified limitations highlight the need for additional research to address the gaps in the thesis The conclusions drawn regarding the relationship between variables and the likelihood of failure serve as a foundation for this future inquiry It is important to note that each financial market, with its unique characteristics, may respond differently Therefore, ongoing research into a variety of financial ratios, alongside macroeconomic indicators such as inflation, remains essential.
Future research should focus on analyzing data over extended periods, including the two to three years leading up to financial distress Additionally, it's important to explore beyond traditional techniques to gain deeper insights.
Logistic Regression and Multivariate Discriminant Analysis, some new methods have proven the advantage of predicting the financial distress, i.e
Neural Networks Thus, these techniques will be taken into consideration in later researches Such studies have a significant contribution to the field of forecasting the failure in Viet Nam
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Method: ML - Binary Logit (Quadratic hill climbing)
Covariance matrix computed using second derivatives
Variable Coefficient Std Error z-Statistic Prob
McFadden R-squared 0.298966 Mean dependent var 0.96334
Akaike info criterion 0.232597 Sum squared resid 28.88697
Hannan-Quinn criter 0.243961 Restr log likelihood -154.351
LR statistic 92.29115 Avg log likelihood -0.11019
Obs with Dep=0 36 Total obs 982
Method: ML - Binary Logit (Quadratic hill climbing)
Covariance matrix computed using second derivatives
Variable Coefficient Std Error z-Statistic Prob
McFadden R-squared 0.298455 Mean dependent var 0.96334
Akaike info criterion 0.232757 Sum squared resid 29.02307
Hannan-Quinn criter 0.244122 Restr log likelihood -154.351
LR statistic 92.13349 Avg log likelihood -0.11027
Obs with Dep=0 36 Total obs 982
Method: ML - Binary Logit (Quadratic hill climbing)
Covariance matrix computed using second derivatives
Variable Coefficient Std Error z-Statistic Prob
McFadden R-squared 0.292518 Mean dependent var 0.96334
Akaike info criterion 0.234624 Sum squared resid 29.0055
Hannan-Quinn criter 0.245988 Restr log likelihood -154.351
LR statistic 90.30054 Avg log likelihood -0.1112
Obs with Dep=0 36 Total obs 982
Dependent Variable: Y Method: ML - Binary Logit (Quadratic hill climbing) Date: 12/20/12 Time: 20:49
Covariance matrix computed using second derivatives
Variable Coefficient Std Error z- Statistic Prob
McFadden R-squared 0.283148 Mean dependent var 0.96334
Akaike info criterion 0.237569 Sum squared resid 29.55923
Hannan-Quinn criter 0.248934 Restr log likelihood -154.351
LR statistic 87.40812 Avg log likelihood -0.11268
Obs with Dep=0 36 Total obs 982
Method: ML - Binary Logit (Quadratic hill climbing)
Covariance matrix computed using second derivatives
Variable Coefficient Std Error z-Statistic Prob
McFadden R-squared 0.217475 Mean dependent var 0.96334
Akaike info criterion 0.258214 Sum squared resid 30.761
Hannan-Quinn criter 0.269579 Restr log likelihood -154.351
LR statistic 67.13468 Avg log likelihood -0.123
Obs with Dep=0 36 Total obs 982
Method: ML - Binary Logit (Quadratic hill climbing)
Covariance matrix computed using second derivatives
Variable Coefficient Std Error z-Statistic Prob
McFadden R-squared 0.210934 Mean dependent var 0.96334
Akaike info criterion 0.26027 Sum squared resid 31.45642
Hannan-Quinn criter 0.271635 Restr log likelihood 154.3505
LR statistic 65.1154 Avg log likelihood 0.124025
Obs with Dep=0 36 Total obs 982
Covariance matrix computed using second derivatives
Variable Coefficient Std Error z-Statistic Prob
McFadden R-squared 0.193098 Mean dependent var 0.96334
Akaike info criterion 0.265877 Sum squared resid 32.44427
Hannan-Quinn criter 0.277242 Restr log likelihood
LR statistic 59.60965 Avg log likelihood
Obs with Dep=0 36 Total obs 982