Introduction of the study
Rationale of the study
The stock market serves as a vital platform for trading medium and long-term securities in the modern economy, involving both individual and institutional investors, such as mutual funds and insurance companies Participants engage in buying and selling stocks for various reasons, including profit generation and corporate control The primary advantage of the stock market lies in its ability to provide a flexible source of capital for business operations Consequently, numerous stock markets have emerged since the establishment 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, boasting approximately 507 listed companies and a total market value of 365 trillion VND by December 31, 2007 Additionally, the exchange includes three securities investment funds and 366 types of bonds, with nearly 298,000 investor accounts opened at securities companies.
During a remarkable period of growth, the Vietnam Index, which reflects the performance of Vietnamese companies on the Ho Chi Minh Stock Exchange, has seen an influx of 7,000 foreign investors This surge marks a significant milestone in the development of the Vietnam Stock Exchange, highlighting its increasing attractiveness to international investors.
However, after a rapid and hot growth, the Vietnamese Stock Market in
The year 2008 concluded with a significant downturn, leading numerous listed companies to encounter financial challenges The economic crisis severely affected these firms, resulting in a 30% reduction in total profits across all listed companies By the end of 2009, 23 companies were notably impacted.
Financially distressed companies can significantly harm market participants, including shareholders, creditors, managers, and individual investors To mitigate these risks, investors can identify specific characteristics associated with financially troubled firms, such as high leverage and low, 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.
The two most widely used methods for financial analysis are Multiple Discriminant Analysis and Logistic Regression These techniques, when applied alongside a selection of financial ratios, lead to the development of 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
Sandin and Porporato (2007) tot nghiep down load thyj uyi pl aluan van full moi nhat z z vbhtj mk gmail.com Luan van retey thac si cdeg jg hg
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, distress techniques are increasingly utilized for various economic purposes, particularly in credit analysis by financial institutions When customers seek loans, these institutions assess their creditworthiness to identify any potential risk of financial distress If such risks are detected, preventive measures may be taken, such as rejecting loan applications or requiring borrowers to implement additional steps to mitigate the risk of default before funds are disbursed.
Determining the most effective technique is a complex question, as each method presents unique advantages and disadvantages The effectiveness of these approaches can vary significantly over time, largely due to the unpredictable nature of individual responses to information.
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 are experiencing this issue Therefore, it is essential to conduct research that explores the significance of financial ratios in forecasting financial distress This study will be valuable for both private entities and governmental institutions in evaluating the financial health of firms.
Research objectives and questions
The primary objective of this thesis is to clarify the relationship between financial ratios and financial distress in listed companies in Vietnam, based on prior research.
- 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 is structured into four chapters Chapter 1 introduces the research's general content, including 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's final results Finally, Chapter 4 discusses the conclusions and implications of the research.
Literature Review
Definition of financial distress
Financial distress is defined in various ways in research According to Dun and Bradstreet (1985), it refers to the interruption of business operations Foster (1986) describes a company in financial distress as one facing significant liquidity issues that necessitate critical operational changes Liquidity problems occur when a company cannot meet its current obligations effectively.
There also have been definitions of financial distress like reduction of dividend and defaults on debt
Various studies have identified different stages of financial difficulty Guthmann and Dougall (1952) outline three stages: technical insolvency, unsupportable debt burden, and reorganization Newton (1975) presents a four-stage model that includes incubation, cash shortage, financial insolvency, and total insolvency Lau (1987) utilizes a five-state model to analyze financial distress, while Somerville (1989) opts for a simpler 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 necessity has sparked extensive discussions on the legal definitions of failure, which offer researchers a framework to classify distressed and non-distressed firms effectively For example, in the context of the Malaysia Stock Exchange, financial distress is identified through several criteria: 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 the firm's loans; and e) restructuring for small borrowers.
A study conducted in the United Kingdom identifies failed firms based on the regulations outlined in the Insolvency Act of 1986 The Act provides five courses of action for these firms: administration, company voluntary arrangement, receivership, liquidation, and dissolution.
In Vietnam, the Law on Bankruptcy, enacted by the National Assembly on June 15, 2004, stipulates that a company may 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 process 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 plan includes several key measures: mobilizing new capital, changing 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.
Vietnamese firms rarely declare bankruptcy in the High Court due to the complexity of administrative procedures and the difficulty in obtaining relevant information Despite operating for over fifteen years, publicly listed companies on the Ho Chi Minh Stock Exchange are not required to file for bankruptcy, even when facing dire financial situations.
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 several articles to the listing regulations In a similar vein, the Ha Noi Stock Exchange adopted Decree 324/QD-SGDHN on June 4, further aligning its practices with these regulatory updates.
In 2010, regulations were established for the listing of securities, categorizing those with unsatisfactory conditions into various statuses, including warnings, control measures, trading halts, and delisting Stock Exchanges are responsible for issuing warning signs and ensuring full market disclosure regarding these securities Warning signs may be removed if listed companies successfully address the issues that led to their warnings, control measures, trading halts, or delisting.
Certain legal classification conditions for each case indicate a financial nature, particularly in instances involving 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
Negative earnings after tax can lead to significant consequences for a company If a publicly listed company reports negative earnings for three consecutive years and its total accumulated losses surpass its equity, it risks being delisted from the Ho Chi Minh Stock Exchange.
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 dated 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) asserts that utilizing financial ratios is the most effective method for identifying companies facing financial difficulties, as these ratios reveal consistent relationships with significant events Ratio models, based on financial statements, highlight differences between stable and unstable firms However, careful interpretation of accounting standards is crucial when analyzing these financial ratios, as they serve as the foundation for financial reporting.
The selection of financial ratios as predictor variables relies on their popularity and predictive power demonstrated in prior research This reliance stems from the absence of theoretical frameworks that establish a causal relationship between financial ratios and bankruptcy Most evidence supporting this connection is empirical, as shown in studies by Jones (1987) and others, including Karels & Prakash (1987), Lam (1994), and Wilson.
Wilson & Sharda (1994) emphasize that the advancement of bankruptcy models is closely linked to the selection of economic variables, which enhances predictive accuracy Jones (1987) highlights the significance of this, noting that numerous studies employing various techniques have yielded consistent ratios.
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 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 However, Wilson and Sharda (1994) contend that the Neural Network method yields superior analysis results when more ratios are utilized, 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 assess whether certain ratios meet the necessary assumptions The research included tests for normality among the selected ratios, revealing that while they do not fully satisfy joint normality assumptions, their deviations differ from those found in other studies Compared to Altman's 1968 study, the ratios analyzed demonstrate an overall improvement in predictive ability.
Karels & Prakash (1987) identified several key financial ratios that align with the seven categories established by Pinches et al (1973) These 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 indicate a company's ability to meet its obligations without disrupting operations A lack of liquidity can lead to difficulties in timely debt repayment (Lam, 1994) Notably, the working capital ratio tends to decrease when companies face financial distress.
Companies in an unhealthy state typically exhibit higher ratios of total debt to total assets (Somerville, 1989) Economic factors such as financial crises, intense competition, and significant fluctuations in interest rates can greatly impact a company's ability to meet its payment obligations.
Hence, levels of leverage are one of indispensable factors in model
Karels & Prakash (1987) highlight that an unstable financial status in firms leads to cash flow issues Cash flow ratios serve as indicators of a company's potential to generate future cash flows Additionally, there is 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 a dividend cut (Somerville, 1989; Lau, 1987).
Activity ratios, including asset turnover, sales per cash, and sales per receivables, are significant for researchers (Zavgren, 1985; Somerville, 1989) These ratios have long-term implications, particularly indicating that companies in distress often exhibit lower values Specifically, the sales per receivables ratio serves as a measure of a company's likelihood of recovering its debts.
Foster (1986) identified the market price of shares as a crucial indicator of bankruptcy, despite it not being a ratio, due to the limitations of financial statements in providing essential information compared to market data Similarly, Karels & Prakash (1987) concluded that financial statements are less effective than the market in predicting bankruptcy probabilities.
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 construction (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 with ratios in a multivariate analysis, leading to the widespread use of Multivariate Discriminant Analysis (MDA) for predicting financial distress MDA enhances the limitations of univariate analysis by capturing the multidimensional aspects of a company, thereby avoiding the conflicting signals often produced by univariate methods.
MDA is based on two key assumptions that are frequently violated: the requirement for multivariate normal distribution of variables and the necessity for identical covariance matrices among predictors across companies To address these issues, Jones (1987) employed log and square root transformations, along with the removal of outliers, to enhance the validity of the first assumption Subsequently, he utilized quadratic discriminant analysis to tackle the second assumption.
The analysis presents contrasting viewpoints, with Altman, Haldeman, and Narayanan (1977) arguing that the quadratic model exhibits heightened sensitivity to the derivation sample, resulting in poor classification performance in the holdout sample.
The validity test's performance contradicts 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 technique's failure to account for errors in data, as well as its inability to manage missing values effectively.
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; when a company exceeds this threshold, it indicates a higher likelihood of insolvency.
This method avoids the restrictive assumptions of MDA but assumes that the costs of type I errors (classifying a bankrupt company as non-bankrupt) and type II errors (classifying a non-bankrupt company as bankrupt) are equal Additionally, it posits that changes in independent variables make midranges of probabilities more sensitive than the extremes.
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
Tests utilizing WESML effectively eliminate biases linked to the assumption of equal type I and type II errors (Zmijewski, 1984) Without appropriate corrections, these methods may yield inaccurate probabilities, particularly if the proportions of failing and stable firms differ between the overall population and the sample (Jones, 1987) To address the costs of misclassification and 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 from Somerville (1989) suggests that Logit Analysis outperforms MDA, while Hamer (1983) also finds that Logit Analysis yields slightly more accurate results compared to MDA However, a study from 1992 does not reach a definitive conclusion on which technique is superior.
Ohlson (1980) demonstrates that the logit model effectively addresses the limitations of the Multivariate 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.
The US stock exchange has seen a study involving 105 failed firms and 2000 non-failed firms over a span of at least three years Three predictive models were developed: the first forecasts firm failure within one year, the second within two years, and the third assesses failure risk over either one or two years The probability of failure for firms in each model is determined using the logistic function.
Jones (1987) argues that Logit analysis models are preferred over MDA due to their strong theoretical foundation for evaluating results Additionally, Harrell and Lee (1985) note that Logit models remain effective even when all MDA assumptions are met.
Artificial Neural Networks (ANN) have been shown to be superior to other methods in predicting financial distress, as evidenced by various studies (Charitou and Kaourou, 2000; Tan and Dihardjo, 2001) However, a significant limitation of ANN is its "black box" nature, which obscures how the network differentiates between failing and non-failing companies (Hawley, Johnson, and Raina, 1990) Additionally, ANN does not provide insights into the contribution of each variable in the final classification, limiting the understanding of variable significance.
Hypotheses
The 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
Sales per cash is inversely associated with the likelihood of business 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 However, the majority of these models are fundamentally rooted in the frameworks established by 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,
This study utilizes the advantages of logistic regression to investigate the influence of financial ratios on financial distress in listed companies in Vietnam.
Research Methods
The model
Logit analysis is a statistical technique that extends the Linear Regression model by incorporating a single dependent variable alongside multiple independent variables While it shares some similarities with the cumulative normal function, Logit analysis is generally more user-friendly from a computational perspective 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
Due to Y being a binary dependent variable rather than continuous, issues like heteroskedasticity and boundary problems arise, as the right-hand side is infinite while the left-hand side is constrained between 0 and 1 To address these challenges, the logit function is employed in the following form:
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 model is utilized to predict the odds of an event occurring, focusing on the range of the logistic distribution rather than forecasting probabilities between 0 and 1 At the midpoint of the distribution, represented by Pi = ẵ, changes in independent variables exert the strongest influence on the probability of selecting a specific option Conversely, near the endpoints, significant fluctuations in independent variables result in only minor changes in probability (Pindyck & Rubinfeld, 1991) The Logit model relies on the cumulative logistic probability function.
Pi represents the probability of companies being in one of two states (Y = 1 or 0) based on eleven financial ratio variables The model predicts one state, while the other state is represented by 1-Pi The logit analysis model calculates the coefficients (βi) using an empirical dataset that includes actual final states and financial ratio values The model's predictive accuracy relies on the alignment between the predicted state (Pi) and the actual state Maximum likelihood estimation is used to determine the model parameters It has been demonstrated that the impact 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.
(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 indicates that there is a lack of a theoretical model for predicting financial distress, which complicates the selection of relevant variables Most studies have relied on empirical processes to derive financial ratios.
The study by Laitinen (2000) highlights the challenges in developing an appropriate set of financial ratios and collecting financial statements It focuses on eight out of nine independent variables selected from Karels & Prakash (1987), which are tested for univariate normality, multivariate normality, and lognormality Although the results indicate that the data is not entirely univariately or multivariately normal, the deviation from multivariate normality is less significant compared to ratios used in other studies Additionally, a comparison with Altman's research reveals noteworthy insights.
& 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) tot nghiep down load thyj uyi pl aluan van full moi nhat z z vbhtj mk gmail.com Luan van retey thac si cdeg jg hg
8 Sales per receivables is computed by dividing Sales by Receivables
The independent variables are incorporated into a discriminating model to predict a company's status, with their respective weights determined through logistic regression analysis.
The dependable variable in this analysis is a company's financial state, categorized as dichotomous variables: state 1 represents non-financial distress companies, while state 2 denotes companies experiencing financial distress A company classified in state 1 is deemed healthy and compliant with listing requirements, whereas state 2 refers to financially distressed companies 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 published annually on December 31 were sourced from two finance websites, www.cafef.vn and www.cophieu68.com The financial ratios used in the model were derived from the data obtained from these statements The sample included 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 those that have been delisted from the Vietnam Stock Exchange, which is still in its early stages The supervision of financial distress is an evolving issue, and there are limitations in research data, particularly regarding the number of affected companies This impacts the selection of the research timeframe The study focuses on a group of 28 listed companies identified as financially distressed, categorized into 9 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 this study, companies were classified as either financially stable or financially distressed based on their financial situation for each year of data Due to the removal of some delisted firms from the sample, there was insufficient financial data for these companies, lacking four consecutive years of records Ultimately, the analysis comprised a total of 982 samples classified as non-financially distressed and 36 samples identified as financially distressed.
Using Eview software, financial ratios were analyzed through a Binary model to determine their relationship with the future state 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 of companies.
One year before failure, six out of eight financial ratios for non-failing companies were higher than those for failing companies Specifically, the mean values of working capital (WOCA), gross profit margin (GROPROM), and cash flow per share (CASPSHARE) for financially distressed companies declined as the failure year approached This trend aligns with previous findings that indicate a negative relationship between cash flow and profitability ratios and the likelihood of failure.
During the failure period, the mean of DEBTTOTAL (total debt to total assets) exhibited an increasing trend, contrasting with lower levels observed in healthy firms This clearly illustrates that financial leverage is positively correlated with the likelihood of failure.
Unexpectedly, companies in distress exhibited a higher SALEPERCA (sales per cash) compared to their non-failure counterparts In fact, 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
WOCA 0.219277 0.221556 0.076828 0.333859 tot nghiep down load thyj uyi pl aluan van full moi nhat z z vbhtj mk gmail.com Luan van retey thac si cdeg jg hg
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 indicators of distress and are crucial for predicting failure probabilities 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 single 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 address the issue of multicollinearity caused by high correlations between two independent variables, it is essential to omit one variable Failing to do so may render both variables insignificant, leading to inconclusive test results.
Based on the correlation analysis of eight explanatory variables, eight potential models were identified, each comprising five independent variables after removing those with high correlation For instance, a model that includes Earnings Per Share (EPS) excludes Cash per Share The following summarizes the results of the logistic regression for 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
SALEPERCA tot nghiep down load thyj uyi pl aluan van full moi nhat z z vbhtj mk gmail.com Luan van retey thac si cdeg jg hg
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 failure, indicating that lower values of these ratios correlate with a higher probability of financial distress Additionally, z-tests confirm 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, indicating that the asset turnover ratio (ATURNOVER) reflects a firm's efficiency in utilizing its assets to generate sales or revenue A company that effectively generates sales will experience increased cash inflows, thereby reducing the risk of financial distress, as supported by the study conducted by Altman and Lavallee (1981).
Sales per receivables (SALEPERRE) indicates a firm's likelihood of collecting revenue post-sale Quick revenue collection allows for faster debt settlement.
Cash flow per share (CASPSHARE) is inversely related to the likelihood of a firm experiencing financial distress, as noted by Westgaard and Van der Wijst (2001) In other words, higher earnings reduce the probability of a firm encountering financial difficulties.
A decrease in 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 is the ratio indicating how many times cash turns over annually, typically higher for companies in financial distress In Models 5, 6, and 7, the relationship between SALEPERCA and the probability of failure was significantly positive at the 5% level, leading us to reject H6 This suggests that a high sales to cash ratio may indicate insufficient cash reserves, potentially resulting in financial difficulties if further financing is not accessible at reasonable rates.
In our analysis of Model 5 and Model 8, we found that the H7 model was not rejected The results revealed that GROPROM had a negative effect on the probability of failure, significant at the 5% level, indicating that higher values 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, shows a significant relationship at the 5% significance level Specifically, the total liabilities to total assets ratio is a crucial financial indicator that positively correlates with the likelihood of a firm experiencing financial distress These findings align with previous research conducted by Mohamed et al (2001) and Nur.
Table 3 presents the McFadden R-squared values for each model, which serve as the coefficient of determination This metric quantifies the percentage of variance in the dependent variable that can be accounted for by the model's independent variables.
In comparison to model 5, where the EPS and ATURNOVER variables were substituted with CASPSHARE and SALEPERRE, the McFadden R-squared of model 1 increased from 21.74% to 29.89% Additionally, the Akaike information criterion for model 1, at 0.2325, was lower than that of model 5.
(0.2582) tot nghiep down load thyj uyi pl aluan van full moi nhat z z vbhtj mk gmail.com Luan van retey thac si cdeg jg hg
The analysis of model 3 and model 5 revealed a decrease in McFadden R-squared from 29.25% to 21.74% when EPS was replaced with CASPSHARE, indicating that EPS has a stronger impact on the probability of failure compared to CASPSHARE Furthermore, the examination of model 7 and model 8 demonstrated that ATURNOVER significantly influences the probability of failure more than 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 failure.
Conclusions
Summary
This study investigates the correlation between various 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.
The 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 probability of failure Additionally, Earnings per Share also plays a significant role in this relationship.
Asset turnover and cash per share are crucial financial ratios that significantly impact a firm's condition Total debt to total assets and sales per cash are positively correlated with the likelihood of a firm experiencing financial distress Additionally, this thesis's findings on profit and activity ratios align with previous research, including Altman's Z-Score model from 1978.
The findings from the logistic regression analysis reveal that financial distress among listed companies on the Vietnam Stock Exchange is primarily caused by inefficient operational activities, which result in losses in earnings per share (EPS), as well as inadequate asset management that hinders revenue generation, reflected in asset turnover.
Understanding the relationship between financial ratios and a company's health is crucial for investors in Vietnam By analyzing these ratios, investors can gain insights into a company's financial stability, helping them identify firms in distress This knowledge enables them to minimize risks when investing in stocks, ultimately leading to more informed investment decisions.
Limitation of the research study
One limitation of the study is that independent variables do not sufficiently explain financial distress, as indicated by a McFadden R-squared value of around 30% This is partly due to the data being collected only from 2007 to 2011, a period with a limited number of companies experiencing financial distress Although the number of distressed firms increased in 2012, it was impossible to gather financial ratios due to the unavailability of financial statements Consequently, the restricted data hindered the identification of additional ratios, such as market ratios, that could influence the probability of failure.
Financial ratios, derived from financial statements, face challenges due to the inherent issues in interpreting accounting standards Additionally, the reliability of these financial statements significantly impacts the outcomes of the analysis, presenting another limitation.
The identified limitations prompt the need for additional research to address the gaps in the thesis The conclusions drawn regarding the relationship between variables and failure probabilities serve as a foundation for this future work However, it is important to note that each financial market exhibits unique characteristics, leading to varied reactions Consequently, ongoing research into a range of financial ratios, including the incorporation of macroeconomic factors like inflation, remains essential.
Future research should focus on analyzing data over an extended period, including the two to three years leading up to financial events Additionally, it is essential to explore beyond traditional techniques to enhance the understanding of these financial dynamics.
Logistic Regression and Multivariate Discriminant Analysis, some new methods have proven the advantage of predicting the financial distress, i.e
Neural networks play a crucial role in forecasting failures in Vietnam, and their techniques will be considered in future research These studies significantly contribute to the advancement of predictive analytics in the region.
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& Accounting, vol 29, no 3, pp 497-520 tot nghiep down load thyj uyi pl aluan van full moi nhat z z vbhtj mk gmail.com Luan van retey thac si cdeg jg hg
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
Obs with Dep=1 946 tot nghiep down load thyj uyi pl aluan van full moi nhat z z vbhtj mk gmail.com Luan van retey thac si cdeg jg hg
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
Obs with Dep=1 946 tot nghiep down load thyj uyi pl aluan van full moi nhat z z vbhtj mk gmail.com Luan van retey thac si cdeg jg hg
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
Obs with Dep=1 946 tot nghiep down load thyj uyi pl aluan van full moi nhat z z vbhtj mk gmail.com Luan van retey thac si cdeg jg hg
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
Obs with Dep=1 946 tot nghiep down load thyj uyi pl aluan van full moi nhat z z vbhtj mk gmail.com Luan van retey thac si cdeg jg hg
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
Obs with Dep=1 946 tot nghiep down load thyj uyi pl aluan van full moi nhat z z vbhtj mk gmail.com Luan van retey thac si cdeg jg hg
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
Obs with Dep=1 946 tot nghiep down load thyj uyi pl aluan van full moi nhat z z vbhtj mk gmail.com Luan van retey thac si cdeg jg hg
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
Obs with Dep=1 946 tot nghiep down load thyj uyi pl aluan van full moi nhat z z vbhtj mk gmail.com Luan van retey thac si cdeg jg hg