The commercial and corporate sector has been significantly expanding in recent years. Businesses have grown in size and complexity over time. Growing businesses face a range of challenges. (Nagy et al., 2018). Businesses, in essence, are entities created by people or institutions with the primary goal of profit maximization; however, there are other equally essential goals such as continuing to compete, developing, and performing social duties in society.
As global economic rivalry heats up, organizations not only strive for maximum profit but also existence. Management’s capacity to manage is intimately linked to the company’s existence (Kwon et al., 2020). Auditors issues an opinion to determine the future viability of the business. Financial statements that will be audited must be prepared by businesses. If there is a very strong indication of the firm’s insolvency, auditors are required to reveal the fact with the viability (going concern) of the client company The going concern assumption is a fundamental principle in the preparation of financial statements. The assessment of an entity’s ability to continue as a going concern is the responsibility of the entity’s management. The appropriateness of the use of the going concern assumption is a matter for the auditor to consider on every audit engagement (Khanifah et al., 2020).
With the help of numerous non-financial institutions, the economy is growing and flourishing. Non-bank financial institutions often known as non-financial firms are businesses that would provide financial and non-financial services without having a banking license (Chepkemoi et al., 2019). Non-financial corporations principally engage in the production of market goods and non-financial services and their financial transactions are wholly distinct from those of their owners. Private and public businesses, holding companies, NGOs, and alliances are examples of non-financial businesses. Non-financial firms have grown in number and form during the Great Recession, playing a critical role in addressing credit demand not provided by traditional banks (Eizaguirre et al., 2019). Non-financial businesses play a significant role in society. Non-financial businesses engage in activities that benefit the nation. The operations of non-financial corporations are heavily impacted by the public’s or consumers’ confidence (Dögüs, 2018).
On January 26, 1959, Bank Negara Malaysia was established (Kitamura, 2020). It is vital to Malaysia’s economic development in the banking and non-financial sectors. According to the Malaysian Securities Law 1993, the Securities Commission was founded in 1993 to promote the growth of the Malaysian securities market (Kim-Soon et al., 2020). Breaches of the Malaysian stock exchange rules and the Malaysian stock exchange listing requirements are taken extremely seriously by the Malaysian Stock Exchange (Fatima et al., 2015) because they have the ability to jeopardize the privileges and protection of an investor.
In recent decades, the use of financial analysis has grown. The goal of financial analysis is to analyze whether an entity is stable, solvent, liquid, or profitable enough to warrant a monetary investment. It is used to evaluate economic trends, set financial policy, build long-term plans for business activity, and identify projects or companies for investment. (Lane & Milesi-Ferretti, 2018). The new age of digital globalization also poses challenges. Companies can enter new markets, but they are exposed to pricing pressures, aggressive global competitors, and disruptive digital business models (Lee & Shin, 2018). Globalization is increasingly defined by the flow of data and information (Danyluk, 2018). All of this posed a rapid issue for the emergence of big, limited, and multi-national corporations.
Predicting company failures is critical since the consequences of business failure result in significant financial and non-financial losses (Balasubramanian et al., 2019). Managers, shareholders, the government, suppliers, consumers, and workers, among other stakeholders, would benefit greatly from a model that could properly anticipate company failure in real-time. Researchers in the past decade have realized that failure does not happen suddenly. Usually, failure take years; therefore, it is necessary to develop an early warning model that can evaluate the strengths and weaknesses of the financial features of companies (Jayasekera, 2018). Classic statistical approaches, data mining, and machine learning approaches were widely used to estimate the likelihood of company failure. Financial distress or insolvency are two examples of financial failure. When a company is insolvent, it means it is unable to fulfill its present commitments on time. Bankruptcy, on the other hand, occurs when a company’s total obligations exceed its fair market worth (Desai et al., 2020). The most common financial statements are profit and loss statements, balance sheets, and cash flow statements, which are used to evaluate the success of a company and its management. Various ratios may be generated from the financial accounts to analyze the current performance and future prospects of the company in issue (Hosaka, 2019).
2. Literature Review
Firms categorized as PN17 (Practice Note 17) on Bursa Malaysia are often financially challenged businesses. The Malaysian Stock Exchange categorizes listed firms in financial distress into two groups: PN4 and PN17 (Alifiah, 2014). The abbreviation PN stands for Practice Note. The Malaysian Stock Exchange launched PN17, which is for financially distressed companies (Iskandar et al., 2012). Corporations that come within the PN17 classification will need to submit a plan to the approving authority to reorganize and resuscitate their business to keep their stock exchange listing. Many investors are perplexed as to why certain firms have become PN17 (Kim-Soon et al., 2020). When closely examined, it appears that many businesses are either poorly managed or have a terrible track record. Investors continue to keep their investment in these PN17 firms for a variety of reasons, including a lack of knowledge about the business’ financial performance and a lack of awareness that they are holding stocks of firms classed as PN17 (Yee, 2018). Moreover, investors may be unaware that these firms have been delisted.
Financial analysis involves using financial data to assess a company’s performance and make recommendations about how it can improve going forward. It plays a crucial role as an indicator of vulnerabilities, thus offering predictability. Therefore, financial ratios remain the key indicator of vulnerability in any firm (Alnori & Alqahtani, 2019; Xu & Wang, 2009). Classical examinations may be unable to discover errors and variances in financial management reporting in some circumstances (Tran & Nguyen, 2020; Du Jardin & Séverin, 2011).
Financial analysis is also employed in review projects to produce clear and accurate financial and accounting reporting (Roychowdhury et al., 2019). For more than 70 years, financial distress prediction models have been explored (Palmer et al., 2004). Empirical research was frequently used to established statistical models, and an attempt to describe the findings using computational equations (Kim- Soon et al., 2013). Beaver (1966) was the one to finish a research project in financial distress. He devised a system known as sophisticated financial ratios. Well ahead, different researchers (Karugu et al., 2018; Bhunia & Sarkar, 2011) from around the globe, conducted a comparable study in this subject, with Altman being the most popular model amongst them. Financial ratios are used by financial analysts to assess a company’s productivity, liquidity, and creditworthiness, as well as management’s competence in the creation and execution of financial investment policies.
Since August 9, 2010, there are 34 PN17 list companies that are listed on the Malaysian Stock Exchange, and these firms have entered the PN 17 List in compliance with existing regulations (Kim-Soon et al., 2020). There are other corporations that were placed on the PN 17 list in 2005 and are yet to fix their financial issues (Yee, 2018). Companies that have been cautioned about not disclosing information or reconsidering their regularization plans are among them. Corporations that did not comply were delisted from the Malaysian Stock Exchange due to their inability to comply with the rules (Najib & Cahyaningdyah, 2020).
Furthermore, several individuals are unaware that they own shares in firms that have been categorized as PN17 firms (Norziaton & Hafizah, 2019). Investors are sometimes unaware of these enterprises’ written-off notifications. Additionally, even with the stock market rebound, almost all investors continue to have concerns about the financial health of several publicly traded firms, prompting numerous inquiries, concerns, and remarks about the future of PN17 (Liloshna et al., 2017). On the PN17 Malaysian companies registered on the Malaysian Stock Exchange, analytical investigations and scientific research are essentially nonexistent (Najib & Cahyaningdyah, 2020).
2.1. Hypotheses Development
The following are the hypotheses that were developed for this empirical research:
H1: There is a significant difference between distress and non-distress PN17 companies.
H2: There are financial distress companies in the non-financial sector that are listed on the Malaysian Stock Exchange.
2.2. Model Altman Z-score
Financial ratios are one piece of information that may be used to forecast a company’s performance, including information regarding impending insolvency, which is important to many individuals, including investors and creditors. In 1968, Altman Edward proposed a methodology for predicting a company’s imminent insolvency. Altman discovered that some financial parameters have greater “predictive power” than others in forecasting financial distress and bankruptcy through research with a sample of firms that had gone bankrupt (Altman, 1968). Altman discovered four financial parameters, known as Z-score that may be used to detect a company’s indebtedness (Altman et al., 2013).
Altman et al. (2017) used a sample of 33 pairs of companies that were bankrupt and not bankrupt to develop the exact formulation of the model, which was able to predict 90 percent of bankruptcy cases a year before they happened. The Altman Z-Score is used to predict the bankruptcy of the business using traditional financial ratios and a statistical method known as the Multiple Discriminant Analysis (MDA) (Chijoriga, 2011). MDA may be used to find the factors that distinguish the existing population and may also be used as grouping criteria (Thai et al., 2014). “MDA generally is Z = V1(X1) + V2(X2) + … +Vn(Xn) where V1 and V2 are parameters (weights) while X1, X2, …, Xn are financial ratios that contribute to predictive models”.
Altman successfully used the financial ratios of the Z-score model to categorize firms into groups with a high chance of bankruptcy or a group of firms that are likely to experience bankruptcy. The Z-score is considered to be 90% accurate in forecasting business failure one year into the future and 80% accurate in forecasting it two years into the future (Prasetiyani & Sofyan, 2020).
The disadvantage of this approach is that there is no precise time limit as to when bankruptcy will occur after the findings are known since Z-scores are lower than the standard established (Lord et al., 2020). The Z-score model is based on historical financial data, which is a big problem in economic decision-making because some of the present circumstances can be different from the past. There is a lack of conceptual base in the Altman Z-score model and a lack of sensitivity to the time scale of failure i.e. time factors may not be fully taken into account. Also, some of the accounting policies used by companies make it difficult to get the required result from the Altman Z-score model. Nonetheless, firms can use the Altman technique to take preventive actions (advance warning) while they are already in a state of bankruptcy (Altman, 2018). The original Altman Z-score formula is as follows:
Z-score = 0.012X1 + 0.014X2 + 0.033X3 + 0.006X4 + 0.999X5
X1 = Working capital/total assets
This equation represents a company’s ability to create net working capital from all of its assets. The gap between current assets and current liabilities is known as working capital.
X2 = Retained earnings/total assets
This ratio represents the company’s capacity to create retained earnings as a percentage of total assets. This metric is important for determining if the company’s cumulative earnings is sufficient to cover its entire assets.
X3 = Earnings before interest and taxes/total assets
This ratio demonstrates a company’s capacity to profit from its assets before interest and taxes.
X4 = Market value of equity/book value of total debt
This ratio demonstrates a company’s capacity to satisfy its market value of equity commitments (common stock). The value of the equity market is calculated by multiplying a company’s outstanding shares by its current market price (per share). The book value of debt is calculated by adding current and long-term obligations together.
The value of Z derived is used to classify a healthy corporation and a bankrupt corporation, namely:
1. If the Z-score is less than or equal to 1.81, the firm is in financial distress and poses a significant risk (Mo et al., 2021).
2. The firm is considered to be in the grey region if its Z-score is between 1.81 and 2.67 (gray area) (Akra & Chaya, 2020). In this situation, the firm is experiencing financial difficulties that need to be addressed by competent management. The firm may risk insolvency if it is too late and improperly handled. So, in this grey area, it’s possible that the firm may go bankrupt, but it’s also possible that it will not. It all relies on how the management can take prompt action to address the firm’s difficulties.
3. When the Z-score is more than 2.67, it indicates that the firm is in good condition and that the risk of bankruptcy is low (Akbar et al., 2019).
3. Research Methodology
The technique for this study must be methodical to conduct an organized investigation of the influence of distressed company indicators. The goal of the study is to justify the best technique by discussing ideas and approaches and choosing the best ratios for their strength.
3.1. Data Source and Samples Selection
The sample of this study includes 84 listed companies in the Kuala Lumpur Stock Exchange (KLSE). Of the 84 companies, 52 are considered high-risk companies and 32 are considered low-risk companies. High-risk companies are companies that were given ratings of 2* and low-risk companies were companies that were given ratings of 7*. Financial and insurance companies were excluded from the list due to their high dependency on economic conditions. The data was collected from Stock Performance Guide, Malaysia (2015 September Edition) for the 82 companies (see Table 1 and Table 2, respectively).
Table 1: List of Companies Categorized as High Risk
Table 2: List of Companies Categorized as Low Risk
3.2. The Trend Approach
The trend approach is used to assess the firm’s overall market price direction. Distressed firms are experiencing a downward trend. The non-distressed firms are on an upward trend. Furthermore, the trend may be used to determine support and resistance (Becchetti & Sierra, 2003).
3.3. Multiple Discriminant Analysis
Multiple discriminant analysis (MDA) is a statistical methodology for categorizing people or things into mutually exclusive and exhaustive groups (quantitative dependent variable) based on a set of characteristics (independent variables) of the people or things (Jaffari & Ghafoor, 2017). MDA creates a discriminant function, which is a function of a set of variables that are evaluated for samples of events or objects and used as an aid in discriminating between or classifying them. The objective of discriminant analysis is to develop discriminant functions that are linear combinations of independent variables that will discriminate between the categories of the dependent variable perfectly.
The investigation was limited to a sample of companies that matched the 82 firms that were chosen from the Malaysian Stock Exchange’s non-financial sector. The Altman (1968) model was used to identify the financial health of the firms to meet the goal of the study defined in this research. Using the Altman Z-Score, financial failure thresholds were used to distinguish between low and high-risk organizations. According to Kim-Soon et al. (2020) and Christopoulos et al. (2019), financial performance was measured using a set of thresholds.
4.1. Group Differences
With the reduced data, the MDA 4-Variable Malaysian data was examined. This data collection was used to create an MDA-based model. There were 404 records in this data collection, however, 16 were eliminated due to outliers. With the Y response, the MDA function was employed with X1, X2, X3, and X4.
Based on the results in Table 3, the mean values for all four independent variables for high-risk companies are lower than the mean values of low-risk companies. Next, we test whether the differences between the high-risk group and low-risk group for the four financial ratios are statistically significant.
Table 3: Group Statistics
In Table 4, the p-value (Sig.) < 0.05 indicates that the group difference between high-risk and low-risk companies is statistically significant for the independent variable. Here X2, X3, and X4, with Sig 0.000, 0.000, and 0.022, have significant group differences between high-risk and low risk companies, while X1 with Sig 0.844 does not have a statistically significant difference between high-risk and low-risk companies.
Table 4: Test of Equality of Group Means
The smaller the Wilks’ Lambda, the more important the independent variable is to the discriminant function (AlKubaisi et al., 2019). Here X2 and X3 have the lowest Wilk’s lambda, 0.836 and 0.838, therefore they are the most mportant variables, followed by X4, 0.938, and then X1, 1.000.
4.2. Independent Variables and Discriminant Function
A pooled within-groups covariance matrix, which may differ from the total covariance matrix, is displayed in the Pooled Within-Groups Matrices (Yee, 2018). The matrix is created by averaging the covariance matrices for each group separately. It is better to consider the correlation rather than the covariance because it is an external quantity (Keskin et al., 2020).
The within-groups correlation matrix (see Table 5) shows the correlations between the independent variables. Here we see a high correlation (0.993) between X2 and X3, and low or no correlation among the other variables. This indicates that a company with high or low X2 will also have high or low X3 (Yee, 2018).
Table 5: Pooled Within-Group Matrices
Wilks’ Lambda uses the eigenvalue to assess the importance of each discriminant function in MDA (Bhunia, & Sarkar, 2011). In this example, the percent of variation explained is 100%. There is only one discriminant function since there are only two groups (Yap et al., 2010). The eigenvalue is the percentage of variation in the dependent variable that the function can explain. The percentage of variation explained in the dependent variable is the Canonical Correlation (see Table 6 and Table 7).
Table 6: Eigenvalues
Note: aFirst 1 canonical discriminant functions were used in the analysis.
Table 7: Wilks’ Lambda
4.3. Discriminant Function for Classification
The discriminant function is the function used in this study to calculate the discriminant score for each company. The Canonical Discriminant Function Coefficients (see Table 8) provides the discriminant function coefficients for the four financial ratios.
Table 8: Canonical Discriminant Function Coefficients (Unstandardized Coefficient)
Using the discriminant function, this study can calculate the discriminant score for all 84 companies. Here group centroids are the average discriminant scores for the companies in the high-risk group and the low-risk group. Therefore, the study uses the two group centroids to establish the cutoff score for classifying a company as high risk and low risk.
Here the high-risk companies have an average discriminant score of −0.399 and the low-risk companies have an average of 0.648. As the number of companies in the two groups is unequal in size, (52 for the high-risk group and 32 for the low-risk group), the optimal cut-off point is the weighted average of the two centroids (Table 9).
Cut off score = 52/84 × (−0.399) + 32/84 × 0.648 = 0
Table 9: Functions at Group Centroids
Note: Unstandardized Canonical Discriminant Functions Evaluated at Group Means.
Using this discriminant function, companies with scores less than 0 will be classified as high risk and companies with scores more than 0 will be classified as low risk (Yee, 2018).
4.4. Discriminant Function Evaluation
The classification results (see Table 10) are used to assess how well the discriminant function works. The accuracy rate of the discriminant model is 67.9% in predicting high-risk and low-risk companies. The model can identify 92.3% high-risk companies, specificity, and 28.1% of the low-risk companies, sensitivity. This is a very conservative model in predicting high-risk companies, and the model is good for risk-averse investors.
Table 10: Classification Results
Note: 67.9% of original grouped cases are correctly classified.
This study has a positive correlation between the discriminant score and Altman’s Z score. The correlation of the discriminant score and Altman’s Z-score is 0.508, and the correlation is statistically significant with a p-value < 0.05. There is a significant correlation between our model and Altman’s Z-score (see Table 11).
Table 11: Correlations
Note: Correlation is significant at the 0.01 level (2-tailed).
Several conclusions may be drawn from this research. To begin, there is a difference in identifying the financial status of low-risk and high-risk companies listed on the Malaysian Stock Exchange in the non-financial sector using the Altman Z-Score 1968 model. Second, several non-financial companies listed on the Malaysian Stock Exchange are experiencing financial difficulties. The findings of this study show that the Altman model may be used to forecast a company’s financial collapse. It dispelled any reservations about the model’s legitimacy and the utility of applying it to evaluate the likelihood of a company’s financial collapse. This is in accordance with research conducted by Kim-Soon et al. (2020), AlKubaisi et al. (2019), Yee (2018), and Bhunia and Sarkar (2011). According to the findings, the Edward Altman model is a good tool for investors to anticipate the financial collapse of organizations.
The findings of this study have significant consequences for investors, creditors, and corporate management. Portfolio managers may make better choices by not investing in companies that are risky and on the verge of a financial failure if they understand the variables that contribute to corporate distress. The findings can be used to offer management early warning indicators of deterioration in the company’s financial condition so that remedial actions may be taken to reduce the risk of financial distress.
Future studies should cover various stock exchanges or bourses, as well as bigger sample sizes of corporations in both categories. In such research, the risk of attrition arises from the fact that a firm may be studied for a period of say 5 years prior to financial difficulty. While such organizations would give a wealth of data that may aid in the development of more accurate financial crisis prediction models, the danger that comes with their inclusion is clear.
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