Predicting financial stress and earning management using ratio analysis.
Generally when thinking of making an investment, what pops up in our mind of course is investing in companies that maximize shareholders' wealth and offer good returns. It is traditional that majority of these companies fall within the first 100 listed companies on the stock exchange based on market capitalization ranking. Many beginning investors in their quest to selecting profitable stocks look for example, at a stock trading at $10 and another trading at $20 and erroneously conclude the latter company is worth twice as much as the former and jump to make decision on that basis (Lesmond et al, 2004). Share price however does not give complete information about the financial performance of a firm as it does not provide the total number of shares at the disposal of each company. Malkiel (2003) argues that going by the efficient market hypothesis, stock market price is less predictable and could not be the sole determinant of company performance. Using market capitalization instead of individual company's share price ensures adequate standardization that gives a true comparison of the value attributed by the stock market to a given company's stock and compares "apples to apples" (Malkiel, 2003). Market capitalization represents the value worth of a company and it can be derived by multiplying the company's share price with the number of outstanding shares.
Being that as it may, the market capitalization of a company does not necessarily mean that it is the absolute fundamental value of a company as it merely represents the value that the "market" is placing on a company. More importantly, since market capitalization is a function of the share price and number of shares outstanding, manipulations of the market capitalization value is possible by fraudulent managers as evidenced in the case of Enron (Henry et al, 2006). In 2001, Enron filed the largest bankruptcy in American history which amounted to $638 million loss in third quarter earnings and upon investigation, the company had revealed that it had overstated four years earnings of $586 million and also hiding off $3 billion of debt by creating fictitious partnership (Vinten, 2002). The resultant effect of the above was the plunge in Enron's share price from $90 to less than $1 due to investors' loss of confidence in their activities and stewardship reports.
In Malaysia, a typical example is the case of Megan unraveled in 2007 when it failed to pay its creditors on the fourth payment. The special audit performed revealed that there are substantial irregularities in the Group's past financial statements, particularly as disclosed in the quarters ending 30 April 2006, 31 July 2006, 31 October 2006 and 31 January 2007, leading to its financial position having been materially misstated in the past. Available evidences implicated the top management as the major culprit in the fraudulent reporting.
The public inclusive of the investors and regulatory authorities became agitated after the incidence why those staggering corporate crime could not have been discovered by the audit firms. Firth et al. (2005) pointed that after all, auditors owe their clients as well as the public the responsibility to exercise due care which includes professional scepticism and judgment in adhering to professional standards in the course of their audit engagements.
As a build-up to tackle the challenge caused by the fraudulent financial reporting, Hogan et al. (2008) noted that regulatory bodies have come up with different laws as is the case in the United States with the setting up of Sarbanes Oxley's (SOX) Act of 2002. Aguilera (2005) explains that SOX saddles regulatory bodies, auditing firms, corporate executives, and boards with additional tasks to solve conflict of interests and increase firm accountability. It is however noteworthy that despite the guidance and deterrents that SOX has brought the reporting world, pockets of lacuna still exist in the standard that fraudulent managers usurp to manipulate financial reports.
As a result, academics and practitioners alike in the quest for solutions have suggested various techniques to detecting fraudulent reporting in order to reduce the information asymmetry between preparers and users of financial reports (Chen and Du, 2009). This study uses Altman Z-Score, Beneish model, as well as M-Score on set of data on small Malaysian firms to identify the presence of financial manipulations in their annual reports.
Historical market capitalization data has shown that the larger the market capitalization becomes, the lower the risk. But the case of Enron and Megan Media cited above presented contrary views that create doubts in the minds of investors as to whether the financial statement of firms are really reliable. This has created negative impression about the auditing process, reduce the integrity of auditors as well as trust in the financial reports.
As part of efforts aimed at reviving the image and quality of financial reporting, this study examined three models (Altman Z-Score, Beneish model and M-Score) aimed at creating a benchmark or framework for identifying fraudulent reporting in small firms listed on the Bursa Malaysia. Thus, the main objectives of this study are first to investigate the probability of bankruptcy on small listed firms on Bursa Malaysia using Altman Z-Score. Secondly, to investigate the possibility of manipulated earnings by small listed firms on Bursa using Beneish Model and M-Score.
Due to the corporate failure, large losses had been suffered by individuals, businesses and even government and the trend shows no end in sight as more casualties are been recorded even after the promulgation of SOX. The purpose of this study is to compliment the concerted efforts of regulators, practitioners and other users of financial reports in making adequate and informed judgment by adding to the literature on fraud detecting techniques. This will serve to reduce the incidence of fraudulent financial reporting and improve the ability of auditors, firms and investors to detect fraud.
Thus, the counter productiveness effect of bankruptcy on the economics at large can be nipped in the bud by protecting the investor and other members of the business community through the use of models that give insights into the going concern nature of listed businesses. The outcome of this study will also assist authorities such as Securities Commissions and Bursa Malaysia to continually monitor the going concern of listed corporations and make informed decisions to avert negative shock to the economy.
The paper proceeds as follows: Section II reviews relevant prior research. Section III describes research method. Section IV is on findings and discussion. Finally, Section V and VI present the concluding remarks and future research and limitation respectively.
Concept of Beneish Model:
The Beneish Model was used to assess the likelihood of earnings manipulation. According to Beneish (1999), this model contains eight ratios that capture either financial statement falsifications that can result from earnings manipulation (DSR, AQI, DEPI and Accruals) or indicate a tendency to engage in earnings manipulation (GMI, SGI, SGAI, LEVI). Companies with a higher score are more likely to be manipulators. The ratio formula are as per in table 1.
Beneish (1997) validates his model in three ways; firstly the model correctly classifies 64% of firms charged with financial reporting violations whereas accrual model identify between 23% and 30% of such firms manipulate their earnings. Secondly, the model distinguished manipulators firm with large accruals and abnormal accruals. Lastly, the model distinguished earnings manipulators from all nonmanipulators in the same industry.
The evidence that financial statement data are useful in detecting manipulation and assessing the reliability of accounting earnings has attracted the attention of professionals and educators. The models have been used as tools for identifying earnings manipulation and assessing earnings quality in financial statement analysis texts (Fridson and Alvarez, 2002).
The result from Beneish research state that companies that manipulated earnings have a mean SGI of 1.607 and a median of 1.411. Meanwhile, when GMI is greater than 1 the company's gross margins have deteriorated and management is motivated to show better numbers. Manipulators sported GMI of 1.193 at the mean and 1.036 at the median. In the depreciation index, the companies that manipulated their earnings show a mean of 1.077 and a median of 0.966.
As for AQI, if it is greater than 1, it means the company has deliberately deferred costs in an effort to increase the bottom line. Companies in the study that manipulated earnings had median AQIs of 1 and mean of 1.254. Sales and receivable typically stay in fairly consistent trend. If the ratio detects a rise in receivables the change might result from revenue inflation. A company may extend more credit to the customer in order to show there is an incremental in their revenue index. Companies that overstated revenue had a mean of DSRI of 1.465 and median of 1.281.
Meanwhile, SGAI interpret an unbalanced increase in sales as compared to the increase in selling and administrative expenses. Companies that manipulated their sales has median for SGAI is 0.96 while mean is at 1.041. As for LEVI, it is included to capture debt covenants incentives for earnings manipulation. Companies that manipulate their debt have a median of 1.030 and mean of 1.111. Lastly, for accruals, it interprets the extent to which managers make discretionary accounting choices to alter earnings. Companies that manipulated their earnings show a mean and median of 0.034 for accruals.
The limitation of Beneish model is that its estimation is based on the financial information for publicly companies. It is not reliable to be used to study private companies. Besides that, the earnings manipulation in the sample done by Beneish (1999) involves earnings overstatement rather than understatement and therefore this model cannot be reliably used to study companies that operate in circumstances that are conducive to decreasing earnings.
Concept of M-Score:
M-Score helps to uncover companies who are likely to be manipulating their reported earnings. Companies with a higher score are more likely to be manipulators. According to Beneish and Nichols (2009) in the research done on the overvalued equity, M-Score captures a unique profile of the overvalued equity. Therefore, M-Score also is being used as one of the indicators to detect manipulation in the companies. The M-Score formula is constructed based on the combination of Beneish Model ratio. The formula is:
M-Score = -4.84 + 0.92xDSRI + 0.528xGMI +0.404xAQI + 0.892xSGI + 0.115xDEPI 0.172xSGAI + 4.679xACCRUALS - 0.327xLVGI
In this M-Score, Beneish found that firms that scored greater than -2.22 were more likely to be earnings manipulators.
Concept of Altman Z-Score:
Altman's Z is one of the best known statistically derived predictive models used to forecast a firm's impending bankruptcy Moyer (2005). The bankruptcy prediction model is an accurate forecaster of failure for up to two years prior to bankruptcy. Altman originally developed the Z-Score based on a small sample of manufacturing firms.
The five variables that was formulated was selected based on several procedures based on Altman research in 1968 where the first procedure is through the observation of the statistical significance of various alternative functions including the determination of the relative contributions of each independent variable, secondly is on the evaluation of inter correlations among the relevant variables, thirdly is on the observation of the predictive accuracy of the various profiles and lastly is based on the judgment of the analyst. The procedures had been improved in the year 2000. The five variables that being selected to be used to measure the bankruptcy of the companies are:
Where X1= Working Capital/ Total Asset
X2= Retained Earnings/ Total Asset
X3= Earnings before Interest and Taxes (EBIT)/Total Asset
X4= Market Value of Equity/ Book Value of Total Liabilities
X5= Sales/ Total Asset
Z = Overall Index
Some modifications had been done to the Altman Z-Score formula in order to suit the research on healthcare Al-Sulaiti and Almwajeh (2007). From the study it shows that the modification of Z-Score formula are able to predict service organizations' success or failure, with the latter being more predictive in a sample of 65 hospitals.
The result of the research done by Gerantonis, Vergos and Christopoulos (2009) showed that Z-Score succeeded in identifying bankrupted and nonbankrupted companies. It also gives good indication for companies that will not face going concern problem in the foreseeable future. However, Z-Score had some drawbacks such as being higher during strong markets, while they appear lower during weak years.
Altman (1968) suggested that companies that have the possibility of experiencing bankruptcy will score less than 1.8, while companies with a Z-Score of 1.8 to 2.99 were in a grey zone in which distress may or may not happen. Lastly, companies that can be considered as financially sound usually have a score of more than 2.99. However, it is suggested that looking more critically into the trend of the Z-Score in order to interpret the result more precisely is a better option simply analyzing the score itself.
The process of data collection is divided into two parts which are: 1) the selection of nonreprimanded and insolvent companies ranked by market capitalization and 2) the selection of the matched pair reprimanded and insolvent companies. 50 reprimanded companies identified as presenting falsified report by the Securities Commission Malaysia were taken from the Bursa Malaysia. Some of the reprimanded companies are classified under the PN17 in the Bursa Malaysia and some were being charged under Securities Commission due to certain irregularities that occur in the respective companies.
As for nonreprimanded and insolvent companies, the data for a five year period from year 2007 to 2011 were selected. Meanwhile, for reprimanded and insolvent companies, the data was selected based on any years that the company being charged under PN17 and charged under Securities Commission. However, due to lack of data, only parts of the samples for nonreprimanded and insolvent companies can be used. Therefore the analysis will be narrowed down where only samples with full information will be used. Table 2 below shows the classification of the samples.
The limitation of collecting of the data is that the Annual Report for several companies is not being kept in the Bursa Malaysia's system. That is the reason why such data collection does equivalent to 200 companies. Besides that, some of the figures in variables such as sales, retained earnings could not be found in the system.
Findings and Discussion:
The data is organized according to the company's result thus, (lower than one to two, greater than two) as in table 3. Each of the company that has the result of more than two regardless of any year is ranked under >2. Meanwhile, the company's result that has not exceeding the two point is placed under the <1-2 regardless of the years.
The result of the Beneish model in table 3 shows that 68.18% of companies ranking above the first hundred by market capitalization listed on the Bursa are having a possibility of earnings management. Meanwhile, the other 31.82% is on the safe side. The result can be compared with the misleading company's result where mostly of the reprimanded companies are having a possibility of earnings management. Table 4 presents the result of Beneish Model of reprimanded and insolvent companies in Malaysia.
M-Score is constructed based on the variables from Beneish Model. The data is organized based on the acceptable level. The acceptable level of M-Score is 2.22. Any figure above 2.22 is considered as the probability that the companies had managed their earnings.
Therefore, companies that had been placed under <1-2 is considered as low possibility of managing the earnings. Meanwhile, the companies that had been placed under >2 is considered as high possibility of earnings management.
Table 5 shows that 96% of the 156 companies that is ranked by market capitalization are having the possibility of earnings management based on the M-Score value. The other 4% is considered in safe place where the possibility of manage the earning is low. The result can be also compared with the reprimanded and insolvent companies where majority of the reprimanded and insolvent companies in Malaysia have the high possibility of earnings management. The table 6 shows the M-Score result for reprimanded and insolvent companies.
Altman Z-Score is a very useful statistic because it allows us to calculate the probability of a score occurring within our normal distribution and it also enables us to compare two scores that are from different normal distributions. Such score can be divided into 4 ranking namely: 1) safe zone, 2) on alert, 3) probability of bankruptcy and 4) red alert. By looking at table 7, it can be concluded that the overall performance of the second and third hundred companies ranked by market capitalization are not as promising as depicted by their respective annual reports.
An in depth analysis reveals that quite a number of companies above the first hundred based on market capitalization are on the 'red alert zone', where up to 45.55% from the total of 180 companies faced with high probability for financial misappropriation. This result is however supported as 50% of the reprimanded companies contributed to 84% contributed to the red alert zone.
The result for the third stage also showed worrying figures as the identified companies may go into bankruptcy in the next two years barring adequate intervention. This would result into high unemployment and a major setback to the economy if 67 companies go bankrupt.
As for the 'safe zone', the investment holdings constitute the larger percentage of companies in this category. From the score, out of 20, 13 companies were involved in investment holdings whilst others comprised of manufacture and sale of electrical and home appliances, manufacturing and marketing of petroleum products, servicing and processing and so forth. Consistent with table 8: the reprimanded and insolvent companies, 2 out of every 3 companies for the safe zone are involved in investment holding whereas the other one is engaged in total logistics provider.
Based on the result analysis, small market companies may experience the possibility of committing earnings management. The small market cap companies may as well experience the possibility of bankruptcy based on the result of Altman Z-Score. From the total of companies, companies that from real estate investment have the greatest fear of committing earnings management that may lead to the bankcruptcy in the near future.
This is very crucial especially to the investors as they might invested their money in poor condition companies. As for the policy makers, the result could alert them to take aware on those companies' performance. This is because those companies could have the possibility to bring harm towards the society in full like in Enron's case.
The number of fraudulent financial reporting is continuously increasing. Thus, the auditing practices nowadays have no choice but to cope with such increase. Many techniques have been implemented to detect fraudulent financial reporting.
The aim of this study is to investigate the probability of bankruptcy on small market cap companies on Bursa Malaysia using Altman Z-Score and to investigate the possibility of manipulated earnings by small market cap companies on Bursa using Beneish Model and M-Score.
Based on the result, it can be concluded that small market cap companies could also have the potential of manipulating earnings. The companies also have the potential of facing bankruptcy in the near future.
Future Research and Limitation:
This research is limited to the financial statement data for small market cap companies. Future research could use sample from top market cap companies to compare the result of fraud using these three ratios analysis.
Received 2 April 2014
Received in revised form 13 May 2014
Accepted 28 May 2014
Available online 27 June 2014
The research was supported by Accounting Research Institute (ARI) through the HICoE grants. Our appreciation also goes to the Research Management Institute of Universiti Teknologi MARA for other forms of support and facilitation.
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Normah Omar, Zuraidah Mohd Sanusi, Zulaikha, Amirah Johari, Intan Salwani Mohamed
Accounting Research Institute (ARI), Universiti Teknologi Mara, 40450 Shah Alam, Selangor Darul Ehsan, Malaysia
Corresponding Author: Dr Intan Salwani Mohamed, Accounting Research Institute (ARI), Universiti Teknologi
Mara,40450 Shah Alam, Selangor Darul Ehsan, Malaysia
Table 1: Summary Of Beneish Model. Variables Formula SGI Sales Growth Index Sales Current Year x Sales Prior Year GMI Gross Margin Index [(Sales Prior Year//COGS prior year) /Sales Prior Year] /[(Sales Current Year //COGS Current Year) /Sales Current Year] AQI Asset Quality Index [(Current Asset + PPE) Current Year] /[(Current Asset + PPE) Prior Year] DSR Days' Sales in Receivables [Receivable Current Year Index /Sales Current Year] / [Receivable Prior Year / Sales Prior Year] DEPI Depreciation Index [Depreciation / (Depreciation + Net PPE) Prior Year] / [Depreciation / (Depreciation + Net PPE) Current Year] SGAI Selling and Administrative [(Selling & Expense Index Administrative Expenses / Revenue) Current Year] / [(Selling & Administrative Expenses / Revenue) Prior Year] LEVI Leverage Index [(Total Debt /Total Assets)Current Year] / [(Total Debt /Total Assets)Prior Year] Accruals Total Accruals to Total [(Working Capital--cash- Assets -depreciation) Current Year] -[(Working Capital- -cash -depreciation) Prior Year] (Source: Beneish, 1997) Table 2: Summary of Data Collection. Method Complete Data Not Complete Beneish Model 156 44 M-Score 156 44 Altman Z-Score 180 20 Table 3: Companies Ranked By Market Capitalization. Ranked <1-2 >2 TOTAL 1-20 7 8 15 21-40 6 6 12 41-60 8 6 14 61-80 6 12 18 81-100 2 10 12 101-120 5 13 18 121-140 3 12 15 141-160 6 12 18 161-180 5 13 18 181-200 11 5 16 Total 59 97 156 Table 4: Reprimanded And Insolvent Companies. Ranked <1-2 >2 TOTAL 1-10 3 7 10 11-20 4 6 10 21-30 4 6 10 31-40 2 8 10 41-50 4 6 10 Total 17 33 50 Table 5: Companies Ranked By Market Capitalization. Ranked <1-2 >2 Total 1-20 0 13 13 21-40 1 11 12 41-60 1 13 14 61-80 0 18 18 81-100 0 12 12 101-120 2 15 17 121-140 0 15 15 141-160 0 19 19 161-180 1 18 19 181-200 1 16 17 Total 6 150 156 Table 6: Reprimanded And Insolvent Companies. Ranked <1-2 >2 Total 1-10 2 8 10 11-20 1 9 10 21-30 2 8 10 31-40 3 7 10 41-50 2 8 10 Total 10 40 50 Table 7: Companies Ranked By Market Capitalization. Ranked Safe On alert Probability of Red alert Total (>3.0) (2.72.99) bankruptcy (<18) (1.8-2.7) 1-20 4 2 9 5 20 21-40 1 0 6 6 13 41-60 3 2 3 11 19 61-80 - 1 9 9 19 81-100 2 0 6 8 16 101-120 2 0 10 6 18 121-140 4 1 4 10 19 141-160 2 1 5 11 19 161-180 2 0 7 10 19 181-200 0 4 8 6 18 Total 20 11 67 82 180 Table 8: Reprimanded And Insolvent Companies. Ranked Safe On alert Probability of Red alert Total (>3.0) (2.72.99) bankruptcy (<18) (1.8-2.7) 1-10 0 0 0 10 10 11-20 1 0 1 8 10 21-30 0 0 3 7 10 31-40 0 0 0 10 10 41-50 2 0 1 7 10 Total 3 0 5 42 50
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|Author:||Omar, Normah; Sanusi, Zuraidah Mohd; Zulaikha; Johari, Amirah; Mohamed, Intan Salwani|
|Publication:||Advances in Natural and Applied Sciences|
|Date:||Jul 1, 2014|
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