BANKING ACADEMY OF VIETNAM ADVANCED PROGRAM GRADUATION THESIS TOPIC: AN APPLICATION OF M-SCORE MODEL TO IDENTIFY EARNING MANIPULATION ON LISTED COMPANIES... DECLERATION I, LE HOANG TU
INTRODUCTION
Statement of the problem
In the market-based economy, financial information has been an indispensable tool onfor making business and investment decision Hence, knowledge quality shown on financial report is extremely vital, impacting directly to information user In fact, during the operation, listed companycompanies are favor of showing beautifully accounting data instead of the real position Misstated or asymmetrical information leading false direction caused devastating consequences to various users, including entrepreneurs, business owner, creditors, investors, etc ,…For listed companies, accounting knowledge required highest quality because of some reasons: types of information acquirers are complex and diversified, in terms of quantity and level; the large number of shareholders move continuously; observing and reflecting equity and earning position become more difficult; information publication must obey more rigid requirement compared to unlisted firms
Therefore, the target to save the whole market transparent should be put on priority
So far, the globe has raised many concerns regarding financial fraud, impactedimpacting on a wide range of market participants It was unimaginable that not only about economic losses sparked from those deception but also how sophisticated those were carried out Especially, Enron Scheme occurred in 2001, with the engagement of
Arthur Andersen, the top 5 biggest audit firm, left people many questions about the responsibility of independent party which ensure fairness and transparency on the market
In pursuit of own benefits, insiders consisting of Board of Management, employee, controlling shareholders or auditors take advantage of holding information, to manipulate financial result for their own purposes
Similar to the popularity in the global economy, there were some cases in Vietnam such as
Vien Dong Pharmacy (2011), Viet Nhat Medical Equipment (2015), Truong Thanh Wood
(2016) It failed to uncover those fraudfrauds because of many explanations, but mainly
Formatted: Indent: First line: 0.39" came from auditing step Also, Vietnam accounting system just started to follow and apply the global standard, and ability to manage the standard is still limited Additionally, the legal system has not catched up with the rapid improvement from financial market For these reasons above, existing weakness in the market is inevitable
To conclude, aside from indigenous macro policy and market improvement, exploring and finding new ways to detect risk when utilizing and analyzing financial report would be a big topic to deep in, so users would be able to make more precious decision.
Rationale for this study
Accounting manipulation havehas been most mentioned across various academic research from decades ago Specifically, a company manipulates earningearnings means that it violates the accounting rules and principles It covers various types of earning management over the boundary of global and national accounting standardstandards and an intervention in the external financial reporting process with the aims of gaining some private bonus Even though some practices were committed as a fraudfraud, others comply legally with financial reporting standard and laws As a result, there are still rooms for firms to control their earning since accounting standard failed to cover the full of behavior
There were some researches before involving financial manipulation One of them basedis based on accruals accounting and introduced by De Angelo, Healy and Jones They added a new dimension to the literature of manipulation in financial information by examining the link between discretionary accruals and audit qualifications Spathis (2002) made use of logistic regression model based on ten variables associated with ratios of asset, earning, cash flow and financial distress However, the most well-known study introduced by Beneish, “The detection of earnings manipulation” (1999) Since then, other researchers followed that foundation to build their own study successfully Those studies consent that all related parties could incorporate Beneish Model to perform earning estimation, to have assurance that financial statement are free from material manipulation
Based on firmthe firm’s prior researches, M-score model has been recognized as an effective tool when applying on wide range of market Although there were several researches applied on Vietnam companies, they did not go deeply on the reasonable basis but only giving M-score result and conclusions Therefore, there is a need for this study to analyze the reason behind the M-score number, andnumber and evaluate the effectiveness of model by comparing numerical results to real events.
Aims and objectives
The main targetstargets of this research is are to provide useful insight on manipulating probability carried out by HOSE and HNX listed companies via financial reports, as following steps:
(1) The comparison and explanation of M-score results among industries
(2) Discrepancy between M-score numbers and audits’ opinions and the evaluation of the model effectiveness
(3) The M-score effectiveness based on several factual cases that were famously mentioned on mediain the media by performing material manipulation.
Methodology and the scope of the study
Methodology: This study gauges the appropriateness of eight factors Beneish model by applying on listed companies on identifying earning manifest (1) this research calculates M-score across sections and makes clear explanation for those numbers; (2) M-score is used to judge the probability of indicator’s effectiveness according to companies given qualified audit’s opinion, (3) the components of Beneish model were analyzed to check whether M-score correctly determines a real event or not
The scope of the study: This study covers all listed companies on Ho Chi Minh and
Ha Noi Stock exchange All financial information collected is from audited report, except the final part that I add unaudited numbers to support observations Financial inputs to calculate M-score limit in the period of 2019 to 2022.
Research structure
This research is divided into 5 chapter as follows:
Chapter I Introduction: States the problems and rationale for this study; defines aims and objectives; introduces methodology and the scope of the study; builds the research structure
Chapter II Literature review: mentions previous related researches regarding authors, methodology and results; and bridges to this study
Chapter III Theoretical basis: introduce some concept of earning manipulation and the Beneish modelmodel
Chapter IV Data and methodology: introducesintroduce target of research and how to get those results
Chapter V Analysis and Discussion:Discussion states and analyzes the results including three subsections.
LITERATURE REVIEW
International researches
Exploration of the ongoing earning manipulation have been discussed for many years, with the vast models and methodologies applied such as the Jones model, the Modified Jones model, the earnings distribution model, Z-score Model and the M-score Model Among those, the M-score Model is one of the most popular methods which has been used and recognized to be a helpful detective tool Messod Daniel Beneish (1999) was the pioneer who went deeply and discovered a new application on financial report His research entitled “The detection of earning manipulation” found a way to identify falsified financial statements, studied a sample of 74 US companies for 10 years (1982-1992) and devised a mathematical model that distinguish manipulated from nonmanipulated companies The resultsresult of the study shows that Beneish found the characteristics of manipulated financial statement such as unreasonable increase in receivables, a decrease in gross profit and asset, an increase in sales growth, and an increase in accruals However, Dr Beneish admitted the limitation that the distortion might not come from manipulation, or the model was analyzed on public companies and might not be applied to private companies The M- score model was firstlyfirst exploited and it could predict about half of the chosen companies involved in earnings manipulation
Then, various researchers across the globe have also agreed the effectiveness of the model by applying it on their own Most authors made use of the original M-score for manipulation detection including 5 and 8 factors Dechow, Patricia and Ge, Weili and Larson, Chad Russell and Sloan, Richard G (2010) carried out a detailed analysis of 2190 accounting and auditing Enforcement release on the period of 1982-2005, using F – score to figure out firm that manipulated quarterly and annual profit based on scaled logistic probability There was a commoncommon sense that overstatement of revenues, misstatement of expenses and capitalizing cost were the most frequently used method They also gave a conclusion that at time of misstatement, accrual quality was low, both financing and non-financial measures of performance were deteriorating Artur Holda (2020) applied
5 and 8-factors model, and each factor value werewas calculated and compared to the benchmark values in order to gauge whether the model was correct or not A conclusionThe conclusion proved 8-factors model worked well rather than 5-factors modelmodel, but it covered only eight non-financial companies listed on Warsaw stock exchange Similarly, Sutainim, Mohammed, and Kamaluddin (2021) used the same approach on 80 non-financial listed firms on Malaysia The prediction accuracy werewas about 60-70 percent and Sales growth, accrual on asset and Day sales on receivables index considered to be significant However, the sample was small and could be a limitation to represent for the whole market Specifically, Nasrin and Arezoo (2017) came into contrasting outcome when they applied the logistic regression on 137 companies in Tehran Stock Exchange from 2005 – 2015 They dismissed the efficiency of model, explained by ineffectiveness and transparency of Tehran Stock exchange, associated with limitation of high inflation.
Domestic researches
Anh.N.H and Linh.N.H (2013) began a researchresearch on 229 non-financial companies listed on Ho Chi Minh Stock Exchange during 2013-2014 They broke down results into 10 distinctive sectors and Commerce and Telecommunication got the highest accuracy, lying nearly 70 percent Phuong.N.C and Tran.N.T.N (2014) analyzed the significance of Beneish model on 30 companies that were detected material mistake on financial reports in 2012
They calculated M-score by Excel and come into result of 53 percentage of accuracy Tuyen.P.T.M(2019)’s research combined M-score by Z-score, identified outcome on 150 listed company HOSE from 2015-2017 She found out that six variables impact on probability of earning management, on descending order as follow: Asset quality, leverage ratio, stock issuance during the year, Gross profit margin, Receivables on Sales, and Z Score Another approach is provided by Tri.N.T, Tu.D.N, Hiep.H.T, and Uyen.N.D.H
(2014) They collected data of 78 listed companies and regressed the model to classify dependent variables FRAUD as 0 or 1 on 21 independent variables based on a fraud triangle including Motive/Pressure, Opportunity and Rationalization The result showed the significance on some variables consisting of ratio of revenue on receivables, inventory on total asset, the ratio of debt on total asset, qualified opinion from auditors, and the deviation from audited number to unaudited one.
Research gap for this study
Taking those limitations into consideration, it is necessary to use the M-score with a bigger sample for better investor protection and contribution to the EM literature in the context of Vietnam Based on the richrich literature reviews, this study selected the Beneish M-score 8-factors Model as a detection tool There are interrelations between the Balance Sheet, Income Statement and Statement of Cash Flow, so that fraud can always pop up when certain numbers do not make sense By making use of a powerful tool, all companies listed on Ho Chi Minh Stock Exchange and Ha Noi Stock Exchange are put into M-score calculation This research also shows results in terms of sectors to assess differences among those, and trace back the past two yearyears from an estimated year, to identify early aggressive earning manipulation with the most updated timeframe from 2019 to 2022 Finally, the research validates the effectiveness of M-score by showing several cases that were given different audits’ opinion.
THEORETICAL BASIS
The concept of earning manipulation
There are many studies around the world giving different definitions of profit management/earnings management It was basically classified into three groups: White, Gray and Black
The first group, "Beneficial (white) earnings management": In this view, earnings management will enhance the transparency of financial statements Profit management is to use the advantage of flexibility in choosing accounting policies to highlight the personal information of business managers about future cash flows Enterprise managers make their own judgments and estimates as tools to provide investors with information about management's expectations for future cash flows of the business Examples of studies belonging to the first group are Ronen and Sadan (1981), Demski, Patell, and Wolfson
(1984), Suh (1990), Demski (1998), Beneish (2001), Sankar and Subra manyam (2001) Regarding to this group, profit management/manipulation would provide longer-term information to investors However, in some respects, it is possible that manipulating activity is on the purpose of information distributors with the intention of driving readers knowledge toward a specific direction
The second group, "Gray earnings management": Adjusting the reported profit data to the extent that it still complies with a standard, and makes the data more beautiful to achieve the specific target Profit management / Profit manipulation is the selection of an accounting policy with the aim of maximizing the benefits of the administrator while it increases the economic efficiency of the enterprise According to Davidson, Stikney and Weil (1987), "Profit manipulation is the intentional process of management to generate desired profits on the financial statements but still complying with accounting standards and principles generally accepted" Following Fields, Lys, and Vincent (2001), "Earnings manipulation occurs when managers make estimates and judgments on accounting data within limits, with either the aim of maximizing corporate value or the exercise of personal opportunities by managers” With the same view, Watts and Zimmerman (1990) argue that managers manipulate profits by modifying accounting data within the bounds of accounting rules, possibly with the aim of increasing firm value or owing to individual opportunistic behaviors According to Scott (2003), profit manipulation is the practice of managers to choose accounting policies in range of regulations to achieve specific purposes
The third group, "pernicious (black) earnings management": The act of manipulating profits through fraud and misrepresentation of financial statements The third group refers to intentional wrongdoing, using fraud to change accounting data, thereby reducing the transparency of financial statements Representing this group of frauds that falsify accounting data are the studies of Schipper (1989), Levitt (1998), Healy and Wahlen
(1999), Tzur and Yaari (1999), Chtourou, Bédard, and Courteau ( 2001), Miller and Bahnson (2002)
On the research of Healy & Wahlen (1999), “Earnings manipulation occurs when managers use their judgments in the preparation of financial statements and in the design of economic transactions to distort reported data or to mislead relevant parties about the company's actual results of operations, or to influence the accounting figures reported on contractual results between the parties" Schipper (1989) also shared the same opinion with Healy & Wahlen (1999) when describing "profit manipulation as a deliberate intervention in the process of disclosing information to the outside for personal gain" According to Miller and Bahnson (2002), “Earnings manipulation is the deliberate manipulation of accounting by management to achieve desired profit figures instead of trying to do real business to achieve that metric, regardless of how analysts forecast the company's profits.” Thus, it can be seen that not in all cases, profit manipulation has negative connotations and profit manipulation is not always fraud in order to distort information on financial statements But regardless of the group of earnings management/profit manipulation (“White earnings management”, “Gray earnings management” or “Black earnings management”), managers make a choice of accounting policies according to regulations or not Beyond regulations, profit manipulation is still intentional behavior of managers for a specific purpose Therefore, within the whole topic, the research team consistently uses the term "profit manipulation" to represent profit management, profit manipulation in the original term of international studies is "earnings management”
According to Hasnan et al (2012), motivation often includes factors that directly motivate and give rise to behavior, and often stem from the needs and desires of individuals or organizations, often occurring in the entity has control defects The concept of motive expresses the reason or purpose of manipulating published profits Chen and Tsai (2010) point out 20 motives that can cause managers to manipulate profits He divided them into three groups: altruistic motives, speculation and pressure from third parties in business activities As stated by Vander Bauwhede and Willekens (2003), a motive is always accompanied by opportunity when performing profit manipulation The researchers present empirical evidence on the reasons why companies in Belgium manipulate profits, including four groups: ensuring stable profits; due to relationships with a number of related parties; to minimize taxes payable and meet stock market expectations and regulations Barbara and Jeffords (1989) also studied profit manipulation due to relationships with several stakeholders
According to Degeorge and his associates (1999), firms have three main reasons to manipulate profits, namely: to avoid reporting losses, to avoid declining reported profits, and to meet the expected profit value of analysts Charoenwong and Jiraporn (2009) studied in Thailand and Singapore; Amar and Abaoub (2010) studied in Tunisia; Amar and Chabchoub (2016) study businesses in France; Halaoua with other partners (2017) in France and the UK provided conclusive evidence on the reasons for the manipulation of profits of enterprises in order to avoid losses and to avoid a decrease in reported profits compared to the previous year Burgstahler and Dichev (1997), Burgstahler and Eames
(2003) and Halaoua (2017) argued that profit manipulation aims to achieve the threshold value of expected returns of analysts
Another motive for profit manipulation is to influence oninfluence regulations and political factors for the enterprises’ interests As Hepworth’s study (1953), the biggest motive leading to manipulating enterprises to stabilize profits is the progressive income tax policy, the higher the income, the higher the tax According to Adhikari and associates
(2005), MdNoor and NorAzam (2007), profit manipulation is to reduce income tax burden, to reduce import taxes (Jones, 1991) and to attract credit (Wong, 1988) With the goal of meeting the listing requirements of the stock market or to attract investors, or to meet the regulations to issue convertible bonds, enterprises must adjust their published profits When businesses fall into financial crisis (DeAngelo et al., 1994), there is a need for low-cost external financing (Dechow et al., 1996) Profit manipulation for the personal future benefit of the manager Because of compensation policies, managers' income depends on net profit, so they manipulate profits to benefit personal interest (Holthausen, 1981; Healy, 1985) Profit manipulation to achieve the annual internal profit plan in order toto minimize the possibility of being fired because the expected profit is one of the requirements in the management's employment contract (Fudenberg and Tirole, 1995), or when a new CEO wants to report significantly improved earnings year-over-year (Murphy and Zimmerman,
From the above studies, it can be seen that the motives of manipulating reported profits mostly include: escape of reporting losses; Guaranteed stable profit; the future benefit of managers; the influence of regulations and political factors on the interests of enterprises; the need of the listing or convertible bond issuance requirements of the stock market; relationships with several stakeholders.
The history and the formulation of Beneish model
The model for detecting earning manipulation used the sample and industry – matched companies during the period from 1982 to 1988 and re-estimate the model’s correction on a holdout sample in the 1989-1992 The model for identification of earning manifest as follow:
Where M is a dichotomous variable assigned as 1 for manipulators and 0 otherwise,
X is the matrix of explanatory variables, and is a vector of residuals
In that study, manipulating firmfirms were overchosenover chosen relative to their true proportion in the population The econometric justification is that such a state-based sample is likely to generate a larger number of manipulators than a random sample would generate, which would make the identification of a model for classifying earnings manipulation difficult However, because estimation of a dichotomous-state model that ignores the state- based sample procedures would yield asymptotically biased coefficient estimates, he used weighted exogenous sample maximum likelihood (WESML) probit as well as unweighted probit The estimation sample run on the 1982–88 period and consisted of 50 manipulators and 1,708 controls Using WESML needed an estimate of the proportion of companies in the population that manipulate earnings Assuming that the population from which the companies were sampled is the population of companies, one estimate of the proportion of manipulators equals 0.0069 (50/7,231) Because he could not assess the validity of this assumption, he evaluated the sensitivity of the model to other specifications of the prior probability of manipulation
In the lack of an economic theory regarding manipulation, he accounted on three sources for picking explanatory variables relied on a financialfinancial statement data First, he covered several indicators about future prospects that beare usually picked in the academic and practitioner literature The presumption was that manipulation was more likely to be appearedappear when companies’ future condition become poorly Next, he considered variables based on two sources including cash flows and accruals Lastly, he collected variables drawn from positive theory research, which hypothesizes contract-based incentives for earnings management The outcome of the search for variables based on financial statement data was a regression model that contained eight explanatory variables The variables were computed from data from the fiscal year of the first reporting violation (specifically the first year for which the company was subsequently required to adjust) I identified seven of the eight variables as indexes because they are used to capture distortions that could arise from manipulation by comparing financial statement measures in the year of the first reporting violation with those in the year prior
M-score factors
The indicators showing the areas of manipulation are expressed by the following formula for M-score:
M8 -factorsfactor = - 4.84 + 0.920 * DSRI + 0.528 * GMI + 0.404 * AQI + 0.892 * SGI+
M5-factors = - 6.605 + 0.823 * DSRI + 0.906 * GMI + 0.593 * AQI + 0.717 * SGI +
0.107 * DEPI where DSRI = Days’ Sales in Receivables Index,
SGAI = Sales, General and Administrative expenses Index,
TATA – Total Accruals to Total Assets
* The DSRI (Day Sales in Receivables Index) is used to assess changes in the level of receivables in relation to the level of sales between year (t) and year (t–1):
DSRI = [net receivables(t) / sales(t)]/ [net receivables (t–1) / sales (t–1)
The DSRI measures whether receivables and revenue (sales) are in balance in two consecutive years Therefore, if an increase in receivables disproportionate to the volume of sales is noticed, then such a change may be understood as an effect of artificially inflating revenues from sales If the company actually manipulates the result through the earlier recognition of revenue, then the effective- ness of this ratio is high However, an increase in receivables in relation to the volume of sales may also result from a change in the company’s credit policy, which is a reaction to an increase in competitiveness on the market
* Another indicator of GMI (Gross Margin Index) is the evaluation of gross margin on sales between the previous and current period
GMI = {[sales (t – 1) – cost of goods sold (t – 1)]/ sales (t – 1)} / {[sales (t) – cost of goods sold (t) / sales (t)}
When the GMI is greater than 1, gross margins have deteriorated The deterioration in sales margin is perceived by the financial market as a negative signal of future probability, and this reduces the effectiveness of the invested capital This situation is associated with a worse business outlook and higher possibility of manipulation It is expected a positive relationship between GMI and the probability of earning manipulation
* The AQI (Asset Quality Index) refers to one of the basic techniques of manipulating financial data, i.e activating costs Asset quality in a given year is the ratio of noncurrent assets other than property, plants and equipment (PP&E) to total assets and measures the proportion of total assets for which future benefits are potentially less certain The asset quality index (AQI) is the ratio of asset quality in year t to asset quality in year t-1 The AQI is an aggregate measure of the change in asset realization risk, which was suggested by Siegel If the AQI is greater than 1, the company has potentially increased its involvement in cost deferral An increase in asset realization risk indicates an increased propensity to capitalize, and thus defer cost It also means that the company’s managers try to transfer part of the operating costs from the profit and loss account to the balance sheet Therefore, it is expected to find a positive relationship between the AQI and the probability of earning manipulation
AQI = {1 – [(current assets(t) + net fixed assets(t)) / total assets (t)]}/ {1 – [(current assets (t –
1) + net fixed assets (t – 1)) / total assets (t – 1)]},
An increase in sales in the company is not a signal of manipulative practice by managers However, as in the case of the gross margin indicator, it represents positive expectations on the part of the capital market, and thus exerts pressure on company managers to achieve forecasts formulated by analysts in terms of the effects achieved (MacCarthy,
* The SGAI (Sales General and Administrative Expenses Index) is used to assess the change in the share of sales and general and administrative expenses in sales revenue between period (t) and period (t – 1)
SGAI = [(selling, general & administrative expense (t) + sales (t)) / sales (t)] / [(selling, general & administrative expense (t – 1) + sales (t – 1)) / sales (t – 1)]
Essentially, the application of the SGAI is related to the assessment of disproportionate changes in sales revenues In this case, the evaluation is carried out in relation to administrative costs and selling expenses The focus on the assessment of the proportionality of changes in sales volumes results from the conviction that any disproportionsdisproportion observed in this area may mean interference of managers in the reporting process Negative deviations within the framework of the presented indicators point to areas that require deeper interest from analysts or auditors Their assessment indicates symptoms of fraudulent activity of managers
Other indicators not included in the five-factor model are:
* DEPI (Depreciation Index), which is the quotient of the value of a measure that determines the ratio of depreciation to gross fixed assets between periods (t – 1) and (t)
DEPI = [depreciation (t – 1) / (depreciation (t – 1) + net assets (t – 1))] / [depreciation (t) / (depreciation (t) + net assets (t))]
The DEPI is the ratio of the rate of depreciation in year t-1 to the corresponding rate in year t The depreciation rate in a given year is equal to Depreciation/(Depreciation + Net PP&E) A DEPI greater than 1 indicates that the rate at which asset are being depreciated has slowed – raising the possibility that the company has revised upward the estimates of assets’ useful lives or choose a new method that is income increasing Thus, it is expected a positive relationship between the DEPI and the probability of manipulation
* LVGI (Leverage Index) is the ratio of the total debt level between year (t) relative to the corresponding ratio in year (t – 1) An LVGI greater than 1 indicates an increase in leverage This variablesThis variable was included to capture incentives in debt covenants for earning manipulation Assuming that leverage followfollows a random walk, the LVGI implicitly measures the leverage forecast error An increase in debt is a bad signal for financial risk assessment This, in turn, increases the managers’ motivation to improve the image of the company in the financial statements
LVGI = [current liabilities (t) / total assets (t)] / [current liabilities (t – 1) / total assets (t – 1)]
* The last indicator of the presented model, TATA (Total Accruals to Total Assets), shows the relation of the total sum of accrual differences to total assets Within this indicator, the total sum of accrual differences is determined as the difference between net profit and cash flows from operating activities (based on the findings of the cash flow statement), or the balance sheet method: TATA = (Δ net working capital– Δ cash and equivalents– Δ income tax – depreciation) / total assets (t)
A high level of TATA means that managers can take advantage of accounting manipulation
Initially, the M-score benchmark for the model was –2.22 An M-score result above this value indicated that the report could be manipulated The model was confirmed in 2004 using a sample size of 120 “manipulators” and 67,366 “non-manipulator” companies during the period 1986 to 2001 and, take into account new calculations, the M-score level was adjusted to –1.99 M.D Beneish finally adopted the M-score –1.78 as threshold for manipulation of financial statementsstatements.
DATA AND METHODOLOGY
Data
The research collected data nearly 600 companies (table 4.1) listed on Ho Chi Minh and Ha Noi Stock Exchange, including M-score calculated from eight factors (or variables) above on the timeframe from 2019 to 2022 This research uses Python as a tool to gather financial information of all firms As a result, some of companies, in reality, are available on these two exchanges but are not included in the collected data owing to different formats or missing value The scope of this research only covers stocks in HNX and HOSE because companies must follow basic standard of financial report to be traded on, and maintain a certain degree of reliability on reflecting true financial position
Table 4.1 Listed Companies by sectors
Methodology
All data related to Sales, Receivables, Cost of goods sold, etc, … were extracted from 24hmoney.vn opened - source data by using Google colab Since i utilized python to query data , a part of those failed to collect owing to inconsistent format Then, I transformed observations (8 factors M-score rather than 5 factor) to conclusion by showing on descriptive statistics and analysis by industries Sectors are covered on this research consisting of Chemicals, Food & Beverage, Personal & Household goods, Real Estate, Industrials good & Services, Retail, Construction & Materials, Basic Resources, Media, Health Care, Utilities, Technology If number is higher than -1.78, a company that year would be classified as earning manipulation I exclude several industries from analysis because they contain only few samples including Automobiles & Parts, Telecommunication, and Oil & Gas Also, because of distinctiveness, the sector of financial services is not covered in this research
Plus, I found out that auditors mostly gave unmodified opinions to companies’ reports As a result, I split this research into 3 main missions: the first section analyses and extracts numbers on all firms collected that are nearly 600 companies totally, to evaluate the probability of manipulation and reason behind those dissimilarity among industries
(Table 4.1) M-score figures are calculated for the period of 4 year from 2019 to 2022 The second one narrow to cover only companies given a modified opinion covering a qualified opinion or disclaimer opinion on a financial report This section extends the prediction power by including year t that is given a modified opinion and two previous years (assigned as year t-1 and t-2) The purpose is to evaluate any chance of manipulation shown by M- score for years ahead The final part identifies several cases on specific firms that drove financial statement toward their motives, andmotives and confirms behavior of manipulation based on M-score result extracted from both unaudited and audited reports These companies were famous on news and media due to their clear behavior of manipulationmanipulation, but their financial reports were given an unmodified opinion for the previous year.
ANALYSIS AND DISCUSSION
M-score for industries
Table 5.1 The probability of earning manipulation (M-score > -1.78)
Overall, the result is likely to show that manipulation event had gradually increased from 25% in 2019 to 35% in 2022 (Table 2) As can be seen clearly, Real estate was the most high and stable probability in earning manuver Noticeably, some industries witnessed extraodinary increase in M-score, resulting to higher number in 2022, including Chemicals,
Basic Resources: M-score higher than – 1.78 range from 22% to 40%, with the peak in 2022, at 40% Despite high ratio of manipulation, the number (M-score), in general, were
Formatted: Indent: First line: 0.5" slightly surpassed the benchmark There are two main types of companies classified on the manipulating group The first team contains leading firms that have competitive strength over other companies and earned rich income They profited from both domestic and international marketmarkets when trade tie was disconnecteddisconnected, and global inflation spread The second one comprised of companies also benefits from inflation and high input cost that results in dramatic sales during 2021 but they made their effort to keep investor expectation by pushing sales On this case, it would result the gap between Operating cash flow and operating income, or factor TATA During the year, some companies achieved impressive profit, but cash flow from operation were not relevant as they increased inventory for anticipating rising comodity price and eased payment policies to trade parners
Chemicals: 14 and 8 of 37 and 36 companies, or 38% and 22% respectively, shows
M-score higher than – 1.78 in 2022 and 2021 It was a reverse trend after ratio of high M- score companies dived in 2020 Similar to Basic Resource, Chemicals sector was favorable in last two years The industry prospect depends mostly on the fluctuation of commodity price during the year Although the market experienced a slight adjustment, there were consensus that chemicals cost would remain new stably high level and be unlikely to fall because tight supply from zero covid policy from China and the war between Russia – Ukraina Improvement in sales mostly skewed the M-score index to high level as companies benefits from heating comodity price and expanding demand
Construction & Materials: The percentage of firms had warning M-score had rose gradually from 21 in 2019 to 36 in 2022 Even though companies in this industry faced various obstacles because of several unexpected events such as the pandemic of Covid 19 and global inflation, they still show how beautiful pictures of finance are The probability increased in 2021 and 2022 reflects higher materials cost and sales Additionally, the market was relatively fragmented and concentrated on some big players As a result, the remaining parts tend to raise their competitiveness by extending credit term that boosting high recevables, leading to hugh discrepancy between operating income and operating cash flow to keep up high stock value which boosted by idle money from investors In reality, some of them began falling down as supportive trend ended in the second half of 2022 but others keep holding good results
Food & Beverage: There are 10 out of 46 companies in the industry show a red flag in 2022, or 22% which slightly increased from 8 companies in 2021, or 17% The demand for this industry is relatively stable, so they are not prone to control their outcome M-score data even though above the benchmark, but there is no dramatic deviation from the value of -1.78 across the industry This result seems matching to the nature of industry
Healthcare: From 2019 to 2022, the average probability of earning manipulation fluctuated around the mean of 27% Gross margin’s industry detoriated (high GMI) significantly in 2022, which implied bad prospects in the future, explained for higher M- score The hike in 2020 to 45% come from high demanđ for health product because of Covid – 19 and started to decline in the two following years after the pandemic eased
Industry goods & service: The chance of manipulation remained stable around 22% from 2019 to 2022 The industry plays a vital role in the whole economy, and demand has been constantly high, not impacted by cylical factors Even though economic activity experienced a standstill owing to the pandemic, the probability only declined by 2 percent, as a result of negative sales growth and gross profit margin However, this section is most unaffected by external elements and low probability is plausible In this case, M-score indicators might reflect correctly how the industry workworks
Media: It seems an upward trend in earning manuver from 20% in 2019 to 43%
2022 Even though lots of companies had higher M-score than -1.78, figures were insignificantly higher from the benchmark Only several firms witnessed a dramatic growth in a year, accompanied by growing economy, leading to unusual sale
Personal & Household Goods: there are 7 out of 24 and 9 out of 27 companies be identified as manipulation in 2022 and 2021 respectively The main causing M-score standard for these firms was that they eased their credit policies to boost sales and retain customers, accompanied with added receivables In 2021 and 2022, some exporting goods benefits from new trade route and market and they recognized big sales, especially garment and textile products However, their operating cash flow much differ to operating income, leading to higher DRSI, TATA and M-score
Real estate: The probability always stay around 50%, comparatively higher than other sectors There are two main distinct characteristics on real estate sector Firstly, they normally exploit financial leverage by increasing debt (higher LVGI) that cause higher probability of immensely unpredictable difficulty As a result, managers have incentives to manifest their financial results to maintain their credit score in front of shareholders and creditors Secondly, they tend to build more inventories to keep their business attractive and allow buyers to defer their payment, so it result to higher DRSI and TATA
Retail: The number of firm manipulation stay around 36 out of 100 in average from
2019 to 2022 TATA indicator mostly stay high owing to movement among receivables, payables and inventories Also, their gross profit margin fluctuated constantly, especially less competitive firms, and depend much on demand elasticity, resulting to high GMI
Technology: The probability is relatively stable above 20% in the period of 2019-
2022, except 2022 The industry has witnessed fast-paced growth lately thanks to the development of technology As a result, several companies increase their sales by easing credit term, and the other witness high SG&A expenses These lead to higher TATA and SGAI
Utilities: The ratio of manipulating firm started to increase from 2020, 7% to 32% in
2022 The industry is quite unchanged, except some companies differ in Operating cash flow and income; and some increased their leverage There are minor signals to classified utility companies as manipulators.
Companies with modified audit opinion
This research estimate the accuracy based on audited companies’ reports claimed as Qualifed or Disclamer Opinion on HOSE and HNX The reason to include not only the year that was given a qualified and disclaimer comment but also two year in the past because an action of manipulation might be carried out before
Table 5.2 M-score of Companies given modified opinion in three consecutive years
No Ticker Opinion Year t Year t-1 Year t-2
As a table above, the model effectiveness had declined if we looked back time Year t come to the conclusion ofconclude 5 out of 25, or about 25%, slightly higher than 4 out
Formatted: Indent: First line: 0.5" of 20 for year t-1 Even though the figures seem low and are unable to be exploited as a powerful predictor, we might bring to the practice as an additional criterion Year t-2 M- score is likely to be insignificant as only 1 out of 16 meet the expectation
VKC: M-score in year t was -1.4 This result comes from two ratios including DRSI and TATA While receivables increased 38% in 2021, revenue went the opposite direction with 14% decrease in value This might indicate that VKC loosen its credit policy to save revenue Also, more money paid to suppliers and increasing inventories caused negative operating cash flow which was -109 billion, compared to 3 billion a profit before tax
JVC: Within three years given qualified opinion from 2019 to 2021, M-score in year
2020 was slightly higher than -1.78 which was -1.73 Asset quality index which was 5.5, was the main reason to cause high M-score Explaining for this situation, the firm disposed several assets worth 80 billion, equivalent to 9.3 % tangible fixed asset
DZM: M-score in year t-1 was uncommon high, at 30.56 Sales in 2020 nearly doubled, went up from 97 billion in 2019 to 190 billion in 2020 That event resulted in high SGI, or sales growth index In 2020, the company had long-term receivables, amounting to
20 billion but without provision This amount account for nearly the remaining part of asset excluding current asset and PP&E in 2020 whilst that amount in 2019 was zero As a result, asset quality index (AQI) reached extremely high, at 80, indicating clear signals of financial manipulation
SDU: M-score in year t-2 was 9.7 Change in receivables on sales (DRSI) caused
M-score high the most While revenue in 2017 stayed only 34 billion, receivables which made up 38% total asset reached 340 billion AS a consequence, ratio of receivables on sale got extraordinarily high in 2017, at 10, compared to only 0.53 in 2016 This might raise concern regarding to ability to create money of SDU Furthermore, ratio of SG&A expenses on sale (SGAI) also follows DRSI, at 11 in 2017 when revenue tumbled
PPC: M-score in year t was -0.88 In 2022, the value of receivables was less than a half of revenue which was 5,278 billion Meanwhile, the ratio of receivables on sales in
2021 was only 20% This change caused high DRSI ratio, at 2.04 Plus, improving sale (SGI) and receivables did exacerbate turnover, and indicated earning control
HU3: M-score in year t was -0.26 The indicator was impacted by three ratios including DRSI, GMI and AQI While receivables accumulated from 101 billion in 2020 to 180 billion in 2021, equivalent to 80% increase, revenue rose only 10% from 120 billion to 115 billion This event might come from loosening credit term to boost turnover Additionally, gross profit margin deteriorated from 14.8% in 2020 to 9.7% in 2021, resulting to high GMI, at 1.5 Asset quality index was dramatically high but it come from expansion of current asset relative to long-term asset It might not be identified as a manipulator based on AQI
HU1: M-score in year t-1 was 2.8 Only Asset quality index (AQI) which was 17 contributed to this significance In 2020, HU1 had uncompleted construction worth 350 billion, and this amount was transferred to revenue in 2021 As a result, the number on that account came back to 0 that made a huge discrepancy between two years However, M- score, in this situation, was incorrect on proving earning scheme
HGM: M-score in year t-1 was 4.6 Change in receivables on sales (DRSI) hiked at
8.2 During 2020, receivables rose 6 times, contrasting to a decrease of 10 percentage in revenue This was a clear indicator of aggressiveness on revenue recognition
Other cases
In this section, the study applies a Man M-score indicator on companies that actually did some material manipulation on their unaudited financial reports The most recent year is assigned as year t As can be seen on the tabletable 4, year t correctly identifies 5 out of 5 companies on the list as a manipulator For year t-1, the appropriateness decreases to only 2 out of 5, equivalent to 40%
Table 5.3 M-score for year t and t-1
CUU LONG PHARMACEUTICAL (HOSE: DCL)
DCL was established in 1997, specializing in manufacturing and trading medicine, capsule and medical equipment Mentioning to DCL in 2013, the company did some misleading on information publicization The massive event of Cuu Long Pharmaceutical
Joint Stock Company was condemned by the press, investors and other interested people from June to July 2014 According to information provided by many domestic financial and audit press sources, during 2013 Cuu Long Pharmaceutical carried out a few round and unusual transactions Specifically, it occurred a closed transaction that took place in 2 days, on 7th and 9th December,December 2013 between Cuu Long Pharmaceutical and Saigon
VPC (a company with 100% ownership of Cuu Long Pharmaceutical), An Tam (a customer that have massive amount of payable in Pharmaceutical) In addition, a number of other closed loop transactions were also performed during this time According to the financial statements of Cuu Long Pharmaceutical, it could be seen clearly that the customer'scustomers’ receivables were always kept at a high level Receivables accounted for largea large proportion on Cuu Long Pharmaceutical’s balance sheet Total receivables from customers on December 31, 2013 were more than 202 billion VND while total assets were only about 611 billion VND, at At the end of the first quarter of 2014quarter of 2014, it was nearly 234 billion VND out of total assets of nearly 621 billion VND Thus,
Formatted: Indent: First line: 0.5" receivables from customers accounted for approximately a third of total assets The recovery of debts from bad customers was also noticeable, particularly the group of 5 largest customers including Thien Son Pharmaceutical Co., Ltd., Dai Nam Pharmaceutical Co., Ltd., Hai Pharmaceutical Co., Ltd Tam, Vietnam Pharmaceutical Joint Stock Company and Anh Dung Pharmaceutical Company Limited It was estimated at about 50 billion VND, more than 18 months overdue With such obvious overdue debt levels, sales contracts with these businesses continued to be signed It was believed that DCL had this intention for several reasons First and foremost, it pushed sales and income to show its good condition in front of readers Secondly, it aimed to issue more stock with higherat a higher price Lastly, by creating more transactiontransactions with intermediate companies, auditors would face difficulties to reveal the truth behind these transactions In conclusion, despite unmodified opinion from big 4 audit firm E&Y, it would be lots of question surrounding the earning quality of Cuu Long Pharmaceutical
Both year t and t-1 indicate that this firm was detected to manipulate its financial result when M-score in two years were 6.8 and 28.7 respectively This M-score result means that the fraud activity already began in 2012 Because the ratio of receivables had been high previously, and receivables started to decrease since 2012 so DRSI ratio was no longer appropriate to identify the company’s scam for both estimated years However, accrual ratio shows the warning sign as the huge gap between operating income and cash flow from operation in 2012 and 2013, resulting to higher M-score Noticeably, after reconciling from audited report to unaudited report, cash flow from operation fell from 210 billion to 156 billion in 2012, and from 404 billion to only 95 billion in 2013 This event proved that companies might overstate revenue, but in reality, there was no actual cash flow to the firm occurring Therefore, M-score might be credible indicator, based on accrual importance, on identifying company’s intention, and this situation could not value the quality of companies that have high receivables on total asset
TRUONG THANH INDUSTRIES WOOD JOINT STOCK COMPANY (HOSE: TTF)
TTF was established in 1993, specializing in manufacturing and exporting various wooden products It used to be the leading enterprise of Vietnam wood production and export Before 2010, TTF borrowed abundant resources to trade in TEAK wood (a high- grade wood) However, due to the economic downturn, the consumption trend shifted to the middle segment that leadedled TTF to be got stuck on tons of debt and inventories During the period from 2011 to 2012, the liabilities stayed high, were around 4 times the equity, and the company was in danger of bankruptcy However, the restructuring at the end of 2013 brought hope to TTF with a series of measures such as issuing shares below par value, selling badselling bad debt, loan interest was written off by banks, along with the acquisition of 49.9% shares by a subsidiary of Vingroup and VND 1200 billionsbillion in value of convertible bonds From the price of 5000 VND/share in 2013, TTF shares reached a record level of 43,600 VND/share in July of 2016 Unexpectedly, on July 19, TTF's shareholders received a notice that Vingroup halted the conversion of a loan worth VND 1202 billion due to the discovery of some "serious deviations" between actual and published financial information On August 2, Truong Thanh Wood shocked investors when it announced a loss of more than 1,100 billion dong in the second quarter, caused by nearly 1,000 billion dong of inventories "evaporated" from balance sheet On August 31,
2016, auditing company E&Y announced the review report of the consolidated interim financial statements of Truong Thanh Wood Industry Corporation (TTF) The opinion of the Auditor shows that the financial situation of TTF was on badin a bad situation The series of data has been retrospectively adjusted for the 2015 financial statements and the first six months of 2016 audit data According to E&Y, these conditions indicated the existence of a materialmaterial uncertainty could cause significant doubt on the firm's capability to continue as a going concern Some notable retroactive adjustment items such as: Adjusted interest expense increased by VND 23.9 billion, provision for doubtful receivables of 2014 and 2015 increased by VND 132.7 and 224.7 billion, respectively, corresponding increase in administrative expenses in 2015 by VND 92 billion; reclassified VND 598.7 billion from "Long-term convertible loan" to "Short-term convertible loan", excluding unrealized interest from the liquidation of an investment of VND 36 billion, etc
The Board of Directors recognized a shortfall when conducting inventory with a value of VND1,051.92 billion, thus reducing the value of inventory by the same amount E&Y was unable to determine when this shortfall arose in previous reporting periods or in the current reporting period In this financial statement, with the loss and loss of inventory of approximately VND 1,052 billion, TTF's cost of goods sold jumped to VND 1,763 billion This was the main reason why Truong Thanh Wood's profit was negative, at VND 1,085.5 billion
As shown on M-score result based on unaudited report, only year t-1 identifies the significance on a number, or 1054 Particularly, two ratios impacting on the significance of M-score were change in Receivables on Sales (DRSI) and discrepancy in cash flow, or operating income minus operating cash flow (TATA) In 2014, receivables decreased by 5%, from 1,563 billion to 1,495 billion whilst receivables doubled from 383 billion to 787 billion, indicating loosening credit term to improve performance Plus, operating income was 147 billion in 2014 but cash flow from operation fell to -515 billion, leading to high number of TATA ratio Interestingly, even though these outcomes were not directly impacted by the fact that the company inflated its inventories but higher goods stock leads to higher cash expenditure to suppliers, and lower operating cash flow In this case, M- score might be a credible indicator in year t-1
On the other hand, M-an M-score which was 1.63 at year t implies the signal of fraud in case of audited report This might reflect the truth numbers after auditors required the company to explain some specific problem and mademake an adjustment Most significantly in 2015, while net operating income was adjusted by audited report, from 286 billion to 168 billion, equivalent to about 40 percent lower, operating cash flow flipped from 389 billion to -219 billion The reason for this discrepancy was that TTF was false to classify the amount of 600 billion to payables instead of convertible long-term bond Therefore, the firm inflated operating cash flow and deflated financing cash flow In this case, M-score is influenced by TATA, or difference in accrual ratio, rather than inventory- related story, and does not explicitly explain for this fraud although the number is considered significantly Similarly, M-score in year t-1 extracted from audited report does not differ from the result in unaudited report
PETRO VIETNAM CONSTRUCTION JOINT STOCK COMPANY (HOSE: PVX)
Petro Vietnam Construction Joint Stock Company was one of subsidiaries of Petro Vietnam The Company constructs industrial nonbuildingnon-building structures and manufacturing buildings, schools, hotels and office buildings It is engaged in the design, manufacturing, installation and repair of drilling platforms, reservoirs, underframes, pressure tanks and technological tube systems used in petroleum exploitation activities In addition, it trades chemicals, materials, machines and equipment used in the petroleum industry
At the end of 2014, PVX recorded revenue of 8,928 billion, nearly doubled the value in 2013 which was 4,962 billion Of which, gross profit margin improved 30% compared to the ratio in 2013, combined with lower cost of interest and SG&A expenses, results in net income of 17 billion Thanks to this performance, PVX escaped from three consecutive negative net income, and continued to stay in Ha Noi Stock Exchange Even though Deloitte agreed on 2014 financial report audited ,audited, it brought up some significant points, specifically in uncollectible receivables and reverse amount regarding several L/C obligations An audited report published in 2013 was also received a qualified opinion from Deloitte when the audit firm could not collect sufficient evidence or alternative processes to prove the existence of receivables from customers, payables to suppliers, prepaid expenses to suppliers and from customers, with the value of 125 billion, 363 billion, 226 billion and 168 billion respectively Also, SG&A expenses declined significantly, in proportion to firm’s turnover These might raise a question that PVX performance actually improved or did some minor adjustments to save the company from negative income
As an unaudited result of M-score for PVX, the numbers were 3.84 and -2.2 in year
2013 (t-1) and year 2014 (t) respectively, and only year t-1 shows the indicator of fraud Explaining for year 2014 as this company recovered a small amount of uncollectible receivables, net receivables did declined that leads to lower DRSI ratio (change in receivables on sales) instead of higher warning signal of manipulation In year 2013, the company was detected as manipulators, mostly come from the discrepancy between operating income and cash flow from operation, resulting to higher ratio of TATA While operating income was negative, with the amount of over 2,400 billion, cash flow from operation showed a contrasting picture, with the amount of 725 billion These results came from additional provision and cutting off significant amounts of receivables, about 2,400 billion in 2013 In this case, although high provision on receivables might create space for future reversion, and large proportion of receivables on total asset, which were 25% and 30% in 2013 and 2014 respectively, might indicate low earning quality, it would be difficult to predict manipulation activity based solely on difference between operating cash flow and operating income, or TATA ratio
The results for 2013 and 2014 audited reports were nearly similar to unaudited versions except that M-score in 2014 slightly surpassed the threshold of manipulation, at - 1.62 The main factor impacts on M-score is TATA, or accrual ratio In this case, M-score in 2014 gives a false indicator because of negative net accrual Additionally, the manipulation was implemented mainly by adjusting uncollectible receivables back, with the marginal amount to save the company from negative earning As a result, the model used only net receivables, and the intention would not be uncovered
PHUONG NAM CULTURAL JOINT STOCK COMPANY (HOSE: PNC)