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Open Access
Research article

Corporate Accruals Practices of Listed Companies in Bangladesh**

md. shamimul hasan1*,
rashidah abdul rahman1,
syed zabid hossain2
1
Accounting Research Institute, University Teknologi MARA, Shah Alam, Malaysia
2
Department of Accounting and Information Systems, University of Rajshahi, Bangladesh
Journal of Corporate Governance, Insurance, and Risk Management
|
Volume 1, Issue 1, 2014
|
Pages 12-43
Received: 01-11-2014,
Revised: 02-15-2014,
Accepted: 02-22-2014,
Available online: 03-28-2014
View Full Article|Download PDF

Abstract:

Corporate accounting scandal is not a new phenomenon and it is the outcome of corporate accruals i.e., accruals by management choice. This study investigated the use of corporate accruals in the financial statements of the listed companies in Dhaka Stock Exchange (DSE) through segregating total accruals into corporate (discretionary) and accounting (non-discretionary) accruals. The average rate of corporate accruals was 35 percent and in many cases, cash flow from operation exceeded the net income, the growth in accounts receivable was faster than sales growth, and inventory growth was not consistent with sales growth. In this context, this study may create awareness of the risk factors of corporate accruals among external users’ of accounting information especially analysts, regulator, policy makers, existing and potential shareholders, lenders, trade creditors, external auditors, researchers, financial advisors, and stock brokers. Consequently, it may reduce the use of management discretion in preparing the financial statements.

Keywords: Corporate accruals, Discretionary accruals, Accounting scandal, Accounting accruals, Non- Discretionary accruals, DSE, Bangladesh

1. Introduction

Corporate swindle has subjugated the financial news in recent years. The scandal at Waste Management (1998), Enron Corporation (2001), Tyco International (2002), WorldCom (2002), Health-South (2003), Freddil Mac (2003), American International Group (2005), Lehman Brothers (2008), Bernie Madoff (2008), and Satyam (2009) to name but a few, emphasizing the will and ability of unscrupulous managers to defraud investors and other stakeholders. These scandals call into the question of reliability of reported earnings. The recent wave of corporate governance failures has raised concerns about the integrity of the accounting information provided to investors and resulted in a drop in investor confidence (Jain et al, 2003; Rezaee 2004; Jain & Rezaee 2006). These failures were highly exposed and ultimately led to the drop of investors’ confidence on accounting information. In Bangladesh, investors’ do not have strong confidence on information provided in annual report (Razzaque 2004). The recent wave of corporate scandals in the United States and elsewhere has dramatized, once again, the severity of the agency problems that may arise between managers and shareholders (Joseph et al, 2004). A principal concern of many users of financial statements has been whether or not earnings are overstated. Companies may be motivated to increase earnings in a particular period to meet analysts’ earnings expectations, to meet debt covenants, or to improve incentive compensation. Importantly, management also may have incentives to lower reported earnings in a particular period.

In the United States, the SEC study (2002), Report Pursuant in Section 704 of Sarbanes-Oxley Act of 2002, reviews 515 enforcement actions between July 31, 1997 and July 30, 2002. The study classified improper accounting practices into four categories: (i) Improper revenue recognition (126 cases) including reporting revenue in advance through techniques, such as holding the accounting period open, billing without shipping (bill and hold), fictitious revenue, and improper valuation of revenue; (ii) Improper expenses recognition (101cases) including improper capitalization, overstating inventory, understating bad debts/loan losses, improper use of restructuring reserves, and failure to record asset impairments; (iii) Improper accounting in connection with business combination (23 cases); and, (iv) Other accounting and reporting issues (130 cases) including inadequate disclosures, failure to disclose related party transactions, inappropriate accounting for non-monetary and round-trip transactions, improper accounting for foreign payments in violation of the Foreign Corrupt Practice Act, improper use of off-balance sheet arrangements, and improper use of non-GAAP financial measures (CFA:2011).

In Bangladesh, the stock market had been crashed twice- one in 1996 and the other in 2010-2011. In 1996, the cause of crash was speculative bubble and in 2010-2011 the cause was asset pricing bubble. In DSE it is observed that, the stock price moves up if the earning per share is higher than that of the same quarter of the previous year. It is an indication of earnings manipulation in order to move up the price or better performance of their stock in the capital market. A probe committee was formed by the government to find out the real causes behind the crash of the capital market in 2010-2011. The probe committee digs out various ways to manipulate the capital market. Among those ways, accounting manipulation was important one which is somewhat responsible for creating asset pricing bubble in the capital market (Probe Committee Report: 2011).

The Institute of Chartered Accountants of Bangladesh (ICAB) provided their opinion and recommendations to the probe committee in 2011. Quality of financial statements of the issuers was one of the concerned issues. Major areas of concern were as follows:

• Quality of earnings

• Non-compliance of accounting standards

• Revaluation of fixed assets

• Poor quality of work of some audit firms that are in the SEC panel of auditors

During investigation, Probe Committee (2011) observed that companies were overstating their assets in the name of revaluation as there was a weakness of the revaluation process in Bangladesh and deferred tax implications were not properly accounted in the financial statement. Even, many companies had issued bonus shares against such unrealized gains which were not legal. The probe committee provides the following information to observe the scenario of the revaluation of assets.

Table 1. Test Case of Overvaluation of Assets

Name

NAV per share in Taka

Before

revaluation

After

revaluation

%

change

Libra Infusions

438

15667

3472

Sonali Aansh Industries

297

2156

626

Rahim Textile

127

785

518

BD Thai Aluminum

142

566

298

Orion Infusion Ltd

20

101

413

Ocean Containers Ltd.

13

50

296

Shine Pukur Ceramic

12

26

120

Eastern Insurance

151

309

104

Probe Committee Report, 2011; NAV = Net Assets Value

Earnings per share (EPS) is an important indicator to justify the share price of a company. In Bangladesh, earnings per share of companies are manipulated in order to hike the offer price in the stock market. Institute of Chartered Accountants of Bangladesh (ICAB) mentioned the manipulating strategies as follows:

1. Annualizing EPS computation – There were instances where issuers annualized the latest quarterly or semi-annual EPS without using latest available full year EPS. Such quarterly/semi annually EPS figures were significantly higher than historical EPS which is most likely to be “managed”

2. Manipulation related to weighted average number of shares in computing EPS- There were instances where issuers had issued a large number of shares closer to the balance sheet date so that such new shares would have lesser impact in computing weighted average number of shares.

3. Issue of shares subsequent to the balance sheet date – There were instances where new shares issued subsequent to the balance sheet date and impact of such new shares were not taken into account in computing EPS in pricing the IPO.

4. Inclusion of exceptional non-recurring income into the EPS computation (example: capital gains).

(Source: Probe Committee Report, 2011)

Management has a unique ability to commit fraud because it often is in a position to directly or indirectly manipulate accounting records and present deceitful financial information. Fraudulent financial reporting often involves management override of controls that otherwise may appear to be operating effectively. Management can either direct employees to perpetrate fraud or solicit help in carrying it out. In addition, management personnel as a component of the entity may be in a position to manipulate the accounting records of the component in a manner that causes a material misstatement in the consolidated financial statements of the entity. Management override of controls can occur in unpredictable ways (CFA: 2011).

In view of the above facts, it is clear that agency issues is a severe problem in the corporate world of Bangladesh and there is a great possibility of using discretionary accruals (choice by management) in the financial statements to achieve desired goals. Discretionary accrual is an important economic variable to assess the quality of earnings. The quality of earnings is directly related with the quality of accruals. Higher discretionary accruals indicate lower quality of earnings and lower discretionary accruals indicate higher quality of earnings. In this context, the main objective of this study is to examine whether there is an existence of discretionary accruals in the corporate financial statements in Bangladesh and if so, assess the level of discretionary accruals. The secondary objectives are to find out some evidences that could lead to accounting irregularities and to refer the risk factors associated with the fraud triangle and common accounting warning signs.

The remainder of the paper is organized as follows. Section 2: presents literature review, section 3: describes various accruals models, section 4: empirical methodology, section 5: discussion of results and section 6: conclusion.

2. Literature Review

Accrual manipulation is an important way to managers to produce a desired earnings number. The company does not change its activities but, rather, opportunistically reports income for an existing activity. Accruals create the opportunity for earnings management because they require managers to make forecasts, estimates, and judgments. The greater the degree of discretion in an accrual, the greater the opportunity for earnings management (Dechow & Schrand, 2004). Generally, managers prefer the manipulation of accruals over the manipulation of real activities. Consequently, managers are likely to resort to the manipulation of real activities only when there is limited scope left for accrual manipulation. The manipulation of both accruals and real activities has severe consequences on the reliability of earnings for decision making. Managerial manipulation reduces the reliability of accounting numbers, leading to reduced conditional conservatism (Juan et al. 2009). The articulation between the income statement and the balance sheet ensures that accruals reflected in earnings also are reflected in net assets. Therefore, an optimistic bias in earnings implies net assets measured and recorded temporarily at values exceeding those based on a neutral application of GAAP. Generous assumptions of managers’ about recognition and measurement in one period reduce their ability to make equally generous assumptions in later periods, if managers want to stay within the guidance provided by accounting regulators and professional groups. Therefore, managers’ ability to optimistically bias earnings decreases with the extent to which net assets are already overstated (Barton & Simko, 2002). Discretionary accruals are accounting adjustments to cash flows that managers can choose within the flexibility of GAAP. Since GAAP allows certain discretion over financial reporting, there is a possibility that accruals contain management’s intention to manipulate information (Beneish 1997; Dechow & Skinner 2000). Previous studies detected earnings management behavior through various methods including the changing of accounting policies (Balsam et al., 1998), discretionary accruals (Jones 1991), real transactions (Barber et al. 1991; Bushee 1998; Cheng 2005), and earnings distribution (Burgstahler and Dichev 1997). Since the middle of 1980s, discretionary accruals have become the primarily focus on detecting earnings management. There are two main reasons. Firstly, accrual is a generally accepted accounting principle. Accruals are used to reduce inconsistencies encountered as a result of difference in timing of the recognition (Dechow & Skinner 2000). Secondly, the accrual technique is less visible and hard to detect compare to the changing of accounting policies or real transactions. As such, accruals open a door for opportunistic earnings management within the requirement of GAAP. Managers believe that accrual technique is a desirable vehicle to achieve their objectives (Dechow 1994; Beneish 2001).

The accrual method began with Healy (1985) and DeAngelo (1986), who used total accruals and changes in total accruals as a proxy for discretionary accruals respectively. These models capture either income-increasing or income-reducing techniques that managers have incentives to employ, however, they misclassified all accruals as discretionary which lead to biased test if earnings management stimulus is correlated to non-discretionary accruals to overcome this limitation. Jones (1991) introduced a linear regression approach to control non-discretionary determinants of accruals. She used change in sales control for non-discretionary accruals of current assets and liabilities; property, plant and equipment control for the non-discretionary component of depreciation expense. The rationale is that a firm’s working capital requirements depend on sales, while its depreciation accruals depend on the level of property, plant and equipment. Then, she uses the residual for regression of total accruals on non-discretionary determinants of accruals as discretionary accrual proxy. However, this model misclassified all revenue as non-discretionary accruals. Dechow et al. (1995) introduced a Modified Jones Model, they adjusted the Jones model by removing credit sales from revenues. However, the modified Jones Model could still yield biased results if no earnings management occurs in credit sales. In the literature, both Jones Model and Modified Jones Model have been widely used in estimating discretionary accruals the proxy for earnings management. Since earnings management is not observable, the validity and reliability of Jones and Modified Jones empirical models have been often criticized (Bernard & Skinner 1996; Wilson 1996; Guay et al. 1996; Beneish 1997; Healy & Wahlen 1999; Thomas & Zhang 2000; Peasnell et al. 2000; Xie 2001; Leuz et al. 2003). Researchers argued that model misspecification problem at least reduces the power of detecting earnings management, and at worst causes researchers to conclude that there is earnings management when none actually exist (e.g., McNichols & Wilson 1988; Dechow et al. 1995). Moreover, it is more likely to detect income increasing earnings management for higher profitable firms and income –decreasing earnings management for lower profitable firms. Likewise, researchers are more likely to detect income-increasing earnings management for lower cash flow firms and income –decreasing earnings management for higher cash flow firms. Accruals are correlated with a firm’s contemporaneous and past performance. Jones and Modified Jones models attempt to control for contemporaneous performance but ignore the past performance. Empirical assessments suggest that estimated discretionary accruals are significantly influenced by a firm’s contemporaneous and past performance. If a firm experiences an unusual performance, for example, has one-time extreme high or low sales; or a fast growth stock exhibit momentum for a period of time, then there is a danger of a false detection of earnings management unless discretionary accruals models can adequately filter out the component that affected by firm performance (Kothari et al. 2005).

So far a few studies on discretionary accruals might have yet been conducted in Bangladesh. The researcher found only one study entitled ‘Earnings Management: An Analysis on Textile Sector of Bangladesh’ (Razzaque et al., 2006). The study was conducted long back (period 1992-2002) using the Modified Jones Model (1995) and confined to the textile industry only. The study did not provide any information about the level of discretionary accruals. Besides, the study did not use separate models for non-discretionary and discretionary accruals. They define discretionary accruals as residuals of total accruals model.

In light of the above, the researchers feel that an in-depth study is urgently needed to measure the level of discretionary accruals in the corporate financial statements in Bangladesh so as to find out the clues that could lead to accounting irregularities. The researchers also feel that the external stakeholders should be acquainted with the risk factors for each condition of the fraud triangle and common accounting warning signs that may lead to reduce the use of management discretion in preparing financial statement.

3. Models of Non-Discretionary Accruals (Accounting Accruals)

A wide variety of non-discretionary accrual models have been employed by previous researchers. Estimating the non-discretionary component of accruals typically involves a regression model. We termed non-discretionary accruals as accounting accruals and discretionary accruals as corporate accruals in the study. The common variants of the most popular models are discussed below:

3.1 The Jones Model (1991)

Jones offers a new and potentially more effective way to estimate non-discretionary accruals in her model. She uses a property, plant and equipment variable (PPE) to control for any changes in non-discretionary accruals arising from the depreciation charge and hence resulting from changes in business activities of the firm. Using the same idea, a sales revenue variable is used to control changes in non- discretionary accruals related to working capital accounts arising from changes in the economic environment of the firm. However, revenues, according to Jones, are not completely exogenous; for example, shipments for merchandise could be postponed in order to postpone recognition of revenue until the next year. The regression is estimated for each sample firm as follows:

$\frac{T A}{L T A}=\beta 1\left[\frac{1}{L T A}\right]+\beta 2\left[\frac{\Delta R E V}{L T A}\right]+\beta 3\left[\frac{P P E}{L T A}\right]+\varepsilon$

Where:  

TA = Total Accruals

∆ REV = Change in Revenues from the preceding year PPE = Gross Value of Property , Plant & Equipment LTA = Lagged Total Assets All variables in her model are scaled by lagged assets to reduce heteroscedasticity. Discretionary accruals (DA), as shown below are computed as the difference between total accruals and non- discretionary components of accruals.

$D A=\frac{T A}{L T A}-\left[\beta 1\left[\frac{1}{L T A}\right]+\beta 2\left[\frac{\Delta R E V}{L T A}\right]+\beta 3\left[\frac{P P E}{L T A}\right]\right]$

Looking at the Jones model, it is clear that the idea of using two variables (∆ REV and ∆ PPE) to control for changes in non-discretionary accruals makes this model potentially more accurate for an analysis of earnings manipulations. However, the assumption that coefficient estimates are stationary over time would create survivorship bias. As well, sales manipulation that can be managed by managers is completely ignored since this model assumes that all revenues in the period are non-discretionary.

3.2 The Modified Jones Model (1995)

Dechow et al. (1995) modify the original Jones model to eliminate the conjectured tendency to measure discretionary accruals with error when discretion is exercised over revenues. The change in revenues is adjusted for the change in receivables in the event period. They assume that all changes in credit sales in the event period proceed from earnings management. They conclude that managing earnings by exercising discretion over the recognition of revenue on credit sales is easier than managing earnings by exercising discretion over the recognition of revenue on cash sales. The regression for the sample is estimated as follows:

$\frac{T A}{L T A}=\beta 1\left[\frac{1}{L T A}\right]+\beta 2\left[\frac{\Delta R E V-\Delta A R}{L T A}\right]+\beta 3\left[\frac{P P E}{L T A}\right]+\varepsilon$

where:

TA = Total Accruals

Δ REV = Change in Revenues from the preceding year

Δ AR = Change in Accounts Receivable from the preceding year

PPE = Gross Value of Property , Plant & Equipment

LTA = Lagged Total Assets

Discretionary accruals (DA), as shown below, are computed as the difference between total accruals and the non-discretionary components of accruals.

$D A=\left[\frac{T A}{L T A}\right]-\left[\beta 1\left[\frac{1}{L T A}\right]+\beta 2\left[\frac{\Delta R E V-\Delta A R}{L T A}\right]+\beta 3\left[\frac{P P E}{L T A}\right]\right]$

3.3 The Extended Jones Cash Flow Model (1999)

Kasznik (1999) adds to modified Jones model changes in operating cash flow as an explanatory variable to explain the negative correlation between cash flow from operations and total accruals. He finds that managers use income-increasing discretionary accruals to manage reported earnings toward their forecast numbers when they have overestimated earnings. In contrast, he finds no evidence that managers use income-decreasing discretionary accruals to manage reported earnings downward when they have underestimated earnings in their forecasts. The regression for the sample is estimated as follows:

$\left[\frac{N D A}{L T A}\right]=\alpha 0+\alpha 1\left[\frac{1}{L T A}\right]+\alpha 2\left[\frac{\Delta R E V-\Delta A R}{L T A}\right]+\alpha 3\left[\frac{P P E}{L T A}\right]+\alpha 4\left[\frac{\Delta C F O}{L T A}\right]+\varepsilon$

where:

NDA = Non-Discretionary Accruals

Δ REV = Change in Revenues from the preceding year

Δ AR = Change in Accounts Receivable from the preceding year

PPE = Gross Value of Property , Plant & Equipment

ΔCFO = Change in cash flows from operation

LTA = Lagged Total Assets

3.4 Modified Jones Model with Book-to-Market Ratio and Cash Flows (2004)

Larcker and Richardson (2004) add the book-to-market ratio (BM) and operating cash flows (CFO) to modified Jones model to mitigate measurement error associated with the discretionary accruals. BM controls for expected growth in operations and if left uncontrolled, growth will be picked up as discretionary accruals. CFO controls for current operating performance. Controlling for performance is important because Dechow et al. (1995) find that discretionary accruals are likely to be misspecified for firms with extreme levels of performance. Larcker and Richardson (2004) note that their model is superior to the modified Jones model in several ways: it has far greater explanatory power, identifies unexpected accruals that are less persistent than other components of earnings, the estimated discretionary accruals detect earnings management identified in SEC enforcement actions, and identifies discretionary accruals that are associated with lower future earnings and lower future stock returns. The regression for the sample is estimated as follows:

$\left[\frac{T A}{L T A}\right]=\beta 1\left[\frac{1}{L T A}\right]+\beta 2\left[\frac{\Delta R E V-\Delta A R}{L T A}\right]+\beta 3\left[\frac{P P E}{L T A}\right]+\beta 4\left[\frac{B M}{L T A}\right]+\beta 5\left[\frac{C F O}{L T A}\right]+\varepsilon$

Where:

TA = Total Accruals

Δ REV = Change in Revenues from the preceding year

Δ AR = Change in Accounts Receivable from the preceding year

PPE = Gross Value of Property , Plant & Equipment

BM = Book-to-Market Ratio

CFO = Cash Flows from Operations

LTA = Lagged Total Assets

3.5 The Performance Matching Model (2005)

Kothari, Leone, and Wasley (2005), develop a performance-matching model. They offer two different approaches. The first involves matching similar firms, which alleviates the need to use an ordinary least square estimate of DA. They detect earnings management by comparing the accruals of firms that are otherwise almost identical. The second, the linear-performance matching model, embodies two modifications of the Jones and modified Jones models: an intercept, and an additional control for lagged rate of return on assets, ROAt-1.

Because the first term in the Jones model is the reciprocal of lagged assets, econometrically, the Jones model does not have an intercept. Deflating by lagged assets is meant to mitigate heteroscedasticity. Finding that heteroscedasticity is still an issue, Kothari, Leone, and Wasley also include an intercept to mitigate it. They find that an intercept yields higher symmetry around zero discretionary accruals, which enhances the power of test for type 1 error.

Roodposhti, Rezaei and Salehi (2012) named this model as Kothari-Jones and Modified Kothari-Jones models and the regression for the sample is estimated as follows:

3.5.1 Kothari-Jones model

$\left[\frac{N D A}{L T A}\right]=\alpha 0+\alpha i\left[\frac{1}{L T A}\right]+\alpha 1\left[\frac{\Delta R E V}{L T A}\right]+\alpha 2\left[\frac{P P E}{L T A}\right]+\alpha 3\left[R O A_{t-1}\right]+\varepsilon$

Where:

NDA = Non-Discretionary Accruals

Δ REV = Change in Revenues from the preceding year

PPE = Gross Value of Property , Plant & Equipment

ROA t-1 = Lagged Rate of Return on Assets

LTA = Lagged Total Assets

3.5.2 Modified kothari-jones model

$\left[\frac{N D A}{L T A}\right]=\alpha 0+\alpha 1\left[\frac{1}{L T A}\right]+\alpha 2\left[\frac{\Delta R E V-\Delta A R}{L T A}\right]+\alpha 3\left[\frac{P P E}{L T A}\right]+\alpha 4\left[R O A_{t-1}\right]+\varepsilon$

Where:

NDA = Non-Discretionary Accruals

Δ REV = Change in Revenues from the preceding year

Δ AR = Change in Accounts Receivable from the preceding year

PPE = Gross Value of Property , Plant & Equipment

ROAt-1 = Lagged Rate of Return on Assets

LTA = Lagged Total Assets

3.6 Performance Matched Free Cash Flow Model

Cash flow is an important basis for accrual measurement (Ingram & Lee 2007). There is considerable body of literature that defines total accruals as the difference between net income and cash flow from operating activities (Dechow, Solan & Sweeny 1995; Xie Davidson & DaDalt 2003). This traditional approach has been extended by Dechow and Ge (2006) who define total accruals as the difference between earnings and free cash flow. Recent research studies have used the free cash flow approach to accruals measurement (Bukit & Iskandar 2009). Free cash flow (FCF) is the combination of cash flow from operating activities and investing activities, which reflects the impact of cash spending on fixed assets and investments. Companies operating with high FCF provide greater opportunities for opportunistic behavior by management. Therefore, it is appropriate to suggest that FCF better reflects accruals for individual firms (Bhuiyan et al. 2013).

$D A=\left[\frac{T A}{L T A}\right]-\left[\alpha 0+\alpha 1\left[\frac{1}{L T A}\right]+\alpha 2\left[\frac{\Delta R E V-\Delta A R}{L T A}\right]+\alpha 3\left[\frac{P P E}{L T A}\right]+\alpha 4\left[R O A_{t-1}\right]+\varepsilon\right]$

Where:

TA = Total Accruals ( Net Income before extraordinary items less free cash flow from operating activities and cash flow from investing activities)

Δ REV = Change in Revenues from the preceding year

Δ AR = Change in Accounts Receivable from the preceding year

PPE = Gross Value of Property , Plant & Equipment

ROAt-1 = Lagged Rate of Return on Assets

LTA = Lagged Total Assets

4. Methodology

4.1 Data and Sample Description

As on June 30, 2012 total listed securities of Dhaka Stock Exchange (DSE) were 511, of which 273 were securities (3 corporate bonds, 8 debentures, 41 mutual funds, & 221 treasury bonds) and 238 were companies. Again, 30 companies were banks, 22 were financial institutions and 45 were insurance companies out of 238 listed companies. Annual reports of 68 listed companies of 2010-2011 out of 141 listed non financial companies were taken as sample in the current study. The relevant data were collected through the survey of financial statements of annual report for the year 2010-2011 of each firm of the sample companies. The relevant pages of the annual report were statement of income (for revenue, net income), statement of cash flow (cash flow from operation), statement of financial position (for accounts receivable, total assets), and schedule of fixed assets (for gross value of fixed assets).

4.2 Measurement of Accounting Accruals Manipulation

Earnings management is predominantly a function of manipulating accruals, so it is intuitive to use the magnitude of accruals as a proxy for earnings quality: the higher the total accruals as a percentage of assets, the greater the likelihood that earnings quality is low. The size of accruals can be used as a rough measure for earnings manipulation, especially in high-accrual firms (Tim Keefe: 2013). Three steps are involved in deriving discretionary accruals i.e., Step-1: Total accruals, Step-2: Accounting accruals (Non-discretionary accruals) and Step-3: Corporate accruals (Discretionary accruals).

4.2.1 Measuring the total accruals

There are two ways to measure the total accruals created in a given period (Tim Keefe: 2013).

1. The Balance Sheet Approach

2. The Statement of Cash Flow Approach

4.2.1.1 The balance sheet approach

Using the balance sheet, we can find the total net accruals by subtracting:

Total Accruals = Accrual Earnings-Cash Earnings

But the balance sheet doesn’t directly tell us what accrual earnings or cash earnings were in the period, so we will have to perform further calculation to retrieve this information.

4.2.1.1.1 Accrual earnings

Net income flows into the balance sheet as retained earnings, which can be found in the owners’ equity section of the balance sheet. Owners’ equity also reflects net distributions to equity holders, and we will need to make some adjustments for these items. So, owners’ equity at the end of the period will be as under:

End Equity = Start Equity+ Accrual Earnings - Cash Dividends-Stock Repurchases+Equity Issuance

To calculate accrual earnings, we can rearrange the equation above and find that it is the difference between ending owners’ equity and beginning owners’ equity, adjusted for dividends, stock repurchases and stock issuances. This adjustment can be summarized as net cash distribution to equity.

$\begin{aligned} \text { Accrual Earnings } & =\Delta \text { Owners' Equity }+\text { Cash Dividends }+\text { Stock Repurchases }-\text { Equity Issuance } = \Delta \text { Owners'Equity }+\text { Net Cash Distributions to Equity }\end{aligned}$

Now, assuming that Assets – Liabilities = Owners’ Equity, we can substitute to get the following equation for accrual earnings:

Accrual Earnings $=\Delta$ Assets $-\Delta$ Liabilities $+$ Net Cash Distributions to Equity * Net Cash Distributions to Equity $=$ Cash Dividends + Stock Repurchases $-$ Equity Issuance

4.2.1.1.2 Cash earnings

To begin, cash earnings must be somehow related to the cash account and can be found by looking at the change in the cash account. The cash account is also affected by net cash distributions to equity holders, and we will need to make some adjustments for these items. So, cash earnings at the end of the period will be as under:

$\begin{aligned} \text { Cash Earnings } & =\Delta \text { Cash }+\text { Cash Dividends }+\text { Stock Repurchases }-\text { Equity Issuance } =\Delta \text { Cash }+\text { Net Cash Distributions to Equity }\end{aligned}$

Total Accruals

The section began with the basic total net accruals equation and then went to define accrual earnings and cash earnings. Now with these definitions in hand we can substitute them in.

Total Accruals=Accrual Earnings-Cash Earnings$=[\Delta$ Assets $-\Delta$ Liabilities $+$ NCDE $*]-[\Delta$ Cash $+$ NCDE $*]$

Total Accruals $=\Delta$ Assets $-\Delta$ Liabilities $-\Delta$ Cash

* NCDE=Net Cash Distribution to Equity

4.2.1.2 The Statement of Cash Flow Approach

Using the statement of cash flow, we can find total accruals with the same basic equation as stated before:

Total Accruals = Accrual Earnings - Cash Earnings

4.2.1.2.1 Accrual Earnings

Calculating total accruals from the statement of cash flow is a bit more straightforward. This is because we don’t need to pull out accrual earnings, because net income is stated right on the report.

Accrual Earnings=Net Income (NI)

4.2.1.2.2 Cash Earnings

Cash earnings can be found from statement of cash flow. Cash flow from operating activities is treated as cash earnings.

Cash Earnings=Cash flow from Operating Activities (C F O)

Total Accruals

$\begin{gathered}\text { Total Accruals = Accrual Earnings - Cash Earnings }=N I-C F O\end{gathered}$

In this study, due to non-articulation issues, the cash flow approach has been used to measure total accruals for each of sample companies by following equation:

$T A\left[\frac{1}{L T A}\right]=\left[\frac{1}{L T A}\right][N I-C F O]$

Where,

TA = Total Accruals

NI = Net Income for the current period

CFO = Cash Flow from Operations for the current period

LTA = Lagged Total Assets [Total assets of the last year]

*Both items in the equation are scaled by lagged total assets in order to reduce heteroscedasticity.

4.2.2 Measuring accounting accruals (Non-Discretionary Accruals)

Using the raw accrual amounts as a proxy for earnings management is a simple method to evaluate earnings quality because firms can have high accruals for legitimate business reasons, such as sales growth. A more complicated proxy can be created by attempting to categorize total accruals into accounting accruals and corporate accruals. The accounting accruals component reflects business conditions such as growth and the length of the operating cycle that naturally create and destroy accruals, while the corporate accruals component identifies management choices. The following two steps are involved in measuring accounting accruals.

4.2.2.1 Measuring Co-efficient Estimates by using Regression Model

The following regression model for the sample has been developed in light of the modified Kothari-Jones Model (2005) as to eliminate possible mechanical relationship between performance metric and current period’s corporate accrual estimate in order to measure the coefficient estimates ($\beta$ value) that are used to segregate the accruals into accounting accruals and corporate accruals components. The regression model for total accruals for the sample firm is estimated as follows:

$\left[\frac{T A}{L T A}\right]=\beta 1\left[\frac{1}{L T A}\right]+\beta 2\left[\frac{\Delta R E V-\Delta A R}{L T A}\right]+\beta 3\left[\frac{P P E}{L T A}\right]+\beta 4\left[\frac{N I}{L T A}\right]+\varepsilon$

Where:

TA = NI – CFO, where NI (net income) is taken from the statement of income

and CFO (operating cash flows) is taken from the statement of cash flows.

Δ REV = Change in Revenues from the preceding year

Δ AR = Change in Accounts Receivable from the preceding year

PPE = Gross Value of Property , Plant & Equipment

NI = Net Income

LTA = Lagged Total Assets

Each $\beta$ is the estimated relationship of the independent variable to the dependent variable, and the error term represents the composite effect of all variables not explicitly stated as an independent variable.

4.2.2.2 Measuring firm’s accounting accruals by using regression Equation

The above coefficient estimates (ß value) are used in the following regression equation to estimate the firm-specific normal accruals (NA) or non-discretionary accruals for our sample firms:

$\left[\frac{A A}{L T A}\right]=\beta 1\left[\frac{1}{L T A}\right]+\beta 2\left[\frac{\Delta R E V-\Delta A R}{L T A}\right]+\beta 3\left[\frac{P P E}{L T A}\right]+\beta 4\left[\frac{N I}{L T A}\right]$

Where:

AA = Accounting (Non-Discretionary) Accruals

Δ REV = Change in Revenues from the preceding year

Δ AR = Change in Accounts Receivable from the preceding year

PPE = Gross Value of Property , Plant & Equipment

NI = Net Income

LTA = Lagged Total Assets

4.2.3 Measuring corporate accruals (Discretionary Accruals)

The value of total accruals and accounting accruals are available at this stage and corporate accruals is the difference between total accruals and the fitted normal accruals, defined as

$C A=\left[\frac{T A}{L T A}\right]-\left[\frac{A A}{L T A}\right]$

Where,

CA = Corporate (Discretionary) Accruals

TA = Total Accruals

AA = Accounting (Non-discretionary) Accruals

LTA = Lagged Total Assets

5. Results and Discussion

The statistical results of our analysis captured the existence of discretionary accruals in the financial statements of listed companies in Dhaka Stock Exchange (DSE) of Bangladesh. The practicing average rate of discretionary accruals of listed non-financial companies is 35 percent in Bangladesh (Annex-1). Table 1 shown below reveals that 12 percent of non-financial listed companies scored 0 to 10 percent (of either sign), 22 percent scored 11 to 20 percent, 16 percent companies scored 21 to 30 percent, 21 percent companies scored 31 to 40 percent, 15 percent companies scored 41 to 50 percent, 7 percent companies scored 51 to 60 percent and 7 percent scored more than 61 percent. Companies with large accruals tend to have large estimation errors. The diligence of earnings is lower when earnings consists mainly accruals. When accruals are large in magnitude, they are likely to contain significant estimation error, which reduces the diligence of earnings. Managers often want earnings to be highly persistent and predictable because these characteristics can improve their reputations with analysts and investors. If such earnings do not annuitize the intrinsic value of the firm, however, the earnings are low quality. Under accrual accounting, current experience is used to make accounting estimates for future periods and these estimates feed back into current- period earnings (Dechow & Schrand; 2004).

Table 2. Distribution of Corporate Accruals

Class interval of

discretionary accruals, (%)

Frequency

(No of companies)

No of companies (%)

< 10

8

12%

11 – 20

15

22%

21 – 30

11

16%

31 – 40

14

21%

41 – 50

10

15%

51 – 60

5

7%

61 <

5

7%

68

100%

The quality of accruals can vary among companies as a function of accruals even in the absence of intentional earnings manipulation. The determination of earnings requires estimations and judgments and some companies require more forecasts and estimates than others. For example, companies in growing industries will typically have high accruals, which raises a question about reliability because accruals are likely to contain estimation errors. Estimation errors reduce earnings persistence (because they must be corrected in future earnings) and are irrelevant for valuation. Therefore, large accruals (of either sign) can indicate great underlying volatility in the company’s operations and low-quality earnings. Accrual accounting opens the door to opportunistic short-run income smoothing that can lead to future restatements and write-downs. In this method, the company does not change its activities but, rather, opportunistically reports income for an existing activity. Examples, increase in income is reducing the allowance for doubtful accounts, capitalizing rather than expensing costs, and avoiding write-offs of assets. Accruals create the opportunity for earnings management because they require managers to make forecasts, estimates, and judgments. Many studies found that high accruals, in absolute magnitude, are a potential “red flag” that indicates companies are engaged in earnings management. In absolute magnitude, the result shows that the average rate of practicing discretionary accruals of listed non-financial companies in Bangladesh is 37 percent (Annex-1). Table 2 reveals that 26 percent of sample companies scored 0 to 10 percent, 24 percent of sample companies scored 11 to 20 percent, 13 percent of sample companies scored 21 to 30 percent, 18 percent of sample companies scored 31 to 40 percent, 7 percent of sample companies scored 41 to 50 percent, 7 percent of sample companies scored 51 to 60 percent and 5 percent of sample companies scored more than 61 percent respectively. Thus, on the whole, 74 percent of the sample companies scored more than 10 percent, which is clearly a ‘red flag’ for all the stakeholders both internal and external. It is also evident that management is abusing their discretionary power through manipulating accounting accruals. As a result,the level of confidence of the external stakeholders on corporate financial reporting is still too low like earlier studies.

Karim (1998) found that financial reporting in developing countries is generally characterized by lack of transparency, adequacy, reliability and timeliness. Ahmed (1982) found that the image and reliability of financial statements prepared by Bangladeshi companies are not up to international standard and in most cases those are dressed up and cosmetics. What they reveal is interesting but what they conceal is vital. That’s why nobody has confidence on those financial statements and hardly anybody uses them for making economic decisions. Rahman (1982) also found that there is no truth in accounting. Accounting is what one wants it to be. Rahman (1982) found that multinational enterprises understate profits through the manipulations of accounting policies. Razzaque (2004) and Hasan (2013) found the same poor level of confidence of the stakeholders on corporate financial reporting in Bangladesh.

Table 3. Distribution of Corporate Accruals*

Class interval of

discretionary accruals, (%)

Frequency

(No of companies)

No of companies (%)

< 10

18

26%

11 – 20

16

24%

21 – 30

9

13%

31 – 40

12

18%

41 – 50

5

7%

51 – 60

5

7%

61 <

3

5%

68

100%

*Absolute Value

In many cases, it is observed that cash flow from operation exceeds net income (Annex -2) like Enron’s case, the growth rate of accounts receivable is faster than sales growth (Annex-3), the growth of inventory is not consistent with sales growth (Annex-4) and it could lead accounting irregularities. Therefore, it can be said that the external users including analysts of accounting information in Bangladesh are not aware about the risk factors of three conditions of fraud triangle and common accounting warning signs.

Statement of Auditing Standard (SAS) No.99 warns practitioners to be alert for the ‘fraud triangle’ or three conditions that are generally present when fraud occurs:

· Incentives or pressures exist that can lead to fraudulent financial reporting, such as pressure to meet debt covenants or analysts’ earnings expectations.

· Opportunities to commit fraud exist, such as poor internal control.

· The individuals themselves are able to rationalize their behavior, such as a desire to get the company through a difficult time, after which they plan to undo their accounting games.SAS-99 provides examples of fraud risk factors for each of the conditions of the fraud triangle (Annexure-5). Based on the SEC, USA studies (2002) and a review of accounting scandals, several common accounting warning signs are apparent (Annex–6).

6. Conclusion

The study focuses on the existence of discretionary accruals in the corporate financial statements published in the annual report of non-financial companies listed in Dhaka Stock Exchange of Bangladesh. There are two different approaches to measure total accruals e.g., The Balance Sheet Approach and Statement of Cash Flow Approach. In this study, due to non-articulation issues, Statement of Cash Flow Approach is used to find out the total accruals for each of the sample companies. Total accruals are scaled by lagged total assets in order to reduce heteroscedasticity.

The division of total accruals into non-discretionary and discretionary accruals (accruals by management choice) is an important area in accounting research. A wide variety of models have been employed by researchers such as The Jones Model, The Modified Jones Model, The Extended Jones Cash Flow Model, The Performance Matching Model (The Kothari-Jones Model and The Modified Kothari-Jones Model), and The Performance Matched Free Cash Flow Model. The detailed discussions of these models have been presented earlier in the later part of literature review section of this study. The performance matched regression model is used for measuring the degree of influence of four independent variables (co-efficient estimates i.e., ß value) over total accruals (dependent variable). Non-discretionary accruals are predicted by using a linear regression equation. The discretionary accrual is the difference between total accruals and non-discretionary accruals. The study found discretionary accruals (accruals by management choice) is practiced by all the sample companies. The average practicing rate of discretionary accruals is 35 percent. The external users of accounting information of public limited companies are not aware of the risk factors of three conditions of fraud triangle and common accounting warning signs as in some cases it is observed that cash flow from operation exceeded the net income like Enron’s case, the growth rate of accounts receivable is faster than sales growth, and the growth of inventory is not consistent with sales growth.

The existence of these red flags (the risk factors) and accounting warning signs does not mean that the company is engaged in accounting fraud. The analysts should take care while performing the evaluation of corporate financial statements with multiple red flags. If too many red flags exist, it is undoubtedly right to tread with caution and it may be best to walk away. It is high time for all the stakeholders to be aware of the possible risk factors associated with each condition of fraud triangle and common accounting warning signs otherwise management may have the opportunity to exercise their discretionary power to achieve their desired goals.

Findings of this study warrant further investigation on decomposition of discretionary components of accruals for each of the listed companies in DSE.

Data Availability

The data used to support the findings of this study are available from the corresponding author upon request.

Acknowledgments

The authors acknowledge the involvements and comments of the following well-known researchers: Ahsan Habib, Auckland University of Technology, New Zealand; Asheq Rahman, Massey University, New Zealand; Zahirul Hoque and Jahangir Alam, La Trobe University, Australia; Jim Rooney, University of Sydney, Australia; Mohammed Omran, University of Queensland, Australia; Helen Samujh, University of Waikato, New Zealand

Conflicts of Interest

The authors declare that they have no conflicts of interest.

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Appendix

Accruals Data

Annex-1

SNTAAA/NDACA/DAAVDA/AVCA
1-0.08064-0.438230.357590.35759
2-0.06167-0.637570.57590.5759
3-0.08714-0.549740.462610.46261
40.12136-0.440440.56180.5618
5-0.02729-0.290670.263380.26338
6-0.07211-0.629080.556970.55697
7-0.03805-0.542230.504170.50417
8-0.06092-0.450330.389410.38941
9-0.00518-0.222450.217270.21727
100.05904-0.096880.155920.15592
110.161430.116070.045370.04537
12-0.176590.00203-0.178620.17862
130.18291-0.188280.371190.37119
14-0.07475-0.216530.141790.14179
150.0049-0.24440.24930.2493
16-0.16673-0.305260.138530.13853
17-0.00043-0.370410.369980.36998
180.00496-0.413050.418010.41801
190.0071-0.571760.578870.57887
200.29875-0.423750.72250.7225
210.45121-0.017460.468660.46866
220.07178-0.08750.159280.15928
230.07282-0.220290.293110.29311
240.22967-0.18820.417880.41788
250.12005-0.306530.426580.42658
260.10662-0.34510.451720.45172
270.02157-0.167240.188810.18881
28-0.00651-0.171830.165320.16532
290.04671-0.344730.391440.39144
30-0.04777-0.138010.090240.09024
31-0.19318-0.376670.183490.18349
32-0.07499-0.170080.095090.09509
330.18148-0.09160.273090.27309
34-0.01703-0.242590.225560.22556
35-0.06439-0.175360.110970.11097
36-0.25379-0.00165-0.252140.25214
37-0.12077-0.500410.379640.37964
38-0.18739-0.189770.002380.00238
39-0.002560.00186-0.004420.00442
40-0.02766-0.100960.07330.0733
410.0345-0.421430.455930.45593
42-0.04145-0.304660.26320.2632
43-0.03830.01452-0.052820.05282
44-0.07612-0.269030.192910.19291
45-0.01012-0.738480.728360.72836
460.00841-0.530880.539290.53929
470.06156-0.131160.192720.19272
480.15144-0.222060.37350.3735
490.12052-0.133840.254370.25437
500.181740.044630.137110.13711
51-0.12495-0.163880.038940.03894
520.08328-0.061090.144370.14437
530.01563-0.154540.170170.17017
54-0.00949-1.030111.020621.02062
550.04107-0.406220.447290.44729
56-0.07635-0.304970.228620.22862
57-0.1331-4.26794.134814.13481
58-0.30365-0.458770.155120.15512
590.15061-0.200530.351140.35114
600.01731-0.436740.454050.45405
610.052870.4179-0.365030.36503
620.14023-0.686490.826720.82672
630.04621-0.193070.239280.23928
64-0.01182-0.39270.380880.38088
65-0.08275-0.486040.403290.40329
66-0.00017-0.318410.318230.31823
670.02171-0.328880.350590.35059
680.08459-0.233410.3180.318
Avaerage35%37%

*TA = Total Accruals, NDA= Non-Discretionary Accruals, DA= Discretionary Accruals, ABDA= Absolute Value of Discretionary Accruals

Negative Accruals Data

Annex-2

SNNegative Total AccrualsSNPositive Total Accruals
1-0.0806410.121359
2-0.0616720.059037
3-0.0871430.161435
4-0.0272940.18291
5-0.0721150.004904
6-0.0380560.004963
7-0.0609270.007101
8-0.0051880.298749
9-0.1765990.451208
10-0.07475100.071779
11-0.16673110.072824
12-0.00043120.229672
13-0.00651130.120054
14-0.04777140.106615
15-0.19318150.021572
16-0.07499160.046713
17-0.01703170.181483
18-0.06439180.034497
19-0.25379190.008413
20-0.12077200.061562
21-0.18739210.151444
22-0.00256220.120523
23-0.02766230.181742
24-0.04145240.083278
25-0.0383250.015629
26-0.07612260.041067
27-0.01012270.150608
28-0.12495280.017309
29-0.00949290.052873
30-0.07635300.140232
31-0.1331310.046212
32-0.30365320.021709
33-0.01182330.084585
34-0.08275
35-0.00017

Faster Growth in Accounts Receivables

Annex-3

SL NoSales CYSales LYΔ RevenueGrowthA/R CYA/R LYΔ A/RGrowth
1409222369378791393304309768%86154875117310024-31155149-27%
2208899878193256165156437138%53188154004301131451433%
359470831302394042923142797%26245892952108-327519-11%
4158314383180527578-22213195-12%148501911815732096692870282%
59749798928881598078682008510%302293636332055981-29762345-9%
69310430398711998-5607695-6%864472016665270-8020550-48%
734686538219284576415401961880%13175315147410-3829879-74%
8238862215221571751172904648%4519025869521673-24331415-35%
96469263166194674-1502043-2%3324122301747030665210%
101427769789620956330806813459130%487570913120948692366622221303%
115805912325502079530578511702016%1810489886453752-68348854-79%
12101459981677359808524100173131%92212584889806477599-585219111-6%
1394992566676931516622256774004537%953496096881553360719427368%
14220466600273719984-53253384-19%954989332492525-22942632-71%
155931559263061238-3745646-6%62724004962700130970026%
1697140170167945762818176825419822%8935735741185346485-291772911-25%
17457763769434418786233449835%1582806221072768775100374548%
18351429516379294228-27864712-7%3596889032540145342874511%
19865921216824257200416640165%3826577123126393327001838022%
202880611405208543250079517890538%85705508447082478738623029782%
213123471032722007682247922702808042%34383566708468695842591487086306%
224501351773692544888088068922%45716437632778531712937905939%
23240978574910289926041380793145134%380378450160094731220283719138%
244022271063312735262789491843629%343047480316068872269786089%
2570916856853955491616961365231%100067414593431384072427669%
2697588294687387162884957842%37793156268970451089611141%
271850615137149177082835884430924%598037857730922072864%
282706541520467307659810832%247131142279099619221188%
297922998457063319728596787312%193153145176829323163238229%
3013917126651368041514236711512%2163556715283483635208442%
3189006700775747244978241428220295119%535004323552376594091123838262%
321247560922510989191414148641781114%2703257156237514047532811668114%
33685512338526455707288139941609716%152535037811411056912711429346548%
341347142446911462578410200884605918%77242134550824917426417217152%
3527150676126848826930184921%463809344459206517888694%
361525430000134731200017811800013%156046100097937700058108400059%
3798002412845874741341493816%4093992441092839-1529150%
382326886100020946040000232282100011%92684200048805300043878900090%
392234047200159508640063896080040%013109600-13109600-100%
406647846013566309039498475561917%1906101221320140235859609944%
41573719257767892320-194173063-25%1910968627388344-8278658-30%
42402855276419612074-16756798-4%104693679119115984-14422305-12%
43113942363572082742841859620758%429163986436201003-7037017-2%
4488149444572933396015216048521%118081196621654975591569990%
45115226574093509452521717121523%2833462325135080319954313%
46468240190434207010340331808%786510426222248-18357144-70%
47681573793622571342590024519%2909081392360000825490805723%
48108808526063398350745410175372%577029624272546947304482677112%
4960172816993898046048211923565154%442592334177520128265072206149%
501768097695134760376142049393431%1037198898457541474579657424127%
515247748947444540211280234683518%2008696513166673108734196542621%
525461234639482901764163221699813%74047917654251011119796906536%
532443657886192440378351925410327%546036962577768167-31731205-5%
541947342997150644469044089830729%138484116415221300-276737184-67%
5557030265438395670718634594749%6256872890176034-27607306-31%
561814459565124104994557340962046%52318405040018075012300330031%
57131634557612051553381111902389%55696134412823661441376835%
582231013380158609447464491890641%220273158235362912-15089754-6%
591744462051152256214122189991015%9724823631027773365-55291002-5%
6035091058021978824374153028142877%77754398262918731714835666524%
6187672579265683653321988925933%195949561958558293740%
621727173751261380664657930937%53819343343620741945726957%
632752091572298756244533353320%244483001014460414303696141%
641780954287111494867866600560960%76224568650459911117857717%
652102832781725860113769726722%0000%
6673497261055426519918070741133%110776095680411574273493863%
675963555575416724235468313410%604746355822245622521794%
68442640850166412261276228589166%54446444330976452134879965%

Inconsistent Growth in Inventory

Annex-4

SL NoSales CYSales LY∆ RevenueGrowthInventory CYInventory LY∆ InventoryGrowth
1409222369378791393304309768%46923572358453491107822331%
2208899878193256165156437138%84261247089406133671819%
359470831302394042923142797%11952326118262661260601%
4158314383180527578-22213195-12%45123293321090119123936%
59749798928881598078682008510%31737604331381543935606041%
69310430398711998-5607695-6%23189309124617621072754786%
734686538219284576415401961880%143522819131175686123471339%
8238862215221571751172904648%7261841562840545977787016%
96469263166194674-1502043-2%934700010152000-805000-8%
101427769789620956330806813459130%2632267551935027586972399736%
115805912325502079530578511702016%64675825153423758211252066921%
12101459981677359808524100173131%47463057625223057730-476751968-9%
1394992566676931516622256774004537%2828333227223919997958913324826%
14220466600273719984-53253384-19%1348807911185607781632001314%
155931559263061238-3745646-6%4016572036377090378863010%
1697140170167945762818176825419822%104005690881405961222599729628%
17457763769434418786233449835%34268049202072891406076070%
18351429516379294228-27864712-7%75681214747118939693211%
19865921216824257200416640165%4089313793474612136147016618%
202880611405208543250079517890538%85088593945005544140083049889%
213123471032722007682247922702808042%64529292683649002576280392669277%
224501351773692544888088068922%4599000044171995844270042010%
23240978574910289926041380793145134%807801205240085542567715663236%
244022271063312735262789491843629%58764569535265010423499559167%
2570916856853955491616961365231%100367939639788053638913457%
2697588294687387162884957842%7639356695526868408810%
271850615137149177082835884430924%719482437680748217387342206%
282706541520467307659810832%172806531175495791-2689260-2%
297922998457063319728596787312%314515290295080069194352217%
3013917126651368041514236711512%34914868734747129716773900%
3189006700775747244978241428220295119%331379759834355326-502975567-60%
321247560922510989191414148641781114%40262318854671791624-645559739-14%
33685512338526455707288139941609716%18150037161819262670-42589540%
341347142446911462578410200884605918%2541688329220707808233461024715%
3527150676126848826930184921%12059302211720630433867183%
361525430000134731200017811800013%86513820007360493000129088900018%
3798002412845874741341493816%1918156217103892207767012%
382326886100020946040000232282100011%53730330004366664000100636900023%
392234047200159508640063896080040%12283726500120398006002439259002%
406647846013566309039498475561917%17563972591701382223550150363%
41573719257767892320-194173063-25%168634337155881884127524538%
42402855276419612074-16756798-4%1488200651317313391708872613%
43113942363572082742841859620758%57329120515763969084-31057033-1%
4488149444572933396015216048521%3190012002685756405042556019%
45115226574093509452521717121523%2561924332215529933463944016%
46468240190434207010340331808%135692738952997884039295042%
47681573793622571342590024519%1964607131286110916784962253%
48108808526063398350745410175372%437246882460477009-23230127-5%
4960172816993898046048211923565154%1932418996103993203589248696186%
501768097695134760376142049393431%566800853551866626149342273%
515247748947444540211280234683518%12360144741623278528-387264054-24%
525461234639482901764163221699813%1392109377100921131738289806038%
532443657886192440378351925410327%70474653769981958949269481%
541947342997150644469044089830729%5967286105115551378517347317%
5557030265438395670718634594749%28459998517262620411197378165%
561814459565124104994557340962046%78500199110913971-32413772-29%
57131634557612051553381111902389%514774187491757780230164075%
582231013380158609447464491890641%26316497215007724411308772875%
591744462051152256214122189991015%128687244377917655150769589265%
6035091058021978824374153028142877%19327742148441481801088626034129%
6187672579265683653321988925933%2806567461975650528309169442%
621727173751261380664657930937%1978163113631338615029345%
632752091572298756244533353320%58058489365776322148085759%
641780954287111494867866600560960%12189263659248451159677911957%
652102832781725860113769726722%7554357492214272-16670698-18%
6673497261055426519918070741133%88622529266716826821905702433%
675963555575416724235468313410%113774226877005102607371630%
68442640850166412261276228589166%144744695816322216311247477%

Annex-5

Risk factors related to incentives or pressures
1Financial stability or profitability is threatened by economic, industry, or entity operating conditions, such as high degree of competition or market saturation, accompanied by declining margins.
2Excessive pressure exists for management to meet the requirements or expectations of third parties, such as need to obtain additional debt or equity financing to stay competitive.
3Information available indicates that management or board of directors’ personal financial situation is threatened by the entity’s financial performance, such as significant portion of their compensation being contingent upon achieving aggressive targets for stock price, operating results, financial position, or cash flow.
4There is excessive pressure on management or operating personnel to meet financial targets set up by the board of directors or management, including sales or profitability incentives goals.
Source: AICPA, SAS No.99, Consideration of Fraud in a Financial Statement Audit, October 2002
Risk factors related to opportunities
1The nature of the industry or the entity’s operations provides opportunities to engage in fraudulent financial reporting, such as assets, liabilities, revenues, or expenses based on significant estimates that involve subjective judgments or uncertainties that are difficult to corroborate.
2There is ineffective monitoring of management, such as domination of management by a single person or small group without compensating controls.
3There is a complex or unstable organizational structure, such as overly complex organizational structure involving unusual legal entities or managerial lines of authority.
4Internal control components are deficient, such as inadequate monitoring of controls, including automated controls and controls over interim financial reporting (when external reporting is required).
Source: AICPA, SAS No.99, Consideration of Fraud in a Financial Statement Audit, October 2002
Risk factors related to attitudes / rationalizations
1Ineffective communication, implementation, support, or enforcements of the entity’s values or ethical standards by management or the communication of inappropriate values or ethical standards.
2Non-financial management’s excessive participation in or preoccupation with the selection of accounting principles ot the determination of significant estimates.
3Known history of violations of securities laws or other laws and regulations or claims against the entity, its senior management, or board members alleging fraud or violations of laws and regulations.
4Excessive interest by management in maintaining or increasing the entity’s stock price or earnings trend.
5A practice by management of committing to analysts, creditors, and other third parties to achieve aggressive or unrealistic forecasts.
6Management failing to correct known reportable conditions on a timely basis.
7An interest by management in employing inappropriate means to minimize reported earnings for tax-motivated reasons
8Recurring attempts by management to justify marginal oe inappropriate accounting on the basis of materiality.
9The relationship between management and the current or predecessor auditor is strained, such as frequent disputes with current or predecessor auditor on accounting, auditing, or reporting matters
Source: AICPA, SAS No.99, Consideration of Fraud in a Financial Statement Audit, October 2002

Annex-6

Common Accounting Warning Signs
1Aggressive revenue recognition
2Operating cash flow out of line with reported earnings
3Growth in revenues out of sync with economy, industry, or peer companies and with growth in receivables.
4Growth in inventory out of line with sales growth or days inventory increasing over time.
5Classification of non-operating or non-recurring income as revenue.
6Deferral of expenses
7Excessive use of operating leases by lessees.
8Classification of expenses or losses as extraordinary or non-recurring.
9LIFO liquidations
10Gross margins or operating margins out of line with peer companies.
11Use of long useful lives for depreciation and amortization.
12Use of aggressive pension plan assumptions.
13          Common use of fourth-quarter surprises.
14Equity method of accounting / frequent use of off-balance sheet SPEs or variable-interest entities.
15Other off-balance sheet financing or guarantees.
Source: Financial Reporting and Analysis, CFA Institute, Level 1, Reading 33, 2011



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Hasan, M. S., Rahman, R. A., & Hossain, S. Z. (2014). Corporate Accruals Practices of Listed Companies in Bangladesh**. J. Corp. Gov. Insur. Risk Manag., 1(1), 12-43. https://doi.org/10.56578/jcgirm010102
M. S. Hasan, R. A. Rahman, and S. Z. Hossain, "Corporate Accruals Practices of Listed Companies in Bangladesh**," J. Corp. Gov. Insur. Risk Manag., vol. 1, no. 1, pp. 12-43, 2014. https://doi.org/10.56578/jcgirm010102
@research-article{Hasan2014CorporateAP,
title={Corporate Accruals Practices of Listed Companies in Bangladesh**},
author={Md. Shamimul Hasan and Rashidah Abdul Rahman and Syed Zabid Hossain},
journal={Journal of Corporate Governance, Insurance, and Risk Management},
year={2014},
page={12-43},
doi={https://doi.org/10.56578/jcgirm010102}
}
Md. Shamimul Hasan, et al. "Corporate Accruals Practices of Listed Companies in Bangladesh**." Journal of Corporate Governance, Insurance, and Risk Management, v 1, pp 12-43. doi: https://doi.org/10.56578/jcgirm010102
Md. Shamimul Hasan, Rashidah Abdul Rahman and Syed Zabid Hossain. "Corporate Accruals Practices of Listed Companies in Bangladesh**." Journal of Corporate Governance, Insurance, and Risk Management, 1, (2014): 12-43. doi: https://doi.org/10.56578/jcgirm010102
Hasan M. S., Rahman R. A., Hossain S. Z.. Corporate Accruals Practices of Listed Companies in Bangladesh**[J]. Journal of Corporate Governance, Insurance, and Risk Management, 2014, 1(1): 12-43. https://doi.org/10.56578/jcgirm010102
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