The effect of mandatory IFRS adoption on earnings management : evidence from the European market

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The effect of mandatory IFRS

adoption on earnings

management: Evidence from the

European market

Master Thesis

Author: Shahnaz Abdoella (10278893)

Institution: University of Amsterdam, Faculty Economics and Business Program: Accountancy and Control

Control Supervisor: dr. A. (Alexandros) Sikalidis

Date: June, 22th 2015

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Statement of Originality

This document is written by student, Shahnaz Abdoella, who declares to take full responsibility for the contents of this document.

I declare that the text and the work presented in this document is original and that no sources other than those mentioned in the text and its references have been used in creating it.

The Faculty of Economics and Business is responsible solely for the supervision of completion of the work, not for the contents.

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Key words: IFRS, earnings management, real earnings management, accrual-based earnings

management

Abstract

Prior studies on the effect of IFRS on earnings management, suggest that IFRS adoption is associated with lower earnings management due to stronger regulations. However there is also evidence that shows that IFRS has no effect or a negative effect on earnings management. Due to the conflicting results in the literature this study examines the effect of mandatory IFRS adoption in Europe on real earnings management and accrual-based earnings management. By researching both accrual-based and real earnings management, I hope to contribute to the literature. The results show that IFRS have a significant impact on earnings management.

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Acknowledgements

I would like to thank my thesis supervisor mister Alexandros Sikalidis for his guidance and support during the writing of my Master thesis. I appreciate his given advice, assistance and suggestions during the whole process. I would also like to thank my family and friends for their support during the stressful time.

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1. Introduction

“If you can't make it good, at least make it look good.” (Bill Gates, founder of Microsoft)

The above mentioned quote is applicable to earnings management because earnings management is mainly used by managers to mislead stakeholders about the performance of the firm (Healy et al. 1999). The literature describes earnings management as an occurrence when managers use their judgment in financial reporting to alter financial reports and to present numbers that don’t reflect the real economic performance of the firm. Earnings management involves activities that changes reported earnings without creating value to the business and their investors (Ball 2006). Managing earnings can sometimes cause harm to the firm and their investors. Managers engage in earnings management to either boost, smooth or reduce earnings. Managers usually boost earnings to increase short term stock prices or to meet or beat last year’s earnings and to avoid reporting losses. Earnings management can be applied to reduce earnings in one period to build up reserves and use those earnings in future periods. Managers may use earnings management as a tool to reduce stock prices prior to management buyouts. In order to reduce corporate risk, managers may apply earnings management to smooth earnings (Merchant & Van der Stede, 2007). There are three categories of earnings management, namely fraudulent accounting, real earnings management and accrual-based earnings management. Fraudulent accounting involves accounting choices that violate GAAP (Gunny, 2010). Fraudulent accounting will not be considered further in this paper. Real activities

manipulation involves activities that change the underlying economic activities of the firm. An example of real earnings management is offering discounts or increasing production. Real earnings management is harder to detect but is usually far more costly than accrual-based earnings

management. This is because real earnings management involves altering actual operating decisions, that has direct consequences for cash flow and it consumes real recourses (Ewert et al. 2005). Accrual-based earnings management involves the selection of accounting methods that represent the underlying economic activities of the firm (Merchant & Van der Stede, 2007). Accruals are applied to shift or adjust the recognition of cash flows over time so that earnings better reflect firm performance (Dechow et al. 2002).

Due to increased globalization and the series of corporate scandals, the need for reliable financial information, uniformity and the necessity to restore the trust in the capital markets, increased the demand for standardized regulations for financial reporting (Cohen et al. 2008). In order to fill these needs and to achieve uniformity in the accounting standards, the International Accounting Standards Board (IASB) developed a “single set of high quality, understandable, enforceable and globally

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6 accepted International Financial Reporting Standards (IFRS) based upon clearly articulated

principles.” 1More than 113 countries around the world require or permit IFRS (U.S. Securities and Exchange Commission). Since the first of January 2005, listed European companies are required to apply IFRS to report simple accounts and consolidated financial statements (Soderstrom et al. 2007). Also non-listed European companies, who aren’t obliged to apply IFRS, choose to apply IFRS for their financial reporting due to the benefits that IFRS provide.2

The literature describes the following benefits of adoption of international accounting standards like IFRS. First IFRS increases decision usefulness by increasing comparability. An improvement in

comparability reduced the costs for investors to compare companies across markets and countries and also decreases risks like estimation risk. With increased comparability, the ability for investors to differentiate between lower and higher quality firms increases (Daske et al. 2008). IFRS reduce cost for multiple reporting, decrease cost of capital for companies, encourage international investment and increase the efficiency of savings allocation (Tendeloo et al. 2005; Chen et al. 2010; Barth et al. 2008). Tight accounting standards like IFRS can reduce the level of earnings management and increase the quality of financial reporting.

Prior studies provide different evidences of the effect of international accounting standards on earnings management. Van Tendeloo and Vanstraelen (2005) investigate whether voluntary IFRS adoption is associated with lower earnings management. Their sample contains German listed companies and their data is from the period before IFRS adoption became mandatory, namely 1999-2001. They only focus on German listed companies because Germany is classified as a code-law country with low investor protection rights (La Porta et al. 2000). In countries with low investor protection rights, the benefits of engaging in earnings management are higher than the costs (Leuz et al. 2003). They also chose Germany because a large number of companies in Germany chose to voluntarily adopt IFRS before it became mandatory. The results show that companies who voluntarily adopted IFRS don’t engage in less earnings management activities than companies who report under German GAAP. When hidden reserves are taken into consideration, there’s no difference in earnings management techniques between companies who apply IFRS and companies who report under German GAAP. So their results show that voluntary IFRS adoption has no effect on real and accrual-based earnings management. Doukakis (2014) also finds evidence that mandatory IFRS adoption has no significant impact on earnings management. Jeanjean and Stolowy (2008) analyze the effect of the mandatory introduction of IFRS on earnings management. They focus their research on IFRS first-time adopters namely Australia, France and the United Kingdom. Early adoption before 2005 was not

1 SEC

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7 possible in these countries. Their results show that the level of earnings management did not decline in Australia and the UK. Earnings management even increased in France after the introduction of IFRS. There’s also evidence in the literature that shows that the adoption of IFRS decreases earnings management (Barth et al. 2008; Christensen et al. 2015). Both studies of Barth et al. (2008) and Christensen et al. (2012) focus on early voluntary IFRS adopters. The contradictions in the literature lead to the following research question of this paper:

“What is the effect of mandatory IFRS adoption on earnings management?”

To examine the effect of IFRS on both real and accrual-based earnings management a quantitative research method is applied. Data is collected from Compustat. In this study real and accrual-based earnings management is measured for 14 member states of the European Union in the period before and after mandatory IFRS adoption in 2005. Real earnings management is measured by the

combination of abnormal levels of the following three proxies, namely cash flows from operation, production cost and discretionary expenses. The modified Jones-model is applied to measure accrual-based earnings management.

Prior literature has mainly researched the effect of IFRS adoption on accrual-based earnings

management. I want to contribute to the literature by also examining the effect of IFRS adoption on real earnings management. As a result I propose that after mandatory IFRS adoption the level of real earnings management decreases and the level of accrual-based earnings management increases.

The results show that IFRS is negatively associated with real and accrual-based earnings management and therefore IFRS have a significant impact on both methods of earnings management. I also find evidence that both earnings management methods are substitutes of each other. This indicates that when one method of earnings management increases (decreases) the other decreases (increases). Tests show that after mandatory IFRS adoption, accrual-based earnings management increases and real earnings management decreases. This could be explained by the fact that IFRS are principle-based and require managers’ judgment and therefore give room for earnings management.

The remainder of this paper is organizes as follows. In the next section related literature is reviewed and the hypotheses are developed. Section 3 shows the variables, the models and discusses the sample. The tests and results are shown in section 4. Finally I discuss the conclusions and limitations. At the end of the paper the references are listed and the appendixes are presented.

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2. Literature review & hypotheses

2.1 Accounting quality

Although there’s no concrete definition for accounting quality, accounting quality can be described as the extent to which financial statement information reflect the underlying economics (Ahmed et al. 2013; Healy et al. 1999). Financial statements are of high quality when they faithfully reflect the economic position and performance of the firm. In the literature the following measures are often used to determine accounting quality: income smoothing, reporting aggressiveness, and earnings management (Ahmed et al. 2013).

2.2 Accounting quality and IFRS

The adoption of IFRS can improve accounting quality if it’s a higher quality standard than local GAAP. A higher quality standard reduces managerial discretion over accounting choices and doesn’t give room for income smoothing or overstatement of earnings (Ahmed et al. 2013). Financial reporting under IFRS could become more value relevance and reliable, and therefore of a higher quality, if the IASB continues to improve the quality of IFRS (Soderstrom et al. 2007).The adoption of IFRS could lead to an improvement of accounting quality for the following reasons. First, IFRS reduces

management discretion by eliminating accounting alternatives. This can reduce the opportunity for opportunistic earnings management. Second, IFRS are principle-based standards and are therefore potentially more difficult to avoid. It is for example more difficult to avoid the recognition of a liability through transaction structuring under principle-based standards. Furthermore IFRS allows fair value accounting. Fair value accounting gives more accurate information about the underlying economic position and performance of the firm (Barth et al. 2008; Ahmed et al. 2013).

Barth et al. (2008) argues that the adoption of IFRS could also lower accounting quality for the following reasons. IFRS limits managerial discretion and thereby eliminates the firm’s ability to report accounting measurements that give a better reflection of the economic position and performance of the firm. When the previous is the case, IFRS could be of lower quality than local GAAP standards. IFRS are principle-based and therefore provide broad guidelines and not specific requirements. The lack of specific guidelines makes a manager’s judgment necessary. When accounting standards allow a high level of managers’ judgment in financial reporting, the opportunity for earnings management increases and that reduces accounting quality (Healy et al. 1999).

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2.3 Accrual-based earnings management

IFRS and GAAP mandate accrual accounting for recognizing revenue en expenses in the financial statement. Accrual-based earnings management is achieved by changing accounting methods like the deprecation method for fixed assets or is achieved by changing estimates used when presenting a given transaction in the financial statement (Zang, 2011). Many studies use discretionary accruals as a proxy to measure accrual-based earnings management. Researchers hereby begin with the total amount of accruals measured as the difference between net income and cash flow from operations. To measure the amount of unexpected accruals and thereby accrual-based earnings management, the total amount of accruals is corrected by proxies for normal accruals. Unexpected accruals are therefore explained as the unexplained part (the residual) of total accruals (Healy et al. 1999). In previous literature, the effect of voluntary and mandatory IFRS adoption on accrual-based earnings management has been researched numerously with conflicting results. Ahmed et al. (2013),

Christensen et al. (2015), Barth et al. (2008), Adibah Wan Ismail et al. (2013) and Capkun et al. (2012) research the effect of voluntary and mandatory IFRS adoption on earnings management and

accounting quality. The results of Ahmed et al. (2013) show an increase in earnings management for mandatory adopters. On the other hand Adibah Wan Ismail et al. (2013) show a significant lower level of absolute value of discretionary accruals during the mandatory adoption of IFRS period. Similarly Cai et al. 2008 also show a decrease in earnings management after mandatory IFRS adoption. Christensen et al. (2015) and Barth et al. (2008) find that voluntary adoption of IFRS is associated with a decrease in earnings management. On the contrary Capkun et al. (2012) find evidence for an increase in earnings management from pre-2005 to post-2005 for early and late voluntary adopters in countries that allowed early IFRS adoption, and for mandatory adopters in countries that did not allow early IFRS adoption. These contradictory results can be explained by self selection (Christensen et al. 2008; Ahmed et al. 2013). Voluntary adopters choose to adopt IFRS to signal to the market that their financial statements are of higher accounting quality. Voluntary adopters therefore have a stronger incentive to report higher quality accounting numbers (Ahmed et al 2013).

2.4 Real earnings management

Roychowdhury (2006) defines real earnings management as a tool that managers use to meet certain earnings thresholds. Real activities manipulation involves measures that deviate from the normal business practices (Roychowdhury, 2006). Real activities manipulation involves actions like offering price discounts or lenient credit terms or cutting in R&D expenses. The research on the effect of accounting standards on accrual-based earnings management is very extensive in existing literature. However the research on the effect of accounting standards on real earnings management is limited.

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10 Ewert et al. (2005) and Doukakis (2014) investigate the effect of accounting standards on both accrual-based earnings management and real earnings management. Doukakis (2014) investigates the effect of mandatory IFRS adoption on both real and accrual-based earnings management. The sample is drawn from 22 European countries in the period before and after mandatory IFRS adoption. The results show that mandatory IFRs adoption has no significant effect on both real and accrual-based earnings management. Ewert et al. (2005) study the effects of tighter accounting standards in a capital market setting on earnings quality. They use earnings management as a proxy for earnings quality and distinguish between accounting and real earnings management. They argue that tighter accounting standards can only restrict the discretion for accrual-based earnings

management, but can do little to restrict real earnings management. So with tighter accounting standards accrual-based earnings may decrease but managers switch to real earnings management. Their results show that earnings quality increases with tighter standards. But this benefit could be outweighed by the fact that managers increase real earnings management because the higher earnings quality increases the marginal benefit of real earnings management. Real earnings

management is more costly and therefore the total cost of earnings management can increase even though when it is expected that total earnings management reduces. The benefit of an increase of accounting quality can also be outweighed because tighter standards can increase rather than decrease expected accounting and total earnings management (Ewert et al. 2005).

2.5 Trade–off between accrual-based earnings management and real

earnings management

Several studies found evidence that accrual-based earnings management and real earnings

management are substitutes of each other. Therefore if one decreases the other will increase. Zang (2011) finds evidence that managers trade-off real and accrual-based earnings management methods based on the relative costliness of both earnings management methods. This indicates that when one activity is more costly, managers engage in more of the other activity. Their results also show that managers adjust the level of accrual-based earnings management according to the realized level of real earnings management. This is because of the timing difference of both methods. Real activities manipulation occurs during the fiscal year and is realized by the fiscal year-end. After this managers can still adjust the level of accrual-based earnings management (Zang, 2011). Cohen et al. (2008) research the occurrence of both real and accrual-based earnings management activities in the period before and after the passage of the Sarbanes-Oxley Act (SOX).

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11 Their results show that accrual-based earnings management declined and the level of real earnings management increased significantly after the passage of SOX. The levels of the two methods of earnings management were opposites in the period before the passage of SOX, accrual-based earnings management increased and real activities manipulation decreased in that period. These results suggest that firms switched from accrual-based earnings management to real earnings management after the passage of SOX, indicating that the two methods are substitutes of each other.

2.6 Hypotheses development

As previously discussed, results in the literature show a decrease in earnings management after voluntary and mandatory IFRS adoption (Christensen et al. 2015; Barth et al. 2008; Cai et al. 2008). However evidence also show that earnings management increases or isn’t effected by mandatory or voluntary IFRS adoption (Ahmed et al. 2013; Doukakis, 2014; Jeanjean et al. 2008; Tendeloo et al. 2005; Capkun et al. 2012). Based on these above contradiction in prior literature I expect that the level of both real and accrual-based earnings management decreases (increases) when the level of IFRS adoption increases (decreases) and the following hypothesis is formulated:

Hypothesis 1: Mandatory IFRS adoption is negatively associated with accrual-based earnings management and real earnings management.

If one level of earnings management, in this case accrual-based earnings management, increases, the other one (real earnings management) decreases. This is because of the evidence in prior literature of both methods for earnings management being substitutes (Cohen et al. 2008; Zang, 2011). I expect that the level of accrual-based earnings management increases after mandatory IFRS adoption because IFRS are principle-based and therefore ask for management’s judgment in

reporting (Barth et al. 2008). When accounting standards allow a high level of managers’ judgment in financial reporting, the opportunity for earnings management increases. (Healy et al. 1999). Because of this expectation, I expect the level of real earnings management to be lower after the period of mandatory IFRS adoption in comparison to the level before mandatory IFRS adoption. Based on these expectations the following hypotheses are formulated:

Hypothesis 2: After the mandatory implementation of IFRS, accrual-based earnings management increases.

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3. Research methodology

In this section the research method, the models and the sample will be described. After the description of the research method, I will first present the models that are applied to measure real earnings manipulation and accrual-based earnings management. Subsequently the models to

measure the relation between IFRS and the two types of earnings management are presented. In the final part of this chapter, the data sample is explained.

3.1 Research method

The focus of the research of this paper is on both real earnings management and accrual-based earnings management in the period before and after mandatory IFRS adoption in Europe. The necessary data is collected from the database COMPUSTAT. Regression models that can be found in the literature are applied to measure earnings management and to measure the relation between IFRS and earnings management.

3.3.1 Accrual-Based Earnings Management

In order to estimate firm i’s discretionary accruals, I use a cross-sectional variety of the modified Jones model. Hereby I rely on prior studies (Kim et al. 2012; Tendeloo et al. 2005; Defond et al. 1997). I follow Cohen et al. (2008) and estimate the following regression:

𝑇𝐴

_𝑖,𝑡

𝐴𝑠𝑠𝑒𝑡𝑠

_{𝑖,𝑡−1}

= 𝛼0

1 𝐴𝑠𝑠𝑒𝑡𝑠

_{𝑖,𝑡−1}

+ 𝛼1

∆𝑅𝐸𝑉

_𝑖,𝑡

𝐴𝑠𝑠𝑒𝑡𝑠

_{𝑖,𝑡−1}

+ 𝛼2

∆𝑃𝑃𝐸

_𝑖,𝑡

𝐴𝑠𝑠𝑒𝑡𝑠

_{𝑖,𝑡−1}

+ 𝜀

𝑖,𝑡 Where:

𝑇𝐴𝑖𝑡 = Total accruals for firm i at year t, 𝐸𝑋𝐵𝐼𝑖𝑡− 𝐶𝐹𝑂𝑖𝑡where EXBI is the earnings

before extraordinary items and discontinued operations and CFO is the operation cash flows taken from the statement of cash flow;

∆𝑅𝐸𝑉𝑖𝑡 = Change in net revenues in year t from year t-1;

𝑃𝑃𝐸𝑖𝑡 = Gross property, plant and equipment;

𝐴𝑠𝑠𝑒𝑡𝑠𝑖𝑡−1 = Lagged total assets;

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3.3.2 Real activities manipulation

For the development of the proxies for real earnings management, I rely on prior studies

(Roychowdhury 2006; Cohen et al. 2005 and Zang 2012). I follow Kim et al. (2012) and consider the following proxies to measure real activities manipulation:

1. Abnormal levels of operating cash flows (AB_CFO). 2. Abnormal levels of production costs (AB_PROD) 3. Abnormal levels of discretionary expenses (AB_EXP)

4. A combined measure of real activities manipulation (COMBINED_RAM)

The abnormal levels of operating cash flows, production costs and discretionary expenses, will be measured as the residual of the normal levels of those three proxies. The combined measure of real activities manipulation is measured by the abnormal levels of operating cash flows minus Abnormal levels of production costs plus the abnormal levels of discretionary expenses (COMBINED_RAM= AB_CFO – AB_PROD + AB_EXP).

a. Operating cash flows

By offering price discounts and more lenient credit term, sales volumes will temporarily increase. These higher sales volumes will disappear when a firm returns to their old prices and credit terms. Additional sales boost current period earnings, however price discounts and lenient credit terms result in lower cash flows in the current period (Cohen et al. 2008; Kim et al. 2012). To estimate the normal levels of operating cash flows, I will follow the model by Roychowdhury (2006):

𝐶𝐹𝑂𝑖,𝑡 𝐴𝑠𝑠𝑒𝑡𝑠𝑖,𝑡−1 = 𝛼1 1 𝐴𝑠𝑠𝑒𝑡𝑠𝑖,𝑡−1 + 𝛼2 𝑆𝑎𝑙𝑒𝑠 𝐴𝑠𝑠𝑒𝑡𝑠𝑖,𝑡−1 + 𝛼3 ∆𝑆𝑎𝑙𝑒𝑠𝑖,𝑡 𝐴𝑠𝑠𝑒𝑡𝑠𝑖,𝑡−1 + 𝜀𝑖,𝑡

For every firm-year, abnormal cash flow from operations (AB_CFO) is the difference between the actual CFO and the expected CFO calculated using the corresponding industry-year model (the residual in the model).

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b. Production costs

Managers can report a lower cost of goods sold (COGS) by increasing production to a level that is more than necessary (overproduction). This way the fixed costs can be divided over a larger number of units (Cohen et al. 2008). In the literature, production cost is calculated as the sum of cost of goods sold (COGS) and change in inventory during the year (𝑃𝑅𝑂𝐷𝑡 = 𝐶𝑂𝐺𝑆𝑡+ ∆𝐼𝑁𝑉𝑡) (Doukakis

2014). COGS is modeled as a linear function of contemporaneous sales (Cohen et al. 2008;

Roychowdhury 2006). I estimate the following model for normal levels of COGS and follow hereby Kim et al. (2012): 𝐶𝑂𝐺𝑆𝑖,𝑡 𝐴𝑠𝑠𝑒𝑡𝑠𝑖,𝑡−1 = 𝛼0 + 1 𝐴𝑠𝑠𝑒𝑡𝑠𝑖,𝑡−1 + 𝛼1 𝑆𝑎𝑙𝑒𝑠𝑖,𝑡 𝐴𝑠𝑠𝑒𝑡𝑠𝑖,𝑡−1 + 𝜀𝑖,𝑡

Where 𝐶𝑂𝐺𝑆𝑖,𝑡= The cost of goods sold in year t.

Similarly, I estimate the model for normal inventory growth using the following equation:

∆𝐼𝑁𝑉𝑖,𝑡 𝐴𝑠𝑠𝑒𝑡𝑠𝑖,𝑡−1 = 𝛼0 1 𝐴𝑠𝑠𝑒𝑡𝑠𝑖,𝑡−1 + 𝛼1 ∆𝑆𝑎𝑙𝑒𝑠𝑖,𝑡 𝐴𝑠𝑠𝑒𝑡𝑠𝑖,𝑡−1 + 𝛼2∆𝑆𝑎𝑙𝑒𝑠𝑖,𝑡−1 𝐴𝑠𝑠𝑒𝑡𝑠𝑖,𝑡−1 + 𝜀𝑖,𝑡

Where ∆𝐼𝑁𝑉𝑖,𝑡 = The change in inventory in year t.

To estimate the normal level of production cost, I use the previous two equations for normal COGS and inventory growth:

𝑃𝑟𝑜𝑑𝑖,𝑡 𝐴𝑠𝑠𝑒𝑡𝑠𝑖,𝑡−1 = 𝛼0 1 𝐴𝑠𝑠𝑒𝑡𝑠𝑖,𝑡−1 + 𝛼1 𝑆𝑎𝑙𝑒𝑠𝑖,𝑡 𝐴𝑠𝑠𝑒𝑡𝑠𝑖,𝑡−1 + 𝛼2 ∆𝑆𝑎𝑙𝑒𝑠𝑖,𝑡 𝐴𝑠𝑠𝑒𝑡𝑠𝑖,𝑡−1 + 𝛼3 ∆𝑆𝑎𝑙𝑒𝑠𝑖,𝑡 𝐴𝑠𝑠𝑒𝑡𝑠𝑖,𝑡−1 + 𝜀𝑖,𝑡

The abnormal level of production cost (AB_PROD) is the residual 𝜀𝑖𝑡from the model.

c. Discretionary expenses

The third proxy for real activities manipulation is abnormal discretionary expenses. Evidence in the literature shows that managers cut discretionary spending to achieve earnings targets and boost current period earnings (Gunny 2010). To estimate the normal level of discretionary expenses, I rely on prior literature (Roychowdhury 2006; Cohen et al. 2008; Zang 2012) and use the following model:

𝐷𝐼𝑆𝐸𝑋𝑃𝑖,𝑡 𝐴𝑠𝑠𝑒𝑡𝑠𝑖,𝑡−1 = 𝛼0 1 𝐴𝑠𝑠𝑒𝑡𝑠𝑖,𝑡−1 + 𝛼1 ∆𝑆𝑎𝑙𝑒𝑠𝑖,𝑡 𝐴𝑠𝑠𝑒𝑡𝑠𝑖,𝑡−1 + 𝜀𝑖,𝑡

Where 𝐷𝐼𝑆𝐸𝑋𝑃𝑇 = Discretionary expenses in year t, defined as the sum of Research & Development

(R&D) expenses and SG&A expenses. Abnormal discretionary expenses (AB_EXP) is measured by the residual 𝜀𝑖𝑡 from the model.

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3.3.3 Relation earnings management and IFRS

To capture the relation between mandatory IFRS adoption and real and accrual-based earnings management, I estimate the following models following Kim et al (2012):

Model 1, relation between discretionary accruals and mandatory IFRS adoption:

𝐴𝐵𝑆_𝐷𝐴𝑡 = 𝛼0 + 𝛼1 𝐼𝐹𝑅𝑆𝑡+ 𝛼2 𝐶𝑂𝑀𝐵𝐼𝑁𝐸𝐷_𝑅𝐴𝑀𝑡+ 𝛼3 𝑆𝐼𝑍𝐸𝑡−1+ 𝛼4 𝐴𝐷𝐽_𝑅𝑂𝐴𝑡−1+ 𝛼5 𝐿𝐸𝑉𝑡−1

+ 𝛼6 𝑅𝐷_𝐼𝑁𝑇𝑡+ 𝜀𝑡

Model 2, relation between real earnings management and mandatory IFRS adoption:

𝑅𝐴𝑀_𝑃𝑅𝑂𝑋𝑌𝑡 = 𝛼0 + 𝛼1 𝐼𝐹𝑅𝑆𝑡+ 𝛼2 𝐴𝐵𝑆_𝐷𝐴𝑡+ 𝛼3 𝑆𝐼𝑍𝐸𝑡−1+ 𝛼4 𝐴𝐷𝐽_𝑅𝑂𝐴𝑡−1+ 𝛼5 𝐿𝐸𝑉𝑡−1

+ 𝛼6 𝑅𝐷_𝐼𝑁𝑇𝑡+ 𝜀𝑡

Where:

𝐴𝐵𝑆_𝐷𝐴 (𝐷𝐴) = Absolute value of discretionary accruals measured by the residual in the cross-sectional modified Jones model.

𝑅𝐴𝑀_𝑃𝑅𝑂𝑋𝑌 = 𝐶𝑂𝑀𝐵𝐼𝑁𝐸𝐷_𝑅𝐴𝑀 (𝐴𝐵_𝐶𝐹𝑂 − 𝐴𝐵_𝑃𝑅𝑂𝐷 + 𝐴𝐵_𝐸𝑋𝑃).

𝐼𝐹𝑅𝑆 = An indicator variable that takes the value of 1 after the period of mandatory IFRS adoption and 0 otherwise.

𝑆𝐼𝑍𝐸 = Natural logarithm of total assets

𝐴𝐷𝐽_𝑅𝑂𝐴 = Industry-adjusted ROA, where ROA is measured as income before extraordinary items, scaled by lagged total assets.

𝐿𝐸𝑉 = (Leverage) Long-term debt scaled by total assets. 𝑅𝐷_𝐼𝑁𝑇 = R&D intensity (R&D expense/net sales) for the year.

3.4 Data sample selection

The sample period extends from 2000 to 2010, 5 years before IFRS adoption became mandatory and 6 years after mandatory IFRS adoption. The sample includes member states that became a member of the European Union before 2000. The empirical approach of this research is in the form of an archival study. For the sample I used existing data from the database of COMPUSTAT. Hereby I obtain data from European listed companies. From my empirical analyses, I exclude financial institutions and banks because of their specific accounting requirements and accrual processes, which differ substantially from other industries. My initial sample consists of 30908 firm-year observations. Firm-year observations with missing data were removed from the initial sample and the final sample consists of 4254 firm-year observations from non-financial firms across 14 European countries.

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16

4. Tests and results

4.1 Descriptive statistics

Table 1 presents the number of firm-year observations per country before and after the exclusion of firm-years with missing data. The firm-year observations are also divided between the pre

mandatory adoption period (2000-2004) and the post mandatory adoption period (2005-2010). The variation in firm-years per country is due to the difference in country size and the availability of data. The united Kingdom has the largest amount of firm-year observations before and after the exclusion. Portugal has zero firm-year observations after the exclusion of missing data. This is because a lot of firm-year observations of Portugal are missing data on R&D expense and change in inventory.

Table 1

Descriptive statistics sample countries (2000-2010)

Country Frequency before exclusion % Frequency after exclusion % Frequency 2000-2004 % Frequency 2005-2010 % Austria 658 2,13 131 3,08 12 1,45 119 3,47 Belgium 915 2,96 146 3,43 30 3,63 116 3,38 Denmark 1096 3,55 185 4,35 37 4,48 148 4,32 Finland 1161 3,76 246 5,78 34 4,12 212 6,18 France 5213 16,87 442 10,39 119 14,41 323 9,42 Germany 5539 17,92 877 20,62 146 17,68 731 21,32 Greece 1592 5,15 208 4,89 25 3,03 183 5,34 Ireland 487 1,58 38 0,89 9 1,09 29 0,85 Italy 1962 6,35 148 3,48 8 0,97 140 4,08 Luxembourg 270 0,87 33 0,78 5 0,61 28 0,82 Netherlands 1046 3,38 135 3,17 42 5,08 93 2,71 Portugal 472 1,53 0 0 0 0 0 0 Spain 1069 3,46 77 1,81 3 0,36 74 2,16 UK 9428 30,5 1588 37,33 356 43,10 1232 35,94 Total 30908 100 4254 100 826 100 3428 100 Mean 2207,7 7,14 303,79 7,14 59 7,14 245 7,14 Minimum 270 0,87 0 0 0 0 0 0 Maximum 9428 30,5 1588 37,33 356 43,10 1232 35,94

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17 Table 2 provides summary statistics of the full sample in the period of 2000 till 2010. Data items are collected from the database of Compustat. Total Accruals deflated by lagged total assets is negative at 0,12 with a standard deviation of 5,39. Similarly, the average absolute value of discretionary accruals (ABS_DA) is negative at 0,01 and a standard deviation of 3,33. On contrary the average value of RAM_PROXY, the proxy for real activities manipulation is positive at 0,45 with a standard deviation of 10,25.

Table 2

Descriptive statistics variables 2000-2010

N Minimum Maximum Mean Median Std.

Deviation

Percentiles

Statistic Statistic Statistic Statistic Statistic Statistic 25th 75th

Total Assets 4253 0 354583 5053,77 180,4 21069,96 35,08 1110,99 Net sales 4253 0 368056 3801,39 170,14 17393,60 25,80 1009,83 Total Accruals 4253 -35032,82 19966,58 -239,97 -4,76 1378,83 -43,69 -0,12 Total Accruals/ass ets t-1 4253 -276,13 106,53 -0,12 -0,04 5,39 -0,09 -0,00 ∆ REV 4253 -206414 246403,58 134,30 4,06 7599,38 -2,17 58,92 ∆ PPE 4253 -393581 460492 135,26 1,60 10252,23 -0,003 22,24 CFO 4253 -1559,66 53189 488,23 10,88 2154,87 0,28 91,07 COGS 4253 -2,16 309670 2356,47 78,28 13241,07 10,24 501,66 ∆ Inventory 4253 -7724 9010 -27,89 -0,13 385,07 -5,92 0,48 Production cost 4253 -254,70 312583 2328,58 77,34 13097,06 10,08 498,37 DISEXP 4253 0,01 52047,00 705,17 41,10 2543,73 8,594 220,29 ABS_DA 4253 -29,45 132,58 -0,01 -0,05 3,33 -0,11 -0,0061 RAM_PROX Y 4253 -243,84 428,29 0,45 0,12 10,25 -0,10 0,39

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18

4.2 Test of hypotheses 1

Hypothesis 1:

Mandatory IFRS adoption is negatively associated with accrual-based earnings

management.

In table 3 the correlation between the variables are shown. Correlation coefficients can vary between -1 en 1. -1 and 1 indicate a perfect (negative) relation and 0 indicates no relation at all between the variables. When a correlation is negative it indicates a negative relation so when one variable

increases, the other decreases. The coefficients indicate a weak relation between the variables when the value lay between (-)0.1 and (-)0.3. The relationship is moderate when the coefficient varies between (-)0.4 and (-)0.6 and there’s a strong relation when the value of the coefficients lay between (-)0.7 and (-)0.9. There’s a negative moderate relation of 0,043 between IFRS adoption and real earnings management measured by RAM_PROXY. Similarly the table shows a weak and negative relation between IFRS and the absolute value of discretionary accruals (ABS_DA) namely -0,012. Overall the results show weak relationships between the variables.

Table 3 Pearson correlation matrix (2000 – 2010)

To test the hypotheses, 2 regressions are performed, namely the regression analyses to measure the relation between IFRS and accrual-based earnings management (ABS_DA) and the second regression analyses is performed to measure the relation between real earnings management (RAM_PROXY) and IFRS. The results of the regressions are shown below in table 4. The actual regression outputs are shown in the appendix. The regression analyses show that there is a significant relation in the negative direction between IFRS adoption and discretionary accruals (-0,227) and real activities manipulation (-0,966). This indicates that when IFRS adoption increases(decreases), accrual-based and real earnings management decreases (increases).

IFRS SIZE ADJ_ROA LEV RD_INT ABS_DA AB_CFO AB_PROD AB_EXP

RAM_ PROXY IFRS 1 SIZE 0,001 1 ADJ_ROA -0,020 0,025 1 LEV 0,035 0,008 -0,016 1 RD_INT -0,009 -0,007 -0,004 -0,004 1 ABS_DA -0,012 0,010 0,196 -0,062 -0,001 1 AB_CFO -0,003 -0,034 0,354 0,048 -0,002 -0,652 1 AB_PROD 0,018 0,138 -0,345 -0,042 -0,001 0,371 -0,684 1 AB_EXP -0,047 0,100 0,288 -0,017 -0,002 -0,015 -0,193 0,038 1 RAM_PROXY -0,043 -0,021 0,499 0,027 -0,001 -0,423 0,568 -0,752 0,592 1

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19 The F test value shows the regression in its entirety is significant. Based on the results of the

regression I don’t reject my first hypothesis. IFRS adoption is significantly, negatively associated with accrual-based earnings management and real earnings management. The results also show that real earnings management decreases with a higher level (-0,966) than accrual-based earnings

management (-0,227) when IFRS increases.

Table 4

Multiple Regression of Accrual-Based Earnings Management and Real Activities Manipulation on IFRS

ABS_DA RAM_PROXY IFRS Coefficient -0,227 -0,966 P-value T-statistics 0,012*** -2,505 0,000*** -3,558 Combined_RAM Coefficient -0,204 x P-Value T-statistics 0,000*** -50,579 ABS_DA Coefficient X -1,842 P-Value T-statistics 0,000*** -50,579 SIZE Coefficient 0,000 0,000 P-Value T-statistics 0,131 -1,511 0,003*** -2,940 ADJ_ROA Coefficient 0,367 1,395 P-Value T-statistics 0,000*** 39,501 0,000*** 56,619 LEV Coefficient -0,485 0,256 P-Value T-statistics 0,005*** -2,799 0,623 0,491 RD_INT Coefficient 0,000 0,000 P-Value T-statistics 0,999 0,002 0,977 0,028 N 4253 4253 R-Square 0,402 0,534 Adjusted R-Square F-Value 0,401 0,000 0,533 0,000 *=10%,**=5%,***=1%

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20

4.3 Test of hypothesis 2

Hypothesis 2:

After the mandatory implementation of IFRS, accrual-based earnings

management increases.

Hypothesis 2a:

After the mandatory implementation of IFRS, real earnings management

decreases

.

Figures 1 and 2 give a graphic result of the collected data on the average of absolute value of

discretionary accruals and real earnings management measured by RAM_PROXY in period from 2000 till 2010. Figure 1 illustrates a decline in the level of accrual-based earnings management after 2000 till 2005. After mandatory IFRS adoption the level of accrual-based earnings management steeply increases till 2007. Figure 2 shows an increase of real earnings management after 2000 till 2005. After mandatory IFRS adoption in 2005 level of real earnings management keeps on declining till 2007. In conclusion figures 1 and 2 show an increase in accrual-based earnings management and a decrease in real earnings management after mandatory IFRS adoption. These results show that accrual-based earnings management and real earnings management are substitutes. These results are supported by the outcomes of the regressions shown in table 4. The association between real earnings management and accrual-based earnings management is significant in the negative direction. Based on the above mentioned results I don’t reject hypothesis 2 and 2a.

-0,3 -0,25 -0,2 -0,15 -0,1 -0,05 0 0,05 0,1 0,15 0,2 0,25 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 ABS_DA Figure 1

Accrual-based earnings management over time, 2000 - 2010

Period of mandatory IFRS adoption

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21 -1 -0,5 0 0,5 1 1,5 2 2,5 3 3,5 4 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 RAM_PROXY Figure 2

Real earnings management over time, 2000 - 2010

Period of mandatory IFRS adoption

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5. Conclusion and limitation

In this paper I examined the effect of mandatory IFRS adoption on both real and accrual-based earnings management. I expected a negative relation between mandatory IFRS adoption and both real and accrual-based earnings management. This indicates that when IFRS increases real and accrual-based earnings management decreases. The results show a significant negative relation between mandatory IFRS adoption and both methods of earnings management. I predict that accrual-based earnings management increases after mandatory IFRS adoption because IFRS are principle-based and require manager’s judgment in reporting. When this is the case, the opportunity for earnings management increases. I find evidence that accrual-based earnings management

increases after the period of mandatory IFRS adoption. Because of previous evidence in the literature that shows that both methods of earnings management are substitutes of each other, I expect that real earnings management decreases after mandatory IFRS adoption because I expect accrual-based earnings management to increase, as explained above. The results show that real earnings

management decline after the period of mandatory IFRS adoption. So in conclusion the results show that IFRS have a significant effect on both methods of earnings management and real and accrual-based earnings management are substitutes for each other.

The limitation to this research that may be taken into account in future research is that I didn’t control for voluntary adopters prior to the period when IFRS adoption became mandatory. Some countries like Germany voluntarily adopted IFRS prior to 2005.

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Appendix

Annex 1

SUMMARY OUTPUT discretionary accruals

RegressionStatistics Multiple R 0,8295 R Square 0,6881 Adjusted R Square 0,6878 Standard Error 3,0137 Observations 4253 ANOVA df SS MS F Significance F Regression 3 85121,1161 28373,7054 3124,1053 0 Residual 4249 38590,2078 9,0822 Total 4252 123711,3239 Coefficients Standard

Error t Stat P-value

Lower 95% Upper 95% Lower 95,0% Upper 95,0% Intercept -0,0246 0,0464 -0,5305 0,5958 -0,1157 0,0664 -0,1157 0,0664 α0 0,6184 0,0481 12,8695 0,0000 0,5242 0,7127 0,5242 0,7127 α1 -0,1130 0,0022 -50,3754 0,0000 -0,1174 -0,1086 -0,1174 -0,1086 α2 0,0410 0,0022 18,8297 0,0000 0,0367 0,0452 0,0367 0,0452

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Annex 2

SUMMARY OUTPUT CFO

RegressionStatistics Multiple R 0,9215 R Square 0,8491 Adjusted R Square 0,8490 Standard Error 3,2004 Observations 4253 ANOVA df SS MS F Significance F Regression 3,0000 244959,3010 81653,1003 7971,7071 0,0000 Residual 4249,0000 43521,9230 10,2429 Total 4252,0000 288481,2240 Coefficients Standard

Error t Stat P-value Lower 95%

Upper 95% Lower 95,0% Upper 95,0% Intercept -0,2052 0,0792 -2,5905 0,0096 -0,3605 -0,0499 -0,3605 -0,0499 α1 -0,9240 0,0515 -17,9406 0,0000 -1,0249 -0,8230 -1,0249 -0,8230 α2 0,2660 0,0648 4,1019 0,0000 0,1388 0,3931 0,1388 0,3931 α3 -0,1390 0,0648 -2,1448 0,0320 -0,2661 -0,0119 -0,2661 -0,0119

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Annex 3

Summary Output DISEXP

Regressionstatistics Multiple R 0,1326 R-square 0,0176 Adjusted R-square 0,0171 Standard Error 6,9029 Observations 4253 ANOVA DF SS MS F Significance F Regression 2 3622,228 1811,114 38,0091 4,3526E-17 Residual 4250 202510,3 47,64948 Total 4252 206132,5 Coefficients Standard

Error T- stat P-value Lower 95%

Upper 95% Lower 95,0% Upper 95,0% Snijpunt 0,7546 0,1706 4,4211 0,00001 0,42 1,0893 0,42 1,0893 α0 0,9663 0,1109 8,7131 4,1671 0,7489 1,1838 0,7489 1,1838 α1 -0,1588 0,1397 -1,1364 0,2558 -0,4328 0,1151 -0,4328 0,1151

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Annex 4

SUMMARY OUTPUT Production cost RegressionStatistics Multiple R 0,9813 R Square 0,9629 Adjusted R Square 0,9629 Standard Error 5,7826 Observations 4253 ANOVA df SS MS F Significance F Regression 4 3695480 923869,9 27628,03 0 Residual 4248 142051,4 33,44 Total 4252 3837531 Coefficients Standard

Error t Stat P-value Lower 95%

Upper 95% Lower 95,0% Upper 95,0% Intercept 0,1019 0,1432 0,7118 0,4766 -0,1788 0,3826 -0,1788 0,3826 α0 1,0105 0,0931 10,8591 0,0000 0,8281 1,1930 0,8281 1,1930 α1 0,3929 0,1172 3,3538 0,0008 0,1632 0,6226 0,1632 0,6226 α2 0,0992 0,1171 0,8467 0,3972 -0,1305 0,3288 -0,1305 0,3288 α3 0,0000 0,0000 0,0088 0,9930 -0,0001 0,0001 -0,0001 0,0001

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Annex 5

SUMMARY OUTPUT relation DA and IFRS

Regression statistics Multiple R 0,6342 R-square 0,4021 Adjusted R-square 0,4013 Standard Error 2,3310 Observations 4253 ANOVA DF SS MS F Significance F Regression 6 15519,0213 2586,5036 476,0174 0 Residual 4246 23071,2046 5,4336 Total 4252 38590,2259 Coefficients Standard

Error T- stat P-value

Lower 95% Upper 95% Lower 95,0% Upper 95,0% Intercept 0,2436 0,0818 2,9790 0,0029 0,0833 0,4038 0,0833 0,4038 IFRS -0,2265 0,0904 -2,5049 0,0123 -0,4038 -0,0492 -0,4038 -0,0492 COMBINED_RAM -0,2041 0,0040 -50,5794 0,0000 -0,2120 -0,1962 -0,2120 -0,1962 SIZE 0,0000 0,0000 -1,5108 0,1309 0,0000 0,0000 0,0000 0,0000 ADJ_ROA 0,3669 0,0093 39,5012 0,0000 0,3487 0,3851 0,3487 0,3851 LEV -0,4846 0,1731 -2,7991 0,0051 -0,8240 -0,1452 -0,8240 -0,1452 RD_INT 0,0000 0,0003 0,0019 0,9985 -0,0007 0,0007 -0,0007 0,0007

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Annex 6

SUMMARY OUTPUT relation RAM_PROXY and IFRS

Regression Statistics Multiple R 0,7306 R-Square 0,5337 Adjusted R-Square 0,5330 Standard Error 7,0023 Observations 4253 ANOVA DF SS MS F Significance F Regression 6 238285,9038 39714,3173 809,9669 0 Residual 4246 208189,9733 49,0320 Total 4252 446475,8771 Coefficients Standard

Error T- Stat P-Value

Lower 95% Upper 95% Lower 95,0% Upper 95,0% Intercept 1,0946 0,2453 4,4629 0,0000 0,6138 1,5755 0,6138 1,5755 IFRS -0,9656 0,2714 -3,5576 0,0004 -1,4977 -0,4335 -1,4977 -0,4335 ABS_DA -1,8419 0,0364 -50,5794 0,0000 -1,9133 -1,7706 -1,9133 -1,7706 SIZE 0,0000 0,0000 -2,9400 0,0033 0,0000 0,0000 0,0000 0,0000 ADJ_ROA 1,3946 0,0246 56,6194 0,0000 1,3463 1,4429 1,3463 1,4429 LEV 0,2558 0,5205 0,4915 0,6231 -0,7647 1,2764 -0,7647 1,2764 RD_INT 0,0000 0,0010 0,0283 0,9775 -0,0020 0,0020 -0,0020 0,0020

The effect of mandatory IFRS adoption on earnings management : evidence from the European market