Identifiable Intangible Assets in Business Combinations · intangible assets across industries was...

Identifiable Intangible Assets in Business Combinations

– A Quantitative Study of US Companies –

Hanna Hedin

Hilda Havert

BACHELOR THESIS IN ACCOUNTING

AT THE DEPARTMENT OF BUSINESS ADMINISTRATION

GOTHENBURG UNIVERSITY

AUTUMN 2014

SUPERVISED BY JAN MARTON AND SAVVAS PAPADOPOULOS

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ABSTRACT

This thesis is a quantitative study of how U.S. companies allocate purchase prices paid in

acquisitions to identified intangible assets in relation to goodwill. It seeks to identify how the

percentage of identified intangible assets in business combinations varies with acquirer firm

characteristics. Since SFAS 141 and SFAS 142 are the two principle-based standards that

regulate how U.S. companies account for acquired intangible assets, the study seeks to

indicate whether these seem to work efficiently, providing the users of financial statements

with relevant information that reflect the economic values of the intangible assets correctly.

The economy seems to have become increasingly knowledge-driven and technology based

during the last couple of decades. This has resulted in intangible assets representing a larger

proportion of balance sheet totals and subsequently the proportion of acquired assets in

business combinations. Problems related to the accounting for intangible assets led to FASB’s

issuance of standards SFAS 141 and SFAS 142 that have brought changes in the way to

account for intangibles. Since few studies have focused on how acquired intangible assets are

identified in business combinations post the issuance of SFAS 141 and 142, it has been of this

study’s objective to provide additional knowledge and increased insight about this. Current

knowledge and previous research literature has guided and supported this study throughout

the hypothesis development, methodology and analysis. A similar methodology as Rehnberg

(2012) used in her thesis studying identification of intangible assets in business combinations,

has been applied but this thesis studies U.S. companies instead of Swedish companies, it

studies acquisitions made between 2010-2014 (the large majority of the observations can be

found between 2011-2013) and it has a significantly greater sample. This was facilitated

through the usage and data collection from database Compustat. Two multiple regression

models are used to draw conclusions about five hypothesis regarding acquirer firm

characteristics relationship to the level of identified intangible assets. The findings of this

paper is that the size and profitability of the acquirer, with a 95% confidence, has a negative

correlation with the percentage of purchase price allocated to identified intangible assets in

U.S. business combinations. The authors do not find that the debt to equity ratio and book to

market ratio of the acquirer can explain this level. The percentage of identified acquired

intangible assets varies between industries and profitability and size affect the percentage of

identified intangible assets differently in different industries. The Energy, Materials,

Industrials, Consumer Discretionary, Information Technology, Telecommunication Services,

and Utilities industries identified significantly less intangible assets in business combinations

than the Consumer Staples industry (43% versus 56,7%). The mean percentage of identified

intangible assets across industries was found to be 47%.

Most findings are not in line with the ones in previous research and the thesis does not attempt

to explain why. One reason could however be that the years of study for this paper is a typical

post-crisis period characterized with impairment of goodwill and other assets whilst the one of

Rehnberg was characterized by significant growth. The study cannot draw a conclusion

whether the levels of identified intangible assets in business combinations observed in this

research indicate efficient principle-based standards but the finding that the level of identified

intangible assets varies between industries is a sign that they do.

KEYWORDS

FASB, SFAS 142, SFAS 141, US GAAP, purchase price allocation, goodwill, intangible

assets, identification of intangible assets, business combinations, incentives in accounting

choices, acquirer firm characteristics.

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ACKNOWLEDGMENTS

We would like to express our gratitude to supervisors Jan Marton and Savvas Papadopoulos

for taking their time and knowledge to provide us with valuable input and guidance

throughout this research. Furthermore we would like to thank our group discussants for giving

us their helpful opinions of improvement and letting us take part in interesting discussions.

Gothenburg, January 4 2015

Hanna Hedin Hilda Havert

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ABBREVIATIONS, SYMBOLS AND DEFINITIONS ABBREVIATIONS

BTM Book to Market Ratio of Common Equity

DE Debt to Equity Ratio

EBIT Earnings Before Interest and Taxes

ESMA European Securities and Markets Authority

FAS Financial Accounting Standards

FDI Foreign Direct Investments

FASB Financial Accounting Standards Board

GICS Global Industry Classification Standards

IAS International Accounting Standards

IASB International Accounting Standards Board

IFRS International Financial Reporting Standards

PPA Purchase Price Allocation

SFAS Statements of Financial Accounting Standards

US GAAP Generally Accepted Accounting Principles in the United States

SYMBOLS

N Sample size

sig Significance

R2

Coefficient of determination

β Unstandardized coefficient (beta)

σ Standard deviation

a Regression intercept

ɛ Error

DEFINITIONS

Fair Market Value The price a willing buyer would pay, and a willing seller would

receive, through an arm’s length transaction in a market with

perfect information.

Company Market Value The sum of all issue-level market values (trading and non-trading

issues). For single-issue companies this is common shares

outstanding multiplied by the month-end price that corresponds to

the period end date.

Control Group The group in a study that does not receive treatment and is then

used as a benchmark to measure how the other tested subjects do.

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TABLE OF CONTENTS

1. INTRODUCTION ................................................................................................ 5 1.1 BACKGROUND AND PROBLEM DISCUSSION ................................................................................. 5 1.2 PURPOSE ....................................................................................................................................... 6 1.3 RESEARCH QUESTION .................................................................................................................... 6 1.4 LIMITATION ................................................................................................................................... 6

2. EMPIRICAL CONTEXT .................................................................................... 7 2.1 THE REGULATORY ENVIRONMENT FOR BUSINESS COMBINATIONS IN THE U.S........................... 7 2.2 IMPORTANT ASPECTS OF ACCOUNTING FOR INTANGIBLE ASSETS IN BUSINESS COMBINATIONS........................................................................................................................................................... 10

3. HYPOTHESIS DEVELOPMENT .................................................................. 15 3.1 EARNINGS MANAGEMENT INCENTIVE ........................................................................................ 15 3.2 CAPITAL MANAGEMENT INCENTIVE ........................................................................................... 15 3.3 DIFFERENCES BETWEEN INDUSTRIES .......................................................................................... 16 3.4 BOOK TO MARKET RATIO ............................................................................................................ 17

4. RESEARCH METHODOLOGY ...................................................................... 18 4.1 RESEARCH DESIGN ...................................................................................................................... 18 4.2 DEPENDENT VARIABLE ................................................................................................................ 18 4.3 INDEPENDENT VARIABLES ........................................................................................................... 18 4.4 HYPOTHESIS TEST ........................................................................................................................ 20 4.5 MODELS ....................................................................................................................................... 20 4.6 DATA COLLECTION ...................................................................................................................... 22 4.7 DATA TREATMENT AND APPLIED TESTS...................................................................................... 22 4.8 CRITICAL REVIEW......................................................................................................................... 23

5. EMPIRICAL FINDINGS .................................................................................. 25 5.1 DESCRIPTIVE STATISTICS ............................................................................................................. 25 5.2 RESULTS AND ANALYSIS .............................................................................................................. 27

6. SUMMARY .......................................................................................................... 42 6.1 CONCLUSIONS ............................................................................................................................. 42 6.2 CONTRIBUTION AND SUGGESTED FURTHER RESEARCH ............................................................. 43

7. SOURCE OF REFERENCE ............................................................................. 45

APPENDIX 1 ........................................................................................................... 47 Industry classification ....................................................................................................................... 47

APPENDIX 2 ........................................................................................................... 48

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1. INTRODUCTION The first section of this thesis is an introduction that will provide the reader with a general

understanding of the area of study. It initiates with a background of the issue and topic at

hand and ends with a purpose and research question that will lay ground for the rest of the

disposition of the thesis.

1.1 BACKGROUND AND PROBLEM DISCUSSION The world economy seems to have become successively more knowledge-driven and

technology-based during the last couple of decades (Rehnberg, 2012). As a result one has

been able to see intangible assets as an increasingly larger proportion of companies’ balance

sheet totals and the importance of well-functioning regulations and guidelines in how to

account for intangibles is therefore crucial. The relevance of financial reports is increasingly

dependent on the quality of the accounting for intangibles.

Researchers and regulators within accounting and financial reporting have long debated the

accounting for intangible assets. The debate centers on the relevance and reliability of how

acquirers account for intangible assets acquired in business combinations. FASB, the

American board that establishes and communicates financial accounting standards and

reporting standards in the U.S. (US GAAP), was the first international standard setter who

answered to this need of redefined regulations and guidelines.

SFAS 141 Business Combinations and SFAS 142 Goodwill and Other Intangible Assets are

two standards that were introduced by FASB in 2001 as a direct effort to improve the way

U.S. companies valuated and accounted for intangible assets in business combinations. SFAS

142 is considered an important step towards fair-value-based accounting.

U.S. firms have experienced an increased requirement for identification of intangible assets in

business combinations since the introduction of SFAS 141 and SFAS 142. The standards have

been intensely debated in research as well as in practice. The large majority of the research

has however focused on the impairment of goodwill but there has hardly been any studies

done on the increased requirement for identification of intangible assets. Motivated by the

debate regarding accounting for intangible assets and that on SFAS 142, this study will be an

empirical study investigating this area further. Not only by explaining how U.S. companies,

covered by SFAS 141 and 142, identify intangible assets in business combinations but also by

researching whether there are any firm characteristics that explain this.

The U.S. is a large economy and consequently U.S. firms may impact the global financial

stability to a larger extent than most other countries’ business operations can. The U.S. has

also been the number one destination for FDI net inflows for over a decade and although the

U.S. slice of global FDI has eroded as competitor nations work to make their economies more

hospitable to global investors, the U.S. share still accounted for 17 percent of the world’s

inward FDI stock in 2012 (OFII, 2014)

The need of stable and well-functioning regulations and standards that can assure accounting

quality of U.S. companies is undeniable. Financial statement users must be able to understand

their investments made in assets and the subsequent performance of those investments. This

requires that financial statements provide relevance, transparency and comparability to the

user. SFAS 141 and 142 are primarily principle-based rules and there are indications within

research that companies' accounting choices are influenced by management incentives related

to financial consequences. The components that make up goodwill have subjective values and

there is a risk that acquiring companies could overvalue goodwill in the business

combinations if the valuation process is not done properly (Rehnberg, 2012). If an acquiring

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company overvalues goodwill, it would have a negative effect on shareholders since they are

likely see a drop in their share values when the company is required to perform goodwill

impairment. In fact, this happened when AOL merged with Time Warner in 2001 (Wallstreet

Journal 2003). When the accountant’s subjective judgment comes into play in the process of

identifying intangible assets, it also becomes more difficult for the users of the financial

statements to verify the information provided and the relevance of the financial statements is

decreased (Watts 2003). Subjective interpretations also result in lower comparability between

firm’s financial statements from a user’s perspective (Rehnberg, 2012). This could have a

negative effect on international trade and investments.

A challenge that companies face when accounting for intangible assets is to make sure that it

corresponds with faithful representation. This can be a tough challenge because intangible

assets often lack active markets for proper valuation and it can be difficult to determine

whether the company meets the requirement of control for an intangible asset or not. One of

the main purposes with the introduction of SFAS 141 and 142 (see section 2.1) was to achieve

relevant and faithfully represented financial information but the question is whether this has

been achieved? Does US GAAP and principle-based standards work efficiently?

1.2 PURPOSE This is a quantitative study that aims to provide additional knowledge of how U.S. companies

under effect of SFAS 141 and SFAS 142 identify and report intangible assets in conjunction

with their accounting for business combinations and whether there are any firm characteristics

that correlate with a certain level of identified intangible assets.

The purpose is also to investigate whether the same conclusions can be drawn and result

produced as Rehnberg (2012) when applying a similar test and methodology but with a larger

sample of specifically U.S. companies under US GAAP instead of IFRS.

1.3 RESEARCH QUESTION How do U.S. companies identify and report intangible assets in business combinations and are

there any specific firm characteristics that explain this?

1.4 LIMITATION This study specifically studies U.S. companies compliant with US GAAP between years

2010-2014 (the sample does not contain all acquisition made during 2010 and 2014 since data

was limited in 2010 and not yet reported in 2014). Due to the limited amount of time, this

thesis will only study a couple firm characteristics of U.S. acquirers: size, profitability, DE,

BTM and industry. The Financials industry (e.g. banks, insurance companies) is excluded

since the regulations and business environment that they operate under is significantly

different from all the other industries.

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2. EMPIRICAL CONTEXT The following chapter describes the empirical context to facilitate the reader’s understanding

of the study. It initiates by describing the regulatory environment in the U.S. and the process

of purchase price allocation and identification of intangible assets. It continues by presenting

the current knowledge and prior studies related to accountancy of intangible assets in

business combinations. The literatures presented have been identified as useful providers of

beneficial information for this study. The hypotheses presented in chapter 4 are motivated by

the findings in the earlier studies presented below.

2.1 THE REGULATORY ENVIRONMENT FOR BUSINESS COMBINATIONS IN THE U.S.

2.1.1 THE PURCHASE PRICE ALLOCATION AND IDENTIFICATION OF

ACQUIRED INTANGIBLE ASSETS This section initiates the empirical context with a shorter description of the identification of

intangible assets and the process of allocating purchase prices paid in business combinations.

The idea is that a basic knowledge of the process will facilitate the reader’s ability to follow

discussions further into the paper when reflecting about how different factors could affect

levels identified intangible assets.

Purchase price allocation (PPA) is a process required of the acquiring company in business

combinations. In the United States, this process is generally conducted in accordance with the

purchase method of SFAS 141 and 142. A PPA categorizes the purchase price into the various

assets and liabilities acquired. The process requires the conduction of acquisition appraisal,

fair market valuation of acquired assets and liabilities, recalculation of the net identifiable

assets from the old balance sheet price to the fair market value and that the level of

unidentifiable intangible assets be determined and reported as goodwill in the transaction. The

fair market value is to be determined per the date of acquisition.

Since the acquiree’s total assets and liabilities are recalculated from book value to their

current market value, the PPA corresponds to a complete balance sheet produced to the real

value of the assets and liabilities per the day of acquisition (Rehnberg, 2012). This can be

seen as the acquirer’s way of expanding the acquiree’s balance sheet based on the book values

to also entail those assets/values, risks and obligations assumed outside of the accountancy

(Johansson 2003). The hidden values are based on what an acquirer think can promote and

generate future cash flows, growth and profitability. The hidden values that an acquirer might

consider and recognize are for example customer and supplier relationships, client registers,

brand name, trademarks, organizational structures, information systems and human resources

(i.e. employee knowledge and skills).

In the PPA process, acquired intangible assets can either be identified or not. If not identified,

they are to be reported as goodwill. For illustrative purposes, assume a business combination

where the acquirer acquirers the acquiree for the purchase price of $30B. The value of the

acquiree’s balance sheet value corresponds to its net identifiable assets (the balance sheet total

subtracted by the acquiree’s existing goodwill and liabilities). Assume that this is appraised to

the value of $8B. Next, the fair market valuation concludes that the net identifiable assets’ fair

market values are worth three times the value reported on the original balance sheet, thus

$16B in write-ups are accounted for. Finally, the difference between the purchase price paid

($30B) and $24B (balance sheet value $8B and fair market value write up $16B) is the

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residual $6B, which is to be reported as goodwill acquired. The acquirer then adds both the

value of the written-up assets ($24B) as well as the goodwill ($6B) onto the balance sheet, for

a total of $30B in new assets on the balance sheet.

2.1.2 FASB AND US GAAP Most companies in the U.S. are subject to the same set of accounting standards, which is the

US GAAP issued by FASB. Public companies are required under SEC rules to prepare

audited, GAAP-compliant financial statements. Private companies are generally not legally

obligated to follow GAAP, but they may need to do so to satisfy lenders, sureties, venture

capitalists, or other stakeholders. All companies listed on the U.S. stock exchange must

however follow US GAAP. This means that a substantial part of U.S. companies follow

SFAS 141R and SFAS 142. Since these firms are very different from each other in terms of

how they are financed and which industry they operate within, it becomes interesting to

compare them and to investigate if there are any patterns of characteristics in combination

with a certain level of identified intangible assets. The comparison gives us a deeper

understanding of how firm characteristics and other variables affect the accounting quality of

acquired intangible assets.

2.1.3 SFAS 142 GOODWILL AND OTHER INTANGIBLE ASSETS SFAS 142 is a standard issued by FASB that replaced SFAS 121 and became effective in July

2001. It addresses how US GAAP compliant entities should report for acquired goodwill and

other intangible assets. Acquiring companies must account for acquired intangible assets in

financial statements upon acquisition and account for goodwill and other intangible assets

after they have been initially recognized in the financial statements.

The introduction of SFAS 142 meant that firms would no longer perform goodwill

amortization but instead do annual impairment tests to see whether the carrying value of

goodwill could be considered to exceed the fair value, and if so perform goodwill impairment.

The introduction of SFAS 142 also meant more specific guidance in how to determine and

measure goodwill. SFAS 142 also differs from SFAS 121 in the way that acquirers now need

to allocate the purchase price paid in a business combination to acquired identifiable

intangible assets based on their fair values. The remainder of the purchase price is to be

recognized as goodwill. The acquirer is to allocate the recorded goodwill to its reporting units

based on the expected benefits, or synergies, each reporting unit obtains from the acquisition.

This is estimated by measuring the fair values of the reporting units before and after the

acquisition (FASB, 2014).

Similar to acquired goodwill, identifiable intangible assets that can be considered to have an

indefinite life, such as a trademark, is not amortized. Identifiable intangible with indefinite

life are to be subject for the same annual impairment test as goodwill. According to FASB,

the majority of the identified intangible assets are considered having finite lives and are

therefore to be amortized over its useful life.

FASBs Reasons for Issuing the 142 Statement and How Its Changes are Supposed to

Improve Financial Reporting

It was noted by both producers (company management) and users of financial statements that

intangible assets are becoming an increasingly important economic resource for many entities.

They also seem to make up a successively larger proportion of the assets acquired in many

business combinations. FASB recognized that there was a need for better information about

intangibles.

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Financial statements are supposed to be improved by SFAS 142 by better reflecting the

underlying economics of the acquired intangible assets. This will help potential and existing

stakeholders such as investors and lenders to make successful and efficient business and

investment decisions.

The increased requirement for disclosures about acquired goodwill and other intangible assets

will provide users with a better understanding what changes in those assets that can be

expected over time. The changes in SFAS 142 are therefore supposed to make it easier to

asses future profitability and cash flows in companies and their different entities.

2.1.4 SFAS 141R BUSINESS COMBINATIONS SFAS 141R is a revised version of SFAS 141 (2001) and issued by FASB in 2007. The

standard is an establishment of principles and requirements for how a publicly traded U.S.

company (or other companies under the effect of US GAAP) as an acquirer is to recognize

and measure acquired identifiable assets, liabilities and other non-controlling interest in its

financial statements. SFAS 141R sets standards and requirements for how a US GAAP

compliant company is supposed to recognize and measure goodwill acquired or gained from

business combinations and what information regarding the business combination that must be

disclosed in the company’s financial statements to provide relevant information from a user’s

perspective. SFAS 141R recognizes that users of the financial statements must be able to

determine the nature and financial effects of business combination and force transparency in

the acquirer’s financial reports.

SFAS 141R sets principles and requirements for how the acquirer:

Recognizes and measures identified assets acquired, goodwill acquired and liabilities

assumed in its financial reports.

Disclose information about the purpose and financial effects of the business

combination

When SFAS 141 was originally issued in 2001 it promulgated changes in the accounting

practice that were a direct effort to improve financial reporting in the U.S. SFAS 141R is not

substantially different from the original SFAS 141 but there are some significant changes. The

scope of SFAS 141R is broader than the one of SFAS 141 since it applies to business

combinations in which control has not been obtained through transferring consideration.

SFAS 141R requires that the acquirer and the acquiree are identified and that the acquisition

date is recognized so that the fair value of the assets and assumed liabilities acquired can be

calculated correctly as of that date. The SFAS 141R Statement also requires that intangible

assets be recognized apart from goodwill if they meet either the contractual-legal criterion or

the separability criterion. Intangible assets lack physical substance and are therefore unlike

tangible assets often hard to recognize and in need of a structured identification process.

One of the major changes that came with the revised version of SFAS 141 (SFAS 141R) was

the requirement that all business combinations be accounted for by the purchase method. The

purchase method recognizes all intangible assets acquired in a business combination while the

previously allowed pooling method in SFAS 141 only recognized those previously recorded

by the acquiree. This was to facilitate comparability and it allowed the investment made in an

acquired entity to be better reflected in the acquirers’ financial statements. The explanation to

this is that the purchase method in contrast to the pooling method account for an acquisition

based on the values exchanged which provides users with information about the total purchase

price paid.

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FASBs Reasons for Issuing the 141R Statement and How Its Changes are Supposed to

Improve Financial Reporting The reason behind the issuance of SFAS 141R is very similar to that of SFAS 142: “to

improve the relevance, representational faithfulness, and comparability of the information that

a reporting entity provides in its financial reports about a business combination and its

effects” (FASB 2007).

SFAS 141R also forced that better information regarding intangible assets was provided to the

users of financial reports. Acquiring companies now have to disclose the primary reasons for

a business combination, the allocation of the purchase price paid and other more specific

information about those assets.

The issuance of SFAS 141R together with IFRS 3 Business Combinations issued by IASB in

2005 (revised in 2007) has had a convergence effect for international reporting standards. It

was an effort by FASB and IASB to foster a better comparability between European and

American companies’, to boost international accounting efficiency and to make it easier for

international investors to do business. SFAS 141R and IFRS 3 are therefore very similar but

not identical.

In conclusion U.S. companies have experienced an increased requirement for identification of

intangible assets since the introduction of SFAS 141 Business Combinations and SFAS 142

Goodwill and Other Intangible Assets as a consequence of FASBs strive to solve the earlier

experienced problems associated with accounting for business combinations. Increased

requirement and a more detailed guidance in how identification of intangible assets is to be

done should reduce US firms’ goodwill at initial recognition. If identification of intangible

assets in business combinations were done properly, goodwill would be relatively small, and

therefore not represent such a material issue. It has been questioned whether this is reality.

There is a need of additional research of how U.S. firms identify intangible assets in their

annual reports since the introduction of SFAS 141 and 142.

2.2 IMPORTANT ASPECTS OF ACCOUNTING FOR INTANGIBLE ASSETS IN BUSINESS COMBINATIONS Accountancy is a process performed by employees within a company and it is ultimately put

together to financial reports issued by the management or board of the company. During this

process, accounting areas covered by principle-based standards such as SFAS 141 and 142,

are sometimes in need of accounting choices partially based on subjective interpretations

(Rehnberg, 2012). The main accounting choice discussed in this paper is the one where

acquired intangible assets are identified or not. When the acquirer performs the PPA, it will

determine the amount of identified intangible assets in a business combination. This will

affect the acquirer’s future cost, balance sheet and therefore also result and profitability since

intangible assets with definite economic life is subject to amortization while goodwill is not.

The debate of SFAS 142 and 141 mainly focuses on those effects that accounting choices

bring to the financial reports and through this indirectly to the users. Earlier studies indicate

that the accounting choices determining the amount of identified acquired intangible assets

seem to be correlated with management incentives and a various set of acquirer firm

characteristics.

2.2.1 ACCOUNTING QUALITY AND ACCOUNTING CHOICES FOR BUSINESS

COMBINATIONS One of the main purposes of financial statements is to provide the user with information that

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enables him or her to make decisions about investment opportunities or other business

oriented opportunities such as partnerships. Barth (2008) explains how accounting quality has

a significant affect on the efficiency of capital markets. Poor accounting quality is likely to

result in capital costs, how effectively capital is allocated and on international capital mobility

(Young & Guenther 2002). Accounting quality is a fundamental condition for investments. If

poor or not regulated investor perceived higher risk and will only be inclined to invest when

the expected return on investment compensate. The same applies on creditors who are more

unlikely to issue loans to companies that cannot ensure accounting quality.

There are different measures to describe accounting quality within firms. IASBs Conceptual

Framework lists the qualitative characteristics of financial information that are required for

quality financial statements from a user’s perspective. The financial information must provide

relevance, faithful representation (completeness, neutrality, freedom from material error),

comparability, timeliness, verifiability and understandability. In line with IASB, Barth (2008)

partially defines accounting quality as the financial reports’ relevance to the user and its

ability to make efficient decision based on it. The Conceptual Framework specifies that the

financial information needs to provide predictive value and confirming value to be relevant

(IASB, 2009).

Prior research has indicated that intangible assets provide valuable information to the users of

the financial reports and thus contributes to their relevance (Rehnberg, 2012). For the

information about intangible assets to be relevant it requires that it is in line with faithful

representation. It has been discussed whether a higher or lower level of identified intangible

assets provide the most relevance and accounting quality the financial statements. One

discussion is of the opinion that comparability between companies’ financial statements is

reduced when a lower proportion of intangible assets are identified since the user will not

have access to the acquired assets character and will therefore not be able to make his or her

own conclusion about the assets value, prospects and future contribution to the profitability of

the company. When reporting goodwill, the user of the financial reports are forced to guess

what type of assets have been acquired and how long their economic lives are, i.e. less

identified intangible assets affect the relevance and comparability of the financial statements

negatively. Rehnberg (2012) defined financial reports as more relevant and of higher quality

when a larger proportion of acquired intangible assets were identified. This opens up for

interesting areas of research. If the quality and relevance of financial statements can be

considered higher when a larger proportion of acquired intangible assets are identified, then

the level of identified intangible assets in business combinations could indicate whether the

principle-based standards especially SFAS 142 work efficiently to provide relevance and

accounting quality to the financial statements.

2.2.2 COMPANIES’ COMPLIANCE WITH REQUIREMENTS FOR BUSINESS

COMBINATIONS IN FINANCIAL STATEMENTS The European Securities and Markets Authority (ESMA) published a report in June 2014,

discussing the importance of accounting quality for intangible assets and transparency during

business combinations. The report studies how EU companies apply IFRS Requirements for

Business Combinations in Financial Statements and whether this is compliant with IFRS or

not. Although the report discuss perceived issues that occur when European companies apply

IFRS 3, the basic problem at hand can be directly related to U.S. companies applying SFAS

142, but most importantly SFAS 141R Business Combinations. The report by ESMA

concludes that EU companies overall provide sufficient and relevant information of their

business combinations in line with IFRS 3 but it also identifies certain areas that are in need

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of improvement. This paper refers to the report issued by ESMA to highlight the importance

of studying how companies identify and account for intangible assets in business

combinations. The report serves as an argument that it is practically motivated to research the

area further to investigate whether SFAS 141 and 142 seem to work efficiently.

2.2.3 ACCOUNTING FOR ACQUIRED INTANGIBLE ASSETS IN BUSINESS

COMBINATIONS IN COMPLIANCE WITH IFRS 3 Rehnberg published a thesis in 2012 discussing how the introduction of IFRS 3 by IASB has

affected the way EU companies account for intangible assets in business combinations. Her

discussion focused on relevance and how the financial statements were affected by accounting

choices. Rehnberg’s thesis serves as an argument that it is theoretically motivated to research

how companies identify acquired intangible assets. It is an area where there has hardly been

any research done and it is a topic of current matter.

Rehnberg found that large companies and companies with a higher percentage of external

financing accounted for a larger proportion identified intangible assets. Rehnberg reasoned

that larger companies tend to be more conservative in their accounting practices and would

therefore choose to continue to amortize and depreciate assets by identify a larger percentage

intangible assets like done prior to the introduction of SFAS 141 and 142. The theory was

that highly indebted companies would find it extra important to communicate the picture of a

stable financial position to stakeholders, as well as disclosing the type of assets owned by

identifying a larger proportion of acquired intangible assets.

2.2.4 ACCOUNTING DISCRETION IN PURCHASE PRICE ALLOCATION Zhang and Zhang published a study in 2007 investigating how companies allocated purchase

prices paid in acquisition between goodwill and identifiable intangible assets. The prediction

of the study was that the issuance of SFAS 142 had created a management incentive to

allocate a greater proportion to goodwill to reduce amortization expenses. The result of their

study was that acquirers that reported small positive earnings were more likely to be

concerned with amortization expenses depressing already small earnings. They were

consequently also more inclined to allocate a larger proportion of the purchase price paid in

acquisitions to goodwill. The authors concluded that there was a strong correlation between

the profitability ranking of a company and the percentage of goodwill allocated to that unit.

The authors reasoned that the incentive to avoid amortization expenses was connected to

management bonus systems based on annual results. An additional motive for earnings

management incentives according to Rehnberg (2012) is the need or desire to meet existing

listing requirement with stock exchanges that demands that a certain level of pre-tax earnings.

The study of Zhang and Zhang also found that there was a positive correlation between the

level of reported goodwill and the acquirer’s anticipated discretion in future goodwill

assessment to avoid reporting impairment. The anticipated discretion was higher if the

acquirer was far from undervalued (low BTM) thus a positive relationship between BTM and

the percentage of identified acquired intangible assets.

PwC published a study in 2009 that found undervalued companies (BTM ratio larger than 1)

more likely to have to perform goodwill impairment. Prior research suggests that an

undervalued company with a higher BTM seem to be more likely to identify a larger

proportion of acquired intangible assets since they are exposed to higher risk of goodwill

impairment (Smith and Watts, 1992). An acquirer with more discretion in future goodwill

assessment (a lower BTM) was found to record more goodwill (Zhang, Zhang, 2007).

13

The myopic behavior of reporting more goodwill to avoid amortization mentioned earlier in

section 2.3 can be bad or even dangerous for both the company and its stakeholders since it

increases the risk for larger future impairment of goodwill. A larger impairment of goodwill

than expected by the shareholders (or other stakeholders) can hurt them badly financially and

it can also jeopardize the survival of the company. There have been several scandals involving

firms that have artificially inflated their balance sheets by reported excessive goodwill value

and that later had to perform massive goodwill impairment (Rehnberg, 2012).

The figure 2.2.4 below illustrates PwC’s findings that the BTM of U.S. publically traded

companies has a positive correlation with the need of future goodwill impairment. The study

refers to their observation in the financial crisis of 2008. As stock prices fell, there was a

significant drop in many U.S. companies’ BTM. Between the third quarters of 2008 up until

the end of the first quarter of 2009, Fortune 500 companies had announced $230 billion of

goodwill impairment, which was more than twice the amount recorded the three earlier years,

combined.

Figure 2.2.4 Breakdown of Trading and Impairments

Additionally, there have been other theories about why there seems to be a positive

correlation between BTM and the amount of identified intangible assets in a business

combination. Authors Zhang and Zhang (2007) wrote in their thesis that acquirers with lower

BTM (overvalued) can be good quality firms are more able to carry out better acquisitions

that generate more synergies. As a result, they would record more goodwill.

2.2.5 BUSINESS COMBINATIONS IN DIFFERENT INDUSTRIES In 2009 KPMG released a study providing insight to how the percentage of purchase price

paid in acquisition varied between industries. The study found that the percentage of purchase

price allocated to goodwill correlated with industry classification. Thus, the finding was that

there exist industry-specific patterns for the identification of intangible assets.

KPMG’s analysis and conclusion behind why patterns differed between industries was that

some industries could meet the contractual-legal criterion and the separability criterion more

easily due to the business environment they operated within and therefore also recognize a

Trading

Below Book

and

Impairment

Not Taken

Since 6/2008;

21%

Trading

Below Book

and

Impairment

Taken Since

6/2008; 23%

Trading

Above Book

and

Impairment

Not Taken

Since 6/2008 ;

44%

Trading

Above Book

and

Impairment

Taken Since

6/2008; 12%

14

larger percentage of the acquired intangible assets apart from goodwill in line with SFAS

141R.

KPMG’s research also found that industry specific value drivers and competitive environment

were vital for the valuation of intangible assets. The forecasted and expected synergies and

long-term growth expectations for business combinations are according to KPMG dependent

on industry specific structures, regulations and value drivers. The operational environment

came in to play in the PPA process when identifying intangible assets.

Most industries were found to identify more than 50% of acquired intangible assets in

business combinations, reporting less than 50% of goodwill. Across all industries, the

Building & Construction industry attributed the lowest value to intangible assets, allocating an

average of just 6.0 % of the purchase price. The industries with the highest percentage of

identified intangible assets were the Consumer Products & Services (57.0%) and Life Science

& Health Care Industry (45.1%).

Rehnberg (2012) tested whether technology intense industries reported a higher percentage of

identified intangible assets but did not find a significant difference. There have however been

other studies indicating existing differences between industry classifications and the level of

identified intangible assets (Godfrey & Jones 1999).

15

3. HYPOTHESIS DEVELOPMENT This chapter develops hypothesizes of the relationship between identified intangible assets in

business combinations and the firm characteristics of the acquirer. The development is based

on the empirical context presented in chapter 2.

3.1 EARNINGS MANAGEMENT INCENTIVE The introduction of SFAS 141 and SFAS 142 in 2001, meant that U.S. companies under the

effect of US GAAP no longer were to perform amortization on goodwill or other intangible

assets with indefinite life (section 2.1.3). This meant that acquiring companies could avoid

amortization expenses by not identifying intangible assets during business combinations and

thus account for larger proportion goodwill. Another way of avoiding the amortization

expense was by assessing the identified intangible assets economic life as indefinite. There

are obviously well defined guidance in when and how acquiring companies should recognize

an acquired asset as identified or not and how to asses its useful life. Rehnberg (2012) did

however emphasize that standards are not strict rules and that IFRS 3, very similar to SFAS

141R and 142 contain a lower proportion of detailed rules. Zhang and Zhang (2007) meant

that this creates a leeway for myopic actions in line with management incentives, especially if

management bonuses are tied to the annual financial performance. The incentive is to identify

less intangible assets in business combinations to avoid amortization expenses and thus report

better annual profit. The first hypothesis is based on Zhang and Zhang (2007) findings in

section 2.3 that acquirers reporting small positive earnings are more likely to be concerned

with amortization expenses and will therefore allocate a larger proportion of the purchase

price paid in acquisitions to goodwill. The hypothesis of this study is developed as follows:

H1: U.S. acquiring companies that report low profitability will identify a smaller proportion

of identified intangible assets in business combinations than those with a higher profitability

ranking.

The expectation based on the earlier studies is in other words that there is a positive

correlation between the profitability ranking of U.S. acquiring companies and the proportion

of intangible assets that they identify in business combinations.

3.2 CAPITAL MANAGEMENT INCENTIVE Intangible amortization does not only decrease the annual result but it also reduces net assets

on a regular basis. As mentioned in chapter 2, Rehnberg (2012) found that large companies

account for more intangible assets separated from goodwill than what small companies do.

There were several theories in her thesis that were believed to explain this correlation. Just

like the management bonus incentive, some companies tie their bonus compensation systems

to different key performance indicators where the balance sheet total might be included (such

as in Return on Capital Employed and other profitability measures). It is also not uncommon

that banks, venture capitalist and other lenders have certain requirements on how big a loan

taker is or how internally financed it is (e.g. a minimum level of debt to equity ratio). The

motive for companies identifying a smaller proportion of acquired intangible assets separated

from goodwill might therefore be that they desire grow their balance sheet totals (since

goodwill is not to be amortized).

The size of a company is often measured as the balance sheet total (either total assets or

equity and liabilities) or measured in the level of annual revenue. If larger companies seem to

identify more intangible assets and therefore more relevant financial statements with better

accounting quality (Rehnberg, 2012 see section 2.2.3), it might be because of their ability,

16

being large, and not because of management incentives. Large acquirers might have a more

developed and advanced accounting division and procedures that can ensure accounting

quality by identifying a larger proportion of acquired intangible assets. From the empirical

context, the second hypothesis is defined as follows:

H2: U.S. acquiring companies with a larger balance sheet total account for a larger

proportion of identified intangible assets in business combinations than what smaller

companies do.

In section 2.2.3, Rehnberg (2012) observed that highly externally financed companies

reported more a higher level of identified intangible assets when acquiring entities. Based on

this the third hypothesis is defined as follows:

H3: U.S. acquiring companies with a higher debt to equity ratio will identify a larger

proportion of identified intangible assets in business combinations than those with a lower

ratio.

The third hypothesis is that there is a positive correlation between debt to equity ratio (DE) in

U.S. companies and the proportion of intangible assets that they identify in business

combinations.

3.3 DIFFERENCES BETWEEN INDUSTRIES If some companies identify a higher proportion of intangible assets in business combinations

it does not necessarily need to be because of management incentives or its size. The

underlying economics in industries can affect the level of recorded goodwill in business

combination (KPMG, 2009, section 2.2.5). If the acquiree is a service company for example,

there will be a significantly larger proportion goodwill recorded than if the acquiree where to

be a producing company. The reason behind this is that the underlying economics is of a more

intangible character in the service industry than in production where the value driven

activities often are based on the quality of machines and other tangible assets and these make

up a large proportion of the balance sheet. In service companies the most valuable assets tend

to be for example trademark, intellectual property and customer lists (KPMG, 2009).

In section 2.2.3 it was mentioned that Rehnberg (2012) found that relevance in acquiring

companies’ financial reports increased when they reported intangible assets separated from

goodwill. There was an indication in earlier studies that this relevance varied between

industries. Motivated by the indication that the underlying economics in industries seem to

create a variance between the level of intangibles identified in acquisitions, this study will

research whether a various set of industries in the U.S. seem to do this to a larger extent. The

fourth hypothesis is developed as follows:

H4a) There is a correlation between the level of identified intangible assets in business

combinations and which industry the acquirer operates within.

H4b) The acquirer firm characteristics profitability, size, DE and BTM explain the level of

identified intangible assets in business combinations differently in different industries.

The fourth hypothesis defined above is that there is a correlation between which industry an

acquirer operates within and the level of identified intangible assets it reports in business

combinations (H4a). It is also predicted, based on the study of KPMG that firm characteristics

might explain the level of identified intangible assets differently between industries due to

17

their underlying economics and industry specific business environment (H4b) (see section

2.5).

3.4 BOOK TO MARKET RATIO The Book to Market ratio (BTM) is a ratio used to measure the value of a company. BTM

attempts to identify undervalued or overvalued securities by taking the book value and

dividing it by market value (market capitalization). A ratio larger than 1 indicates that stock is

undervalued and thus overvalued if the ratio falls below 1.

In section 2.4 it was presented that a study pursued by PwC had found that positive

correlation existed between BTM and goodwill impairment. Zhang and Zhang’s found that

there was a positive correlation between the level of reported goodwill and the acquirer’s

anticipated discretion in future goodwill assessment to avoid reporting impairment. It is

therefore expected that undervalued acquiring companies (BTM larger than 1) will report less

goodwill and therefore identify a higher percentage of intangible assets in business

combinations.

Since BTM has been found to be an important factor of goodwill recognition in earlier studies

this research finds it motivated to investigate whether U.S. acquiring companies with a higher

book to market ratio on common equity will identify a larger proportion of identified

intangible assets in business combinations than those with a lower ratio. The fifth hypothesis

is developed as follows:

H5: U.S. acquiring companies with a higher BTM on common equity will identify a larger


ratio.

18

4. RESEARCH METHODOLOGY This chapter aims to describe the research methodology and the data collection process

applied in this thesis. The chapter presents which models are used to test the hypotheses

developed in chapter 3 and the way which data has been processed. It ends with a critical

review of the thesis’ validity and reliability.

4.1 RESEARCH DESIGN This paper seeks to empirically investigate and draw conclusions about how U.S. companies

identify intangible assets in business combinations and whether there are any firm

characteristics that explain this. The paper will draw conclusions about the research question

and the developed hypothesis by applying a quantitative method based on a multiple

regression analysis similar to Rehnberg (2012). Secondary data is used, which has been

retrieved from database Compustat. The quantitative character of the methodology and the

way which data is presented in Compustat facilitates the use of a big sample size (N =1948).

4.2 DEPENDENT VARIABLE Compustat does not provide a list of all acquisitions made per year but provides data of

companies’ annual financials where acquired intangible assets (identified) and acquired

goodwill can be seen as miscellaneous items. We do not know how many acquisitions that

have been made each year, only the number of acquirers.

Table 4.2.1 Definition of Dependent Variable

Variable

abbreviation

Variable name Definition

Y The ratio of identified intangible

assets

Identified intangible assets

acquired divided by the sum

of acquired goodwill and

identified intangible assets

4.3 INDEPENDENT VARIABLES All variables are related directly to the acquirer and the data is from the same year as the

acquisition was made.

Table 4.3.1 Definition of Independent Variables

Variable

Abbreviatio

n

Variable

Name

Definition Variable Equation

BTM Book to

Market

Ratio

The book to market ratio

of the acquirer’s common

equity the year of the

acquisition.

BTMBook Value of Common Equity

Market Value of Common Equity

DE Debt to

Equity

Ratio

Debt to equity ratio

measures how externally

financed a company is.

DE Liabilities

Stockholders Equity

BTM = book value o f common equ ity

market value o f common equ ity

19

I Industry A dummy for each

industry in our sample

classified accordance to

GICS from Compustat.

PROF Profitabilit

y

A continuous variable

that measures how

profitable a company is.

PROFEBIT

Total Assets

SIZE Size The acquirer’s balance

sheet total defined as

total assets. A continuous

variable calculated as its

logarithm to obtain

normal distribution and a

more exact result.

SIZE log(Total Assets)

The industries are studied in regard to which two-digit GICS code they belong to. The two-

digit GICS code is the lowest, most grouped level of the Global Industry Classification

Standards (GICS). GICS is a way of classifying which industry publically traded companies

belong to and it is among others used in Compustat. The GICS index was developed by

Morgan Stanley Capital International and Standard & Poors. The GICS index was used in

Rehnberg’s thesis to classify industries as well, which makes it easier to compare the results

of this study to hers. The nine industry sectors that will be studied are listed and described in

Table 4.3.2 below.

Table 4.3.2 Industry Definitions and Description

GICS Code Industry Name Description

10

15

20

25

30

35

45

50

55

Energy

Materials

Industrials

Consumer

Discretionary

Consumer Staples

Health Care

Information

Technology

Telecommunication

Services

Utilities

Oil, gas, coal and consumable fuels.

Chemicals, construction materials e.g. sand, wood, paper, metals.

Aviation, defense, construction, machines, trucks, ships, related

services.

Consumer services and cars, kitchen appliances, furniture,

clothes, media,

Consumer beauty supplies, food, beverages, other everyday

commodities.

Health care equipment and services, biotechnology,

pharmaceuticals.

Computers, software, hardware and related services.

Telephone network operators and network operators.

Electricity, gas, water utilities and other power producers.

*Please observe that the Financials Sector (GICS 40) is excluded, therefore only 9 out of the

10 GICS sectors are studied in this paper.

20

**Information based on Morgan Stanley Capital International (2014)

4.4 HYPOTHESIS TEST In this paper we test five hypotheses developed in chapter 3 based on the prior research in the

empirical context of chapter 2. The significance level is chosen to be 0.05 (or equivalently,

5%) for all hypotheses, thus only variables with a p-value of less than 5% will be significant.

This gives the study a risk of type I error (the fixed probability of wrongly rejecting the null

hypothesis when it is in fact true). The null hypothesis will be rejected in favor for the

alternative hypothesis if results show significance lower than 5%. The conclusion is then that

the alternative hypothesis is likely to be true with a 95% confidence.

4.5 MODELS A multiple regression analysis is appropriate for this study since we aim to explain how

different variables of firm characteristics explain the proportion of identified intangible assets

in relation to goodwill (Y). Using a multiple regression analysis makes it possible to see

which variables are most likely to affect the proportion of identified intangible assets.

The regression model analysis was complemented by univariate analysis (see the descriptive

statistics in section 5.1) and multivariate analysis (see its conclusions in section 5.2.1).

For the industry dummies (Model 1 and 2) and the interaction variables between industry and

the continuous variables size, profitability DE and BTM (Model 2), the control group was

chosen as the Consumer Staples Industry (I30). The Consumer Staples Industry was chosen as

the control group since it was identified as the most extreme industry in terms of identifying

the highest level intangible assets in business combinations on average. Using the most

extreme industry as control group makes it easier to analyze and draw conclusions about the

results later on in the thesis.

MODEL 1

Y = a +DE i,t SIZE i,t PROF i,t BTMi,t +I10i,tI15i,t

+I20i,tI25i,t

I35i,tI45i,t

+I50i,tI55i,t

Model 1 provides information about whether the size, profitability, DE, BTM, industry

classification of the acquirer explains the level of identified intangible assets in business

combinations (Y). The significance level and the sign of the coefficient (B) are of importance

to draw conclusions about above aspects in a more detailed manner.

MODEL 2

Model 2 provides the same information as the first model, but additionally facilitate the

unique test of whether size, profitability DE and BTM explain the percentage of identified

intangible assets (Y) differently in different industries. Model 2 is broken down into four

submodels studying size, profitability, DE and BTM one at the time together with the

different industry dummies as interaction variables. These interaction variables are the main

variables of interest in Model 2. The sign of the coefficient along with the significance level is

of interest to conclude whether the firm characteristics explain Y differently between

industries.

MODEL 2A EQUATION

Y = a +BTMi,t +I10i,tI15i,t

+I20i,tI25i,t

I35i,tI45i,t

+I50i,tI55i,t

+BTMi,t I10i,tBTMi,t I15i,t


BTMi,t I35i,tBTMi,t I45i,t


SIZE i,t PROF i,t DE i,t

MODEL 2B EQUATION

21

Y = a +DE i,t +I10i,tI15i,t

+I20i,tI25i,t

I35i,tI45i,t

+I50i,tI55i,t

+DE i,t I10i,tDE i,t I15i,t


DE i,t I35i,tDE i,t I45i,t


SIZE i,t PROF i,t BTMi,t

22

MODEL 2C EQUATION

Y=a+PROFi,t +I10 i,tI15 i,t +I20 i,tI25 i,tI35 i,tI45 i,t +I50 i,tI55 i,t +PROFi,t I10 i,tPROFi,t I15 i,t

+PROFi,t I20 i,tPROF I25PROFi,t I35 i,tPROFi,t I45 i,t +PROFi,t I50 i,tPROFi,t I55 i,tSIZEi,t DEi,t BTMi,t

MODEL 2D EQUATION

Y=a+SIZEi,t +I10 i,tI15 +I20 i,t +I25 i,t +I35 i,t +I45 i,t +I50 i,t +I55 i,t +SIZEI10 i,t +SIZEI15 i,t

+SIZEI20 i,t +SIZEI25 i,t +SIZEI35 i,t +SIZEI45 i,t +SIZEI50 i,t +SIZEI55 i,tPROFi,t DEi,t BTMi,t +

4.6 DATA COLLECTION

4.6.1 SOURCES In order to conduct the tests of this paper, a collection of secondary data has been retrieved

from the Compustat database. The Compustat database is the source of data for this study and

it has been accessed through WRDS. Compustat provides U.S.-wide, financial and accounting

data of US companies. This enables the use of a quantitative statistical method to analyze the

relationship between the variables of interest.

4.6.3 SAMPLE OF COMPANIES Since the authors of this study seeks so explain how U.S. companies identify and account for

intangible assets obtained through acquisition after the introduction of SFAS 141 and 142 in

2001 the data set is first restricted to those U.S. companies that have made acquisitions

between years 2002-2013. Unfortunately there was a problem accessing the data for years

prior to 2010. Acquired goodwill and acquired intangible assets both started being collected in

Compustat in 2011. There were few companies reporting total intangible assets and total

goodwill posts for two consecutive years, which made it impossible to link an acquisition to a

specific year prior to 2010. Due to the data limitation the study has had to use 2010 as the first

year of observation. The sample of companies has not been restricted to those who are still

active today. As a result, there are companies observed that are not in business today. The size

of the sample is 1948 observations. The large amount of observations is possible because of

the quantitative character of the study and how the data is presented in Compustat.

4.7 DATA TREATMENT AND APPLIED TESTS

4.7.1 ELIMINATION OF UNWANTED DATA This research only studies acquisitions, thus only companies reporting annual acquired

identified intangible assets or acquired goodwill larger than zero were included in the data.

Since it could not be assumed that an empty data post implied that the value was zero, all

observations that where not complete in providing information for all variables were

eliminated.

Observations where stockholders’ equity was negative has been eliminated as well as the

negative observations for common equity, resulting in no negative BTM or DE observations

in the sample. The reason for eliminating the negative values was that they gave an unrealistic

result when the variables were computed. Eliminating the unwanted data decreased the

sample size from 2312 to 1948.

4.7.2 ROBUSTNESS TEST AND SENSITIVITY ANALYSIS Two types of robustness tests were applied to see whether the results of model 1 and 2

changed when their assumptions were altered. The first test was by performing a sensitivity

23

analysis on the results prior to and post winsorization. The second test was by studying how

the results changed when observations equaling 1 or 0 for the dependent variable were

excluded. The results and sensitivity analysis can be seen in section 5.2 but are explained

further below in section 4.7.2.1 and 4.7.2.2.

4.7.2.1 TREATMENT OF EXTREME VALUES – WINSORIZING

The data needs to be normally distributed in order to use multiple regressions. The original

variables’ data contained outliers (observations with extreme values). This was a problem

since it resulted in a data that was not normally distributed and the outliers could distort the

variable coefficients. In order to get satisfying results the outliers were treated by a common

method called winsorizing. Winsorizing is the transformation of statistics by limiting extreme

values in data to reduce their influence and distortion on the data distribution. We have

applied a 98% winsorization, which means that the 2nd

and 98th

percentile of the data

distribution has been censored. A 98% winsorization was chosen since this was the level that

provided the best results in terms of adjusted coefficient of determination.

4.7.2.2 TREATMENT OF THE OBSERVATIONS EQUALING 1 OR 0 FOR Y

When studying the error distribution post winsorization of the extreme values for the

independent variables, it was apparent that the high frequency of observations for Y equaling

zero or one created a scatter plot where the error distribution was not randomly spread like it

should be. The scatter plot showed that there was systematic tendency in the residuals that

was not captured in the model. The standard errors are not efficient.

In this study we have run the regressions with and without 1 and 0 to see if there was any

difference in the results. We are of the opinion that if there is no significant difference

between the results prior to and post the elimination of observations equaling 1 or 0 for Y,

then these observations can be included without any problem.

4.8 CRITICAL REVIEW

4.8.1 VALIDITY AND RELIABILITY It is important that the reader of a study can ensure its quality. In terms of research, it is of the

general opinion that validity and reliability represent a substantial part of its quality. This

section will specifically report all factors and choices that have been made and could affect

the level of validity and reliability. It defines validity as how well the results and conclusions

made answer the thesis’ purpose and research question. Reliability is defined as whether

several, independent researches could reach the same results and conclusions based on the

same method as applied in this study.

In consideration of the scarce resources of this research, it has only been realistic to study a

limited amount of variables. The variables have been chosen based on prior studies’

commonly found significant variables to see what results can be drawn based on a relatively

large sample of only U.S. companies. There are reasons to suspect that additional factors

could affect the amount of identified intangible assets in business combinations. The risk is

that there only is an immaterial statistical correlation between this level and the variables

BTM, DE, size and profitability and that there are other variables not studied that would

explain its variance better. This risk has been reduced by the authors by not choosing

variables that have not been significant before and only using already existing theories and

knowledge to motivate them and the method applied. It should be emphasized that there is no

way to provide statistical insurance of causality between the dependent and independent

24

variables. The study only proves that there is statistical probability that there is or is not a

correlation between them.

The sample only includes U.S. companies and although SFAS 141R Business Combinations

is very similar to IFRS 3, one should be cautious when projecting the results on other

economic areas, such as the European Union. The difference in business environment, other

parts of the regulatory environment and capital markets between the U.S. and EU should be

considered.

The acquiring companies have been classified into industries in accordance to their GICS

codes. The Financials sector (e.g. banks, insurance companies) is excluded since the

regulations and business environment that they operate under is significantly different from

all the other industries.

We wanted to observe as many acquisitions made post the issuance of SFAS 141 and 142 as

possible since a smaller difference between the sample size and population size provides more

relevant results. We also wanted to study as many consecutive years as possible to see if any

trends or patterns could be identified for Y. Unfortunately this method for collecting data for

years 2002-2009 required information of observed acquirers’ balance sheet posts in two

consistent years and there were too few acquiring companies whose balance sheet posts were

reported like this. As a result, the study only includes acquisitions between 2010-2014 where

acquired goodwill and acquired intangible assets has been reported specifically. Only five

observations can be found in 2010 and only 13 in 2014. The study has not included year as a

control variable in the regression models due to the few amount of years studied.

The fact that the Compustat database is one of the most commonly used sources in this field

of research, should emphasize the reliability of its data. Not all databases specify companies’

annual acquired goodwill and intangible assets separately (for example Worldscope) but

Compustat does. As a result, there have been no assumptions about the amount of goodwill

and identified intangible assets acquired a certain year.

There are some manual operations, which expose the risk of human errors, these are the

calculation of the variables. The equations have been linked between the original data to

minimize the manual operations.

25

5. EMPIRICAL FINDINGS

5.1 DESCRIPTIVE STATISTICS Among the acquirers studied, most acquirers operated within the Information Technology

industry (31,8% i.e. 619 acquisitions). Only 17 acquirers (0,9%) operated within the Utilities

Industry (see table 5.1.2). Among the years studied only 2011-2013 contain 100 percent of the

acquirers those years as seen in table 5.1.1. Only 5 acquisitions are studied from 2010 since

Compustat did not provide complete data for acquired intangible assets until 2011 (see section

4.6.3). Since 2014 is not over yet, it cannot be assumed that the number of acquisitions

reported in Compustat and therefore also studied in this research, represent 100 percent of the

total acquisitions in 2014.

Table 5.1.1 Acquirers per year

Year Observation Frequency Approximate Percent

2010 5 0,3

2011 645 33,1

2012 667 34,2

2013 617 31,7

2014 13 0,7

Total 1948 100

Table 5.1.2 Acquirers per industry

Industry Observation Frequency Approximate Percent

Energy 96 4,9

Materials 128 6,6

Industrials 444 22,8

Consumer Discretionary 245 12,6

Consumer Staples 80 4,1

Health Care 292 15,0

Information Technology 619 31,8

Telecommunication Services 27 1,4

Utilities 17 0,9

Total 1948 100

Table 5.1.3 Descriptive analysis of variables

Variable Observations Mean Std. Dev. Min Max

BTM 1948 0,527525 0,3726690 0,0552 1,8207

DE 1948 1,652926 2,2009339 0,1267 12,5880

PROF 1948 0,063379 0,0966524 -0,2977 0,2370

SIZE 1948 3,036518 0,7879004 1,2557 4,7354

Y 1948 0,457794 0,2265794 0,0000 1,0000

Variable definition: see section 4.3

Figure 5.1.1

26

Figure 5.1.1 shows the mean value of identified intangible assets ratio (Y) for 9 industries.

The higher the mean value of Y is the more intangible assets have been identified in

acquisitions within that industry and thus less goodwill have been presented. A mean value of

1 would mean that an industry would tend to identify all acquired intangible assets and a

mean value of 0 that all acquired intangible assets tend to be accounted for as goodwill. When

analyzing the result of the level of identified intangible assets in different industries, there

seems so be a fairly even level between most industries. The Energy, Industrials, Information

Technology and Utilities industry identify about 43-44% of the acquired intangible assets

(and thus 56-57% goodwill), which represents the lowest level of Y between the studied

industries. The next group that tend to identify slightly more intangible assets in business

combinations are the Telecommunication Services and Consumer Discretionary industry with

the mean value of 47% identified intangible assets (53% reported goodwill). The Health Care

and Materials industry reported about 49-51% identified intangible assets, thus about 49-51%

of goodwill. The one industry that seems to stand out and differ is the Consumer Staples

industry which accounts for the highest level of identified intangible assets about 57%, only

accounting 43% of the acquired intangible assets total as goodwill. Referring to section 4.2 it

seems like this study can come to the same conclusion as earlier studies. It seems as if some

industries identify more intangible assets than others. The Consumer Staples industry

identifies more in relation to the other industries. The Energy, Industrials, Information

Technology and Utilities Industry identifies in average about 13-14% less intangible assets in

business combinations than what the Consumer Staples industry does.

43,0%

50,7%

42,9%

47,2%

56,7%

49,4%

43,5%

47,2%

43,2%

Energy

Materials

Industrials

Consumer Discretionary

Consumer Staples

Health Care

Information Technology

Telecommunication Services

Utilities

Mean Percentage of Identified Intangible Assets (Y)

Industry

Mean Percentage of Identified Intangible Assets in Business Combinations by Industry

27

5.2 RESULTS AND ANALYSIS

5.2.1 MULTIVARIATE ANALYSIS

The variables were studied one by one to see whether the results changed for each when they

did not correlate with any of the other variables. The multivariate analysis showed that the

significant variables that correlate with Y are size and profitability no matter what

combinations of variables they were included in. What industries gained significance did

however vary indicating that there seems to be an intercorrelation between industries. The

industries Materials, Industrials, Consumer Staples, Health Care and Information Technology

gained significance most often. The results of the multivariate analysis were also that BTM

and DE did not explain the level of identified intangibles assets in business combinations.

They did not gain significance no matter what combination of variables they were included in.

Adjusted coefficient of determination remained very low for all variables (1-4%).

5.2.2 REGRESSION RESULTS

This section presents the results of the regression models below, starting with Model 1,

followed by the Model 2A)-D). As mentioned in chapter 6, all test results are based on an

linear regression model with winsorized extreme values.

REGRESSION RESULTS FOR MODEL 1

Model 1 provides information about whether the size, profitability, DE, BTM, industry

classification of the acquirer explains the level of identified intangible assets in business

combinations (Y). The significance level and the sign of the coefficient (B) are of importance

to draw conclusions about above aspects in a more detailed manner

MODEL 1 EQUATION

To test how the percentage of identified assets varies with firm characteristics and industry

the following model is used:

Y = a +DE i,t SIZE i,t PROF i,t BTMi,t +I10i,tI15i,t

+I20i,tI25i,t

I35i,tI45i,t

+I50i,tI55i,t

Table 5.2.1Model 1 Regression Results

Variable B(Coefficient) Coefficient

Sig

Standard

Error

BTM -0,002 0,863 0,014

DE 0,001 0,819 0,002

Profitability -0,163**

0,006 0,059

Size -0,036****

0,000 0,007

Energy Industry (I10) -0,137****

0,000 0,034

Materials Industry (I15) -0,055 0,081 0,032

Industrials Industry (I20) -0,142****

0,000 0,027

Consumer Discretionary Industry (I25) -0,100****

0,000 0,029

Health Care Industry (I35) -0,084***

0,003 0,028

28

Information Technology Industry (I45) -0,148****

0,000 0,027

Telecommunication Services Industry (I50) -0,115**

0,020 0,050

Utilities Industry (I55) -0,138**

0,020 0,059

Constant 0,695****

0,000 0,034

* p < 0.05, **p < 0.025, *** p < 0.005, **** p < 0.0005

Adjusted R2 for Model 1 is 0,045

N=1948

Table 5.2.1 shows that the adjusted coefficient of determination of Model 1 only obtains a

value of 0,045. Model 1 thus only will explain 4,5% of the variances of Y. The Standard Error

corresponding to each variable value is an estimate of how well the prediction of the

coefficient is estimated. A smaller standard error means a more precise estimate.

As seen in table 5.2.1, the coefficient of variables profitability and size is negative with -0,163

and -0,036 respectively and the standard error of 0,059 and 0,007. These values support a

negative correlation between them and the value of Y at the significance level of 5 %.

The variables DE, BTM and industry Materials I15 are insignificant and therefore there is no

correlation between them and Y. DE and BTM thus do not explain the variance of identified

intangible assets in business combinations at the significance level of 5%.

The industry Materials do not significantly differ from the control group industry Consumer

Staples but the Consumer Discretionary, Telecommunication Services, Utilities industries

Energy, Industrials and Information Technology industries do. These have a negative variable

coefficient (B) as expected indicating that companies within the Energy, Industrials and

Information Technology industry identify less intangible assets in business combinations.

REGRESSION RESULTS FOR MODEL 2 Model 2 provides the same information as the first model, namely whether size, profitability,

DE and BTM explains the level of identified intangible assets in business combinations,

whether the variance in Y can be explained by the acquirer’s industry classification. The

unique test in Model 2 is if size, profitability DE and BTM explain Y differently in different

industries. Model 2 is broken down into four sub models studying size, profitability, DE and

BTM one at the time together with the different industry dummies as interaction variables.

These interaction variables are the main variables of interest in Model 2. The sign of the

coefficient along with the significance level is of interest to conclude whether the firm

characteristics explain Y differently between industries.

MODEL 2A) BTM AS INDUSTRY BASE VARIABLE The unique test in Model 2A) is whether BTM explain Y differently in different industries (in

relation to the control group, the Consumer Staples industry). In this model the interaction

between BTM and the industry dummy is the main variable of interest.

MODEL 2A EQUATION

Y = a +BTMi,t +I10i,tI15i,t

+I20i,tI25i,t

I35i,tI45i,t

+I50i,tI55i,t



BTMi,t I35i,tBTMi,t I45i,t


SIZE i,t PROF i,t DE i,t

Table 5.2.2 Model 2A) Regression Results

29

Variable B

(Coefficient)

Coefficient

Sig

Standard

Error

BTM -0,015 0,816 0,066

DE 7,559E-005 0,975 0,002


0,005 0,059

Size -0,035****

0,000 0,007

Energy Industry (I10) -0,078 0,180 0,058


Industrials Industry (I20) -0,017**

0,018 0,045

Consumer Discretionary Industry (I25) -0,128**

0,006 0,047

Health Care Industry (I35) -0,058 0,206 0,046


0,000 0,043

Telecommunication Services Industry (I50) -0,112 0,179 0,084

Utilities Industry (I55) -0,229 0,096 0,137

Energy Industry (I10)* BTM -0,097 0,270 0,088

Materials Industry (I15)* BTM 0,048 0,583 0,087

Industrials Industry (I20)* BTM -0,059 0,415 0,073

Consumer Discretionary Industry (I25)*

BTM

0,052 0,478 0,074

Health Care Industry (I35)* BTM -0,055 0,463 0,075

Information Technology Industry (I45)*

BTM

0,079 0,259 0,070

Telecommunication Services Industry

(I50)* BTM

0,001 0,993 0,108

Utilities Industry (I55)* BTM 0,134 0,467 0,184

Constant 0,700****

0,000 0,046

* p < 0.05, **p < 0.025, *** p < 0.005, **** p < 0.0005

Adjusted R2 for Model 2A) is 0,052

N=1948

As seen in Table 5.2.2, the interaction variable between industry and BTM shows significance

for the Industrials Industry ( I20), Consumer Discretionary Industry (I25), and Information

Technology (I45). All the significant variables have a negative variable coefficient (B). None

of the interaction variables (BTM*Industry) are significant. The conclusion from Model 2A)

is therefore that at significance level of 5 %, only the Industrials, Consumer Discretionary and

Information Technology industries are significantly different from the Consumer Staples

industry in how BTM explain the level of identified intangible assets in acquisitions (Y). The

30

Table 5.2.2 also shows that the adjusted coefficient of determination is quite low (5,2%) and

the standard errors range between 0,002 for DE to 0,137 for the Utility industry.

MODEL 2B) DE AS INDUSTRY BASE VARIABLE In this model the interaction between DE and the industry dummy is the main variable of

interest.

MODEL 2B EQUATION

Y = a +DE i,t +I10i,tI15i,t

+I20i,tI25i,t

I35i,tI45i,t

+I50i,tI55i,t



DE i,t I35i,tDE i,t I45i,t


SIZE i,t PROF i,t BTMi,t

Table 5.2.3 Model 2B) Regression Results

Variable B

(Coefficient)

Coefficient

Sig

Standard

Error

BTM -0,002 0,879 0,014

DE 0,011 0,294 0,011


0,008 0,059

Size -0,036****

0,000 0,007

Energy Industry (I10) -0,107**

0,016 0,045


Industrials Industry (I20) -0,125***

0,001 0,036


0,006 0,038



0,000 0,035


Utilities Industry (I55) 0,118 0,278 0,109

Energy Industry (I10)* DE -0,014 0,317 0,014

Materials Industry (I15)* DE -0,015 0,279 0,014

Industrials Industry (I20)* DE -0,007 0,545 0,011

Consumer Discretionary Industry (I25)* DE 0,003 0,829 0,012

Health Care Industry (I35)* DE -0,027* 0,025 0,012

Information Technology Industry (I45)* DE -0,012 0,322 0,012

Telecommunication Services Industry (I50)*

DE

-0,021 0,179 0,016

Utilities Industry (I55)* DE -0,115**

0,005 0,041

Constant 0,672****

0,000 0,040

* p < 0.05, **p < 0.025, *** p < 0.005, **** p < 0.0005

Adjusted R2 for Model 2B) is 0,053

N=1948

31

Table 5.2.3 shows that the coefficient of determination is quite low (0,053). The significant

interaction values with DE are the Health Care and Utilities industry. The model will explain

5,3% of the variances of Y.

32

MODEL 2C) PROFITABILITY AS INDUSTRY BASE VARIABLE In this model the interaction between profitability and the industry dummy is the main

variable of interest.

MODEL 2C EQUATION

Y=a+PROFi,t +I10 i,tI15 i,t +I20 i,tI25 i,tI35 i,tI45 i,t +I50 i,tI55 i,t +PROFi,t I10 i,tPROFi,t I15 i,t

+PROFi,t I20 i,tPROF I25PROFi,t I35 i,tPROFi,t I45 i,t +PROFi,t I50 i,tPROFi,t I55 i,tSIZEi,t DEi,t BTMi,t

Table 5.2.4 Model 2C) Regression Results

Variable B

(Coefficient)

Coefficient

Sig

Standard

Error

BTM 0,005 0,742 0,014

DE 0,001 0,633 0,002

Profitability -0,223 0,425 0,280

Size -0,034****

0,000 0,007


0,000 0,043



0,000 0,037


0,010 0,039

Health Care Industry (I35) -0,084**

0,021 0,037


0,000 0,035

Telecommunication Services Industry (I50) -0,108*

0,049 0,055


Energy Industry (I10)*PROF 0,559 0,164 0,401

Materials Industry (I15)* PROF 0,387 0,352 0,416

Industrials Industry (I20)* PROF 0,365 0,248 0,316


PROF

0,002 0,996 0,327

Health Care Industry (I35)* PROF -0,025 0,934 0,298


PROF

0,027 0,926 0,291


(I50)* PROF

-0,657 0,236 0,554

Utilities Industry (I55)* PROF -1,551**

0,011 0,609

Constant 0,690****

0,000 0,041

*p < 0.05, **p < 0.025, *** p < 0.005, **** p < 0.0005

Adjusted R2 for Model 2C) is 0,051

N=1948

33

Adjusted R2 of 5,1% mean that the model will explain 5,1% of the variances in Y. The only

interaction variable with profitability that has significance is the Utilities industry.

MODEL 2D) SIZE AS INDUSTRY BASE VARIABLE In this model the interaction between size and the industry dummy is the main variable of

interest.

MODEL 2D EQUATION

Y=a+SIZEi,t +I10 i,tI15 +I20 i,t +I25 i,t +I35 i,t +I45 i,t +I50 i,t +I55 i,t +SIZEI10 i,t +SIZEI15 i,t

+SIZEI20 i,t +SIZEI25 i,t +SIZEI35 i,t +SIZEI45 i,t +SIZEI50 i,t +SIZEI55 i,tPROFi,t DEi,t BTMi,t +

Table 5.2.5 Model 2D) Regression Results

Variable B

(Coefficient)

Coefficient

Sig

Standard

Error

BTM -0,002 0,870 0,014

DE 0,001 0,758 0,002


0,006

0,060

Size -0,069**

0,011

0,027

Energy Industry (I10) -0,341**

0,012

0,135


Industrials Industry (I20) -0,258**

0,011

0,101

Consumer Discretionary Industry (I25) -0,177 0,103 0,108

Health Care Industry (I35) -0,226*

0,027

0,102

Information Technology Industry (I45) -0,260**

0,007 0,096


Utilities Industry (I55) 0,067 0,751 0,211

Energy Industry (I10)*SIZE 0,062 0,119 0,040

Materials Industry (I15)* SIZE 0,019 0,638 0,041

Industrials Industry (I20)* SIZE 0,037 0,234 0,031


SIZE

0,024 0,472 0,033

Health Care Industry (I35)* SIZE 0,045 0,149 0,031


SIZE

0,035 0,230 0,029


(I50)* SIZE

0,029 0,598 0,056

Utilities Industry (I55)* SIZE -0,058 0,336 0,060

Constant 0,800****

0,000****

34

*p < 0.05, **p < 0.025, *** p < 0.005, **** p < 0.0005

Adjusted R2 for Model 2D) is 0,044

N=1948

Adjusted R2 of 0,044 indicates that 4,4 % of the variation of the variance in Y is explained by

the model. None of the interaction variables with size are significant.

5.2.3 SENSITIVITY ANALYSIS

WINSORIZATION

This section studies whether the results in model 1 change when applying a 98%

winsorization in relation to not performing winsorization. This was performed to study how

sensitive the multiple regression model were to observations with extreme values in the

sample (Read more about winsorization and its contributions to results in general in section

4.7.2.1). Table 5.2.3.1 shows the regression result prior to winsorization and table 5.2.1 shows

the regression result after winsorizing the equivalent information but for winsorized variables.

As can be studied, adjusted R2

is increased by 0,001, the histogram found below only

indicates a marginal improvement of the normality in the residuals. When observing the

distribution of the residuals it can be seen that the observations for Y equaling 1 and 0 is part

of the explanation why the residuals are not completely normally distributed. It is however

considered to be fairly normally distributed. The significance for profitability now indicates

that there is a correlation between profitability and Y. The variable profitability gains

significance post winsorization, indicating that it is correlated with Y. BTM and DE are

insignificant both prior to and post winsorization. The standard errors are slightly worsened

post winsorizing the variables.

Please see Appendix 2.1 to see how the winsorization has censored the extreme values,

resulting in a narrower gap between the minimum and maximum value and with a lower

standard deviation.

NORMALITY OF THE RESIDUALS PRIOR TO WINSORIZATION

Table 5.2.3.1

Variable B

(Coefficient)

Coefficient

Sig

Standard

Error

BTM 0,008 0,134 0,005

DE 0,001 0,050 0,000

Profitability 0,024 0,164 0,017

Size -0,044****

0,000 0,006


0,000 0,034



0,000 0,027


0,000 0,029

35


0,004 0,028


0,000 0,026

Telecommunication Services Industry (I50) -0,107* 0,031 0,049

Utilities Industry (I55) -0,128* 0,031

0,059

Constant 0,700****

0,000

0,033

*p < 0.05, **p < 0.025, *** p < 0.005, **** p < 0.0005

Adjusted R2 for Model 1 before winsorization is 0,044

N=1948

FIGURE 5.2.3.1, NORMALITY OF THE RESIDUALS BEFORE WINSORIZATION

FIGURE 5.2.3.2, NORMALITY OF THE RESIDUALS 98% WINSORIZATION

APPLIED

EXCLUDING VS. INCLUDING THE OBSERVATIONS EQUALING 1 AND 0 FOR Y

As mentioned in section 4.7.2.2, one of the assumptions of the linear regression model is that

the residuals are uncorrelated, normally distributed and that their variances do not vary with

the effects being modeled. When the distribution of the residuals in the regression models was

36

studied it does however show a tendency of error correlation (see figure 5.2.3). It is a major

concern when performing a regression analysis since it can invalidate tests of significance.

When studying the error distribution it was apparent that the high frequency of observations

for Y equaling zero and one created a scatter plot where the error distribution was not

randomly spread like a normal scatter plot with uncorrelated errors should look like. It shows

that there is a systematic tendency that this research model does not capture. The standard

errors are not efficient.

A test of the results when including respectively excluding the observations of 1 and 0 for Y

has been produced for a simpler comparison of how the error inefficiency affects the results

(see the results in Appendix 2).

When 1 and 0 for Y are excluded, the range between the maximum and minimum value of the

residuals were narrowed and the SSE (sum of squared errors of prediction) lower, indicating a

tighter fit of the model. The adjusted coefficient of determination decreased by 0,6 percent. A

few variables significant prior lost their significance when excluded and the other way around

for some of the other variables, not indicating a certain trend the overall significance changes.

Although it could be argued that the distribution of residuals are improved when excluding

them the values of 1 and 0 for Y, there is no significant difference between the results prior to

and post the elimination of observations equaling 1 or 0 for Y and these observations have

been considered to be included without any problem. It is of the authors’ opinion that the best

option for the results is to include them.

The argument for including the values of 1 and 0 is that the purpose of the study is to observe

all relevant purchase price allocations in intangible assets. The research does not want to

exclude those acquisitions where 100% or 0% of the acquired intangible assets have been

identified, these observations are believed to provide important information, just like the rest

of the observations for Y. There is no indication that indicates something different.

In figure 5.2.3 and 5.2.4 below it is easy to see the differences between the distribution of

residuals of Model 1 when observations equaling 1 and 0 for Y are included respectively

excluded. Figure 5.2.3 when 1 and 0 are included shows that there is a pattern in the

distribution of residuals that are not captured by Model 1.

The scatter plot for Model 2A)-D) were practically identical to the one in Model 1 and has

therefore not been illustrated in this paper, they show the very same pattern of the residuals.

Eliminating the observations equaling 1 and 0 for Y reduces the sample studied with 80 (50

observations equal 1 and 30 observations equal 0).

Figure 5.2.3 Model 1 Scatter plot – Residual distribution when observations equaling 1

and 0 for Y are included (N=1948)

37

Figure 5.2.4 Model 1 Scatter plot – Residual distribution when observations equaling 1

and 0 for Y are excluded. (N=1868)

5.2.2 ANALYSIS OF THE REGRESSION MODELS RESULTS This section analyses the regression model results and connects them with the empirical

context to draw conclusions about whether the hypothesis formulated in chapter 4 are to be

rejected or not.

In the hypothesis development in section 3.1, earlier studies conducted by Zhang and Zhang

(2007) showed that there was a strong correlation between an acquirer’s profitability ranking

and the percentage of purchase price allocated to identified intangible assets. Less profitable

companies were more inclined to record less identifiable intangibles and thus reducing

amortization expenses to maintain higher net worth (positive correlation). This led to the

following hypothesis:

H1: U.S. acquiring companies that report high profitability will identify a higher proportion

of identified intangible assets in business combinations than those with a lower profitability

ranking.

When studying the results of the regression models a positive coefficient of profitability

would show that a higher proportion of intangible assets would be identified in business

38

combinations when profitability increases. The empirical finding is that profitability is

significant (p-value < 0,05) and therefore can explain variances in Y. The empirical finding

does however show a negative coefficient for profitability of -0,163. This means that an

increase in the acquirer’s profitability would likely result in a 0,163 % lower percentage of

identified intangible assets in business combinations. Although the difference is significant,

the coefficient of profitability shows a negative value and the first hypothesis must therefore

be rejected.

The findings differ from the ones in prior research by showing the inverse relationship

between profitability and identified intangible assets of what was expected.

The prediction of this study was that the issuance of SFAS 142 had created a management

incentive in especially unprofitable companies, to allocate a greater proportion to goodwill to

reduce amortization expenses. (Zhang, Zhang 2007). The incentive is to identify less

intangible assets in business combinations to avoid amortization expenses and thus report

better annual profit.

The second hypothesis was development based on the findings of Rehnberg, 2012 presented

in section 2 finding that large companies account for more intangible assets than small

companies (Rehnberg, 2012). This was explained as the consequence of larger companies

being more conservative and bound to accounting traditions, thus choosing to continue

amortize and depreciate assets like done prior to SFAS 141R and 142. Another theory was

that a large company might have better ability to perform quality accounting, thus identifying

a larger proportion of intangible assets. The second hypothesis was defined as follows:

H2: U.S. acquiring companies with a larger balance sheet total account for a larger

proportion of identified intangible assets in business combinations than what smaller

companies do.

In order to not rejecting the hypothesis the variable size would need to receive a p-value lower

than 0,05 and it would have to be positive. The regression result from Model 1 is that there is

a negative coefficient (-0,036) between size and the proportion of intangibles accounted for

during business combinations. Variable size is the logarithm of total assets and its coefficient

therefore states the /100 change of the level of identified intangible assets (Y), given a

percentage change in total assets. Although the p-value of variable size is less than 0,05

showing that there seems to be a correlation between Y and size, the sign of the variable

coefficient is negative and not in line with the second hypothesis above. The hypothesis is

rejected. These finding therefore differ from Rehnberg’s (2012).

The third hypothesis developed in section 2.2.3 was motivated by Rehnberg (2012) who

found that more externally financed companies would report a larger proportion of intangible

assets. She explained this relationship as a consequence of highly in-debt companies finding it

extra important to communicate the picture of a stable financial position to stakeholders, as

well as disclosing the type of assets they own. The third hypothesis was defined as follows:

H3: U.S. acquiring companies with a higher debt to equity ratio will identify a larger


ratio.

39

The expectation based on the findings from the empirical context was that the hypothesis

would be rejected, finding a negative correlation between the debt to equity ratio (DE) and the

proportion of identified intangible assets in business combinations.

When applying the hypothesis to our regression in Model 1, DE is not significant. Neither is it

significant in any of the other models, this indicates that DE doesn’t explain the ratio of

intangible assets accounted for during business combinations. The lack of significance also

tells that there isn’t a negative correlation either. The hypothesis is rejected.

It is of the research’s finding that there is no correlation between debt to equity ratio and the

ratio of intangible assets accounted for during acquisitions. The findings differ from previous

research that found the variable to correlate with the level of identified intangible assets.

Based on the earlier findings of KPMG (2010) and (Godfrey & Jones 1999) indicating that

there seems to exist a difference between industries and the way they identify intangible

assets in business combinations, the following two hypothesis were developed in chapter 3:

H4a) There is a correlation between the level of identified intangible assets in business

combinations and which industry the acquirer operates within.

H4b) The acquirer firm characteristics profitability, size, DE and BTM explain the level of

identified intangible assets in business combinations differently in different industries.

The fourth hypothesis did in other words predict that not only would Y be explained by

acquirer’s industry classification but it would also be explained differently by the acquirer’s

firm characteristics size, profitability, DE and BTM differently depending on which industry

the acquirer operated within.

When interpreting the regression result, industry dummies (I10…55) are of interest to conclude

whether hypothesis H4a) is to be rejected or not and the interaction variables, where industry

dummies have been multiplied with size, profitability, DE and BTM, are the variables of

interest to conclude the same about H4b). The industry dummy variables (I10…55) gaining

significance (p-value <0,05) are considered to identify a significantly different level of

identified intangible assets in business combinations in relation to the base dummy industry

Consumer Staples (I30). The value of the variable coefficient will indicate whether the

industries tend to identify more or less in relation to the Consumer Staples industry.

The study’s regression results from Model 1 show that all industries except the Materials

industry (I15) gain significance of a p-value lower than 0,05. The research concludes that all

industries expect the Materials industry identify a significantly level of intangible assets in

business combinations than the Consumer Staples industry does, thus hypothesis H4a) can not

be rejected: there is a correlation between the level of identified intangible assets in business

combinations and which industry the acquirer operates within. The level of Y varies in

between industries.

When expanding the interpretation of the regression results for Model 1 that connects with

H4a) it is observed that all of the industries have a negative variable coefficient. This

indicates that the Consumer Staples industry is the industry identifying the highest level of

intangible assets in business combinations (Y). This observation is strengthened by the pair

wise correlation analysis that was performed in this paper, which led it to choose the

Consumer Staples industry as the extreme base dummy. The Energy, Industrials, Consumer

Discretionary, Health Care and Information Technology industry have particularly low p-

40

values indicating that these industries have a stronger correlation with Y (differs from the null

hypothesis) in relation to the Telecommunication Services industry and Utilities industry.

The values of the coefficient variables indicate that the Information Technology industry (I45)

tend to identify a level of Y that differs from the one in the Consumer Staples industry the

most. When studying figure 5.1.4 in chapter 5 from the Descriptive Statistics, it does indeed

show that the Information Technology industry are one of the industries that differs the most

from the Consumer Staples industry but it is not the one identifying the lowest mean level of

acquired intangible assets, it does however only differ 0,6%.

When interpreting the interactive variables between industry and the other firm

characteristics, those variables gaining significance (p-value <0,05) are concluded to represent

those industries where the firm characteristics explain the level of identified intangible assets

significantly different from the Consumer Staples industry.

The regression results from Model 2 shows that BTM and size does not explain Y

significantly different between industries in relation to the Consumer Staples industry (Model

2A and D) but that DE does for the Utilities industry (I55) and Health Care industry (I35)

(Model 2B) as well as profitability for the Utilities industry (I55)(Model 2C). The results show

a mixed finding in terms of the “trueness” of hypothesis H4b). The authors finds the H4b) to

be rejected for the firm characteristics BTM and size but accepted for DE and profitability

since at least one industry significantly differs in term of their affect on Y in relation to the

Consumer Staples industry. All three significant interaction variables Health Care industry

(I35)* DE, Utilities industry (I55)* DE and Utilities industry (I55)* PROF has negative

coefficients indicating meaning that DE in the Health Care industry is less positively

correlated with Y, compared to DE in the Consumer Staples industry. DE and profitability in

the Utilities industry is less positively correlated with Y, compared to DE and profitability in

the Consumer Staples industry.

The fifth and last hypothesis developed in chapter 3 was based on PwC’s findings that BTM

had a positive correlation with goodwill impairment and Zhang and Zhang’s finding there was

a positive correlation between the levels of reported goodwill and the acquirer’s anticipated

discretion in future goodwill assessment to avoid reporting impairment. Based on this

empirical context, it was expected that undervalued acquiring companies (BTM larger than 1)

would report less goodwill and therefore identify a higher percentage of intangible assets in

business combinations. The fifth hypothesis was developed as follows:

H5: U.S. acquiring companies with a higher BTM on common equity will identify a larger


ratio.

A positive coefficient of BTM would be equivalent with a higher proportion of identified

intangible assets during business combinations when BTM is high. The finding from Model 1

is that BTM has a negative coefficient and then identified intangibles is lower when BTM is

higher, this result is not significant and therefore the conclusion is that there is no correlation

for BTM which leads to that the hypothesis is not supported.

This differs from previous research since previous research found a positive correlation and

this thesis did not find a correlation at all.

All conclusions drawn about the original hypothesis have differed from previous findings

except from hypothesis 4 showing differences in how industries identify intangible assets in

41

business combinations. An attempt of explaining why the findings of this research differ from

prior findings is not pursued but referred to as suggested further research. There is no

immediate explanation to why our findings differ from prior research. All prior research are

either based on IFRS 3 or SFAS 142 which are substantially similar, the findings were not

concluded so long ago and there are no particular sample characteristics that could explain the

differences of the findings. The only factor that potentially could explain the differing

findings between this paper and the one of Rehnberg is that she studied years 2005 to 2007,

which was a period of significant growth where no impairments were performed. The years of

study for this paper are 2010 to 2014, which were a typical post crisis period characterized by

impairments of goodwill and other assets.

42

6. SUMMARY

This chapter presents the conclusions that have been drawn based on the findings and

analysis in chapter 7. The conclusions concern the findings of this research and their

consistency with prior research presented the Empirical Context of chapter 2. This chapter

answers the research question defined in chapter 1, highlights how the findings of this

research has served its purpose and provides suggestions for further research within the area

of this paper.

6.1 CONCLUSIONS The purpose of the study was to provide additional knowledge of how U.S. companies under

effect of SFAS 141 and SFAS 142 identify and report intangible assets in conjunction with

their accounting for business combinations and whether there are any firm characteristics that

correlate with a certain level of identified intangible assets. The purpose was also to

investigate whether the same conclusions could be drawn as the conclusion Rehnberg made in

2012. The conclusions of this section will fulfill the purpose of this thesis and answer how

U.S. companies identify and report intangible assets in business combinations and if there are

any specific firm characteristics that explain this.

The finding of this paper is that there are acquirer firm characteristics that explain how U.S.

companies identify intangible assets during business combinations. Among the firm

characteristics studied in this research these are profitability and size in terms of the acquirer’s

logarithmic total assets. Profitability has been found to have a negative relationship to the

identification of intangible assets, indicating that more profitable and acquirer is, the less

acquired intangible assets will be identified in business combinations. The conclusion of size

is that it explains the level of identified intangible assets in business combinations with a

negative relationship. This means that when the total assets of the company increase the

identified intangibles decreases. In previous research size and profitability have been found to

have a positive correlation with the intangibles allocated during business combination. The

debt to equity ratio and book to market ratio of an acquirer is not found to have a significant

correlation with percentage of identified intangibles assets accounted for during business

combinations.

The finding in this paper is not in conjunction with the ones of Rehnberg’s thesis in 2012

when investigating correlations between firm characteristics and the level of identified

intangible assets in business combinations. The authors have not made an attempt of

explaining why but suggest this for further research.

The authors draw the conclusion that the Industrials, Energy, Utilities and Information

Technology are the industries that identifies the lowest proportion of acquired intangible

assets (on average 42,9-43,5%) and that the Consumer Staples industry identifies the highest

proportion (on average 56,7%)

The authors find that the Energy, Materials, Industrials, Consumer discretionary, information

technology, telecommunication services, utilities industries identified significantly less

intangible assets in business combinations than the consumer staples industry. The health care

industry (I15) was however not found to identify a significantly different percentage of

intangible assets in relation to the consumer staples industry. The conclusion drawn was

subsequently that there is a correlation between the level of identified intangible assets in

business combinations and which industry the acquirer operates within. The level of Y varies

between industries.

43

The authors find that the acquirer firm characteristics BTM and size do not explain the level

of identified intangible assets acquired differently in different industries but that DE and

profitability of an acquirer partially do. More specifically DE does explain the level of

identified intangible assets in the Utilities Industry (I55) and Health Care industry (I35) in

relation to the Consumer Staples industry and profitability explains the level differently

between Utilities Industry (I55) and the Consumer Staples industry. DE in the health care

industry is less positively correlated with Y, compared to DE in the Consumer Staples

industry. DE and profitability in the Utilities industry is less positively correlated with Y,

compared to DE and profitability in the Consumer Staples industry. The finding that the

percentage of identified intangible assets varies differently between industries is in

conjunction with the study of KPMG (2009).

The empirical context presented that a higher percentage of identified intangible assets could

indicate that SFAS 141R and 142 worked more efficiently by providing relevance and more

quality financial reports. In the study of KPMG (2009), most companies were found to have

identified more than 50% of the acquired intangible assets. This study does however show the

mean percentage of identified intangible assets across industries as 47%, which is lower than

the result of KPMG studying prior years. A trend analysis should be performed to study

whether the level of identified intangible assets seem to increase or decrease. If decreasing it

could indicate that SFAS 141and 142 are not working efficiently. The study cannot draw a

conclusion whether the levels of identified intangible assets in business combinations

observed in this research indicate efficient principle-based standards (that SFAS 141R and

142 work efficiently). This study is careful in drawing conclusions about the accounting

quality between industries. Even if the Consumer Staples industry identified a higher

proportion than all other industries, it cannot be assumed that companies within this industry

generally produce more relevant and quality financial statements. As mentioned in the

empirical context KPMG (2009) stated that some industries will naturally identify a higher

ratio and it can therefore not be concluded that a higher Y corresponds to a higher relevance

in accounting quality when compared between certain industries. Since some of the industries

are so different in terms of value drivers and business operations, it is difficult to determine

whether the differences observed in the identification of acquired intangible assets indicates

higher relevance and quality of these industries’ financial statements. The fact that the level of

Y varies between industries is a sign that SFAS 141 and 142 are working efficiently. The

underlying economics of industries differ and so should also the level of identified intangible

assets in business combinations. If there were no differences in this level, it would have

indicated a problem.

6.2 CONTRIBUTION AND SUGGESTED FURTHER RESEARCH The results of this study contributes to the field by providing additional knowledge of how

different firm characteristics might affect the percentage of identified intangible assets and the

accounting quality for business combinations under SFAS 141 and 142. The findings have

indicated relationships between the acquirer characteristics profitability, size, DE and BTM

that differ from previous studies. The findings might be of interest of other researches

interested in pursuing further studies of the area and seek to explain why some of the

relationships found in this paper differ from previous findings. The information provided

could benefit several external parties such as stakeholders and other users of financial reports

e.g. market analysts and standard-setting bodies and auditors who must approve a company’s

financial reporting and disclosure in relation to any acquisitions it makes.

Since this study primarily provides information of how SFAS 141 and 142 compliant

companies have accounted for acquired intangible assets during years 2011-2013, it provides

44

such information that would be of interest to standard-setters and regulators wanting to study

trend post the issuance of the two standards. The results of this paper are primarily intended to

provide additional insights and knowledge to the area of research that ultimately can assist

standard-setting bodies in their development of the standard for accounting for intangible

assets. Since it appears that the variance of identified intangible assets varies by firm

characteristics, influenced by incentives and partially affecting accounting quality the rules

and regulations of SFAS 141 and 142 should be reviewed in order to obtain the optimal

balance between principles and rules within the standards in terms of faithful representation

and user relevance.

Since this study only observes acquisitions made primarily between 2011-2013, further

studies of how US GAAP compliant U.S. companies identify intangible assets could try to

access observations for 2002-2009 as well as those acquisitions in 2014 that are yet to be

reported in order to see how the trends develop over time.

The previous findings of Zhang and Zhang (2007) showed that certain CEO characteristics

such as age, but especially management compensation systems could explain the variance in

identified intangible assets in business combinations. There was an indication that if

management’s bonuses were closely tied to the annual result, there was an incentive to

allocate a larger proportion of the purchase price paid to goodwill. Older CEOs were also

found to report a larger proportion of goodwill than younger ones. The author’s theory behind

this is that older CEOs are more likely to care about short-term accounting earnings and

bonuses and therefore will avoid amortization expenses by identifying less intangible assets in

business combinations. This research has been unable to study these CEO characteristics since

the Compustat access at hand did not allow filtering out this information. It is a suggestion for

future studies to further research this area in purpose of deepening the knowledge of how

identification of intangible assets can be explained by earnings incitements.

Another suggestion for further research is to study if U.S. acquiree characteristics affect the

way that the acquirer identifies the acquiree intangible assets. Previous studies have studied

acquiree characteristics but this paper has not been able to find any of such studies, recently

updated, specifically focusing on U.S. companies for a larger sample. This study had no

access to acquiree information in Compustat and has therefore not been able to include it in its

spectrum of research. The authors did not either have database access to information related to

an acquirer’s reporting units. Zhang and Zhang found that acquirer’s with multiple reporting

units experienced greater accounting discretion and therefore tended to allocate a greater

proportion of the purchase price paid in an acquisition to goodwill since the risk of future

goodwill impairment was less than for those acquirers with single reporting units. Further

research could include multiple reporting units as an explanatory variable to see whether the

same results could be found for a greater sample of specifically U.S. firms.

45

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FASB (Financial Accounting Standards Board) (2001).” Statement of Financial Accounting

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Fields, T. & Lys T. & Vincent, L. (2001); “Empirical research on accounting

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opportunism”, Journal of Business Finance & Accounting vol. 29 (7) & (8).

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information och styrning”.

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industry study” (2009)

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Morgan Stanley Capital International

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edition, Pearson Education

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United States – 2013 Report” www.ofii.org

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market response to goodwill impairment announcements”

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förvärvskalkylering” (Accounting for intangible assets in connection with business

combinations). Diss., Gothenburg:, University of Gothenburg, School of Business Economics

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47

APPENDIX 1 Industry classification

The nine industries studied in this paper are classified by GICS. The industries are

summarized for a quick and easily accessible overview in table 4.3.1 in chapter 4 but here

complemented with a more specified description. The description is in compliance with the

one issued by Morgan Stanley Capital International in 2014. Please observe that the

Financials Sector (I40) is excluded, therefore only 9 out of the 10 GICS sectors are studied.

The Energy Industry Sector (I10)

Companies operating within the energy sector primarily offer and/or produce products and

services related to coal, gas, oil exploration and consumable fuels.

The Materials Industry Sector (I15)

Companies classified under the materials sector are engaged in chemicals and construction

materials such as sand, wood, paper and metals.

The Industrials Industry Sector (I20)

Companies operating in the industrials sector manufacture or/and sell products and/or services

related to e.g. aerospace and aviation, construction, machines, trucks, ships.

The Consumer Discretionary Industry Sector (I25)

Companies providing products and services to the consumer market related to media,

marketing, cars, kitchen appliances, furniture, clothes etcetera.

The Consumer Staples Industry Sector (I30)

Companies classified as Consumer Staples operates as producers, distributors or retailers for

consumer beauty supplies, food, beverages and other everyday commodities.

The Health Care Industry Sector (I35)

Companies that operate in the health care industry manufactures health care equipment,

provide health care services or research, produce or/and sell products and services related to

biotechnology and pharmaceuticals.

The Information Technology Industry Sector (I45)

Information technology companies produce, sell or distribute products and services related to

e.g. computers, software, and hardware.

The Telecommunication Services Industry Sector (I50)

Companies in the telecommunication services industry sector are telephone network operators

and network operators.

The Utilities Industry Sector (I55)

Companies in business producing, selling or distributing electricity, gas, water utilities and

other power producers.

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APPENDIX 2 Sensitivity Analysis

Winsorization

Table A.1.1 Descriptive analysis of variables before a 98% winsorization


BTM 1948 0,574319 0,9359917 0,0000 24,1077

DE 1948 2,330869 13,1566092 0,0227 469,4708

PROF 1948 0,050086 0,3118785 -

11,2536

0,7751

SIZE 1948 3,034255 0,8222225 -0,5528 5,3526

Variable definition: see Table 4.3.1

Table A.1.2 Descriptive analysis of variables after a 98% winsorization


BTM 1948 0,527525 0,3726690 0,0552 1,8207

DE 1948 1,652926 2,2009339 0,1267 12,5880

PROF 1948 0,063379 0,0966524 -0,2977 0,2370

SIZE 1948 3,036518 0,7879004 1,2557 4,7354

Variable definition: see Table 4.3.1

Treatment of observations equaling 1 and 0 for Y

Table A.2.3 MODEL 1 Regression Result with 1 and 0 included

Variable B

(Coefficient)

Coefficient

Sig

Standard

Error

BTM -0,002 0,863 0,014

DE 0,001 0,819 0,002

Profitability -0,163

** 0,006 0,059

Size -0,036

**** 0,000 0,007


0,000 0,034



0,000 0,027


0,000 0,029


0,003 0,028


0,000 0,027


(I50) -0,115

** 0,020 0,050

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Utilities Industry (I55) -0,138**

0,020 0,059

Constant 0,695

**** 0,000 0,034

*p < 0.05, **p < 0.025, *** p < 0.005, **** p < 0.0005


N=1948

Table A.2.4 MODEL 1 Regression Result with 1 and 0 excluded

Variable B

(Coefficient)

Coefficient

Sig

Standard

Error

BTM -0,010 0,473 0,014

DE 0,000 0,885 0,002

Profitability -0,133

** 0,017 0,055

Size -0,034

**** 0,000 0,007

Energy Industry (I10) -0,108***

0,001 0,033



0,000 0,026


0,012 0,027



0,000 0,025


(I50) -0,073 0,110 0,046


Constant 0,653

**** 0,000 0,032

*p < 0.05, **p < 0.025, *** p < 0.005, **** p < 0.0005


N=1868

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