Value exploration and materialization in diversification strategies

ORI GINAL RESEARCH

Value exploration and materialization in diversificationstrategies

Mark E. Holder • Aiwu Zhao

� Springer Science+Business Media New York 2014

Abstract The commonly accepted explanation in early studies to diversification discount

is that diversification destroys value because of operational inefficiency. Such argument

neglects the value of growth potentials incorporated in the value measures such as market-

to-book ratio. Our study indicates that diversification activities are strategic decisions that

will change the real options of a firm and will create value impacts that are different from

those caused by changes in operational efficiency. We find that diversification activities,

especially unrelated diversification activities, carried out by below average performers tend

to increase firm value, in terms of market-to-book ratio, as a result of exploring for new

growth opportunities. Whereas diversification activities carried out by above average

performers tend to decrease firm value as a result of materializing excess capability. The

result indicates that value changes around diversification are different for different firms. In

addition to changes in operational efficiency, changes in growth potentials play a role in

explaining diversification discount phenomenon as well.

Keywords Diversification � Resource-based view � Real options � Firm

value

JEL Classification G12 � G34

1 Introduction

Empirical studies have shown that diversified firms trade at a discount compared to stand-

alone firms (Lang and Stulz 1994; Berger and Ofek 1995). Early studies usually explain the

diversification discount phenomenon with the conclusion that diversification destroys value

M. E. HolderInstitute for Financial Markets, Washington, DC 20006, USA

A. Zhao (&)Department of Management and Business, Skidmore College, Saratoga Springs, NY 12866, USAe-mail: [email protected]

123

Rev Quant Finan AccDOI 10.1007/s11156-014-0434-8

because diversified firms tend to show inefficiency in asset allocation, in management

capability, or in other measures of operational efficiency. However, such an argument does

not explain why firms diversify if diversification is ex ante inefficient. Some studies apply

agency cost theory and suggest that private benefits to managers, such as empire building,

benefits extracting, and position securing, are the driving motive for diversification

(Baumol 1967; Shleifer and Vishny 1989). But empirical research (Yang 2006) does not

find evidence that firms with weaker corporate governance are more likely to diversify than

firms with stronger governance.

Recent studies try to find an answer to the puzzle and have shown that diversification

discount does not necessarily reflect value-reducing effects related to declining operational

efficiency. Gomes and Livdan (2004) argue that diversification is a rational choice for firms

with declining or no growth in existing business lines to explore new opportunities. They

suggest that firms with lower values are more likely to diversify, so a cross-sectional

discount related to diversified firms is not sufficient to show whether diversification

activities would increase or decrease firm value, because the lower value might be mostly

driven by pre-diversification characteristics of the firms.

However, the studies that explain the diversification discount by justifying diversifi-

cation behavior neglect the value of future potentials incorporated in the value measures

and cannot be used to explain the value impact of diversification for stronger performers.

The commonly used measures in cross-sectional studies on diversification effects are

market multipliers such as market-to-book (M/B) ratio. One important piece of information

reflected in market-to-book (M/B) ratio is the value of real options. Based on Myers

(1977), market value has two components: growth options and present value of assets-in-

place. Investors evaluate the market value of a firm by discounting all future possible cash

flows, so the growth potentials of a firm are incorporated in its market value even though

these potentials have not been materialized as real assets investments (De Andres-Alonso

et al. 2005).

Multiple things can affect value measures such as market-to-book (M/B) ratio. As a

result, even though decreased operational efficiency may lead to lower value measures, the

converse is not necessarily true. A firm that operates its existing assets efficiently with a

high return-on-assets ratio can still have relatively low market-to-book (M/B) ratio because

of the lack of new growth opportunities.

Diversification activities, which are associated with real asset investments, are strategic

decisions that can change the growth potential of a firm and will create value impacts that

are different from those caused by changes in operational efficiency. For example, when a

firm adds a new line of business that is similar to its existing business, it may extract excess

value from its unique resources because of economies of scale. But heavy investment in

closely related business sectors also makes it more difficult to switch gears if the market of

existing business deteriorates, because the firm now faces more exit costs than before if it

decides to change the direction of its business.

Our study will study the value impacts around diversification and try to differentiate

between the operational efficiency and real options argument. We formalize the two groups

of arguments into their corresponding theoretical paradigms. Different predictions on the

change of value measures are generated from these two groups of arguments. These

contrasting predictions are the basis of the conceptual framework of this study. Since

previous research has indicated the endogeneity of diversification decisions and the

comparability between diversified and focused firms has also been questioned (Campa and

Kedia 2002; Villalonga 2004a), we do not compare the value of diversified firms with that

of corresponding groups of focused firms. Instead, we examine the value impact of

M. E. Holder, A. Zhao

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diversification by only focusing on firms that have diversified into multiple segments from

single segment and by comparing the value differences between before and after

diversification.

The argument that focuses on the value impact of operational efficiency follows the

resource-based view of firm and transaction cost economics (Villalonga and McGahan

2005). The resource-based view argues that the value of excess capabilities in resources

such as superior production and managerial skills, patents, marketing abilities, and con-

sumer goodwill can be enhanced through economies of scale. Under this framework,

unrelated diversification processes will generate more transaction costs than related

diversification processes, because it is usually more difficult to transfer the resources to a

different segment than to a similar segment. Therefore, unrelated diversification will be

less beneficial or even create negative impacts to firm value when compared to related

diversification.

The argument that focuses on the value impact of future potentials follows the real

options theories. The real options approach applies financial options theory to real assets

investments. Financial options give the owner the right, but not the obligation, to buy or

sell a security at a given price. Companies holding real options have the right, but not the

obligation, to exploit different opportunities in the future (Rappaport and Mauboussin

2001). Similar to financial options, the value of real options increases with the uncertainty

level of the investment environment and with the time horizon to delay the investment.

Any exercise of potential opportunities will end the time value of the options. Under the

real options framework, related diversification tends to reduce market-to-book (M/B) ratio,

because a related diversification is a process for a firm to materialize its potential growth

opportunities. In related diversification, the prior portion of the firm’s growth potential

reflected in market value will turn into assets-in-place, leading to a lower market multiplier

ratio (Bernardo and Chowdhry 2002). On the other hand, from the real options approach,

unrelated diversification is expected to have a more positive value impact than related

diversification, a prediction in opposition to that of the operational efficiency argument,

because adding a segment with a business that is different from existing industry may add

growth options to a new area. For firms with closely related segments, the value of growth

options to new areas will be much lower, because it is more costly to enter than to expand.

This paper proceeds as follows. In the next section, we review the literature that links

the diversification discount to operational inefficiency and the studies that question the

inefficiency argument. We also outline the theoretical framework of the real options

argument. In Section 3, we present the methodology applied in our study, with empirical

evidence reported in Section 4. Section 5 concludes the paper.

2 Literature review

2.1 Literature on diversification discount

There is no unified definition of diversification in previous empirical studies. Some studies

examine diversification effects by focusing on merger and acquisition activities (Morck

et al. 1990; Hubbard and Palia 1999; Anderson et al. 2011). There are also studies using the

number of segments reported by the firms to decide companies’ diversification levels

(Lang and Stulz 1994; Berger and Ofek 1995). Studies based on segment data usually do

not take how the new segment is created into consideration. We define that a company has

a related diversification when it reports a new segment or acquires a new business line

Diversification strategies

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whose first two digits of the SIC code are the same as those of its existing segments.

Unrelated diversification means the first two digits of the SIC code of the new segment are

different from those of its existing segments. Firm expansion only refers to increases in

investment within existing segments and no new segment or business line is added.

Much of the research regarding the impact of diversification on firm value focuses on

the benefits of synergy from diversification. The dominant theory on diversification in this

field is the resource-based view (Villalonga and McGahan 2005; Wernerfelt 1984). The

resource-based view argues that the value of excess capabilities in resources such as

superior production and managerial skills, patents, marketing abilities, and consumer

goodwill can be enhanced through economies of scale.

However, empirical evidence from early studies indicates that benefits from diversifi-

cation can be hard to achieve. Diversified firms are usually found to be traded at a discount

compared to stand-alone single segment firms. For example, both Lang and Stulz (1994),

who use Tobin’s q as value measure, and Berger and Ofek (1995), who use market

multipliers such as market-to-book, market-to-sale, and market-to-EBIT as value mea-

sures, find that diversified firms trade at a discount for about 15 % compared to stand-alone

firms.

Based on the above observation, different explanations on the costs of diversification

have been studied. The studies on the negative impacts from diversification usually focus

on cross-subsidization to unprofitable lines and higher management costs due to infor-

mation asymmetry. Berger and Ofek (1995) attribute the value loss to cross-subsidization

from better-performing segments to poor segments. Shin and Stulz (1998) and Scharfstein

and Stein (2000) find similar evidence and argue that managers in poorly performing

segments have strong incentive to maintain their positions and to lobby the top manage-

ment, especially the opportunity costs to these managers of taking away from productive

work to engage in lobbying are lower.

Studies that consider the diversification discount a result of costs outweighing benefits

usually find that diversified firms are less efficient than focused firms and related diver-

sification shows superior performance, in terms of return-on-assets (ROA), over unrelated

diversification (Bettis 1981; Markides and Williamson 1994, 1996), because in a more

diversified environment, the synergy implementing process would become more difficult.

However, the inefficiency argument falls short of explaining why firms still choose to

diversify if diversification is ex ante inefficient. Villalonga (2004b) indicates that from

1990 to 1996, the number of firms that diversified is almost the same as the number of

firms becoming more focused. Some studies apply agency cost theory and suggest that

private benefits to managers, such as empire building, are the driving motive for diversi-

fication (Baumol 1967). Shleifer and Vishny (1989) also indicate that diversification can

help managers extract more compensation as the assets amount under their management

increases. Increase in assets also reduces the probability for the management to be replaced

because it is more difficult to acquire a big firm than a small firm.

However, the entrepreneurship related motivation does not necessarily lead to man-

agement behaviors that will destroy the firm value (Stein 1997; Scharfstein and Stein

2000). In addition, Yang (2006) finds that firms with weaker corporate governance are not

more likely to diversify than firms with stronger governance, and they do not lose more

value around diversification either. Therefore empirical evidence suggests that agency

problem may exist in some diversification activities, but such argument is not sufficient to

explain the overall diversification discount phenomenon.

Besides the difficulty of agency arguments in justifying the motivation for diversifi-

cation, the conventional explanation of the value destroying impact of diversification has


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also been challenged by some recent studies that question the comparability between single

segment firms and segments within a conglomerate. Villalonga (2004a) documents that the

average size of segments in diversified firms is larger than that of focused firms in the same

industry. The conventional calculation of the excess value of diversified firms is the

difference between Tobin’s q or market multipliers such as M/B ratio of a diversified firm

and the weighted average q or M/B ratio of its segments as if the q or M/B ratio of each

segment were the average of the focused firms in its industry. Because it is widely doc-

umented that asset size and value ratios are inversely related, the value ratios assigned to

the segments of a conglomerate would be upwardly biased. As a result the documented

cross-sectional diversification discount is likely exaggerated. Similarly, Campa and Kedia

(2002) observe that diversifying firms have higher value than exiting or disappearing firms

in their industry, but lower value than firms remaining focused in the industry. They

suggest that since exiting firms tend to be low value firms, the industry median market

ratios will be boosted up, creating a cross-sectional value discount for diversifying firms.

Such discount is due to changes in industry composition rather than changes in intrinsic

value.

The embedded shortcoming in the matching method between diversified and focused

firms challenges the view that the discount is caused by inefficient management of

diversified firms. As a result, more recent studies start to provide rationalized explanations

to diversification strategies and suggest that the discount can still be generated as a result of

a rational choice of firms. Gomes and Livdan (2004) suggest in their theoretical models

that diversification discount can be generated even based upon the profit-maximization

diversification model and it is an optimal choice for low or no growth firms to explore new

growth opportunities. The diversification discount reflects that firms that choose to

diversify are usually low or no growth businesses with low M/B ratios. Bernardo and

Chowdhry (2002), on the other hand, also suggest in their theoretical model that diversi-

fication may as well lead to lower M/B ratio for good performers as a result of materi-

alizing growth potentials into real investments. But in He’s (2009) empirical study, after

controlling for endogeneity of diversification decision, the author identifies a diversifica-

tion premium rather than diversification discount. The theoretical arguments and empirical

evidence from this group of studies indicate that in order to justify diversification as a

rational strategy, it is necessary to take companies’ performance prior to diversification

into consideration, since poor and good performers face different opportunities and

potentials.

Our study rationalizes diversification decision as Gomes and Livdan (2004) and Ber-

nardo and Chowdhry (2002) do. We follow a real options approach similar to Bernardo and

Chowdhry (2002). But our argument covers diversification scenarios for both low and high

growth firms. Our empirical tests are not based on the excess value of diversified firms by

matching with focused firms. Instead, we study the value differences before and after

diversification of diversifying firms.

2.2 Literature on real options

Real options theories have important implication on diversification study, because it is a

convention in finance literature to proxy the growth options using M/B ratio, which hap-

pens to be the major value measure for diversification effect as well.

To link the real options idea to firm value, Dixit and Pindyck (1999) indicate that

expandability of operations gives rise to a call option and to invest is to exercise the option.

On the other hand, Abel et al. (1996) illustrate theoretically that a firm making the


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investment partially or totally reversible acquires a put option. Both the call and put

options have holding value or time premium because the uncertainty in the future may

generate an adverse circumstance under which the firm may regret its decision of having

exercised the option early.

Based on the real options theory, De Andres-Alonso et al. (2005) indicate that M/B ratio

can be illustrated as MB¼ AIPþ

PðROÞ

AIP, in which, AIP stands for assets-in-place, usually

measured by the book value of the assets; RO stands for real options, which are the

different valuable alternative strategies available to a firm. Real options theory suggests

that the more valuable choices the firm holds, the higher the M/B ratio will be.

The irreversibility feature of real investments provides new interpretations to some of

the phenomena documented previously as evidence for inefficient allocation of resources.

For example, because of the irreversibility of real investment, firms facing low or no

growth will not exit one market easily, because when the market situation changes and

there comes the chance to gain from the low growth segment, it will be much less costly to

expand an existing segment than to start a new segment. The identified over-investment in

low M/B industries within diversified firms might be a result of irreversibility of real

investment, rather than that of an active investment choice. Studies (Alvarez 1999; Molls

and Schild 2012) have shown theoretically that at the optimal exit threshold, operating

revenues may be well below costs, meaning that production can be optimal even when net

cash flows are negative because the value of future productive potentials may be higher

than current loss.

Following this rationale, a diversified corporate structure reduces the exit cost for low

growth business segments. Diversified firms can voluntarily close low growth business

segments at an optimal time, whereas focused firms would be more likely to be forced to

go through bankruptcy process when they exit the market. The high costs related to

bankruptcy process and the limitation in choosing an optimal time to exit for focused firms

indicates that low growth firms are expected to gain when they choose to diversify into

unrelated businesses.

Though most of the real options discussions so far focus on investing or disinvesting

decisions related to isolated single projects, the value impact of real options can be

extended easily to explain interrelated multiple-project cases such as diversification. The

real options view suggests that firms with good potentials in existing businesses will have

high value in call options because of the expandability. At the same time, these firms have

minimal or no put options because of the high exit costs of disinvesting. Under the

diversification investment scenario, when a firm with great future in existing business

expands into related new business segments, it exercises a call option, and should expect a

decrease in M/B ratio.

Assuming the M/B ratio after diversification is AIPaþCOaþPOa

AIPa, in which CO stands for call

option, PO stands for put option, and the sub-a stands for after diversification. The assets-

in-place after the diversification (AIPa) is usually larger than the assets-in-place before the

diversification because diversification is an activity in need of real asset investments. The

call option (COa) value decreases after the firm exercises the investment. Since the busi-

ness of the old and new segments has a high level of correlation, related diversification

does not provide the firm more flexibility to exit, so the value impact of put options (POa)

is minimal. As a result, under this scenario the overall value, in terms of M/B ratio,

decreases after the diversification.

When a firm with good potential in existing business diversifies into an unrelated

business, it also exercises a call option based on its existing resource, because some of the


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operational capabilities are transferable across different industries. But the creation of

expandability in a new business gives rise to a new call option related to the new segment.

At the same time, the value of put options increases. Since some common resources are

now shared by multiple business lines, the exit cost related to disinvesting one segment

decreases.

The M/B ratio after the diversification for this scenario can be illustrated asAIPaþCOaoþCOanþPOa

AIPa, in which COao stands for the call option related to the old business line

after the diversification; COan stands for the call option related to the new business line

after the diversification; and POa stands for the put option generated after the diversifi-

cation. The call option value related to original resources decrease, while call option value

related to new business lines increases, and the put option value increase as the exit costs to

terminate either business line decreases. The overall change in M/B could be either

positive or negative, but is expected to experience less decrease than under related

diversification scenario.

For a firm with not much potential in existing business, it usually has little competitive

advantage and low or no expandability in current segment, so the rise of expandability in

new business and the increase in put options in either business lines will affect firm value

positively and outweigh the impact of exercising the prior negligible call option. The

overall change in M/B for this case should be positive.

When a firm with not much potential in existing business adds a closely related seg-

ment, it will not gain much from either the creation of call option or put option. The firm is

not able to increase its call option value because the industry of new segment is closely

related to its old business, in which it lacks competitive advantage. The put option value is

also limited because the high correlation between the old and new segments will not

provide much flexibility for the firm to exit either business line. On the other hand, since

the value of real options prior to diversification is minimal, the firm has not much to lose

from the diversification process either. The overall change in M/B ratio for this case is

unclear, but should be less beneficial than under the unrelated diversification scenario.

Though the real options idea is believed to depict a more realistic investment decision

process than the conventional NPV method, the complexity in measuring the real options

value has hindered its application in practice. Most of the real options academic studies are

on the theoretical conjecture level. Our study fills the gap in the literature by providing the

empirical evidence on real options as a source of firm value.

2.3 Previous examination on value changes around diversification

The difficulty of cross-sectional studies on diversification effects in generating a conclusive

result calls for an examination on value changes around diversification, which is the

approach this study follows.

Previous effort in this direction has not been very successful and has generated mixed

results. The evidence based on stock price reactions to diversification is not consistent. For

example, John and Ofek (1995) show that in late 1980s, the market reacts positively when

diversified firms decide to increase focus and sell asset. On the other hand, positive

response to diversification has also been identified. Hubbard and Palia (1999) show that

announcements of diversifying acquisitions during the 1960s and 1970s conglomerate

merger wave give rise to positive returns. For the same period, Matsusaka (1993) also

observes similar phenomena that acquirers in unrelated purchases realize positive


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abnormal returns upon the announcement of a merger, whereas those in related acquisitions

realize negative abnormal returns.

Besides the problem of mixed results, event study using stock price reaction is not an

ideal method to investigate diversification effect, because the market response can be

distorted by the price level paid or negotiated between the parties and does not necessarily

reflect the diversification impact itself. Loughran and Vijh (1997) show that 5-year post-

acquisition stock price performance can be either positive or negative depending on the

type of acquisition and the method of payment applied during the transaction.

Our study will use yearly average measures rather than abnormal return to avoid the

interference in value measurement generated by the acquisition procedure. Yang (2006) is

one of the few studies that have examined the change in average valuation measures such

as M/B ratio prior to and after diversification, but she does not identify any significant

result on the value change around diversification. The reason is that Yang (2006), as well

as previous diversification effect studies, has not considered the fact that there are different

diversification scenarios, and we should expect to see different value changes around

diversification. Our study will follow the theoretical framework in real options literature.

We divide the diversification activities into different categories based on the firm per-

formance prior to diversification and whether the firm adds in related or unrelated business

lines.

3 Date, methodology and research design

The dominant method used in previous diversification studies is to carry out cross-sectional

comparisons based on value measures between diversified and focused firms. The rationale

is that if diversification improves or impairs firm performance, it is expected that diver-

sified firms will be traded at a higher or lower value than a portfolio of corresponding

focused firms (Lang and Stulz 1994). Empirical studies usually draw the conclusion that

diversification reduces firm value because cross-sectional evidence shows that diversified

firms are traded at a discount compared to comparable portfolios of stand-alone firms. Such

evidence, however, is not sufficient to show whether diversification activities increase or

decrease firm value for the following two reasons.

First, the degree of diversification is not the only difference between diversified and

focused firms. It is found that diversified and focused firms face different investment

opportunities (Maksimovic and Phillips 2002). The tests and conclusions based on

matching a diversified firm with a portfolio of focused industry median firms are not

appropriate because these two groups of firms are not comparable.

Second, cross-sectional evidence cannot determine whether the value measure of a

diversified firm reflects the pre- or post-diversification firm features. Especially, it is found

that low growth firms use diversification strategy to pursue new growth opportunities

(Gomes and Livdan 2004). The cross-sectionally lower value identified for diversified

firms may reflect more pre-diversification features of the firms rather than the diversifi-

cation effect itself.

In our examination, we investigate the diversification effect by comparing the value

measures of the firm before and after diversification instead of inappropriately matching a

diversified firm with a portfolio of focused firms. In so doing, our tests are constrained to

reflect the impact of diversification strategy without the interfering of other factors.


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3.1 Hypotheses

Our study use the M/B assets ratio, calculated as the sum of market value of equity and

book value of debt divided by the book value of assets, as the main value measure. Because

it is a convention in finance literature to proxy the growth options using M/B ratio and

many of cross-sectional studies on diversification discount draw the conclusion based on

the M/B ratio, using M/B ratio in our study can make it feasible to compare our research

results with those of previous studies.

The resource-based view and the real options approach have contrasting predictions on

the change of value measures for different types of firms under different diversification

scenarios.

Under the resource-based view framework, when a firm with good potential in existing

business adds related business lines, the diversification activities are expected to affect

M/B ratio positively. When the firm diversifies by adding unrelated business lines, the

diversification will be less beneficial or even create negative impact to firm value. The

wider the diversification level, the more negative the value impact would be, because wider

diversification is expected to have higher level of transferring costs as indicated by the

resource-based view. When companies with less potential in existing business area

diversify into unfamiliar business, it is expected that M/B ratio will go down, because such

firms are unlikely to have excess capabilities or unique resources to carry over to other

areas and the diversification process can generate additional transferring costs. When such

firms add another closely related segment, the value change effect is uncertain because

neither the gain from excess capabilities nor the costs of transferring is expected to be

significant.

Under the real options framework, diversification is expected to have a negative impact

on M/B ratio when a firm with good potential in existing business decides to add related

business lines, because the firm exercises its growth options. When such firm chooses to

diversify into an unrelated business, the overall impact on M/B ratio is hard to predict

under this scenario, even though adding a business segment of a new industry can create a

call option for the new industry and a put option to exit either segment at an optimal time.

The difficulty lies in the limitation of the method used in separating related and unrelated

diversification activities. Empirical studies usually determine whether it is a related or

unrelated diversification by comparing the first two digits of the SIC codes between new

and old segments. An unrelated diversification based on such classification, however, can

still be a decision to materialize excess capabilities, which is an exercise of the call options,

rather than a decision to create new growth opportunities, because some excess capabilities

in resources such as superior managerial skills and marketing abilities can be applied

across different industries.

For companies with less potential in existing areas, unrelated diversification will affect

M/B ratio positively, because there is minimal downward pressure on M/B ratio from

exercising growth options for this group of low growth firms, and the increase of call and

put options will dominate. For these firms, the value impact of related diversification will

be unclear. On one hand, these low growth firms do not have high value growth options to

exercise; on the other hand, the positive value impacts from the newly generated call and

put options related to the new segment will be limited, because the new segment’s business

has a high correlation with the old business.

Based on the real options argument illustrated above, the theoretical framework sug-

gests the following predictions:


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H1 When a firm with good potential in existing business diversifies into related business

lines, M/B ratio is expected to be affected negatively;

H2 When a firm with good potential in existing business diversifies into unrelated

business lines, M/B ratio will be affected either positively or negatively;

H3 When a firm with less potential in existing business diversifies into unrelated business

lines, M/B ratio is expected to be affected positively;

H4 When a firm with lower potential in existing business diversifies into related business

lines, the change in M/B ratio is unclear.

3.2 Define variables

3.2.1 Dependent variable

Since the focus of our study is to look at the value impact related to diversification, the

change of average M/B ratio (DMB) around diversification will be used as the dependent

variable.

Following the real options framework, MB¼ AIPþ

PðROÞ

AIP. Since assets-in-place (AIP)

usually increases when the firm carries out a diversification or expansion investment, the

change in direction of M/B will mainly depend on the change in real options value. So we

use the change in M/B ratio (DM/B) around the diversification to proxy the diversification

effects to test the predictions based on real options arguments. The change in M/B ratio

will be calculated as D MB¼ M

B

� �a� M

B

� �b; in which M

B

� �a

stands for the M/B ratio after

diversification and MB

� �b

stands for the M/B ratio before diversification.

3.2.2 Resource-based view related explanatory variables

Resource-based view theory suggests that M/B ratio will decrease as a result of reduced

efficiency level when management costs exceed the value gain from transferable resources.

This claim reflects three groups of explanatory variables.

The first group includes efficiency measures, which will be proxied by ROA. The

measure we use, change in return-on-assets (DROA), will be the change in the initial

business segment only. It is not unusual for new business lines to have negative profit

margin at the beginning stage and that will affect the firm’s overall profitability level. A

decrease in return-on-assets on the firm level because of adding a new business line does

not lead to the conclusion that there is a decrease in efficiency level. So efficiency

examination will be constrained to the initial business line only.

The second group is cost measurement, including management costs and agency costs.

Transaction cost theories indicate that the higher the diversification level, the more difficult

it is for top management to control the firm because of the information asymmetry between

top management and divisions. Herfindahl index (HER) based on segment assets will be

used to measure the management costs within the company. An increase in diversification

level is expected to have a negative impact on firm value because of higher level of

transaction costs.

Agency costs will be measured by managerial ownership (MOWN) following Scharf-

stein and Stein (2000). Smaller management equity stakes indicate higher levels of agency


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problems between corporate management group and investors and are expected to have

negative impact on M/B ratio.

Finally, the third group, following Morck and Yeung (1991), is the unique resources that

will benefit the firm from economies of scale. They are measured by the sum of R&D and

advertising expenditures scaled by total assets (RDA), and the intangible assets scaled by

total assets (INTA). According to the resource-based view, firms with higher level of

unique resources tend to benefit more from diversification than firms with less unique

resources.

3.2.3 Real options theory related explanatory variables

In order to distinguish the real options argument from the resource-based view argument,

the determinants of real options are also included as explanatory variables.

According to Myers (1977), the difference between the market value and assets-in-place

is the value of growth opportunities, which depend on future discretionary investments.

Within the real options framework, whether the firm has the rights or holds the options to

exercise the investments opportunities establishes the starting point to identify the deter-

minants of real options.

One factor commonly identified that will affect a firm’s ability to make optimal

investment decision is debt. For a firm with long-term debt, part of the value from prof-

itable projects goes to debt holders. So a leveraged firm will only invest in project whose

expected payback is higher than initial investment plus the payment to debt holders. As a

result, firms may forgo positive NPV projects when there is risky debt in the capital

structure. Because the interest conflicts between debt holders and equity holders, debt is

perceived as a discouraging factor for an efficient exercise of real options (Myers 1977).

Since tough and uncertain business environment can help a firm learn more to improve

its investment decision in the future than easy and stabilized environment, an increase in

the volatility of gross profit is considered to have a positive relationship with the values of

real options the firm is holding (PAstor and Pietro 2003). On the other hand, when a firm is

experiencing relatively steady profitability, it is usually considered a result of successive

modification in operation, which is an action of exercising growth options and will lead to

a decreased value in M/B ratio.

Kester (1984) and Kadiyala (2000) indicate that the maturity of growth options, which

has a positive relationship with the value of growth options, depends on two major factors:

the nature of investment opportunities and the competitiveness of the market. For pro-

prietary resources possessed by a firm, such as a patent with no close substitutes, the

project could have a very long maturity date, indicating that intangible assets level, such as

R&D investment, has a positive relationship with the value of growth options. If diver-

sification strategy is an exercise of real options, then the more growth options the firm

possesses, the greater the decrease in M/B is expected after the diversification.

On the other hand, studies (Kester 1984; Kadiyala 2000; Chang and Chen 2012) also

illustrate that in a highly competitive market, even growth options backed by proprietary

resources need to be exercised early to prevent from losing potential market opportu-

nities. This idea is consistent with Kulatilaka and Perotti’s (1998) perception on the

impact of early mover advantages on growth options. Since a more volatile new market

brings more growth opportunities than a less volatile or more mature market, it is

expected to see bigger increase in M/B ratio when the firm diversifies into a new market

with high volatility.


123

The following factors are included as explanatory factors as suggested by the real

options studies: leverage; profitability volatility; R&D and intangible assets level; and

industrial volatility of the new industry the firm diversifies into.

Because debt is perceived as a discouraging factor for the efficient exercise of options

(Myers, 1977), a decrease in debt level is expected to increase M/B ratio. So change in debt

(DLEV) is expected to have a negative relationship with the change in M/B ratio (DMB)

based on the real options approach.

Second, real options arguments also indicate that M/B ratio is positively related to

profitability volatility. Following Makhija (2003), we calculate the profitability volatility

(VROA) as the standard deviation of quarterly ROA of the firm for the corresponding year

before and after the diversification. An increase in profitability volatility suggests an

increasing capability in operation modification, thus leads to an increased M/B ratio.

Third, R& D expenditure level (RDA) and intangible assets level (INTA), both cal-

culated as the percentage of total assets, are expected to be positively related to the amount

of growth options a firm holds (De Andres-Alonso et al. 2005). Since the more growth

options the firm possesses, the bigger the decrease in M/B ratio will be generated from

diversification. We expect to find that high R&D and intangible asset firms will result in a

greater decrease in M/B ratio when they use diversification as a means to materialize

growth opportunities.

Furthermore, the higher the market volatility level, the more the growth options will be

obtained through new market entry. Industrial level volatility of monthly returns (IVOLR)

will be used as the measure for market volatility, which is expected to have a positive

effect on the change in M/B ratio around diversification. The industry volatility measured

here refers to the new industry the firm diversifies or expands into, because the early entry

advantage is related to the new market.

3.2.4 Control variables

Because the market value of a firm will be affected by the overall market and industrial

level factors, overall market trend, proxied by S&P 500 M/B ratio, and industrial trends,

measured by industry median M/B ratios, are included in the regression to eliminate the

impacts from non-firm-specific factors.

Other control variables that affect a firm’s M/B ratio but without any certain predictions

in the direction of change either based on the real options approach or on the resource-

based view include firm size and market share. Firm size (SIZE) is expected to have close

relationship with M/B ratio, but different theories have different predictions. Within the

real options framework, size could have either positive or negative relationship with M/B

ratio. On one hand, increased size can be a result of exercising growth options and should

be related to a lower value in M/B ratio (Bernardo and Chowdhry 2002). On the other

hand, size reflects a firm’s abilities to raise fund and will affect M/B positively (De Andres-

Alonso et al. 2005). In strategic literature, size is found to be able to affect M/B both

negatively and positively as well (Makhija 2003). On one hand, bigger size helps the firm

win more market power. On the other hand, the bigger the size, the more difficult it is to

manage the firm effectively. So firm size (SIZE), which is calculated as the natural log of

assets value, is added in as a control variable without certain prediction on the direction of

its contribution.

The second control variable is market share (MKTSH), calculated as the percentage of

sales of the individual firm’s initial business segment relative to the total sales of all firms

in its industry. Based on strategy literature, market share is usually considered to be


123

positively related to M/B ratio because firms with larger shares of the sales have more

competitive advantage (Makhija 2003). However, firms with larger market share tend to be

bigger in size as well, a fact that may associate with lower firm value. So the overall value

impact is unclear for the change in market shares.

3.3 Empirical methodology

There are two major sets of empirical tests to carry out in this research. First, we use

nonparametric sign-tests and nonparametric Kruskal–Wallis tests to examine the value

differences before and after diversification to test the hypotheses as suggested by the real

options argument. Second, we use regression tests to compare the explanatory power of the

resource-based view and real options approach on diversification effect. There are four

groups of explanatory variables included in the main regression test.

The first group includes changes in overall market level market-to-book ratio

(DMKTMB) and changes in industry level market-to-book ratio (DIMB). They are used to

eliminate impacts from overall market level and industrial trends.

The second group includes variables suggested by the resource-based view, such as

changes in profitability (DROA), changes in diversification level (DHER), management

ownership prior to diversification (MOWN), R&D level prior to diversification (RDA), and

intangible assets level prior to diversification (INTA). Change in profitability (DROA),

which is used to proxy the change of operation efficiency in the initial business segment, is

expected to have a positive relationship with changes in firm’s market-to-book ratio

(DMB) if the resource-based view is valid. Increases in diversification level, which is

related to a decrease in Herfindahl index (HER), reflect increases in transaction costs and

tend to exert negative impact on M/B ratio, so changes in Herfindahl index (DHER) is

expected to have positive relationship with changes in firm’s market-to-book ratio (DMB)

based on the resource-based view. Higher level of management ownership will reduce the

agency costs between management group and stockholders, so diversification activities

carried out by firms with higher level of management ownership (MOWN) prior to

diversification are expected to bring more positive impacts to firm value. Morck and Yeung

(1991) indicate that unique resources such as R&D and advertising expenditures and

intangible assets are the underlying possessions that help a firm realize the benefit of

economies of scale, so we expect to see that higher R&D level (RDA) and intangible assets

level (INTA) prior to diversification is related to a more positive change in M/B through

diversification activities if resource-based argument is valid.

The third group of variables includes changes in debt level (DLEV), changes in prof-

itability volatility (DVROA), industry volatility of the new segment (IVOLR), R&D level

(RDA), and intangible assets level (INTA) prior to diversification as suggested by the real

options approach. Change in debt level (DLEV) is expected to be negatively related to

change in market-to-book ratio (DMB) because higher leverage will lead the firm to make

sub-optimal investment decisions. Changes in profitability volatility (DVROA) is sug-

gested to have positive relationship with changes in market-to-book ratio (DMB), because

an increase in profitability volatility means an increase of the possibility to learn more in

operation capability and a decrease in volatility reflects the result of successive operation

moderation. A high level of industry volatility (IVOLR) in new segment is expected to

increase M/B ratio because a volatile industry means more opportunities for the firm.

Under the real options framework, pre-diversification R&D and intangible assets levels

(RDA and INTA) are expected to have negative relationship with the change in M/B ratio

under the scenario when firms try to materialize excess capacities through diversification,


123

because firms with high R&D and intangible assets level will result in a bigger decrease in

real options’ values when they materialize their potentials. However, things may be dif-

ferent under the scenario when firms try to explore new growth opportunities through

diversification. The usefulness of R&D and intangible assets may not be able to be

reflected in an under-performing firm. As a result, high R&D and intangible assets may

bring up firm values for firms searching for new chances.

The fourth group includes two variables, changes in firm size (DSIZE) and changes in

market share (DMKTSH). They are used to control the value impacts of factors other than

those suggested by resource-based view and real options approach.

The full model including all four groups of variables is as follows:

DMB ¼ b0þ b1DMKTMBþ b2DIMBþ b3DROAþ b4DHERþ b5DMOWN

þ b6DLEV þ b7DVROAþ b8IVOLRþ b9RDAþ b10INTA

þ b11DSIZE þ b12DMKTSH

ð1Þ

The predicted signs of the variables as suggested by different arguments are listed as

follows.

RBV RO

DLJSLB ? ?

DIMB ? ?

DROA ?

DHER ?

MOWN ?

DLEV –

DVROA ?

IVOLR ?

RDA ? ;

INTA ? ;

DSIZE

DMKTSH

3.4 Data

Our empirical investigation is based on the reported segment data. The sample firms are

drawn from COMPUSTAT Industry Segment Database. We follow the data selection

convention in diversification studies, which is the method used in Berger and Ofek (1995),

to clean the data set, so that we will have a dataset consistent with other research. We

exclude firm years that have segments in financial sector (SIC 6000-6999) and have sales

less than $20 million. Any industry, classified by using firms’ first two digits of SIC code,

with less than five single-segment firms is also excluded.

To avoid the distortion that could be caused by the transaction prices and methods

applied during the actual acquisition process, our study uses yearly average measurement

to examine the value impact of diversification. We define the year when the firm reports a


123

new segment as documented in COMPUSTAT as year 0. The value comparison around

diversification will exclude year 0 and be only based on performance between years before

year 0 and years after year 0. This approach will allow us to remove the impacts of

transaction procedure and take all types of diversification into account.

The value change examination ranges from a minimum of 1-year average value measure

to a maximum of 5-year average value measure to provide a short to medium term profile

of the diversification effects. But the most reliable evidence on diversification effect should

be based on shorter-term comparison, because the longer the comparison period, the more

likely that new restructuring will take place and change the firm’s diversification profiles

(Gary 2005).

Since previous studies have documented that the dramatic difference in M/B ratios is

between single segment firms and firms with two segments (Lang and Stulz 1994), the

comparison in our study focuses on the case where a single segment firm diversifies and

becomes a multiple-segment firm.

To avoid the impact of the credit crisis, our study focuses on the period from 1997 to

2006. We identify a total of 11,397 firms with segment data available from COMPUSTAT

from 1997 to 2006 (Table 1). During this period, 3,214 focused firms chose to diversify

and 1,054 diversified firms became focused firms.

Our observation indicates that firms have not tried to avoid diversification activities and

chose to become more focused even though diversification discount is a widely docu-

mented phenomenon. We also observe that most of the diversification activities happened

during year 1998 to year 2000 period (Table 2, Panel A). Among the 3214 ‘‘focused to

diversified’’ events during the 11-year period, 985 of them, or about 30.65 % of the whole

sample, happened in 1998. For segment addition events happened in already diversified

firms, more than 50 % of them took place during the 3-year period from 1998 to 2000

when the market experienced a boom (Table 2, Panel B).

This preliminary observation indicates the possibility that diversification decisions may

be affected by external market conditions and may not have consistent value impacts in

different periods.

4 Empirical results

4.1 Descriptive statistics

Based on the real options argument, firms with different potentials in existing business are

expected to have different value changes around diversification, so we classify the firms

into groups first. The M/B ratio, which is the proxy for growth opportunities in finance

literature, is used to do the classification.

We compare a firm’s M/B ratio 1 year before diversification to its industry average. If a

firm’s M/B ratio in the year before diversification is lower than 95 % of the industry

median, it is considered a firm with less potential in existing business; if its M/B ratio is

higher than 105 % of the industry median, it is considered a firm with good potential in

existing business. In between are average performers. Industry classification is based on the

first two digits of firms’ SIC codes. Previous literature (Whited 2001; Villalonga 2004a)

has indicated that diversified and focused firms are not comparable. Since our firm clas-

sification is based on performance prior to diversification when the firms are still single

segment firms, so we only include single segment firms for each industry when we cal-

culate the industry median M/B ratio.


123

Table 1 Number of observations

Types of firms Number of firms

Focused to diversified 3,214

Diversified to focused 1,054

Always diversified 1,641

Always focused 5,488

Total 11,397

Data are drawn from COMPUSTAT Industry Segment Database for the years 1997–2006. We follow thedata selection convention in diversification studies, which is the method used in Berger and Ofek (1995). Weexclude firm years that have segments in financial sector (SIC 6000–6999) and have sales less than $20million. Any industry, classified by using firms’ first two digits of SIC code, with less than five single-segment firms is also excluded

Table 2 Number of firms that add new segments (1997–2006)

Year Number of observation % of Whole sample

Panel A: Number of focused firms that diversify

1997 91 2.83

1998 985 30.65

1999 570 17.73

2000 383 11.92

2001 338 10.52

2002 242 7.53

2003 178 5.54

2004 219 6.81

2005 194 6.04

2006 14 0.44

Total 3,214 100.00

Panel B: Total number of segment addition events for diversified firms

1997 176 2.68

1998 1,259 19.15

1999 908 13.81

2000 1,561 23.74

2001 693 10.54

2002 531 8.08

2003 463 7.04

2004 492 7.48

2005 442 6.72

2006 50 0.76

Total 6,575 100.00

Data are drawn from COMPUSTAT Industry Segment Database for the years 1997–2006. We follow thedata selection convention in diversification studies, which is the method used in Berger and Ofek (1995). Weexclude firm years that have segments in financial sector (SIC 6000–6999) and have sales less than $20million. Any industry, classified by using firms’ first two digits of SIC code, with less than five single-segment firms is also excluded


123

Tab

le3

Des

crip

tive

stat

isti

csof

focu

sed-t

o-d

iver

sifi

edfi

rms’

mar

ket

-to-b

ook

rati

os

1yea

rbef

ore

div

ersi

fica

tion

Yea

rP

re-d

iver

sifi

cati

on

per

form

ance

Nu

mb

ero

fo

bs.

Med

ian

M/B

Mea

nM

/BS

tan

dar

dd

evia

tio

nC

hi

Sq

.aP

r[

Chi

Sq

.

19

97

Bel

ow

ind

ust

ryav

erag

e2

80

.94

0.9

30

.34

29

.16

0.0

00

Ab

ov

ein

du

stry

aver

age

36

2.1

23

.11

2.5

0

Ind

ust

ryav

erag

e6

0.9

61

.15

0.4

6

19

98

Bel

ow

ind

ust

ryav

erag

e4

31

0.8

90

.97

0.4

44

04

.38

0.0

00

Ab

ov

ein

du

stry

aver

age

33

82

.25

2.6

71

.75

Ind

ust

ryav

erag

e8

71

.13

1.2

80

.46

19

99

Bel

ow

ind

ust

ryav

erag

e2

45

0.9

00

.98

0.4

62

27

.46

0.0

00

Ab

ov

ein

du

stry

aver

age

18

82

.44

3.7

85

.50

Ind

ust

ryav

erag

e3

81

.13

1.3

90

.60

20

00

Bel

ow

ind

ust

ryav

erag

e1

35

0.8

40

.97

0.5

31

50

.24

0.0

00

Ab

ov

ein

du

stry

aver

age

14

03

.07

5.4

97

.66

Ind

ust

ryav

erag

e1

31

.28

1.2

70

.51

20

01

Bel

ow

ind

ust

ryav

erag

e1

29

0.8

71

.06

0.7

21

22

.66

0.0

00

Ab

ov

ein

du

stry

aver

age

11

93

.70

6.2

58

.66

Ind

ust

ryav

erag

e2

91

.58

2.1

91

.30

20

02

Bel

ow

ind

ust

ryav

erag

e1

11

0.6

70

.77

0.4

38

4.3

20

.00

0

Ab

ov

ein

du

stry

aver

age

78

2.5

55

.95

11

.33

Ind

ust

ryav

erag

e1

71

.00

1.6

01

.05

20

03

Bel

ow

ind

ust

ryav

erag

e6

70

.66

0.7

90

.72

78

.83

0.0

00

Ab

ov

ein

du

stry

aver

age

61

2.3

05

.59

8.1

0

Ind

ust

ryav

erag

e1

41

.38

1.5

40

.58

20

04

Bel

ow

ind

ust

ryav

erag

e7

80

.78

0.7

70

.32

86

.44

0.0

03

Ab

ov

ein

du

stry

aver

age

56

2.6

15

.41

11

.90

Ind

ust

ryav

erag

e2

01

.15

1.2

20

.46


123

Tab

le3

con

tin

ued

Yea

rP

re-d

iver

sifi

cati

on

per

form

ance

Nu

mb

ero

fo

bs.

Med

ian

M/B

Mea

nM

/BS

tan

dar

dd

evia

tio

nC

hi

Sq

.aP

r[

Chi

Sq

.

20

05

Bel

ow

ind

ust

ryav

erag

e7

80

.89

1.0

30

.62

76

.15

0.0

00

Ab

ov

ein

du

stry

aver

age

61

2.7

97

.15

13

.14

Ind

ust

ryav

erag

e1

01

.49

1.4

90

.44

Fir

ms

are

clas

sifi

edin

toca

tegori

esbas

edo

nth

eir

M/B

rati

os

1yea

rbef

ore

div

ersi

fica

tion

toit

sin

dust

ryav

erag

e.If

afi

rm’s

M/B

rati

oin

the

yea

rbef

ore

div

ersi

fica

tio

nis

low

erth

an9

5%

of

the

ind

ust

rym

edia

n,

itis

con

sid

ered

ab

elo

win

du

stry

aver

age

firm

;if

its

M/B

rati

ois

hig

her

than

1.0

5%

of

the

ind

ust

rym

edia

n,

itis

con

sid

ered

anab

ov

ein

du

stry

aver

age

firm

.In

bet

wee

nar

ein

du

stry

aver

age

firm

s.In

du

stry

clas

sifi

cati

on

isb

ased

on

the

firs

ttw

od

igit

so

ffi

rms’

SIC

cod

es.O

nly

sing

lese

gm

ent

firm

sar

ein

clu

ded

for

each

indu

stry

wh

enca

lcu

lati

ng

the

indu

stry

med

ian

M/B

rati

o.

Th

ista

ble

isu

sed

tolo

ok

atw

het

her

the

abo

ve

clas

sifi

cati

on

met

ho

dg

ener

ates

two

dis

tin

ctiv

eg

rou

ps

of

firm

sth

atfi

tto

the

nee

do

fth

est

ud

y.

Th

ed

escr

ipti

ve

stat

isti

csfr

om

the

tab

lein

dic

ate

that

the

dif

fere

nce

sin

the

M/B

rati

os

bet

wee

nb

elow

and

abo

ve

indu

stry

aver

age

firm

sar

est

atis

tica

lly

dif

fere

nt,

soth

ecl

assi

fica

tio

nis

val

idfo

rth

ep

urp

ose

of

the

stu

dy

aC

hi

Sq

uar

eis

ob

tain

edfr

om

Kru

skal

–W

alli

sT

est,

wh

ich

isca

rrie

do

ut

bet

wee

nb

elow

and

abo

ve

ind

ust

rym

edia

nfi

rms


123

We first look at whether the above classification method generates two distinctive

groups of firms that fit to the need of our study. The descriptive statistics from Table 3

indicate that the differences in the M/B ratios between below and above industry average

firms are statistically different, so the classification is valid for the purpose of our study.

We then investigate what factors may be relevant to firms’ tendency to diversify. As

indicated in Table 4, during the sample period, the median M/B ratios of diversified firms

are not significantly different from those of the non-diversified firms. Furthermore, in some

specific years, such as 1997, 2000, and 2001, firms that diversified tend to have higher M/B

ratios than non-diversified firms. This evidence does not support Campa and Kedia’s

(2002) argument that diversification discount is generated because diversified firms tend to

be low growth firms prior to diversification.

Next, we look at the descriptive statistics of firm level variables that are suggested by

the resource-based view and the real options approach.

From Table 5, we notice that below industry median firms tend to have lower profit-

ability ratio than above industry median firms prior to diversification. After diversification,

the average profitability increases for below industry average firms, whereas there is no

statistically significant change in profitability for above industry average firms. In addition,

below industry average firms tend to have wider level of diversifications compared to

above industry average firms. The results show that the diversification will not lead to a

decrease in operational efficiency. Instead, for below industry average firms, diversification

tends to increase a firm’s operational efficiency in terms of return-on-assets.

We also find that there is no significant difference in management ownership concen-

tration between these two groups of firms prior to diversification. This agrees with Yang’s

(2006) finding that firms with weaker corporate governance are not more likely to diversify

than firms with stronger governance.

For variables suggested by the real options approach, we find that below industry

median firms tend to carry more debt than above industry median firms prior to diversi-

fication. The debt level tends to increase after diversification for both groups of firms.

Firms with high M/B ratios tend to have higher level of profitability volatility than firms

Table 4 Industry level comparison on differences in M/B ratios between diversified and non-diversifiedfirms

Year No.industry

Diversified firmsmedian M/B

Non-diversified firmsmedian M/B

S-value Prob.

1997 34 1.285 1.116 113.5* 0.051

1998 58 1.089 1.120 -150.0 0.249

1999 53 1.029 1.084 -162.5 0.152

2000 40 1.372 0.947 177.0** 0.015

2001 41 1.144 0.964 143.5* 0.062

2002 44 0.844 0.968 -62.0 0.476

2003 36 1.035 1.066 37.0 0.568

2004 39 0.946 1.041 -76.0 0.295

2005 37 1.228 1.263 37.5 0.579

2006 10 1.518 1.534 10.5 0.322

Overall 392 1.105 1.082 2,625.0 0.243

* Significant at 10 % level

** Significant at 5 % level


123

Ta

ble

5D

escr

ipti

ve

stat

isti

csof

firm

level

var

iable

s

Type

of

firm

sN

o.

obs.

Pre

-div

ersi

fica

tion

level

,m

edia

nC

hi

Sq

Pr[

ChiS

qC

han

ge

around

div

ersi

fica

tion

med

ian

S-v

alue

Pro

b.

Ret

urn

-on-A

sset

(RO

A)

of

init

ial

segm

ent

Bel

ow

ind.

aver

age

717

0.0

54

26.3

9\

0.0

001

0.0

13

18,8

30.5

0.0

01

Above

ind.

aver

age

569

0.0

95

-0.0

04

-480

0.9

03

Aro

und

ind.

aver

age

126

0.0

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123

with low M/B ratios before diversification. The profitability volatility tends to decrease

after diversification for both groups of firms. It also seems that industries with higher level

of volatility of market returns are more likely to be the candidates of new segments for

both groups of firms when they diversify. There is no significant difference in industry

volatility between the groups of industries they choose. Above industry average firms

usually have higher level of R&D expenditures than below industry average firms, whereas

we do not observe significant difference in intangible assets level from the balance sheets

between these two groups.

Finally, above industry median firms are smaller than below industry median firms in

terms of assets value prior to diversification. Both groups of firms tend to see increases in

assets scale after diversification. If only the sales from initial segments are considered,

there is no significant difference between these two groups of firms before diversification in

terms of market shares. After diversification, the market shares of products and services

from primary segment decrease for both groups of firms.

Table 7 Value changes of related versus unrelated diversification activities

Firm type Diversification type Median S_value Prob. Chi Sq Pr [ ChiSq

Panel A: Difference from industry median M/B before diversification

Below industry average Related -0.552 -73,576.5 \.0001 1.788 0.181

Unrelated -0.479 -29,842.5 \.0001

Above industry average Related 0.821 35,438 \.0001 0.567 0.451

Unrelated 0.872 25,840.5 \.0001

Panel B: Value change around diversification

Below industry average Related -0.037 -3,987 0.168 9.423 0.0021

Unrelated 0.074 4,097.5 0.0055

Above industry average Related -0.524 -14,771 \.0001 0.061 0.8045

Unrelated -0.489 -8,969.5 \.0001

Diversification activities are categorized into unrelated and related diversification groups. A diversificationactivity is defined as related diversification when the newly added segment has the same first two digits ofSIC code as those of the old segment and unrelated diversification when the two digits are different

Table 8 Logit model on the possibility of having positive or negative value change in different diversi-fication scenarios

Parameter Estimate Standard Error Chi Square Pr [ ChiSq

Panel A: Analysis of maximum likelihood estimates

Intercept -0.3422 0.0806 18.0262 \.0001

Pre-diversification performance -1.0349 0.1132 83.5039 \.0001

Relatedness 0.3916 0.1098 12.7248 0.0004

Effect Point estimate 95 % Wald confidence limits

Panel B: Odds ratio estimates

Pre-diversification performance 0.355 0.285 0.444

Relatedness 1.479 1.193 1.834


123

Overall, we notice that below industry average firms tend to have bigger size and lower

profit margin. They invest less in R&D, carry more debt, and operate in less volatile

environment. Above industry average firms are smaller in size with higher profit margin.

They invest more in R&D, carry less debt, and operate in relatively more volatile

environment.

4.2 Value changes around diversification

We calculate firms’ average M/B ratios before- and after-diversification for various time

lengths, ranging from 1 year to up to 5 years based on data availability. The longer the

time range, the more likely a firm’s M/B ratio will reflect impacts other than diversifica-

tion. So we will put more weight on the results based on 1-year M/B ratios when we draw

our conclusion.

We first use 1-year average M/B ratios. The value change is calculated as the difference

between the M/B ratio in the first year after diversification and the M/B ratio in the year

before diversification. Industry trend has been taken out by deducting the median industry

M/B ratio change from the firm’s M/B ratio changes.

Overall, the 1-year M/B ratio changes show that below industry average performers tend

to observe significant increase after diversification at a median level of 0.041; while above

industry average firms see a significant decrease in their M/B ratios at a median level of

-0.602 (Table 6). Similar result has been shown in yearly data as well. Out of a total of

8 years, there are 5 years where below industry average firms experience significant

increase in M/B ratio around diversification. There are 2 years where they experience no

significant change in M/B ratio after diversification, and only in 1 year, year 1999, below

industry average firms observe a decrease in M/B ratio. In contrast, above industry average

firms consistently experience decreases in M/B ratio after diversification over the sample

period.

Then we look at the difference between the various longer-term average M/B ratios

after diversification and the 1-year M/B ratio before diversification (Detailed results are

available upon request). We find that above industry average performers consistently show

decreases in value after diversification when using 1- to 5-year average M/B ratios. For

diversification of below industry average firms, the changes are significant when using 1-

and 4-year ratio with an increase at 0.041 and 0.021 respectively. The changes measured

by 2-, 3-, and 5-year ratios are minimal and not significant.

To further our examination to test the hypotheses, we carry out the value comparison

tests by categorizing diversification activities into unrelated and related diversification

groups.

Table 9 Difference in M/B ratio between focused-to-diversified firms and industry average firms

Time periods Firm M/B median Compare to industry median

S-value Prob.

Pre-diversification 1.2545 46,973.5 0.2204

Post-diversification 1.138 -225,437 \.0001

Prior to diversification, there is no significant difference in M/B ratio between focused-to-diversified firmsand industry average firms. After diversification, diversified firms do have lower M/B ratios compared toindustry median


123

Table 10 OLS regressions testing the resource-based approach versus real options approach

Full model Resource-based model Real options model

Panel A: Include both below and above industry median firms

Intercept -0.1488 0.1109 -0.1097

-(0.56) (0.74) -(0.46)

DLJSLB 0.4574 0.4449 0.4488

(0.99) (0.96) (0.97)

DIMB 0.3757*** 0.3877*** 0.3762***

(3.10) (3.18) (3.09)

DROA -0.0360 -0.0920

-(0.44) -(1.14)

DHER -0.2219 -0.2049

-(0.71) -(0.65)

MOWN 0.0137** 0.0143**

(2.47) (2.56)

DLEV -0.0205 0.0062

-(0.04) (0.01)

DVROA 0.0185*** 0.0190***

(3.16) (3.29)

IVOLR 1.2423 1.5297

(1.10) (1.35)

RDA -5.9080*** -5.7472*** -6.0386***

-(8.03) -(8.26) -(8.21)

INTA -0.4277 -0.4252 -0.4173

-(0.89) -(0.88) -(0.88)

DSIZE -0.2860*** -0.3032*** -0.2827***

-(4.57) -(4.87) -(4.53)

DMKTSH 1.5716 1.4723 1.2574

(0.47) (0.44) (0.38)

N 566 566 566

Adjusted Rsq 0.1844 0.1718 0.1787

Panel B: Include only below industry median firms

Intercept 0.1440 0.0775 0.1494

(0.88) (0.77) (1.01)

DLJSLB 0.9246*** 0.9046*** 0.9231***

(3.17) (3.13) (3.19)

DIMB 0.1498* 0.1682** 0.1509*

(1.89) (2.11) (1.92)

DROA 0.0090 -0.0189

(0.21) -(0.46)

DHER -0.0165 0.0099

-(0.08) (0.05)

MOWN -0.0027 -0.0059

-(0.10) -(0.23)


123

Table 10 continued


DLEV 0.4298 0.4277

(1.15) (1.16)

DVROA 0.0088** 0.0086**

(2.54) (2.60)

IVOLR -0.4269 -0.4304

-(0.61) -(0.63)

RDA 1.7294** 1.2285* 1.7021**

(2.52) (1.88) (2.53)

INTA -0.3529 -0.3514 -0.3466

-(1.21) -(1.20) -(1.20)

DSIZE 0.0553 0.0747 0.0569

(0.79) (1.11) (0.82)

DMKTSH 2.0999 2.5107 2.0063

(0.28) (0.34) (0.27)

N 281 281 281

Adjusted Rsq 0.0881 0.070 0.098

Panel C: Include only above industry median firms

Intercept -0.9219 -0.2907 -0.6568

-(1.52) -(0.98) -(1.22)

DLJSLB -0.5680 -0.4295 -0.5494

-(0.58) -(0.43) -(0.56)

DIMB 0.5817** 0.4454* 0.5446**

(2.24) (1.78) (2.12)

DROA 0.1889 -0.0342

(0.61) -(0.11)

DHER -0.8406 -0.7810

-(1.27) -(1.18)

MOWN 0.0137* 0.0146**

(1.87) (2.00)

DLEV -0.4635 -0.3997

-(0.47) -(0.41)

DVROA 0.0388** 0.0364

(2.54) (2.44)

IVOLR 2.7491 2.9849

(1.09) (1.20)

RDA -10.7376*** -10.2782*** -10.8317***

-(8.60) -(8.89) -(8.79)

INTA -0.0517 -0.1757 0.1326

-(0.05) -(0.18) (0.13)

DSIZE -0.2067** -0.2485** -0.1840*

-(2.09) -(2.51) -(1.87)

DMKTSH 2.1921 1.7229 1.1192

(0.48) (0.37) (0.25)


123

We define a diversification activity as related diversification when the newly added

segment has the same first two digits of SIC code as those of the old segment and unrelated

diversification when the two digits are different.

The comparison test results in Table 7 show that the positive value impact from

diversification for below industry average firms is mainly driven by unrelated diversifi-

cation activities. Prior to diversification, there is no significant value difference between

related and unrelated diversified firms for below industry median firms. After diversifi-

cation, unrelated diversification is associated with significant value increase. The median

level increase in M/B ratio is 0.074, higher than the 0.041 level when we have not

separated unrelated diversification from related diversification. The value impact of related

diversification to below industry median firms is not significantly different from zero. This

finding is consistent with our theoretical arguments based on real options theory.

There is no significant difference in value changes between unrelated and related

diversifications for above industry average firms. This is consistent with the argument that

superior operation capabilities are transferable across products and industries. So no matter

it is related or unrelated diversification, when above industry median firms diversify, the

option exercising effects dominate.

We also use logit model to test the possibility of having positive or negative value

change in different diversification scenarios (Table 8).

The variables are defined as follows:

Dependent variable ¼1; if value change is positive;

0; if value change is negative;

(

Independent variable 1 pre - diversification performanceð Þ

¼1; for above industry median firm;

0; for below industry median firm;

(

Table 10 continued


N 233 233 233

Adjusted Rsq 0.3274 0.308 0.319

The dependent variable is the change of M/B ratio (DMB) around diversification, calculated as the dif-ference between the 1-year M/B ratios after diversification and the 1-year M/B ratio before diversification.Changes in overall market level market-to-book ratio (DMKTMB) and changes in industry level market-to-book ratio (DIMB) are used to eliminate impacts from overall market level and industrial trends. Change inprofitability (DROA), changes Herfindahl index (DHER), and the managerial ownership (MOWN) arevariables based on resource-based view. Unique resources of firms are measured by the sum of R&D andadvertising expenditures scaled by total assets (RDA), and the intangible assets scaled by total assets(INTA). Changes in debt level (DLEV), changes in profitability volatility (DVROA), and industry volatilityof the new segment (IVOLR) are explanatory variables based on the real options approach. Changes in firmsize (DSIZE) and changes in market share (DMKTSH) are control variables



*** Significant at 1 % level


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Independent variable 2 relatednessð Þ ¼1; for unrelated diversification;

0; for related diversification;

(

The model has a Likelihood Ratio = 96.47, Pr [ v2 \ 0.0001. The results from logit

model indicate that pre-diversification above industry median firms are more likely to have

a negative value impact from diversification than below industry median firms. Unrelated

diversification is more likely to bring positive impact to firm value than related

diversification.

The results from above examination process indicate that diversification activities have

considerable impacts on firm value. The major reason why there is no conclusive result

generated from previous inter-temporal studies is that firms’ pre-diversification perfor-

mance has not been taken into consideration. Pre-diversification below industry average

firms are expected to have totally different value change from above industry average

firms. In addition, value decreasing is not a common phenomenon associated with all types

of diversification activities. In most cases, firms with less potential in existing business

prior to diversification will see increases in value measure after diversification; while firms

Table 11 Explanatory power of the RBV and RO models

Pooled data Poor performers only Good performers only

F Value Pr [ F F Value Pr [ F F Value Pr [ F

Resource-based view

All five explanatory variables 14.83 \.0001 1.82 0.1091 17.17 \.0001

Excluding RDA and INTA 2.24 0.0829 0.02 0.9959 1.93 0.1258

Real options approach

All five explanatory variables 16.07 \.0001 2.92 0.0139 18.56 \.0001

Excluding RDA and INTA 3.9 0.0089 2.93 0.0343 3.28 0.0219

Table 12 Detecting multicollinearity of independent variables by VIF

VIF Pooled data Below industry average firms Above industry average firms

DLJSLB 1.106 1.136 1.116

DIMB 1.101 1.157 1.247

DROA 1.058 1.116 1.193

DHER 1.055 1.042 1.153

MOWN 1.013 1.011 1.047

DLEV 1.047 1.160 1.041

DVROA 1.064 1.147 1.232

IVOLR 1.155 1.087 1.353

RDA 1.189 1.193 1.332

INTA 1.045 1.050 1.071

DSIZE 1.073 1.178 1.111

DMKTSH 1.037 1.062 1.063

For typical social science research, multicollinearity is considered not a problem if VIF is smaller than 4


123

Table 13 Regression with reduced number of variables

Extended dataset Original dataset


Intercept 0.1244* 0.1350

(1.88) (1.64)

DLJSLB 0.5543 0.4231

(1.50) (0.93)

DIMB 0.4599*** 0.4079***

(5.27) (3.42)

MOWN 0.0081** 0.0145***

(2.06) (2.63)

DVROA 0.0095* 0.0194***

(1.94) (3.41)

RDA -4.4247*** -5.5538***

-(7.50) -(8.16)

DSIZE -0.2683*** -0.2893***

-(5.67) -(4.73)

N 805 566

Adjusted Rsq 0.1427 0.1885


Intercept 0.0380 0.0185

(0.91) (0.34)

DLJSLB 0.8771*** 0.8690***

(3.92) (3.08)

DIMB 0.1434*** 0.1424*

(2.77) (1.84)

MOWN -0.0008 -0.0012

-(0.34) -(0.05)

DVROA 0.0069** 0.0090***

(2.38) (2.75)

RDA 1.3376** 1.7495***

(2.55) (2.74)

DSIZE 0.0949** 0.0885

(2.10) (1.35)

N 401 281



Intercept -0.1112 -0.0869

-(0.77) -(0.50)

DLJSLB -0.2772 -0.4976

-(0.35) -(0.52)

DIMB 0.7810*** 0.6191**

(3.99) (2.51)

MOWN 0.0145** 0.0143**

(2.02) (2.02)


123

with good potential in existing business always observe decreases in value measure after

diversification.

Our results challenge the argument that diversification discount reflects pre-diversifi-

cation rather than post-diversification firm characteristics (Campa and Kedia 2002; Lang

and Stulz 1994). As we observe from Table 3, low M/B ratio firms are not more likely to

diversify than high M/B ratio firms. We compare the numbers of below and above industry

median firms that diversify each year over the 9-year sample period. We have an

F = 1.687 and Prob. = 0.238 for the ANOVA test, indicating there is no significant dif-

ference between the numbers of observations for these two groups of firms. Moreover, the

value decrease related to diversification activities is more driven by the value changes of

firms with good potentials. So Campa and Kedia (2002) might be right to argue that firms

choose to diversify as a means to move away from low growth industries, but this argument

has only provided the reason for some diversification activities and cannot be used to

explain the overall diversification discount phenomenon.

Our results do not reject the existence of diversification discount. Since the median

increase in M/B ratio for below industry average firms is around 0.02; whereas the median

decrease in M/B ratio for above industry average firms is about -0.5. As a result, the

overall median M/B ratio after diversification may be well below market average. Table 9

shows that prior to diversification, there is no significant difference in M/B ratio between

focused-to-diversified firms and industry average firms. However, after diversification,

diversified firms do have lower M/B ratios compared to industry median. So cross-sec-

tionally diversification discount does exist.

Table 13 continued

Extended dataset Original dataset

DVROA 0.0241** 0.0396**

(1.98) (2.77)

RDA -7.9045*** -10.2116***

-(8.03) -(9.27)

DSIZE -0.2552*** -0.1990**

-(3.30) -(2.08)

N 322 233


The dependent variable is the change of M/B ratio (DMB) around diversification, calculated as the dif-ference between the 1-year M/B ratios after diversification and the 1-year M/B ratio before diversification.Changes in overall market level market-to-book ratio (DMKTMB) and changes in industry level market-to-book ratio (DIMB) are used to eliminate impacts from overall market level and industrial trends. Change inprofitability (DROA), changes Herfindahl index (DHER), and the managerial ownership (MOWN) arevariables based on resource-based view. Unique resources of firms are measured by the sum of R&D andadvertising expenditures scaled by total assets (RDA), and the intangible assets scaled by total assets(INTA). Changes in debt level (DLEV), changes in profitability volatility (DVROA), and industry volatilityof the new segment (IVOLR) are explanatory variables based on the real options approach. Changes in firmsize (DSIZE) and changes in market share (DMKTSH) are control variables. In order to increase the samplesize, fewer independent variables are included in the regression. The sample size is increased from 566 to805





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Table 14 OLS regressions testing the resource-based approach versus real options approach: using Tobin’sq as the value measure



Intercept -0.0537 0.0432 -0.0811

-(0.29) (0.41) -(0.49)

DLJSLB -0.0663 -0.0735 -0.0586

-(0.21) -(0.23) -(0.18)

DIMB 0.1592* 0.1640** 0.1603*

(1.92) (1.99) (1.94)

DROA -0.0141 -0.0261

-(0.25) -(0.48)

DHER 0.0792 0.083

(0.37) (0.39)

MOWN 0.0137 0.0093

(0.18) (0.12)

DLEV 0.0481 0.0499

(0.14) (0.14)

DVROA 0.0036 0.0038

(0.90) (0.97)

IVOLR 0.4730 0.4904

(0.61) (0.63)

RDA -1.7187*** -1.6346*** -1.7057***

-(3.40) -(3.45) -(3.39)

INTA 0.0998 0.1069 0.0812

(0.30) (0.33) (0.25)

DSIZE -0.2336*** -0.2371*** -0.2356***

-(5.43) -(5.59) -(5.52)

DMKTSH -0.0237 -0.0439 0.1096

-(0.01) -(0.02) (0.05)

N 566 566 566

Adjusted Rsq 0.0711 0.0740 0.0757


Intercept 0.1627 0.0621 0.1197

(1.47) (0.86) (1.19)

DLJSLB 0.5512*** 0.5073** 0.5677***

(2.79) (2.47) (2.87)

DIMB -0.1064** -0.0830 -0.0943*

-(1.99) -(1.48) -(1.77)

DROA 0.0360 0.0007

(1.27) (0.02)

DHER 0.1685 0.2100

(1.24) (1.47)

MOWN 0.0450 0.0212

(1.21) (0.55)


123

Table 14 continued


DLEV 0.7583*** 0.7697***

(3.01) (3.05)

DVROA 0.0111*** 0.0100***

(4.72) (4.44)

IVOLR -0.6504 -0.6947

-(1.38) -(1.48)

RDA 1.2236*** 0.5971 1.1909***

(2.66) (1.30) (2.62)

INTA 0.1168 0.1295 0.1017

(0.59) (0.62) (0.51)

DSIZE 0.0174 0.0545 0.0238

(0.37) (1.14) (0.50)

DMKTSH -0.7752 0.0181 0.1827

-(0.15) (0.00) (0.04)

N 281 281 281

Adjusted Rsq 0.1207 0.0142 0.1157


Intercept -0.6536 -0.1275 -0.6331

-(1.39) -(0.53) -(1.55)

DLJSLB -1.0113 -0.9727 -1.1028

-(1.36) -(1.31) -(1.49)

DIMB 0.2652 0.3644* 0.2872

(1.36) (1.96) (1.49)

DROA -0.2089 -0.1444

-(0.89) -(0.64)

DHER -0.2433 -0.1189

-(0.48) -(0.24)

MOWN -0.2824 -0.2890

-(0.83) -(0.85)

DLEV -0.4843 -0.4259

-(0.66) -(0.59)

DVROA -0.0131 -0.0108

-(1.13) -(0.97)

IVOLR 2.6134 2.4966

(1.36) (1.31)

RDA -2.9254*** -2.4254*** -3.0597***

-(3.08) -(2.80) -(3.28)

INTA 0.6170 0.5953 0.5895

(0.82) (0.79) (0.80)

DSIZE -0.2620*** -0.2671*** -0.2599***

-(3.48) -(3.59) -(3.51)

DMKTSH 0.5223 -0.0354 0.2343

(0.15) -(0.01) (0.07)


123

Our results are more supportive to the explanation of real options argument, which

argues that when a firm with less potential in existing business diversifies, investors see the

possibility of exploring new growth opportunities; whereas when a firm with good

potential in existing business diversifies, investors are more likely to interpret it as a

decision to materialize the excess capacity and the firm’s M/B ratio tends to decrease as a

result of exercising the growth options. Our test results do not support the predictions

suggested by resource-based view, in which above average firms are expected to see more

positive value contributions from diversification than below average performers. The

empirical results show that the opposite is true that below average firms are expected to see

more positive value contributions from diversification activities.

4.3 Regression tests

In order to investigate the value contributions of the factors as suggested by the real options

and resource-based view arguments, we look at the results generated from regression

analysis.

Since the value changes around diversification are different between below and above

industry average firms as shown in comparison tests, it is likely that diversification

activities are interpreted differently by investors for these two groups of firms. So in the

regression analysis, we first include both below and above industry average firms, and then

run the same regressions again for these two groups separately (Table 10).

When we pool both below and above industry average firms in the sample, our full

model, which includes all the explanatory variables as suggested by the resource-based

view and the real options approach, has an adjusted R-square of 0.1844. When we include

below industry average firms only, the adjusted R-square of the full model drops to 0.0881;

whereas when we include above industry average firms only, the adjusted R-square

increases to 0.3274. Similar evidence is identified for the resource-based view model and

the real options model. For the resource-based view model, when we include both below

and above industry average firms, the regression has an adjusted R-square of 0.1718; when

below average firms are included only, the adjusted R-square drops to 0.07; when above

average performers are included only, the adjusted R-square increases to 0.308. For the real

options model, when both groups are included, the adjusted R-square is 0.1787; when

below average firms are included only, the adjusted R-square drops to 0.098; when above

average firms are included only, the adjusted R-square increases to 0.319.

The above results indicate that diversifications carried out by below or above industry

median firms have different value implications. The differences are driven by three factors,

Table 14 continued


N 233 233 233

Adjusted Rsq 0.0952 0.0942 0.1005

The dependent variable is the change of Tobin’s q around diversification, calculated as the market value ofcommon equity plus total assets minus the book value of common equity, divided by total assets





123

the pre-diversification management ownership level (MOWN), R&D level (RDA), and the

change in firm size (DSIZE).

The pre-diversification management ownership level (MOWN) has significant positive

contribution to firm value when above average firms diversify, but no significant contri-

bution when below average firms diversify. Since diversification activities carried out by

above average firms are considered strategies to materialize growth potentials, based on the

agency cost argument, it is more likely to see that firms with higher level of management

ownership to function more efficiently, because the operation outcome will affect the

management groups’ benefits directly. Whereas diversification activities carried out by

below average firms are mainly driven by the purpose of searching for new growth

opportunities. Whether or not the exploration will be successful is not totally under control

by the management group. The outcome is less certain and hard to tell in short term. So

management ownership becomes a less important factor under this scenario.

R&D level prior to diversification has a very significant negative contribution to above

average firms’ diversification. But when below average firms diversify, it is beneficial to

firms with higher R&D level. The difference in value contribution lies in the difference in

investors’ interpretation on the impact of R&D level for below and above average firms.

For above average firms, which have high pre-diversification M/B ratios, the future benefits

that can be generated by R&D input have already been reflected in the market value. So a

real assets investment through diversification that materializes the future potential will

bring down the M/B ratio. For below average firms, the unsatisfactory performance con-

strains the utility of different resources the firms possess, including R&D input. A search

for new growth opportunities through diversification can increase the chances for the

resources to generate future benefits. So a higher R&D level before diversification will lead

to a bigger increase in firm value for below industry average firms.

The change in firm size does not have significant contribution to change in firm value in

the case of below average firms’ diversification. But it is negatively related to the change in

firm value in the case of above average firms’ diversification, a relationship that is con-

sistent with the widely documented negative relation between size and firm value. We

think the reason still lies in the differences in the goals of diversification between below

and above average performers. When diversification is carried out to transfer excess

capability like the case with above average performers’ diversification, a larger increase in

size means a bigger scale of exercise of growth options. When diversification is carried out

to explore new opportunities, size would be a lesser important factor comparing to the

potentials that can be brought up by new opportunities.

The above results confirm our findings in the previous value comparison section that

below and above average firms’ diversification activities are two different strategies and

should be studied separately. It is not appropriate to discuss an overall value impact of

diversification activities as many previous studies did.

We then use F-statistics to investigate the contribution of each group of variables based

on the real options approach and the resource-based view respectively. This step helps us

examine the explanatory power of the two contrasting arguments (Table 11).

We find that when we drop the three factors suggested by the resource-based view,

which include the change in operational efficiency, the change in diversification level, and

the pre-diversification management ownership level, we have an F = 2.24,

Prob. [ F = 0.0829 for the pooled data, an F = 0.02, Prob. [ F = 0.9959 for the below

average firms’ case, and an F = 1.93, Prob. [ F = 0.1258 for the above average firms’

case. This result indicates that the resource-based view factors have explanatory power to

the changes in firm value around diversification and these factors explain the above


123

average firms’ diversification scenarios better than the below average firms’ diversification

scenarios.

For the real options model, when we drop all five factors, we have an F = 2.92,

Prob. [ F = 0.0139 for below average firms, and an F = 18.56, Prob. [ F \ 0.0001 for

above average firms. When we drop only three factors, but not R&D level and intangible

assets level, we have an F = 2.93, Prob. [ F = 0.0343 for below average firms, and an

F = 3.28, Prob. [ F = 0.0219 for above average performers. So the real options model

has significant explanatory power to the changes in firm value around diversification for

both the below average and above average firms’ diversification scenarios.

We then test whether there is multicollinearity problem among independent variables.

Variance inflation factors (VIFs) of all independent variables are calculated as shown in

Table 12.

For typical social science research, multicollinearity is considered not a problem if VIF

is smaller than 4. Since the VIFs of all the independent variables are smaller than 4, there is

no multicollinearity problem in the regression.

We also carry out robust tests by expanding the sample size for the regression tests. In

our primary regression model, we include a total of 12 independent variables. Because not

every focused-to-diversified firm has data available for all the independent variables, the

sample size for our regression model reduced to 566 firms. In order to increase the sample

size, we run the regression again with fewer independent variables included. There are five

independent variables that have been identified to have significant contribution to value

impact of diversification from previous regressions. They are value changes on industrial

level (DIMB), management ownership (MOWN), changes in profitability volatility

(DVROA), R&D level (RDA), and changes in firm size (DSIZE). When we include these

five variables, in addition to the value changes on the overall market level (DLJSLB)

variable in the regression, the sample size increases to 805. With the extended data set,

each variable shows value impact that is similar to the case when only 566 firms are

included (Table 13).

In addition, we use Tobin’s q, which is the most widely used value variable in diver-

sification studies, to replace M/B and run the regression tests again. We calculate Tobin’s

q following the commonly used approach in the literature, which is dividing the market

value of common equity plus total assets minus the book value of common equity by total

assets. The regression tests based on Tobin’s q show similar results to those based on M/B

ratios (Table 14). When we pool all diversification events together, the adjusted R-square

of the full model is 0.0711. When we only include the below industry average firms, the

adjusted R-square of the resource-based view model drops to 0.0142, whereas the adjusted

R-square of the real options model increases to 0.1157. When we only include the above

industry average firms, the adjusted R-square of the resource-based view model increases

to 0.0942, and the adjusted R-square of the real options model increases to 0.1005 as well.

These results indicate that real options model explains the diversification activities in

below industry average group better.

5 Conclusion

Our study provides an answer to the diversification discount puzzle existed in previous

literature. We categorize firms into below and above industry median groups based on their

pre-diversification performance. We find that diversification activities have different value

implications to below and above average firms. Diversification activities carried out by


123

below average performers are considered an effort to explore new growth opportunities and

firm values tend to increase after diversification. Diversification activities carried out by

above average performers are considered a move to materialize excess capability.

In our examination, we investigate the diversification effect by comparing the value

measures of the firm before and after the diversification and avoid the inappropriate

method of matching a diversified firm with a portfolio of focused firms. We compare the

explanatory power of the resource-based view and the real options approach on the value

impact of diversification. The examination indicates that both resource-based view and real

options approach have explanatory powers to the value change around diversification. In

addition to changes in operational efficiency, changes in growth potentials play a role in

explaining diversification discount phenomenon as well. In general, resource-based view

explains the above average firms’ diversification scenarios better. Real options approach

can explain the value impact of diversification for both the above average and below

average firms’ diversification scenarios. Our study shows that the traditional explanation

attributing the lower value of diversified firms to inefficiencies in asset allocation and

management capability is not well-founded.

We also find that lower than average performance of a firm tends to suppress the

usefulness of resources such as R&D input. Diversification may create new value creating

opportunities for such resources, so high R&D input may generate more future benefits for

previously below average performers.

The results in our study indicate that it is inappropriate to talk about an overall value

impact of all types of diversification activities. Our study does not support the argument

that diversification discount reflects pre-diversification rather than post-diversification

impact (Campa and Kedia 2002; Lang and Stulz 1994) either. However, our results do not

deny the cross-sectional evidence that diversified firms tend to have a lower value com-

pared to focused firms. But such evidence is a collective outcome of the value increase of

below average performers in exploring new opportunities and the value decrease of above

average performers in exercising growth options. It is not an evidence of value destroying

effect of diversification.

The results from our research show that diversification is not ex ante inefficient. No

matter whether it is to materialize the benefits of economies of scale by high M/B ratio

firms, or it is to explore new growth opportunities by low M/B ratio firms, diversification

activities are rational choices for firms to take.

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Value exploration and materialization in diversification strategies