1. The Market’s Reaction to Corporate Diversification:
What Deserves More Punishment
By: Ryan Holcomb and Larisa Zimoglyad
June 29, 2013
Capstone Final

2. Section 1: Introduction
One of the main focuses of modern investment theory has been the relationship between the
shareholders and management of a firm. Many different areas and fields have devoted time and energy
to studying this interesting dichotomy and for good reason. In theory, the main goal of the management
of public corporations is to maximize shareholder value---although the extent to which this duty is
upheld is often debated in court---and thus arises the question of what falls into the realm of fiduciary
responsibility for the firm and what investors can and should implement more efficiently themselves.
One aspect of this relationship was studied by Franco Modigliani and Merton Miller and resulted in the
Modigliani–Miller theory (MM theory). Modigliani and Miller stated that a firm’s capital structure only
added value to the extent that interest payments on debt provided a tax shield to earnings. Individual
investor preferences regarding the capital structure are irrelevant because investors can change the
degree of leverage within their portfolios more efficiently and at a smaller cost than firms can.
Diversification follows along parallel lines. Investors can diversify more cheaply than firms; compare
brokerage fees with investment banker fees. Since investors are not homogenous in their diversification
needs, it is more efficient and practical to leave diversification to shareholders.
Diversification is a main theme in modern investment and theorists and practitioners utilize the concept
of the efficient frontier to help show the benefits of having a well-diversified portfolio of assets. The
underlining theory of diversification and the efficient frontier is that investors are compensated for
enduring risk. However, there is more than one type of risk and the financial theory demonstrates that
investors are only compensated for risks that cannot be diversified away. The broadest categories that
risk is divided into are systematic risk and unsystematic risk. Discussing the latter first, unsystematic risk
represents the risks inherent in owning a single investment. There are many individual events that could
negatively affect a single firm or industry. However, by diversifying across a multitude of industries and
asset classes the theory of diversification states that these individual uncorrelated events will mitigate
each other. For example, if an individual has a large number of uncorrelated assets the theory states
that the probabilities of a negative random event striking an individual asset class or sector and a
positive random event in one of the other investment sectors offset one another and that will stand to
mitigate the risks inherent in single investments. Thus, investors can eliminate unsystematic risk
through diversification and therefore will not be compensated for enduring risks that can be eliminated.

3. In contrast, systematic risk represents the uncertainty involved in providing capital to risky markets
where there is a possibility of loss or reduction of principal. Systematic risk is ubiquitous in the sense
that it can negatively impact all asset classes and investments simultaneously. Thus, there is no
possibility of diversifying systematic risk away and therefore investors require compensation for bearing
the risks of the market. Each asset class represents the opportunity for differing systematic exposure
and thus offers the investor alternative levels of expected return. The aforementioned discussion states
that investors should diversify across different asset classes to reduce unsystematic risk and to gain
exposure to the returns promised by systematic risk.
The main question we wish to ask in this paper is whether the market rewards or punishes firms for
engaging in diversification. If investors can diversify in a more cost effective way, firms that diversify
should trade at a discount compared to firms who don’t diversify. However, managers give many
reasons for why diversification at the firm level makes sense and adds value.
Lang and Stulz (1993) examined how the market values diversified firms and showed that there is a
negative relationship between firm diversification and Tobin’s Q, a measure of the value the market
places on a firm. They used cross-sectional data from 1984 as well as time series data from 1976-1988
obtained from COMPUSTAT. Lang and Stulz concede toward the beginning of their paper that the
conclusions reached by studies of this nature are heavily influenced by the sample period. For this
reason, we will examine whether the same relationships hold using only cross-sectional data from 2009,
also obtained from COMPUSTAT. In addition, we examine the effects on their findings of using different
measures of firm diversification, namely Palepu’s (1985) measure of entropy, as well as the
traditional Herfindahl–Hirschman Index. Both measures will be discussed in greater detail in the
definition section.
In addition, we hypothesize that the relationship between Tobin’s Q and diversification becomes even
more negative if the diversification is unrelated to the firm’s core business. In some sense, we believe
that firms are punished more by the markets for stepping too far out of the bounds of their core
business.

4. The paper proceeds as follows. Section 2 provides definitions of terms used throughout this paper as
well as how those terms are interpreted. Section 3 reviews the literature in this area of study. Section 4
describes our methodology and provides the reasoning behind the variables we used as well as the
expected relationships. This section also describes our data and the sources used to obtain it. Section 5
shows our results and Section 6 analyzes our results to arrive at conclusions. Section 7 discusses
potential future research questions. Section 8 summarizes our conclusions and highlights our
contribution to the current literature.
Section 2: Definitions
Throughout this paper, we will refer to different measures commonly used in the diversification
literature. For the reader unfamiliar with how these measures are calculated and interpreted, we
proceed to define the key measures and provide sources for further information.
Herfindahl-Hirschman Index
The Herfindahl-Hirschman Index (HHI) is most commonly used to measure the concentration of an
industry. This measure is calculated by taking the sum of the squared weights, where the weights
represent the market share of an individual firm. For example, in an industry with one firm, the HHI
would be 10000 (1*(1002)). A more interesting example would be an industry with four firms, each with
25% of the market share. The HHI for this industry would be 2500 (4*(252)). A final example to consider
would be an industry with 4 firms, but one firm controls 70% of the market and the remaining firms
each have 10%. In this case, the HHI would be 5200 (702+3*(102)). In general, the formula for HHI is
∑
where si is the percentage of the ith firm’s market share.
It should be noted that the HHI ranges from approximately zero to 10000, where approximately zero
means a very high level of competition and 10000 indicates that the industry is a monopoly.
Additionally, some people use decimals instead of percentages, which reduces the range from
approximately zero to 1.

5. For our purposes, we looked at each individual firm in our sample as one would look at an industry and
calculated the HHI. We took the weight of each operating segment within the firm, squared it, and
added up the individual segments. An HHI of 1 meant that the firm conducted business within only one
segment, as defined by a specific four digit SIC code. Smaller HHI’s reflected an increasing degree of
diversification. For more information on the Herfindahl-Hirschman Index, see Rhoades (1993).
Tobin’s Q
Tobin’s Q is a slightly more complex version of a market to book ratio. The formula has several
variations. The variation we used was:
(
)
Tobin’s Q conceptually looks at the sum of the market value of equity and the market value of debt over
the replacement value of assets. In our calculation, it is assumed that the book value of debt equals the
market value of debt and that the book value of assets equals the replacement value of assets. This
metric was calculated for all firms in our sample using 2009 data.
A higher value of Tobin’s Q indicates that the market is valuing the company at a greater multiple of
book value. It is also similar to the widely accepted P/E ratio, except that Tobin’s Q is expanded to the
firm level, not just the equity of the firm. We will use Tobin’s Q as a measure of the premium or discount
the market is applying to specific firms. Tobin’s Q has some limitations that are widely addressed in the
literature. A discussion of this follows in the literature review.
Palepu’s Measure of Entropy
Palepu’s entropy measure as aforementioned is a different metric used in testing the relationship
between performance and diversification of firms. The following are four formulas used to calculate the
degree of total diversification, related diversification, and unrelated diversification. One would first use
the RDj formula to find the related diversification as a result of operating in several business segments
within an industry group j. This is then used as an input to find the overall related diversification. It can
be interpreted as simply taking a weighted average of the RDj where the weights are the total sales to
industry j, Pj. The third equation calculates the unrelated diversification as basically a weighted sum of

6. the industries the firm is operating in as measured by industry sales Pj. The final equation can be proved
by algebra, and simply states that total diversification is the sum of related and unrelated diversification.
∑
(
)
∑
∑
( )
Choi and Russell (2005) provide a slightly more expanded explanation of the calculation. Alternatively,
one could reference the original Palepu (1985) article.
Our calculation began with unrelated diversification. The first step was to cross reference the NAICS
codes and the income statement for a particular firm to separate the firms divisions. Using the income
statement makes it necessary to eliminate all categories that do not contribute significantly to the
overall revenue. Since firms are required to only disclose segments that contribute more than ten
percent of revenue, this was the test used to determine significance.
We then can use the
aforementioned formula to calculate the unrelated diversification. For illustrative purposes, consider a
firm that has revenues broken down into two categories of 88.67% and 11.57%, the calculation would
be as follows:
UD= [(.8667 ln (1/.8667)) + (.1157 ln(1/.1157))] = .374
This process can be expanded for as many different categories as necessary. To calculate the related
diversification we need to examine the relatedness between categories by breaking down the categories
into further subsets and using the above formula. For example if the 88.67% category can be broken
down into the following subsets with their respective weights, X1 (51%), X2 (37%), X3 (6%), X4 (4%), and X5
(3%), the calculation would be:
RD= .8667 [(.51ln(1/.51)) + (.37ln(1/.37)) + (.06ln(1/.06)) + (.04ln(1/.04)) + (.03ln(1/.03))] + .1157
[(.78ln(1/.78)) + (.09ln(1/.09)) + (.03ln(1/.03))] = 1.025

7. Once again this can be expanded to incorporate as many variables as necessary. The formula for related
diversification was similarly applied. Finally, total diversification can intuitively be found by summing
both related and unrelated diversification.
Section 3: Literature Review
The question of whether it is profitable for firms to diversify is not a new one and this section focuses on
the previous work of several authors and their contribution to the study of firm diversification.
Specifically, we will be examining Montgomery (1994) in “Corporate Diversification,” Wernerfelt and
Montgomery (1988) in “Tobin’s Q and the importance of Focus in Firm Performance,” Rumelt (1982) in
“Diversification Strategy and Profitability,” and finally Lang and Stulz (1993) in “Tobin’s Q, Corporate
Diversification and Firm Performance.” The following section will discuss each of these papers in turn
and present their results and conclusions. These results will serve as a benchmark against which we
compare our own results in Section 6.
Montgomery starts her paper out by asking the question of: why do firms diversify? As has been stated,
the common belief in modern finance is that diversification is more easily and efficiently implemented
by investors. However, there are still conglomerates and firms still utilize mergers and acquisitions (M &
A) to expand into unrelated industries. So the big question is why do firms diversify? Montgomery,
based on the research of several other authors, suggests that market power, resource-view, and agency
view are the reasons why firms, for better or for worse, still diversify. The following is a discussion of
each rationale, respectively.
Montgomery, building on the work presented by Edwards (1955) in “Conglomerate bigness as a Source
of Market Power,” points out that it was the common belief held by economists that diversified firms
would have an advantage over undiversified firms for three reasons: cross-subsidization—the ability of
firms to engage in “predatory pricing” behavior in certain markets because its other markets can
temporarily subsidize the other segment, mutual forbearance—the reduction in competition between
firms that compete in multiple markets due to “interdependence,” and finally reciprocal buying—where
large firms prevent small firms from competing because of the “interrelationships” between large firms.
These rationales for corporate diversification all fall under the heading of the market-power view of

8. corporate diversification and scholars that believe in this theory argue that there is a positive
relationship between diversification and performance.
The agency view of why firms diversify is based on the disconnect between the owners of a firm and the
managers of the firm; specifically, the managers of a firm have divergent goals and desires from those of
the firms owners and since ownership is spread out amongst many owners, it is hard for owners to
prevent managers from taking less profitable routes. Managers pursue strategies that may not be in the
best interest of owners for three reasons: empire building—this refers to the inherent desire of
managers to control more assets in the attempt to be seen as more powerful or important, managerial
entrenchment—this refers to managers engaging in diversification activities to concentrate on areas the
manager is good at in order to increase his or her importance to the firm and thereby provide job
security, and finally—managers may pursue diversification strategies to reduce the risk of the firm even
though investors can do this far more efficiently. All three of these rationales for why managers may
pursue diversification strategies have one thing in common. They sacrifice the maximization of
shareholder wealth for personal benefits which is the definition of an agency cost. Thus, the agency view
theory would predict a negative relationship between diversification and profitability.
The resource view is based on the work of Penrose (1959) and presented in her work “The Theory of the
Growth of the Firm.” Montgomery explains that the resource view leads a firm to diversify because of
three reasons: the “indivisibility” of resources—pertaining to the need to attain economies of scale for
certain high cost investments, the malleable nature of some resources—which provide opportunities for
penetration into new markets, and finally because in the course of normal operations a firm has the
opportunity to continually develop new “productive services”. Montgomery also points out that there
are different dimensions that describe the productive factors that firms have and this leads to the
conclusion based on a resource view that different firms will have different optimal diversification levels.
The difference in dimensions has to do with the specificity of resources. Specifically, firms that have
resources that are very specific in nature will have limited scope in its ability to transcend its current
market segments. Furthermore, firms that have resources that are more general in nature might be
able to take advantage of other markets to increase efficiency or may find greater profit margin in
another sector. Thus, firms with specific resources may be optimal at lower levels of diversification
whereas firms with more malleable resources may be optimal at higher levels of diversification.

9. The rationales mentioned above provide some explanations for why firms may choose to diversify.
However, these rationales don’t answer the question of whether they should diversify and if it makes
economic sense for companies to diversify. Based on her previous works and the work of other
pertinent authors on the subject matter, Montgomery concludes that the typical firm that chooses
higher levels of diversification will not attain increased wealth for its shareholders. Firms that choose the
diversification path are on average less likely to satisfy owners. Montgomery believes the underpinnings
of this hypothesis can be contributed to the analysis of the three reasons why firms diversify that were
presented above.
Specifically, Montgomery believes that there is evidence for both the resource based view and agency
view. However, she believes there is strong evidence against the market-power view of diversification.
In explaining her opinion on the market-power view of diversification, Montgomery points to previous
work which studies the relationship between concentration and diversification. Based on studies by
Berry (1974) and Caves (1981), the data showed that a positive correlation between diversification and
industry concentration in highly concentrated industries did not exist. It should be noted that increases
in concentration were only observed in new or less concentrated industries. These results lead
Montgomery to conclude that there is little evidence for the market-power view as there is no
relationship between diversification and increased market-share in competitive industries. Based on
Lang, Stulz and Walking’s (1991) and Kaplan and Weisbach’s (1992) works on free cash flow,
Montgomery posits that there is definite evidence for the theory of agency view in relation to
diversification. These studies focused on the excess of free-cash flows, defined by Jensen (1986) as the
free-cash flow above the amount necessary for normal NPV projects. They found that there is a negative
relationship between the amount of excess free-cash flow and the performance of firms.
This
relationship is characterized by the idea that excess amounts of free-cash flow will be invested by
managers into less optimal investments that benefit managers but hurt investors. Montgomery also
states that there is large evidence that firms with more specific resources tend to diversify less than
firms with less specific resources, which is consistent with the resource-based reason for diversifying.
This research led Montgomery to the conclusion that diversification is largely unprofitable, although she
concedes that this research question is far from being conclusively answered.
In a prior paper titled “Tobin’s Q and the importance of Focus in Firm Performance,” Montgomery,
working with Wernerfelt, (1988) seek to explore the profitability of firms by analyzing the relative

10. importance of industry and focus on a firm’s Tobin’s Q. Specifically, they wish to test the theory that
largely diversified firms perform sub-optimally because of the firm’s inability to reproduce competencies
in a large amount of separate markets compared to focused firms who are better able to implement
competitive advantages. The following is a presentation of their methodology and results.
The first distinction Wernerfelt and Montgomery make is the divergence from the traditionally used
accounting measures to determine profitability to the use of Tobin’s Q, which they believe is a more
accurate measure. Specifically, they believe that traditional accounting measures fail to account for firm
differences in systematic risk, temporary disequilibrium effects, tax laws, and accounting conventions.
Tobin’s Q is calculated as a firm’s total capital market value divided by the replacement value of its
assets. Theoretically, the Tobin Q measure corrects for all of the above mentioned biases and thus
represents a more stable measure of firm profitability across multiple firms and industries. Tobin’s Q
represents the dependent variable for the analysis.
To construct the independent variable measuring diversification, Wernerfelt and Montgomery multiply
the firm’s total sales against the industry average capital output ratio and utilize the concentric index to
arrive at the total amount of diversification. The concentric index utilizes the data from sales from
industry and weighs the correlation by using the SIC code reference number (Wernerfelt & Montgomery
P.3). The following are the two equations that Wernerfelt and Montgomery used in their regressions:
∑
∑
∑
∑
∑
∑
In Wernerfelt and Montgomery’s first equation, the betas represent the effect on profitability from
industry, γ is the shared effect, and theta represents the effect from focus or diversification. Equation
two adjusts for problems pertaining to “industry specific” intangible assets and thus includes measures
of marketing, research and development, and the replacement value of intangible assets.
Wernerfelt and Montgomery’s results show that as expected, most of the variation in profitability can
be explained by industry effects. Furthermore, there is evidence that diversification does play a role in
the variability of returns for firms, which leads Wernerfelt and Montgomery to the conclusion that there

11. is some loss involved when firms spread their competencies to thin. They take these results to be an
argument for the revisionist theory that diversification does not enhance returns. Presented below are
FIGURE 1: With Correction for Marketing and R&D
the tables and graphs that Wernerfelt and Montgomery used in their research paper. Figure 1 shows the
R2 and adjusted R2 for the different regressions they ran as well as the p-values for models that
corrected for marketing and research and development investment. Figure 2 shows the same chart but
doesn’t include correction for marketing and research and development investment. These two figures
correspond directly to the equations presented above.

12. Figure 2: Without correction for marketing and R&D
Rumelt (1982) in “Diversification Strategy and Profitability” explores the relationship between
diversification and profitability. Specifically, Rumelt argues that there is a statistically significant
difference in the profitability of diversified firms and that the firms who diversify into related industries
outperform those who diverge significantly from their core competencies. To test his theory, Rumelt
constructs seven tranches of diversification which range from single business units to unrelated
businesses. To construct the tranches, Rumelt calculates several metrics and then measures the firms
against them to determine what level of diversification the firm exhibits. The metrics include a firm
specialization ratio (Rs)—the fraction of revenues from the largest business unit, a firm related-core ratio
(Rc) —the fraction of revenue from its largest group of related businesses, a related ratio (Rr)—the
fraction of revenues from the largest group of semi related businesses, and a vertical ratio (Rv)—the
fraction of revenues from products that share the same raw materials. Once the metrics are calculated

13. Rumelt compares them to established benchmarks to categorize the degree of diversification; the
benchmarks are presented below (Rumelt P.3).
Rumelt’s data sample includes the 500 largest corporations in the US for the years 1949, 1959, 1969,
and 1974. A random sample of the 500 largest firms was taken for each of the aforementioned years.
The diversification levels of the firms were analyzed over the period 1949-1974 and the firms were
placed into one of the seven tranches based on the above mentioned metrics. A table of the percentage
breakdowns by category is displayed below.
Based on the dataset, Rumelt notes that there is a large decline in single business units over the
specified time period. Furthermore, businesses early in the study focused on related diversification,
whereas, later in the time period firms focused more on unrelated diversification.
To measure the profitability of diversification over the time period, Rumelt utilized return on invested
capital which is defined as:
(
)

14. where Y is net income after taxes, I is the interest rate on long-term debt, and K equals the sum of
owner’s equity and long-term debt (Rumelt P.5). Rumelt’s regression function is presented below.
∑
∑
Based on the regression results included below, Rumelt concluded that his study was significant and that
his model was able to show the benefits and consequences of diversification. As can be seen in the
table below, unrelated diversification significantly underperforms that of the related constrained group
which is an indication that related diversification is indeed profitable. Rumelt concludes his paper by
discussing the reasons for the premium returns experienced by the related diversification firms. He
argues that economies of scope—developing a lower cost per unit through expanded sales, idiosyncratic
investment—the specificity of competitive advantages that lead to related diversification, and uncertain
imitability—the creation of barriers to entry through productive processes, are the reasons for higher
returns.
The final paper that we would like to draw attention to is Lang and Stulz’s “Tobin’s Q, Corporate
Diversification and Firm Performance” (1993). The main argument presented by these authors is that
firms turn to diversification when they have exhausted all internal growth opportunities and thus seek
to expand into new markets. The results presented by Lang and Stulz show that there is a negative

15. relationship between diversification and firm performance as measure by Tobin’s Q. Their research
focuses on the approach presented by LeBaron and Speidell (1989) which is called the “chop-shop”
approach. This approach permits comparisons of a diversified firm’s Q measure to that of a stand-alone
firm. Essentially this approach allows the researchers to analyze how the conglomerate firms would
perform had they been operated as stand-alone firms. Lang and Stulz argue that if diversification really
does add value, then this should be reflected in higher Q-values for diversified firms compared to that of
stand-alone segments. The following is a presentation of their results and conclusions regarding the
profitability of diversification.
One important aspect of their analysis is that they looked at not only time-series data but also crosssectional data. They realized that the profitability of diversification can be more accurately measured
during one static period as opposed to over several time periods because the results become heavily
dependent on the time periods analyzed. Furthermore, using a static time period allows the researchers
to avoid the use of a risk adjustment that could have potentially negative effects on the analysis. Like
the previous articles we have discussed in this paper, Lang and Stulz utilize Tobin’s Q because of its
diverse benefits and its apt ability to measure the performance of firms. The researchers utilize a
modification of Tobin Q presented by Lindenberg and Ross (1981) which provides a way to calculate the
replacement costs of assets.
Lang and Stulz’s data is compiled using COMPUTSTAT from the year 1984. The graph below comes from
their paper and displays the correlations between HHI and Tobin’s Q which stands to show the
relationship between diversification and profitability.

16. As can be seen from the above table, there is a significant negative correlation between diversification
and profitability. Below is also a table which displays the means and medians of both highly diversified
and more focused companies. The graph illustrates that the means and medians of specialized firms are
higher than that of diversified firms regardless of which measure is being used. According to the table,
the mean and median for the entire sample is 1.11 and .77 respectively for the year of 1984. In
addition, the table also shows that the mean and median values for specialized firms exceed their
diversified counterparties by 39% and 31% respectively. Lang and Stulz argue that this is further
evidence that diversification does not add value to owners (Lang and Stulz P. 19).
Table three presents regression data from Lang and Stulz where they construct a dummy variable to
take on the property of being more or less diversified in order to measure the marginal benefit of
diversification. This regression provides more evidence for their theory as the regression results are
significant and shows that diversified firms, on average, have a lower Q-rating. Based on these results,
Lang and Stulz argue that diversification does not, on average, lead to an increase in value to the
organization.

17. Section 4: Data and Methodology
Our findings in reviewing the literature indicate that the question of whether diversification adds value
is still largely unanswered. Some find significant discounts for diversified firms, others argue that there
may be good reasons for diversification, and yet others distinguish based on how related the
diversification is to the firm’s core business. For this reason, we collected data and performed our own
analysis. The data for our analysis came from two different sources. We were able to obtain the
breakdown of operating segments from 2009 for 388 firms from COMPUSTAT. From this we were able
to calculate the HHI for each firm. Furthermore, we would like to thank Karin Schnarr for providing
additional data on the 388 companies as well as performing many of the calculations.

18. Our review of the literature suggested that there were benefits to adding additional measures of
diversification. Stan Xiao Li and Royston Greenwood (2004) point out that Palepu’s entropy measure is
the preeminent choice for testing the relationship between diversification and performance. This
measure was not used by Lang and Stulz. We will examine the effects of using these measures while also
using the more common HHI metric.
The following table breaks down our sample of firms by the different diversification metrics and shows
the average and median Tobin’s Q as well as a count of the number of firms that fall under that
category. The first category is pure play firms. The categories progress to higher levels of diversification.
The last category shows those firms that are highly diversified. For the related and unrelated
diversification metrics, the last couple categories are combined to provide a reasonable number of firms
in each category.
Not diversified
Highly diversified
HHI
Average Tobin's Q
Median Tobin's Q
Count
1 1>x>.8
.8>x>.6
.6>x>.4
.4>x>.2
.2>x>0
2.340119 1.752236 1.781966 1.643909 1.703855 1.758242
1.827088 1.486365 1.496348 1.461802 1.510884 1.571115
90
33
47
92
114
12
Total Diversification
Average Tobin's Q
Median Tobin's Q
Count
0 0<x<.5
.5<x<1
1<x<1.5
1.5<x<2
2<x<3
2.034387 2.048399 1.864682 1.714184 1.780025 1.646143
1.578312 1.695231 1.588718 1.489933 1.580462 1.445543
23
74
129
109
43
10
Related Diversification
Average Tobin's Q
Median Tobin's Q
Count
0 0<x<.5
.5<x<1
1<x<1.5
1.5<x<3
1.784187 1.811128 1.914233 1.875241 2.010917
1.555855 1.452866 1.594203 1.516314 1.715888
93
104
120
59
12
Unrelated Diversification
Average Tobin's Q
Median Tobin's Q
Count
0 0<x<.5
.5<x<1
1<x<1.5
1.5<x<2
2.082656 1.802089 1.58639 1.505698 1.742659
1.743211 1.505454 1.365018 1.397169 1.707081
179
73
96
32
8
A close examination of the table reveals some very interesting results. Looking just at the HHI, increased
diversification results in a decreased Tobin’s Q. From this we can conclude that diversifying reduces

19. value. However, looking at the entropy measure of total diversification reveals that a small amount of
diversification actually results in an increased average and median Tobin’s Q, although the results seem
to oscillate. The related diversification metric clearly shows that increasing diversification in related
fields results in an increasing Tobin’s Q. Unrelated diversification also shows a clear discount for
increasing diversification with an anomaly for the highest category of diversification. Our next step was
to examine the correlations between the relevant variables.
The following correlation matrix includes the relevant variables as well as significance levels. Although
some of the correlations are very close to zero, they are still significant, which provides justification for
including them in regressions. Because of the inconclusiveness of some aspects of the above table, we
feel that regression analysis will better synthesize the results. It should be noted that the correlation
between HHI and Tobin’s Q in our data is very similar to the correlation found by Lang and Stulz.
Drelated
Dunrelated
Dtotal
Assets
Sales
Tobin’s
Q
Market
Value
HHI
1.00
Drelated
-0.28*
1.00
Dunrelated
0.64*
0.55*
1.00
Dtotal
0.08***
0.14*
0.18*
1.00
Assets
0.03
0.09***
0.10**
0.64*
1.00
Sales
0.07
-0.23*
-0.13*
-0.15*
-0.12**
1.00
Tobin’s Q
0.11**
0.09***
0.16*
0.64*
0.69*
0.16*
1.00
Market Value
-0.03
-0.57*
-0.48*
-0.13**
-0.09***
0.24*
-0.10**
1.00
HHI
Significant at the: *.01, **.05, ***.1. Otherwise, not significant, except diagonals.
Section 5: Results
To determine the effect of diversification on the market’s valuation of firms as measured by Tobin’s Q,
we ran four different regressions, one for each of the different diversification measures along with
return on assets, market value, and net sales against Tobin’s Q. Market value was included to control for
size differences between firms. Return on assets controls for differences in efficiency. Net sales is a
commonly suggested control to use when using Tobin’s Q. The following table summarizes the betas of
the diversification metric for each regression, along with the R2, the adjusted R2 and the P-value.
We can see that the regression involving HHI and Unrelated Diversification were highly significant with a
P-value of 0.0000. Total Diversification was significant at the 5% confidence level, and related
diversification wasn’t significant when compared to standard measures of significance.

20. Beta
R2
Adjusted R2
P-value
HHI
0.6562
0.3224
0.3153
0.0000
Total Diversification
-0.1860
0.2932
0.2858
0.0110
Related Diversification
0.1138
0.2846
0.2771
0.1770
Unrelated Diversification
-0.4099
0.3197
0.3126
0.0000
Metric
Section 6: Conclusion
The regression results presented in Section 5 are consistent with a lot of the results that we found while
reviewing the literature. When interpreting the regression using HHI, it should be kept in mind that a
decreasing HHI corresponds to an increase in the amount of diversification of the firm. The regression
using HHI shows a positive relationship between HHI and Tobin’s Q, so when HHI decreases, Tobin’s Q
also decreases. Stated in different terms, when the firm’s diversification increases, the market’s
valuation of the firm decreases. This confirms the findings of Montgomery and Wernerfelt and Lang and
Stulz that diversification destroys value.
Using the entropy measure for total diversification confirms that there is a negative relationship
between increased diversification and decreased market valuation. However, the beta for this
regression is not as significant and the beta is much lower in absolute value than the beta in the HHI
regression. This suggests that the discount may not be as bad as show by HHI.
The regression using the related diversification was not statistically significant. However, the results
show that as related diversification increases, Tobin’s Q also increases. If this regression was statistically
significant, it would provide support to Rumelt’s argument that related diversification increases value.
The main contribution of this paper is examining the difference between the discount applied to
diversification as measured by total diversification versus the discount applied to unrelated
diversification. We couldn’t find any studies that others performed to evaluate if engaging in unrelated
diversification is worse than simply engaging in diversification. Using unrelated diversification as the
measure of diversification revealed that for a given increase in diversification, the impact on Tobin’s Q is
a much larger decrease than the decrease from just total diversification. In percentage terms, the beta
associated with unrelated diversification is 54.6% larger than the beta associated with total
diversification.

21. We also conducted a t-test to determine whether the difference between the betas was statistically
significant. Our calculated t-statistic was -2.538 ((-0.4099-(-.186))/.08821). To determine the critical
value of t, we looked at a Student’s T distribution with 383 degrees of freedom. This converges to the
normal distribution and results in a critical value of -1.96. Therefore, we conclude that the discount the
market applies to firms that diversify outside of their line of business is significantly larger than the
average discount applied to firms for engaging in diversification.
Section 7: Recommendations for Future Analysis
As Lang and Stulz noted, the results of this type of analysis are highly dependent on the data and time
period analyzed. For this reason, the exercise of repeating this analysis with more recent data would not
be in vain, even if it just served to confirm the findings of others. Another question that could be
addressed is the flip side of one of our questions, namely does the market reward firms for related
diversification. Although there is a lot more in the literature on this topic, the findings are still not
entirely conclusive.
Section 8: Summary
The main contribution of this paper is to show that there is a significant discount in the market
for engaging in unrelated diversification. We suggest to management of firms to avoid
unrelated diversification, as it does not maximize shareholder value. Related diversification is
acceptable and may result in a higher valuation. However, this result is inconclusive with the
data we used.
1
0.0882 is the standard error of the beta corresponding to unrelated diversification.

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