Managerial Myopia A New Look

Report as inappropriate Your report has been received

Description

Shared by: benbenzhou

Tags

Managerial, Myopia, A, New, Look

Stats

views:: 27
posted:: 7/30/2010
language:: English
pages:: 42

Public Domain

Document Sample

Managerial Myopia and Corporate Investment

Jaideep Chowdhury
Department of Finance
Virginia Tech
Blacksburg, VA
jaideepc@vt.edu

and

Alexei V. Ovtchinnikov
Owen Graduate School of Manangement
Vanderbilt University
Nashville, TN
Alexei.Ovtchinnikov@owen.vanderbilt.edu

November 17, 2009

Abstract
We develop theoretical justification of lower investment cash flow sensitivity and lower
investment Q sensitivity for myopic managers. The age of the CEO is used as the main proxy for
managerial myopia. We provide empirical evidence of managerial myopia by showing that the
investment-Q sensitivity and investment - cash flow sensitivity are lower for myopic managers.
Our results suggest that faced with one unit increase in growth opportunities, there is a 20.833%
(41.66%) drop in the increase in investments when there is one (two) standard deviation increase
in CEO age. We show that faced with one dollar increase in cash flow, there is a 5.319%
(10.638%) drop in the increase in investments when there is one(two) standard deviation increase
in CEO age. This indicates significant deviations from optimal investments as CEO become older.
This investment distortion is more prominent when the corporate governance is weak which calls
for better corporate governance in the firms. 1

1
We would like to thank Dr Dilip Shome , Dr Vijay Singhal , Dr Raman Kumar , Dr Ozzie Ince and all the
seminar participants at Department of Finance, Virginia Tech for their helpful comments.

0
1. Introduction
Myopia is the tendency of managers with short horizon to invest sub optimally, diverting
resources from the long-term value maximizing projects to short-term share price maximizing
projects. Myopic managers inflate current earnings and stock price at the expense of long run
benefits of the firms. Managerial Myopia is a concern for corporations and academics alike.
There is debate in the corporate finance literature as to whether myopic behavior exists.
One camp of academics, like Stein (1988, 1989), Porter (1992), Graham, Harvey and Rajagopal
(2005) argue that managerial myopia is a serious issue which leads to investment distortions.
Another camp of researchers, notably Jensen (1986), have reasoned that if we believe that the
markets are efficient, then the managers cannot systematically fool the market by shifting
resources from long-term value enhancing investments to short-term current earnings and current
stock price boosting investments. Myopia is an important issue because if myopic behavior is
prevalent among the managers, the managers will not invest optimally reducing the long run
value of the firms.
In this paper, we look at a specific aspect of managerial myopia namely investment
distortions. Ideally a myopic manager should overinvest in short-term projects and under invest in
long run projects. Given the difficulty in separating long-term and short-term investments as well
as given the difficulty of measuring optimal level of investment, it is difficult to test
overinvestment in short-term and underinvestment in long-term projects. Furthermore, looking at
the total investments is not a viable way of measuring managerial myopia. This is due to the
invisibility of intangible investments and because one does not know what are the projects the
managers could have but did not invest in. We argue that instead of measuring investments at
level, looking at the change in the investment in the face of incremental growth opportunity and
incremental cash flow can serve as a better method of capturing managerial myopia.
First, we identify a variable which can serve as a good proxy for managerial myopia. We
show that age of the CEO is a good proxy for managerial myopia. The idea is that the older the
CEO is, lesser is the time she has left in office and shorter is her managerial horizon. We
document empirically that the older managers on average spend smaller amount on Research and
Development and Capital Expenditure. The firms with older CEOs have higher earnings per share
and higher retained earnings. One can argue that there is a selection bias and the younger
managers tend to manage the younger firms, for example the start up firms, where Research and
Development and Capital expenditures should be higher. In order to control this selection
problem we adjust for industry. One important advantage of using CEO age as a proxy for the
manager's myopic behavior is that CEO age is exogenous and does not suffer from the problem of

1
endogenity in a regression set up. The second myopia variable we use is stock holding of the
CEO in the company. Because most of the stock a CEO holds is vested, more is the stock holding
of the CEO, less myopic the CEO becomes.
We investigate these following important questions. First question is that does managerial
myopia distort the sensitivity of investment to growth opportunity? More specifically, is it true
that the investment sensitivity to growth opportunity is lower for firms with myopic managers?
The motivation for asking this question is as follows. The market does not have any mechanism
to find out if the levels of investment are optimal for the firms. The optimal level of investment is
known only to the managers. The managers invest in both tangible and intangible assets. A
manager can get away with suboptimal investments by diverting resources from intangible assets
to boost current earnings. Intangible assets are hard to measure and are “invisible” by nature.
Examples of intangible assets include client coverage, employee satisfaction. Reducing
investment in these assets is difficult to distinguish from reduction in operating costs. As it is
difficult to measure the optimal amount of investment and hence underinvestment or
overinvestment, we are going to look at the marginal increase in investment in face of growth
opportunities. If the manager is myopic, she will invest less than a non myopic manager when
faced with the same increase in growth opportunities. Coefficient of Q captures the increase in
investments when faced with one unit increase in growth opportunities. Our results suggest that
when faced with one unit increase in growth opportunities, there is a 20.833% (41.66%) drop in
the increase in investments when there is one (two) standard deviation increase in CEO age. This
indicates significant deviation from optimal investments as CEO becomes older. Firms give up
as much as 20.833% ( 41.66%) in investments as there is one ( two ) standard deviation increase
in CEO age.
The second question we ask is if the investment - cash flow sensitivity is lower for firms
with myopic managers. The intuition behind lower investment cash flow sensitivity is that the
future cash flows from long-term investments are less valuable to myopic managers compared to
non myopic managers. Hence a myopic manager will prefer short-term projects, which provide
short-term cash flows, over long-term investments like human capital investments. If there is one
extra dollar to be invested, the myopic manager will invest a lesser fraction of that one dollar in
capital expenditures compared to a non myopic manager. Therefore, reduction of investment cash
flow sensitivity can serve as an evidence of managerial myopia. The preference of a myopic
manager of short-term projects over long-term projects leads to lesser investment sensitivity to
cash flow as well as lesser investment sensitivity to growth opportunities. Our results indicate that
faced with one dollar increase in cash flow, there is a 5.319% (10.638%) drop in the increase in

2
investments when there is one(two) standard deviation increase in CEO age. This drop in the
increase in investments indicates that the firms deviate from their optimal investments when CEO
becomes older.
Empirically the growth opportunities are measured by Tobin’s Q. There are
measurement problems of Tobin’s Q as Q captures both growth opportunities and
overvaluation/undervaluation. The third question we ask is that after we control of the
misevaluation in Q, is investment Q sensitivity lower for myopic managers? In order to
distinguish between firms’s over valuation and its investment opportunity; we use a technique to
capture the intrinsic value of the firm. Using the analysts' forecasted earnings per share of the
firm, Dong,Hirshleifer and Teoh (2007) developed a measure of the intrinsic value of the firm.
We use their measure to find out the intrinsic value of the firm. The firm's over valuation is
calculated as the ratio of the market value of the firm to the intrinsic value of the firm. After
controlling for misevaluation of firm, we still find that investment Q sensitivity decreases for
myopic managers. We find that even after we control for misevaluation in Q, investment Q
sensitivity is still lower for myopic managers.
The next logical question that follows is why is the manager is able to act myopically? If
a manager behaves myopically, is it due to weaker corporate governance? We provide evidence
that the firms with weak corporate governance have more myopic managers. This can explain
why the myopic managers are allowed to behave myopically. This has serious policy
implications in terms of better corporate governance of the firms.
This paper contributes to three different strands of the literature. First and foremost, it
contributes to the managerial myopia literature by providing both theoretical model and empirical
evidence of managerial myopia. It shows that CEO age can serve as a good proxy for managerial
myopia. It further illustrates theoretically and empirically how firms with myopic managers
should have lower investment sensitivity to cash flow and lower investment sensitivity to Tobin’s
Q.
The second strand of literature where this paper makes contribution is the ever expanding
investment cash flow sensitivity literature. We regress investment on cash flow and Tobin’s Q
(which is believed to capture the investment opportunity). We use CEO age which is by itself an
exogenous variable and interact it with cash flow and Tobin’s Q. We show that the two
interaction terms CEO age with cash flow and CEO age with Tobin’s Q are significantly negative
suggesting that that investment Tobin’s Q sensitivity and investment cash flow sensitivity are
lower for firms with myopic managers.

3
The third strand of literature is that of corporate of governance. We show that the
managers are allowed to act myopically because of weaker corporate governance. Firms with
myopic managers are firms with weaker corporate governance which calls for better governance
mechanisms in these firms.

2. Theoretical model and Hypothesis development.
In this section, we develop a model of managerial myopia based on the theoretical
framework of Q model of investment as summarized by Hubbard (1998). We develop our
hypothesis which we test in section 4 and 5 of the paper.

2.1 A model of Managerial Myopia.
The value of the firm at time period t is given by the present value of all the future profits.
In ideal scenario, the manager chooses investment I t to maximize the value of the firm at time

t.
∞
Vt ( K t ,θt ) = max It E[∑ β s [π ( K t + s ) − C ( I t + s , K t + s , γ t + s ) − rt + s I t + s ] (1)
s =0

where, π is the profit function, C is the cost of adjustment function, K t is capital stock at period

t,
I t is the total investment made in period t. rt is the cost of capital goods which is discussed

below and expressed in equation 5.
We introduce myopia in the model in the form of principle agent problem between the
manager and the share holders. If the utility of the manager is Vt ( K t , θ t ) , the manager will

maximize the value of the firm, given by equation 1 and there is no problem of managerial
myopia. But the problem arises when the utility of the manager depends not only on the long-
term value of the firm Vt ( K t , θ t ) but also on the short-term current profits. The utility of the

manager is composed of two components. The first component of managerial utility is her
current perks which is a fraction “a” of the current earnings of the firm. The second component of
managerial utility is the (1-a) fraction of the value of the company.
Managerial utility is given by
U t = a[π ( K t ) − C ( I t , K t , γ t ) − rt I t ] + (1 − a )Vt ( K t , θ t ) (2)

where “a” reflects the preference of the manager for current profits [π ( K t ) − C ( I t , K t , γ t ) − rt I t ]

over the value of the firm Vt ( K t , θ t ) . Higher is the value of a, the manager acts more myopically

4
and prefers current profits more than the value of the firm. “a” captures the intensity of principle
agent problem and propensity of the managers to act myopically.
Managerial myopia arises because the investors do not know the managerial utility
function U t correctly. More specifically, the outside world does not know the value of “a”. Only

the manager knows the value of “a”. We will argue later that even if the investors know the value
of “a”, the manager can get away with myopic behavior without getting punished by the market.
If a=0, the managerial utility is reduced to Vt ( K t , θ t ) and the manager maximizes the value of the

firm in order to maximize her utility. In this case, we do not have any problem of managerial
myopia. The investment the manager chooses to maximize Vt ( K t , θ t ) is the first best investment

level (say that the investment level is ItF ). The shareholders want the manager to invest ItF in

order to maximize the value of the firm. The problem is the share holders do not know the value
of optimal, first best level of investments. Let I tS be the level of investment which maximizes U t .

Clearly, investment level of I tS is suboptimal from the share holder’s point of view.

The amount of investment the manager chooses I tS is observable to the outside world,

but the first best level of investment ItF is not observable. Further investments in intangibles are

very hard to measure. The manager can divert investments from some hard to measure assets to
short-term projects in order to boost the current earnings of the firm. Some of the examples of
these hard to measure assets are expenditures on client coverage and employee satisfaction. As
the first best level of investment ItF is not known and because of the “invisible” nature of some

of the intangible assets, the manager can get away with sub optimal level of investments. Even if
the market is efficient, the manager can act myopically as argued by Stein (1989). The manager
can take some actions which are not perfectly observable to the market giving her an opportunity
to act myopically. Further, the first best optimal level of investment ItF is only known to the

manager. Hence the share holders expect the managers to act myopically and discount the price
of the shares of the firm accordingly. The manager knows that the share holders expect her to
behave myopically. She has no mechanism at her disposal to convince the share holders that she
will not act myopically. Hence, the manager acts myopically.
The capital accumulation constraint is given by
K t = (1 − δ ) K t −1 + λt I t + (1 − λt −1 I t −1 ) (3)

5
The manager makes an investment I t , but only λt fraction of that investment enter the capital at

time t in order to increase current profits. The rest of the investment gets materialized in the next
period. An example of this type of investment is human capital investment, which does not pay
off immediately. The manager decides on λt , i.e. the manager decides what fraction of the current

investment increases current profits and what fraction of the current investment increases
tomorrow’s profits. This, in turn, means, the manager decides on the nature of investments to be
made.
The firm has some internal wealth from the last period t-1, denoted by Wt −1 . The wealth

from last period Wt −1 is assumed to be exogenous when making the investment decision in

period t. The manager does not reinvest the entire last year’s internal funds Wit . Due to the

principle agent problem between the manager and the share holders, the manager will invest only
(1-a) fraction of the previous period’s funds. The total investment is funded by the internal fund
(1 − a )Wit −1 and borrowing Bt .

I t = (1 − a)Wit −1 + Bt (4)

The outside world does not know the value of “a” and the total amount of investments
I t . Intangible assets are hard to measure and are “invisible” in nature. Examples of intangible
assets are client coverage, employee satisfaction. Reducing investments in these assets is difficult
to distinguish from reduction in operating costs. The amount of investments in tangible assets is
known to the outside world but the amount of investment in intangible assets or “invisible
investments” are not known to the external investors. Hence, the outside world does not know the
level of total investments I t . The level of internal funds Wt −1 and borrowing Bt is known to

the market.
The cost of investing (1-a) fraction of the Wt −1 in the firm is the risk free rate rf . We

introduce imperfection in the capital market by arguing that the cost of external financing is
increasing with the amount borrowed because of the principle agent problem between the external
investors and the manager.
rt = rf , I t ≤ Wit −1
Bt (5)
rt = rf + r ( ), I t > Wit −1
K t −1

The problem of the manager is to maximize her own utility by choosing λt and I t subject

to capital accumulation constraint (3) and borrowing constraint (4).

6
max It ,λt U t = a[π ( Kt ) − C ( I t , Kt , γ t ) − rt I t ] + (1 − a)Vt ( Kt ,θt ) (6)

Solving the maximization problem (see Appendix A1 for details), we get,

I λ d + λγ − r
t 1− a 1 2 − a Wit − 1 t it f
=[ ][ ]q + +
K 1 − δ 2e + α 2e + α K 2e + α
t −1 t −1 (7)
W
= Aq + B it − 1 + C
K
t −1
λ d + λγ − r
1− a 1 2−a t it f
where A = [ ][ ] B= C=
1 − δ 2e + α 2e + α 2e + α
Equation (12a) captures the investment cash flow sensitivity and investment q sensitivity.

Comparative Statistics
d 1− a 1
[ ][ ]<0, (8)
da 1 − δ 2e + α
d
In other words, A<0
da
The coefficient of q falls as myopia parameter a increases. This leads us to our first
hypothesis.

Hypothesis 1: The Investment-Q sensitivity is lower for myopic managers.
Intuition: Tobin’s Q captures growth opportunities of a firm. Suppose there be an unit increase
in growth opportunities for two firms, one with a myopic manager (with high a) and another with
a less myopic manager (with low a). The less myopic manager will tap the increase in growth
opportunity more than the more myopic manager. As a result, the less myopic manager will
invest more than the more myopic manager when there is 1 unit increase in growth opportunities.
The investment q sensitivity of the less myopic manager will be higher compared to the
investment q sensitivity of the more myopic manager.
Using the graphical framework of Hubbard (1998), we illustrate in figure 1 the reduction
of investment Tobin’s Q sensitivity for a myopic manager. In figure 1, the inverse capital demand
function for a non myopic manager is D0 D0 and the inverse capital supply function is S (W0 ) .

D0 m D0 m is the inverse capital demand function for myopic managers. The inverse capital
demand function will be steeper for a myopic manager. If there is one unit decrease in the cost of
capital, the increase in capital demand will be less for myopic managers. A myopic manager

7
discounts the future cash flows at a higher rate compared to a non myopic manager. For example,
let the cash flow be constant C for all the future periods. The cost of capital is r and but the
myopic manager discounts the future cash flows at a rate m.r where m is the myopia parameter. m
is greater than 1 for a myopic manager and is equal to 1 for a non myopic manager. Investment
demand is given by
C C C C
I =C+ + + + .... =
1 + mr (1 + mr ) (1 + mr )
2 3
mr
(9)
dI C
=− 2
dr mr
As m > 1 for a myopic manager, the absolute value of the slope of the capital demand function
will be lower in case of a myopic manager. The inverse capital demand function for a myopic
manager D0 m D0 m will be steeper than the inverse capital demand function for a non myopic

manager D0 D0 .Suppose there is one unit increase in the growth opportunities. For the non

myopic manager, the capital demand function will shift outward from D0 D0 to D1 D1 . This leads

to an increase in equilibrium capital demanded from K0 to K1 . For the myopic manager, the

capital demand function shift from D0 m D0 m to D1m D1m . Equilibrium capital demanded increases

from K0 to K2 . The investment Tobin’s Q sensitivity for a non myopic manager is K0 K1

whereas the investment cash flow sensitivity for a myopic manager is K0 K2 . This illustrates the

reduction of investment Tobin’s Q sensitivity for myopic manager, the reduction given by K1

K2 .
Let us consider the investment cash flow sensitivity. The coefficient of cash flow is
2−a
given by B = .
2e + α
d
B<0 (10)
da
The coefficient of cash flow falls as a increases.

Hypothesis 2: The Investment-Cash flow sensitivity is lower for myopic managers.
W
Intuition: Cash Flow is a proxy for the normalized wealth of the firm it − 1 .Investment cash
K
t
flow sensitivity arises due to capital market imperfections. If there is one extra dollar of cash to

8
be invested, the myopic manager will invest smaller fraction of that one dollar in capital
expenditures compared to a non myopic manager.
Suppose, in figure 2, the wealth of the firm increases from W0 to W1 . Following Hubbard

(1998) the inverse capital supply function shifts outward from S (W0 ) to S (W1 ) as shown in

figure 2. In case of a non myopic manager, the capital demand increases from K0 to K1

(investment cash flow sensitivity) due to increase of firm’s internal funds from W0 to W1 . In case

of a myopic manager, the capital demand increases from K0 to K1m . Hence investment cash flow

sensitivity of a myopic manager is lower in magnitude compared to that of non myopic manager,
the magnitude of reduction being K1m K1

There is a significant literature on if Tobin’s Q, measured by market value of asset to
book value of asset, captures the true growth opportunities. Many papers have argued that
Tobin’s Q proxy both for growth opportunity and misevaluation of the firm. We argue after we
control for firm misevaluation, investment Q sensitivity should still decrease as the myopia
increases. This brings us to the third hypothesis.

Hypothesis 3: After controlling for the firm misevaluation, investment fundamental Q
sensitivity is lower for myopic managers.
Intuition: Myopic managers should invest less compared to a non myopic manager when faced
with a unit increase of growth opportunity. True growth opportunity can only be evaluated after
we control for the misevaluation component of Q.
Now the logical question is why the managers are allowed to act myopically? The answer
to this question is related to governance mechanisms of the firms. We hypothesize that firms with
poor governance have managers who act myopically because the monitoring mechanisms of those
firms are weak. This brings us to our last and fourth hypothesis.

Hypothesis 4: Myopic behavior of managers is more prevalent in firms with weak corporate
governance.
Intuition: Myopic behaviors of managers destroy firm value because the firms do not invest the
optimal amount. Good corporate governance can prevent managers from being detrimental to
firms’ long term value creation. Managers will be able to act myopically only when the corporate
governance mechanisms are weak.

9
3. Data and Methodology
Our sample consists of all US firms listed on NYSE, AMEX or NASDAQ that are
present in CRSP and COMPUSTAT for the period January 1993 to December 2004. Our sample
begins in 1993 because we require data on CEO identity. That data comes from ExecuComp
which commences in 1993. We also require earnings forecast data from I/B/E/S. We exclude all
financial services firms (SIC 6000-6999) and utility firms (SIC 4900-4999). We also exclude
firms with assets less than 10 million dollars and firms with incomplete data required for the
analysis. All variables are defined in the Appendix A2.

4 Results
In this section, we test the four hypothesis proposed in the paper and document our
results. We begin by justifying why CEO age can serve as a good proxy for managerial myopia.
We show that the investment cash flow sensitivity and investment Q sensitivity is reduced as the
CEO becomes older. The results hold good even after we control for misevaluation in Q. Finally,
we provide empirical evidence that this lowering of investment Q sensitivity and investment cash
flow sensitivity is enhanced when corporate governance is weak.

4.1 Myopia Proxy
In table 1, we analyze whether CEO age can be considered a reasonable proxy for
managerial myopia. In table 1, we divide the firms into different groups based on CEO age. We
calculate the mean Research and Development expenditures and mean Capital Expenditure of the
different groups of firms. If a manager is more myopic, she will reduce investments in those
categories of items which do not generate revenue immediately. Research and Development
expenses decreases as the CEO age increases. For example, the ratio of Research and
Development to total assets is 0.084 for the firms whose CEO age is below 40, but this ratio is
0.061 for CEO age group of 41 to 50, 0.046 for CEO age group of 51 to 60, 0.037 for CEO age
group of 61 to 65 and so on. So we can see that the ratio of Research and Development to total
assets is systematically decreasing with managerial age. Further, we note that the correlation
between managerial age and the ratio of Research and Development to total assets is -0.208 and
statistically significant. 65 is the usual retirement age of the CEOs in US. As a result, we look at
the CEOs who are close to retirement, i.e., the CEOs who are between 61 and 65. Further we look
at the CEOs who are above 65. Correlation 1 is the correlation between the CEO age and
operating performance variables. We report correlation coefficient 1 without eliminating any firm
whose CEO is above age 65. Correlation 2 is the correlation between the CEO age and the

10
operating performance when the CEO age is censored at 65. We censor the CEO age at 65
because 65 is the average retirement age of the CEOs in USA. We find that the correlation
coefficient between capital expenditure and CEO age is also negative, -0.119 and statistically
significant. Negative and significant correlation between Research and Development and CEO
age and between Capital Expenditure and CEO age provides justification to use CEO age as a
proxy for managerial myopia. Additional advantage of CEO age is that age is exogenous to
investment, cash flow and Tobin’s Q.
If older CEOs act myopically, they inflate the operating performance of the firm. We
document that there is a positive and statistically significant correlation between Earnings per
share and CEO age and between Retained Earnings and CEO age which further reinstates our
belief in CEO age being a good proxy for managerial myopia.
We test for the difference in these above variables between the firms with CEO above
age of 65 and firms with CEO below age 40. We perform a t test and report the t statistics value
for the test. The t stat 1 is testing the difference between the firm level variables between the
firms with old CEO, characterized by CEO above age of 65 and the firms with young CEO, age
less than 40. The younger CEOs spend significantly more on Research and Development and
Capital Expenditure compared to older CEOs. Further t stat 2 is testing the difference between the
means of these variables of the firms with old CEO, characterized by age between 61 and 65, and
firms with young CEO, where age is below 40. We find that the CEOs who are under 40 spend
significantly more on Research and Development and Capital Expenditure compared to CEOs
between the age of 61 and 65.
It may be possible that systematically the younger managers are CEOs of firms which are
in those industries which require more Research and Development and Capital Expenditures. In
order to control for this selection problem we adjust for industry. All the firm level variables’
industry mean are calculated with industry being defined by the three digits SIC code. The
industry mean are subtracted from the firm level variables to get industry adjusted firm level
variables. After adjusting for industry, we still find that older managers spend less on Research
and Development and less on Capital Expenditure. Firms managed by older managers have
higher retained earnings and higher earnings per share, even after controlling for industry. Hence
we can infer that after controlling for any possible selection bias, the younger managers still
spend more on Research and Development and Capital Expenditure compared to old managers.
We conclude from table 1 that CEO age is a good proxy for managerial myopia. For the rest of
the empirical tests, manager’s age serve as the main proxy for managerial myopia.

11
The second myopia variable is negative of the percentage of shares owned by the CEO.
Most of the stock a CEO holds are vested and hence, more is the stock holding of the CEO, she
will be less myopic in her behavior. If the manager owns a lot of shares of the company and most
of her shares are vested, her interests are more aligned with the long run share holders. She is less
likely to behave myopically and less likely to under invest in long-term capital investments like
Research and Development.
The managers invest in both tangible and intangible assets. We have argued that the
outside investors know the levels of tangible investments but do not know the level of intangible
investments because of the “invisibility” of intangible assets. Some of the intangible assets are
hard to measure. A manager can get away with suboptimal investments by diverting resources
from intangible assets to boost current earnings. Examples of intangible assets are client coverage,
employee satisfaction. Reducing investment in these assets is difficult to distinguish from
reduction in operating costs. For the investment cash flow Tobin’s regression, we use the data
definition from Kaplan and Zingales 1997. Investment is COMPUSTAT 128, which is capital
expenditure. Clearly, we have left out the hard to measure “invisible” investments from our
definition of investments because we do not know their values. Myopic managers tend to divert
resources from these hard to measure assets to boost current earnings. Hence, by leaving out these
intangible assets from the measure of investments, our estimates of the coefficient of the
interaction term of myopia and Tobin’s Q and coefficient of the interaction term of myopia and
cash flow are conservative in nature. If we could have included the “invisible” investments in our
measure of investments, these coefficients of interaction terms would have been much stronger.
There is a considerable literature on what are the variables which influence investments. The
controls we use, following Hovakimian (2009), are sales growth, firm size, firm age, leverage,
and asset tangibility, dummy for bond rating, dividend payout and financial slack.

4.2 Tests of Hypothesis 1 and 2:
We employ standard investment regression where a firm investment is regressed on
Tobin’s Q, cash flow and other control variables. To test hypothesis 1 and 2, we interact Tobin’s
Q and Cash Flow variables with a proxy for managerial myopia. We expect these two interaction
terms to be negative. We therefore run the following regression.

I CFit
it = α + β1q + β 2 + f (controls) + ϑit Myopia * q + ρit Myopia * CF + ε it
K Kit −1
it − 1 (11)

12
The first column in table 2 is the standard investment regression model used in the literature. In
the second column, we include interaction terms of myopia and Tobin’s Q and myopia and cash
flow. CEO age is our primary measure of myopia. If hypothesis 1 is correct, we will expect the
coefficient of interaction of CEO age and Tobin’s Q to be negative and statistically significant,
which is what we observe. Coefficient on Tobin’s Q CEO age interaction term is -0.001 and is
statistically significant with a p value of 0. This provides evidence in support of hypothesis 1. If
hypothesis 2 is correct, the coefficient of interaction term of CEO age and Cash Flow will be
negative and statistically significant, which is what we find in column 2. Coefficient on CEO age
and Cash Flow interaction is -0.001 and is statistically significant with a p value of 0 providing
evidence in support of hypothesis 2.
The standard deviation of CEO age is 7.5. If we change CEO age by one standard
deviation, the Tobin’s Q x CEO age coefficient becomes -0.0075 and if we increase CEO age by
two standard deviations, the Tobin’s Q x CEO age coefficient becomes -0.015. The coefficient of
Tobin’s Q in column 1 of table 2 is 0.036. So if there is one standard deviation change in CEO
age, the coefficient of Tobin’s Q decreases by 20.833% (-0.0075/ 0.036). If the CEO age changes
by two standard deviations, the coefficient of Tobin’s Q decreases by 41.66 % (-0.015/ 0.036).
Coefficient of Q captures the increase in investments when faced with one unit increase in growth
opportunities. Our results suggest that when faced with one unit increase in growth opportunities,
there is a 20.833% (41.66%) drop in the increase in investments when there is one(two) standard
deviation increase in CEO age. This result clearly has huge economic impact as firms deviate
from optimal investments as CEO becomes older. Firms give up as much as 20.833% ( 41.66%)
in investments as there is one ( two ) standard deviation increase in CEO age.
If we change CEO age by one standard deviation, the Cash Flow x CEO age coefficient
becomes -0.0075 and if we increase CEO age by two standard deviations, the Cash Flow x CEO
age coefficient becomes -0.015. The coefficient of Cash Flow in column 1 of table 2 is 0.141. So
if there is one standard deviation change in CEO age, the coefficient of Cash Flow decreases by
5.319% (-0.0075/ 0.141). If the CEO age changes by two standard deviations, the coefficient of
Cash Flow decreases by 10.638% (-0.015/ 0.141). Our results indicate that faced with one dollar
increase in cash flow, there is a 5.319% (10.638%) drop in the increase in investments when there
is one(two) standard deviation increase in CEO age. This drop in the increase in investments
indicates that the firms deviate from their optimal investments when CEO becomes older.
The second measure of myopia is the negative of the percentage of shares owned by the
CEO. In column 3 of table 2, we find that the coefficient of the interaction term of NegOwnership
and Tobin’s Q is negative and significant -0.001 (p value 0) providing evidence for Hypothesis 1.

13
Using the same logic as above, we argue that this indicates significant deviation from optimal
investments, which is economically significant.
Investment Cash flow sensitivity can be regarded as a symptom of underinvestment
caused by inflated external cost. Capital market imperfections result in higher cost of borrowing
leading to lower amount of investment by the firm. The firm will invest less than what it would
have invested if there were no capital market imperfections. Another view is that instead of
external funds being too much expensive, the internal fund is too cheap leading the manager to
over invest (Jensen 1986). If the managerial stock holding increases, her interests become more
aligned with the share holder’s interest. The manager has less incentive to waste cash leading to a
lowering of investment cash flow sensitivity. But if we believe in the underinvestment story, as
the managerial stock holding increases, the manager will be more reliant on internal funds leading
to an increase in the investment cash flow sensitivity. Hadlock (1998) found out that the
investment-cash flow sensitivity increases as the managerial stock holding increases. This
relationship reverses at higher levels of managerial stock holding. After the managerial stock
holding cross a certain level, the investment cash flow sensitivity decreases with the increase of
managerial stock holding. Cash Flow sensitivity of investment is positive, up to a certain level of
managerial holding (5 percent) and when managerial holding is beyond 5 percent, the relationship
is negative. He provided an agency based explanation of this empirical fact. His result is
consistent with the underinvestment story of cash flow sensitivity stated above.
NegOwnership is negative of shares owned by the manager. Interaction of NegOwnership and
cash flow in column 3 is zero and insignificant seemingly rejecting hypothesis 2. But we have to
interpret this rejection in light of overinvestment interpretation and underinvestment
interpretation of Investment Cash Flow sensitivity. We note that the investment cash flow
sensitivity can increase or decrease with increase of managerial stock holding and this can be
interpreted as either underinvestment or overinvestment problem as has been tested by Hadlock
(1998) who showed that the investment cash flow sensitivity with respect to managerial
ownership is non linear. We find support for Hadlock's results in column 4. NegOwnershipL5 is -
min (5, percentage of shares owned by manager). NegOwnershipG5 is -max (0, percentage of
shares owned by manager -5). NegOwnershipL5 captures stock holding of CEO up to 5 percent.
If a CEO owns stocks above 5 percent, NegOwnershipL5 will be -5 and the percentage of stock
holding above 5 percent is captured by NegOwnershipG5. Interaction term of Cash Flow and
NegOwnershipL5 is negative -0.005 and significant whereas the coefficient of the interaction
term of cash flow and NegOwnershipG5 is positive 0.001 and insignificant. This is consistent
with Hadlock's results. But we have to be careful in interpreting the coefficient of interaction of

14
cash flow with NegOwnershipL5 and cash flow with NegOwnershipG5. Coefficient of the
NegOwnershipL5 and cash flow is negative and significant supporting hypothesis 2. We can say
that as long as the stock holding of the CEO is below 5 percent, the CEO acts myopically and
investment cash flow sensitivity falls. But we should be aware that there can be some agency
based explanation of this result as has been pointed out by Hadlock. Similarly, the coefficient of
the interaction term of cash flow and NegOwnershipG5 is positive and insignificant which does
not support hypothesis 2. It may be the case that if the manager holds significant percentage of
company shares (more than 5 %), any change in stock holding of the manager may not reflect
myopic behavior by the manager. If the managerial stock holding exceeds 5% of the total
company stocks, NegOwnership may not be a good proxy for managerial myopia. The overall
inference we draw from table 2 suggest that there is evidence that myopia leads to lowering of
investment cash flow sensitivity and lowering of investment Tobin’s Q sensitivity.

4.3 Tests of Hypothesis 3
4.3.1 Investors’ Irrationality
Until now, we have assumed that the managers and investors are rational. In other words,
we have assumed that the stock price reflects the true value of the stock and there is no problem
of mispricing. But this assumption is a bit farfetched as the literature has provided evidence
suggesting that the stock price is often over valued or undervalued. One problem with table 2 is
that we have assumed that Tobin’s captures the true growth opportunities of the firm. It may be
the case that Tobin’s Q not only captures the growth opportunity but also the misevaluation of the
firm. In that case, even if the coefficient of interaction term between myopia with Tobin’s Q is
negative and significant, one cannot reliably argue that it is truly capturing the lowering of
investment sensitivity with respect to the firm’s growth opportunities. We use a new measure of
misevaluation based on residual income model as used by Dong, Hirshsliefer, Teoh(2007) which
is discussed in Appendix A3. Introduction of this new term will capture the misevaluation of the
firm and we will be able to disentangle the effect of misevaluation from Tobin’s Q.
Our regression setup is given by
I CFit
it = α + β1q + β 2 + β3 MisValuation + f (controls) + ϑit Myopia * q
K Kit −1 (12)
it − 1
+ ρit Myopia * CF + ε it
If Hypothesis 3 is true, ϑit is negative and significant. As we have controlled for misevaluation,

we can argue that now Tobin’s Q captures true growth opportunities. Hence, we can infer that if

15
ϑit is negative and significant, investment sensitivity to growth opportunities is lower for firms
with myopic managers.
Table 3 reports the results of regression setup of equation 12. After we include the
misevaluation term, the interaction term of Tobin’s Q and myopia is negative and significant.
CEO age is the main proxy for managerial myopia. The results with CEO age as the proxy for
managerial myopia are reported in column 2 of table 3. The interaction term of CEO Age and
Tobin’s Q is negative and significant (supporting hypothesis 3). If we use negative of shares
owned as a proxy for myopia, we find that the coefficient of the interaction of myopia and
Tobin’s Q is negative and significant supporting hypothesis 3. Even after controlling for
mispricing in Q, the coefficient of the interaction term of Tobin’s Q with myopia is negative and
significant providing empirical support for hypothesis 3. If the CEO age changes by one standard
deviation, the coefficient of Tobin’s Q decreases by 41.66 %( -0.002*7.5/ 0.036). If the CEO age
changes by two standard deviations, the coefficient of Tobin’s Q decreases by 83.33 % (-
0.002*2*7.5/ 0.036). Our results suggest that after controlling for mispricing in Q, when faced
with one unit increase in growth opportunities, there is a 41.66 %( 83.33%) drop in the increase in
investments when there is one(two) standard deviation increase in CEO age. This point to huge
deviation from optimal investments as CEO becomes older. Further, there is evidence that
investment cash flow sensitivity is lower for myopic managers when negative of shares owned is
used as a proxy for myopia (column 4 and 5). Coefficient of Cash Flow x NegOwnershipL5 is -
0.006 and statistically significant.
Results in this table depend crucially on the measure of mispricing. It may be possible the
mispricing measure is not capturing the true mispricing of the firm. As a result, we test hypothesis
3 with respect to an undervaluation measure which depends not on investor irrationality but on
managerial irrationality.

4.3.2 Managerial Irrationality
Still now, we have assumed that the manager is rational. But it may be the case that the
manager herself is irrational. It may be the case that the manager perceives that the stock is
overvalued or undervalued, may be the manager is too optimistic about the firm. In table 4, we
incorporate a measure of managerial optimism in our regression setup. We define managerial
optimism as inmonex/optionVal, where inmonex is the value of unexercised exercisable option of
the manager. We explain this measure of managerial optimism in Appendix A4. The reasoning
behind this measure is that if the manager is optimistic about the firm's stock and believes that the

16
stock is under priced, then the manager will not exercise her own options. Hence, managerial
optimism captures the managerial perception if the stock is undervalued.
I CFit
it = α + β1q + β 2 + β3UnderValuation + f (controls ) + ϑit Myopia * q
K Kit −1 (13)
it − 1
+ ρit Myopia * CF + φitUndervaluation * Myopia + ε it
If Hypothesis 3 is true, ϑit is negative and significant.

Table 4 reports the results of regression setup of equation 13. In the second column, we
use CEO age which is our primary measure of managerial myopia. The coefficient on the
interaction terms, CEO age and Tobin’s Q is negative and significant supporting hypothesis 3.
Further, the interaction term of CEO age and cash flow is negative and significant providing
evidence for hypothesis 2. When we use negative of shares owned by the manager as a measure
of myopia (column 3,4,5 of table 4), the interaction of Tobin’s Q and myopia is negative and
significant, supporting hypothesis 3.
The coefficients of the interaction terms of CEO age and Tobin’s Q and interaction term
between CEO age and Cash flow are negative and significant, even after controlling for
mispricing using two different measures of mispricing. So we can safely infer that after we
control for misevaluation in Q, so that Q is believed to capture true growth opportunities, there is
strong evidence of lowering on investment Q sensitivity and investment cash flow sensitivity.

4.4 Tests of Hypothesis 4: Corporate Governance
Given that there is evidence that the managers can act myopically, the next logical
question is why the manager are allowed to act myopically. We provide empirical evidence that
myopic behavior is more prevalent when the corporate governance is weak. We use three
corporate governance measures. The first measure is Gompers, Metrick, Ishii governance index.
Gompers, Metrick, Ishii (2003) constructed this index using 24 unique governance rules. Higher
is the value of the index, weaker is the share holder rights and weaker is the corporate governance.
In table 5, we use GIM index as a measure of corporate governance. We attempt to test if a
manager behaves myopically due to weak corporate governance. As the GIM Index increases,
the managers should act more myopically. We divide our sample of firms into lower GIM firms
and higher GIM firms. We define industry by 2 digits SIC code. For each firm, we calculate the
median GIM index for that industry. If a firm is above (below) the median industry GIM index,
we call that firm a high (low) GIM firm. Higher GIM Index firms have weaker share holder’s
rights and hence have weaker corporate governance. Column 2 and 3 of table 5 document the

17
results for the low GIM and high GIM firms. As we can see, the interaction terms (Tobin’s Q
with CEO age and Cash Flow and CEO Age) are negative and significant for both the low GIM
and high GIM firms. As both the interaction terms are negative for both the low and high GIM
firms (columns 2 and 3), it is difficult to infer that as GIM index increases, the managers act more
myopically. To make any conclusion regarding higher GIM index leading to more myopic
behavior on the part of the manager, we interact both the interaction terms, Tobin’s Q with CEO
age and Cash flow with CEO age, with GIM Index and document the results in column 1. We
find that both the interaction terms (Tobin’s Q with CEO age with GIM Index and Cash Flow
with CEO age with GIM Index) are negative and statistically significant indicating lowering of
investment cash flow sensitivity and investment Tobin’s Q sensitivity due to weaker share
holders’ rights, as captured by higher GIM Index. This is evidence that myopic behavior of the
managers may be enhanced due to weaker corporate governance supporting hypothesis 4.
We argue that underinvestment by the managers of the firms are possible even for those
firms with greater share holders rights as it is almost impossible to find out what should be the
optimal level of investment and due to the “invisible” nature of intangible investments. The share
holders can observe the amount of investment, but they cannot observe the investments which are
forgone or what are the exact investment opportunities for the firms. The true investment
opportunities facing the firms are known only to the managers. The best corporate governance
measures for testing if good corporate governance reduces managerial myopia should be those
measures of governance which reduce the agency problems between the managers and the share
holders. If a measure of corporate governance aligns the interests of the manager with the
interests of the share holders, then an increase in that measure of corporate governance should
ideally reduce the propensity of the manager to act myopically.
We use the shares owned by the managers as a measure of corporate governance and
report the results in table 6. Given the fact that most of the shares owned by the managers are
vested, more is the shares owned by the managers, less should be the propensity to act myopically.
Column 1 and 2 of table 6 report the results for the top three and bottom three deciles of the firms,
based on the stock ownership of the CEO. For both the top three deciles and the bottom three
deciles, interaction term of Tobin’s Q and CEO age is negative and significant. So it is difficult to
infer that as the CEO’s stock ownership increases, the interaction term for Tobin’s Q and CEO
age is becoming less and less negative. Similarly, the interaction term for cash flow and CEO age
is negative and significant for both groups of firms. In column 3, we include the interaction term
of Tobin’s Q with CEO age and Stock ownership of CEO and the interaction term of Cash Flow
with CEO age with Stock ownership of CEO. The coefficient of the interaction term of Tobin’s Q

18
with CEO age and Stock ownership of CEO is positive and significant indicating that as the CEO
stock ownership increases, the magnitude of the interaction term of Tobin’s Q and CEO age is
becoming less negative. This suggests less myopic behavior by the managers as corporate
governance, captured by managerial stock ownership, increases supporting hypothesis 4. The
coefficient of the interaction term of Cash Flow with CEO age with Stock ownership of CEO is
not significant. Hadlock showed that the relationship of investment cash flow is non linear. Cash
Flow sensitivity of investment is positive, up to a certain level of managerial holding (5 percent)
and when managerial holding is beyond 5 percent, the relationship is negative. We divide the
Ownership into OwnershipL5 and OwnershipG5. OwnershipL5 is min (5, percentage of shares
owned by manager). OwnershipG5 is max (0, percentage of shares owned by manager -5).
OwnershipL5 captures stock holding of CEO up to 5 percent and the percentage of stock holding
above 5 percent is captured by OwnershipG5.
Column 4 reports the results when we divide the Ownership into OwnershipL5 and
OwnershipG5. The interaction term Cash Flow with CEO age with OwnershipL5 is positive and
significant whereas the interaction term Cash Flow with CEO age with OwnershipG5 is not
significant. As the managerial ownership increases up to 5 percent, more is the CEO ownership;
less is the magnitude of the interaction term Cash Flow and CEO age. Further, more is the CEO
ownership, the coefficient of the interaction term of Tobin’s Q and CEO age decreases. This is
compatible with our hypothesis 4. Higher is the corporate governance, measured by managerial
stock ownership; lower is the propensity of the manager to act myopically. In column 5, we report
results after interacting Tobin’s Q x CEO age with OwnershipL5 and OnwershipG5. Up to
managerial ownership of 5 percent, more is the CEO ownership; less is the magnitude of the
interaction term Cash Flow and CEO age and less is the magnitude of the interaction term of
Tobin’s Q and CEO age. This confirms our intuition of less myopic behavior with more
managerial ownership.
Finally, we use pay performance sensitivity as a measure of corporate governance to test
managerial myopia and report the results in table 7. Higher the pay performance sensitivity more
is the incentive for the managers to inflate share prices of the firms and act myopically. Hence,
managers of firms with high pay performance sensitivity should act more myopically compared
to the firms with lower pay performance sensitivity.
Firms are divided into deciles based on pay performance sensitivity. We use Aggarwal
and Samwick (1999) to calculate the pay performance sensitivity. See Appendix A2 for more
details. Higher is the pay performance sensitivity, greater is the incentive to the CEO to boost the
current share price and act myopically.

19
We divide firms into top 3 and bottom 3 deciles based on pay performance sensitivity. As
reported in column 1 of table 7, firms in top three deciles based pay performance sensitivity act
myopically given that the coefficient of the interaction term of Tobin’s Q and CEO age and the
coefficient of interaction term of Cash Flow and CEO age are negative and statistically significant.
As documented in column 2, the coefficient for the interaction term of Tobin’s Q and CEO age
for the bottom three deciles firms based on pay performance sensitivity is zero and statistically
insignificant giving evidence that CEOs of those firms do not myopically. But the interaction
term of Cash Flow and CEO age is negative and statistically significant for the firms with lower
pay performance sensitivity. In order to investigate if the interaction terms of Tobin’s Q with
CEO age and Cash Flow with CEO age increase in magnitude with increase in pay performance
sensitivity, we introduce the interaction term of Tobin’s Q with CEO age with Pay Performance
Sensitivity and the interaction term of Cash Flow with CEO age with Pay Performance Sensitivity
(column 3). It is documented that both these interaction terms are negative and statistically
significant providing evidence that as pay performance sensitivity of CEO compensation
increases, the CEO act more myopically, supporting hypothesis 4.
This measure of corporate governance, pay performance sensitivity of CEO
compensation, is a reliable measure to investigate if corporate governance reduces managerial
myopia as this measure involves the interests of the managers directly. Other corporate
governance measures like GIM index is not very good measure to test the relationship of
governance and myopic behavior because it is difficult for the outsiders to find out where a CEO
could have invested and did not invest. It is difficult for outsiders like shareholders to correctly
assess the investment opportunities of the firm. Only the manager of a firm knows precisely the
exact investment opportunities of the firm. Hence, the corporate governance measures which are
related to the interests of the manager, like managerial stock ownership and pay performance
sensitivity of CEO compensation, are the best corporate governance measures when one is testing
if corporate governance reduces myopic behavior of the manager. Based on measures like pay
performance sensitivity and stock ownership by the managers, we conclude that there is evidence
that good corporate governance reduces managerial myopic behavior thereby providing support
for hypothesis 4.

5. Robustness Tests
As a part of the robustness check, we perform a number of robustness tests. First, we
deflate investment and cash flow by assets. The results are presented in table 8. In the interaction
terms of Q and Myopia and Cash Flow and myopia are negative and significant.

20
We further use a different measure of investment. Investment is COMPUSTAT data 128
plus research and development data46. Cash Flow is the sum of earnings before extraordinary
items, item 18, and depreciation, item 18. Both investment and cash flow is deflated by asset,
which is item 6, at the beginning of the fiscal year. We report the results in table 9. Still the
results we obtained in table 2 hold good.
If we use a new definition of investment, Investment being the sum of COMPUSTAT
item 260 plus item 261 plus item 263 plus item 264 plus item 265 plus item 265 plus item 266,
the results hold good. We do not report the results with this new definition in the paper.
We can safely infer that our results of lowering of investment Tobin’s Q sensitivity and
investment cash flow sensitivity due to greater CEO age or higher negative stock ownership of
managers are not applicable only to some specific definition of investment and cash flow. We can
obtain similar results when we define investment differently and when we deflate investment and
cash flow by asset, instead of PPE. The results in tables 8 and 9 serve as robustness checks for
our main results documented in table 2.

6. Conclusion
In this paper, we provide an alternative methodology for testing managerial myopia. : A
myopic manager is expected to invest sub optimally, diverting resources from the long-term value
maximizing projects to short-term share price maximizing projects. Given the difficulty in
separating long-term and short-term investments and measuring the optimal level of investment,
it is difficult to test overinvestment in short-term and underinvestment in long-term projects.
Further, looking at the total investment level is not a viable way of measuring managerial myopia
because one does not observe where the manager could have invested and did not invest. Also,
there is the problem of invisibility of intangible investments. We argue that instead of measuring
investments at level, looking at the change in the investment in face of incremental growth
opportunity and incremental cash flow can serve as a better method of capturing managerial
myopia. We provide theoretical justification of lowering of investment cash flow sensitivity and
investment Tobin’s Q sensitivity due to managerial myopia. We show empirically that investment
cash flow sensitivity and investment Tobin’s Q sensitivity is indeed lowered in presence of
managerial myopia. Our results indicate that faced with one unit increase in growth opportunities,
there is a 20.833% (41.66%) drop in the increase in investments when there is one (two) standard
deviation increase in CEO age. We show that faced with one dollar increase in cash flow, there is
a 5.319% (10.638%) drop in the increase in investments when there is one(two) standard
deviation increase in CEO age. Thus, we provide evidence that firms with myopic managers

21
divert from optimal investments. Further, we document evidence that the managers can get away
with suboptimal investment due to weak corporate governance. This calls for better governance
of firms as managerial myopia result in suboptimal investment strategies which are detrimental to
the long-term value maximization of the firms.

22
Appendix A1
We solve the maximization problem equation 6 from section 2.1. We rename equation 6 as A1.

max It ,λt U t = a[π ( Kt ) − C ( I t , Kt , γ t ) − rt I t ] + (1 − a)Vt ( Kt , θt ) (A1)
∞
where Vt ( K t , θt ) = E[ ∑β
s =0
s
[π ( K t + s ) − C ( I t + s , K t + s , γ t + s ) − rt + s I t + s ]

The first order condition for maximization of managerial utility with respect to λt is given by
1
a[π ( Kt ) − C ( I t , Kt , γ t )] = [1 − a][ − 1]q (A2)
1− δ
Where q is the marginal q and is given by
∞
q = E[ ∑ β s (1 − δ ) s [π ( K ) − C (I ,K ,γ )−r B ] (A3)
k t+s k t+s t+s t+s k t+s
s=0
The first order condition for maximization of managerial utility with respect to I t is given by
1− a B B
q = [r + r ( t ) + rIt ( t ) Bt + C ( I ,K ,γ )] (A4)
1− δ f Kt −1 Kt −1 I t+s t+s t+s
This is the marginal q specification as developed in Hayashi (1982) and Hubbard(1998) except
for the introduction of managerial myopia in terms of “a” and capital market imperfection in
Bt
terms of r ( ) . When a=0 and there is no capital market imperfection, then the equation
Kt −1
reduces to
q = [ rf + C ( I ,K ,γ )][1 − δ ]
I t+s t+s t+s
which is the familiar marginal q specification.
When the cost of adjustment is linearly homogeneous in investment and capital, the marginal q
becomes equal to average q (Hayashi 1982). We use Hubbard (1998)’s cost of adjustment
functional form which is linearly homogeneous in investment and capital.
It α
C ( I t + s , Kt + s , γ t + s ) =
− dt − γ it )2 Kt −1
( (A5)
2 Kt −1
B
We also need to provide a functional specification for r ( t ) . Let us assume that the rate of
Kt −1
B B
interest is a linear function in r ( t ) . r ( t ) is given by
Kt −1 Kt −1
B B
r ( t ) = e. t , e > 0 (A6)
Kt −1 Kt −1

Bt
With the specification of C and r ( ) , the first order condition, equation (9) is reduced to
Kt −1

23
I λ d + λγ − r
t 1− a 1 2 − a Wit − 1 t it f
=[ ][ ]q + +
K 1 − δ 2e + α 2e + α K 2e + α
t −1 t −1 (A7)
W
= Aq + B it − 1 + C
K
t −1
λ d + λγ − r
1− a 1 2−a t it f
Where A = [ ][ ] B= C=
1 − δ 2e + α 2e + α 2e + α
Equation (A7) captures the investment cash flow sensitivity and investment q sensitivity. We
rename equation A7 in the text as equation 7.

24
Appendix A2
The various data definitions are as follows:
Sales are Data 12 from COMPUTSTAT. Research and Development is Data 46 in
COMPUSTAT and it is normalized by either sales or assets ( Data 6). PPE (Plant Property and
Equipment) is defined as Data 30 which is annual capital expenditure to PPE divided by Total
PPE at year t-1. Capex is defined as Capital Expenditure Data 128 divided by assets at year t-1.
ROE is calculated as the ratio of net income (data172) in year t by book value (data60) in year t-1.
ROA is calculated as the net income in year t divided by asset (data6) in year t-1. Data 58 is
earnings per share. The EPS is data58. Retained Earnings is data36. Retained earnings ratio here
is the ratio of data36 to data6.
For the investment cash flow Tobin’s regression , we use the data definition from Kaplan and
Zingales 1997. Investment is COMPUSTAT 128. Cash flow is the sum of earnings before
extraordinary items, item 18, and depreciation, item 18. Both investment and cash flow is deflated
by capital, which is net property, plant and equipment, item 8, at the beginning of the fiscal year.
Tobin’s Q is the market value of asset divided by the book value of asset. Market Value of asset
is the sum of the book value of asset and market value of equity minus the sum of the book value
of common equity item 60 and balance sheet deferred taxes item 74. Market value of equity is the
product of data25 and data199.
Asset tangibility is defined as the book value of a firm’s net fixed capital (item 8) divided by the
total assets (item 6). A dummy variable is used for bond ratings. If a firm has a rating of BBB- or
higher by the Standard and Poor’s, the dummy is set to one. Leverage may also affect firm
investment. Low leverage increases the firm’s ability to raise more external financing. Leverage
is defined as the sum of long term debt (item 9) and short term debt (item 34) divided by the total
assets (item 6). Dividend payout has been a common proxy for financial constraint. Further, low
dividend paying firms may be those firms who have higher growth opportunities and may want to
invest more. Dividend payout ratio is defined as the cash dividend paid (item 127) to net income
(item 172).
We use CRSP database to calculate the firm beta. The annualized cost of equity is calculated
using CAPM. We define industry by the three digit industry code.
We use RiskMetrics database (formerly IRRC) for corporate governance measures. We use three
corporate governance measures, Gompers, Metrick, Ishii corporate governance index and the
percentage of independent shareholders, pay performance sensitivity of CEO compensation and
managerial Ownership
We get executive compensation data from ExecuComp. Main myopia variable is CEO age.
NegOwnership is - percentage of shares owned by CEO. NegOwnershipL5 is -min(5,percentage
of shares owned by manager). NegOwnershipG5 is -max(0,percentage of shares owned by
manager -5). Myopia1 and NegOwnership are without winsorising. Percentage of shares owned
by executives is defined as shrown divided by shrsout divided by 10. shrsout is the common
shares outstanding. shrown is the shares owned by the executive. We get forcasted EPS from the
I/B/E/S database. Using the methodology defined in the above section, we calculate the intrinsic
value of the firm and the over valuation of the firm. Managerial Optimism is defined as inmonex
divided by optionVal. Inmonex is the unexercised exercisable options. optionVal is the total value
of options in managerial compensation. The basic intuition is that if the manager is optimistic
about his company, then he is not going to exercise his option. Holding in-the-money options is a
good proxy for managerial optimism as introduced in the literature by Malmendier and Tate
(2004). We calculate the value of old options as the sum of INMONEX and INMONUN.
INMONEX is the value of the unexercised exercisable options. INMONUN is the value of
unexercised unexercisable options. The new options are defined as BLK-VALU, which the value
of new options granted in ExecuComp. Total option value is the sum of old options and new
options.

25
Following Aggarwal and Samwick ( 1999), CEO compensation is composed of three
components : flow compensation, the change in the value of stock holding and the change in the
value of stock options. Flow compensation is easily calculated as TDC1, which is available from
ExecuComp. TDC1 is composed of salary, bonus, total value of stock options, long-term
incentive payouts, other annual compensation and all other, as is defined in ExecuComp manual.
The change in the value of stock holding is defined as the percentage of stocks held by the CEO
at the beginning of the fiscal year multiplied by shareholder dollar return. Total returns to
shareholders are reported in ExecuComp in percentages. The dollar return is defined as the
percentage total return multiplied by the market value of the firm at the beginning of the fiscal
year. Once we have the dollar return to shareholder, we can calculate the change in the value of
stock holding. The change in the value of stock options is a bit difficult to calculate. We calculate
the value of old options as the sum of INMONEX and INMONUN. INMONEX is the value of
the unexercised exercisable options. INMONUN is the value of unexercised unexercisable
options. The new options are defined as BLK-VALU, which the value of new options granted in
ExecuComp. Total option value is the sum of old options and new options. Change in the option
value is the value of the option in year t minus the value of the option in year t-1. The total value
of CEO's compensation package is defined as the sum of the flow compensation, the change in
the value of stock holding and the change in the value of stock options. The variance of preceding
five years stock returns is termed as variance and is used a proxy for stock's risk. We calculate
CEO tenure using BECAMECEO from ExecuComp, which gives us the date an individual has
become the CEO. CEO tenure acts a proxy for her abilities when we run pay performance
sensitivity regressions.

26
Appendix A3
Estimation of Misvaluation and Investor Irrationality :
We use the Residual Income Model to calculate the intrinsic value of a firm. This procedure have
been used in Lee, Myers and Swaminathan(1999) and more recently by Dong, Hirshleifer and
Teoh(2007). The intrinsic value of the firm can be expressed as the summation of the book value
and the discounted value of an infinite sum of expected residual incomes.
Et [[ ROE (t + i ) − re (t )]B (t + i − 1)]
∞
V (t ) = B (t ) + ∑
i =1 (1 + re (t ))i
where B(t) is the book value of equity at time t, re (t ) , is the firm's annualized cost of equity
capital and ROE(t+i) is the return on equity for period t+i.
We use a three-period forecast horizon:
[ f ROE (t + 1) − re (t )]B(t ) [ f ROE (t + 2) − re (t )]B(t + 1)
V (t ) = B(t ) + +
1 + re (t ) [1 + re (t )]2
[ f ROE (t + 3) − re (t )]B(t + 2)
+
[1 + re (t )]2 re (t )

where we assume that the forecasted value for year 3 continues in perpetuity. This is the exact
procedure by Dong,Hirshleifer and Teoh(2007) to calculate the intrinsic value of the firm.
The forecasted ROE are computed from the forecasted EPS, using the formula below.
f EPS (t + i )
f (t + i ) =
ROE

B (t + i − 1)
Where we calculate B (t + i − 1) as
B(t + i − 1) + B (t + i − 2)
B (t + i − 1) =
2
We calculate B (t + i ) as
B (t + i ) = B (t + i − 1) + (1 − k ) f EPS (t + i )
where k is the dividend payout ratio given by
D (t )
k=
EPS (t )
with D (t) being the dividend at period t and EPS (t) being the Earnings per Share in period t.
We calculate beta using CAPM and using the CAPM beta, we calculate the annualized cost of
equity re (t ) .Having calculated the intrinsic value of the firm, we get a measure of over valuation
by dividing the market value of the firm by the intrinsic value of the firm.
E
Misvaluation =
V
Where E is the market value of equity and V is the intrinsic value of the firm calculated above.

27
Appendix A4
Estimation of Misevaluation and Managerial Irrationality
In the previous subsection, the firms are not correctly priced because of irrational investors. Now
we introduce irrational managers. We build a measure of managerial optimism. The idea is that
the manager is more optimistic about her firm if she thinks that the firm is undervalued. Hence
managerial optimism is a measure of undervaluation as perceived by the manager.
inmonex
Optimism = where inmonex is Unexercised exercisable options. Inmonex
optionVal *1000
is a variable in the ExecuComp database. optionVal is the total value of options in managerial
compensation.
If the manager perceives that the stock is undervalued, the manager will not exercise her
exercisable stock options believing that the stock is underpriced. In this case, the variable
optimism is a proxy for managerial perception of undervaluation of the firm.

28
References

Bushee B,1998. The Influence Of Institutional Investors On Myopic R and D Investment
Behavior. Accounting Review, 73, 305-333.

Chevalier J , Ellison G ,1999. Career Concerns Of Mutual Fund Managers. Journal of Finance,
114, 389-433.

Dechow P M , Sloan R G,1991. Executive Incentives and the Horizon Problem: An empirical
investigation. Journal of Accounting and Economic, 14, 51-89.

Dong Ming, Hirshleifer David, Teoh Siew,2007 . Stock Market Misvaluation and Corporate
Investment. Working Paper.

Graham John R, Harvey Campbell R, Rajgopal Sivaram, 2005. The Economic Implications of
Corporate Financial Reporting. Working Paper.

Hansen G, Hill C,1991. 1991. Are Institutional Investors Myopic? A Time Series Study Of Four
Technology Driven Industries. Strategic Management Journal, 12, 1-16.

Hadlock Charles J, 1998. Ownership, Liquidity, and Investment. RAND Journal of Economics,
29, 487-508.

Hayashi,F.,1982. Tobin's Marginal Q And Average Q:Neoclassical Interpretation. Econometrica,
50, 731-753.

Hovakimian Gayane,2009. Determinanats of Investment Cash Flow Sensitivity. Financial
Management, 38, 161-183.

Himmelberg,C.P.,Peterson,B.C., 1994. R&D and Internal Finance:a panel study of small firms in
high-tech industries. Review of Economics and Statistics, 76, 38-51.

Hubbard R Glenn, 1998. Capital-Market Imperfections And Investment. Journal of Economic
Literature, 36, 193-225.

Lundstrum L Leonard. 2002. Corporate Investment Myopia: A Horserace Between The
Theories. Journal of Corporate Finance, 8, 353 – 371.

Jarrel G, Lehn K, Marr W. 1985. Institutional Ownership, Tender Offers and Long-Term
Investments. Unpublished SEC Report.

Kaplan S , Zingales L, 1997. Do Investment Cash Flow Sensitivity Provide Useful measures Of
Financing Constraints. Quarterly Journal Of Economics, 112, 1, 169-215.

Lee Charles M C, Myers James, Swaminathan Bhaskaran. 1999. What is the Intrinsic Value of
the Dow? Journal of Finance, 54, 5, 1693-1741

McConnell John J, Wahal Sunil. 2000. Do Institutional Investors Exacerbate Managerial Myopia?
Journal of Corporate Finance, 6 ,307-329.

29
Narayanan M.P,1985.Managerial Incentives For Short-Run Results. Journal of Finance, 40,
1469-1484.

Narayanan M P, 1987. Managerial Incentives For Short-Term Results: A Reply. Journal of
Finance, 42, 1103-1104.

Porter M E, 1992. Capital Disadvantage: America’s Failing Corporate Investment System.
Harvard Business Review, 70, 65-82.

Noe T H, Rebello M J,1997. Renegotiation,Investment Horizons And Managerial Discretions.
Journal of Business, 70,385-407.

Shleifer A ,Vishny R W ,1989. Management Entrenchment: The Case Of Manager-Specific
Investments. Journal of Financial Economics, 25,123-139.

Stein J C , 1988. Takeover Threats And Managerial Myopia. Journal of Poltical Economy, 96,61-
80.

Stein J C,1989. Efficient Capital Markets, Inefficient Firms: A Model Of Myopic Corporate
Behavior , Quarterly Journal of Economics, 104. 665-669.

Stein J C,1996. Takeover Rational Capital Budgeting In An Irrational World. Journal of Business,
69, 4, 429-455.

30
Table 1
Descriptive Statistics of means of firm level variables and correlation with CEO age

Research and Development is data46 in COMPUSTAT. Sales is data12 and assets is Data6. R and D is normalized by sales and by assets. Industry adjusted R&D by Sales(Assets)
is defined as R&D by Sales ( Assets) minus Industry Mean R&D Sales(Assets), where Industry is defined by three digits SIC code. Capital Expenditure ratio is data30 at year t
divided by book value of PPE at year t-1. Industry adjusted Capital Expenditure ratio is Capital Expenditure ratio minus Industry mean capital expenditure ratio. Data 58 is
earnings per share. The EPS is data58. Industry adjusted EPS is defined as EPS minus Industry mean EPS. Retained Earnings is data36. Retained earnings ratio here is the ratio of
data36 to data6. Correlation 1 is the correlation between the CEO age and operating performance variables. Correlation 2 is the correlation between the CEO age and the operating
performance when the CEO age is censored at 65. The t stat 1 is testing the difference between the operating performance variable between the old CEO, characterized by CEO
above age of 65 and the young CEO, age less than 40. t stat 1 is testing if the mean of the operating performance of old CEO is greater than mean of the operating performance of
the young CEO. t stat 2 is testing the difference between the operating performance of the old CEO, age is between 61 and 65 , and young CEO, age is below 40.

full sample Under 40 41 to 50 51 to 60 61 to 65 66 to 70 Above 70 Correlation 1 t stat 1 Correlation 2 t stat 2
R&D by Sales 0.055 0.087 0.073 0.053 0.040 0.047 0.046 -0.178 -9.620 -0.203 -10.120
4 ,300 94 815 2 ,327 761 188 115 0.000 0.000 0.000 0.000
R&D by Assets 0.048 0.084 0.061 0.046 0.037 0.039 0.039 -0.208 -6.740 -0.213 -11.200
4 ,300 94 815 2 ,327 761 188 115 0.000 0.000 0.000 0.000
Indus Adj R&D By Sales 0.012 0.020 0.021 0.012 0.004 0.003 0.004 -0.120 -3.850 -0.203 -5.120
4 ,300 94 815 2 ,327 761 188 115 0.000 0.000 0.000 0.000
Indus Adj R&D By Assets 0.006 0.015 0.011 0.005 0.002 -0.002 0.000 -0.114 -4.120 -0.213 -3.950
4 ,300 94 815 2 ,327 761 188 115 0.000 0.000 0.000 0.000
Capital Expenditure 0.291 0.633 0.361 0.271 0.253 0.233 0.224 -0.119 -10.970 -0.144 -13.670
6 ,914 138 1443 3 ,683 1 ,204 291 155 0.000 0.000 0.000 0.000
Indus Adj Capital Expenditure -0.021 0.218 0.023 -0.036 -0.038 -0.070 -0.077 -0.077 -7.680 -0.083 -9.170
6 ,914 138 1443 3 ,683 1 ,204 291 155 0.000 0.000 0.000 0.000
Earnings Per Share 1.702 1.295 1.438 1.818 1.855 1.391 1.534 0.077 2.200 0.135 6.880
8 ,224 195 1800 4 ,289 1 ,362 359 219 0.000 0.028 0.000 0.000
Indus Adj. Earnings Per Share 0.668 0.501 0.468 0.759 0.765 0.390 0.507 0.044 -1.090 0.093 3.560
8 ,224 195 1800 4 ,289 1 ,362 359 219 0.000 0.277 0.000 0.000
Retained Earnings 0.322 0.286 0.300 0.328 0.330 0.329 0.382 0.078 3.050 0.060 2.440
8 ,090 181 1803 4 ,191 1 ,343 355 217 0.000 0.002 0.000 0.015
Indus. Adj. Retained Earnings 0.069 0.047 0.051 0.072 0.075 0.072 0.129 0.067 2.280 0.043 1.470
8 ,090 181 1803 4 ,191 1 ,343 355 217 0.000 0.002 0.000 0.143

31
Table 2
Fixed Effect Regression of Investment on Cash Flow and Tobin's Q

The sample covers firms from COMPUSTAT from 1993 to 2004. NegOwnership is - percentage of shares owned by
CEO. Dependent variable is Investment scaled by PP&E. Investment is COMPUSTAT 128. Cash flow is the sum of
earnings before extraordinary items, item 18, and depreciation, item 18. Both investment and cash flow is deflated by
capital, which is net property, plant and equipment , item 8, at the beginning of the fiscal year. Tobin’s Q is the market
value of asset divided by the book value of asset. Market Value of asset is the sum of the book value of asset and
market value of equity minus the sum of the book value of common equity item 60 and balance sheet deferred taxes
item 74. Market value of equity is the product of data25 and data199. NegOwnershipL5 is -min(5,percentage of shares
owned by manager). NegOwnershipG5 is - max(0,percentage of shares owned by manager -5). CEO age and
NegOwnership are without winsorising. The data definition is from Kaplan and Zingales 1997. We control for firm age,
firm size, asset tangibility, sales growth, and leverage, dummy for bond rating, dividend payout and financial slack.
Regressions are estimated with fixed firm effects and year effects.

Variables [1] [2] [3] [4] [5]
Tobin's Q 0.036 0.107 0.031 0.031 0.030
(12.2) (6.51) (9.13) (9.25) (7.86)
Cash Flow 0.149 0.224 0.150 0.143 0.151
(27.63) (8.12) (25.99) (21.75) (22.47)
Ceo Age 0.003
(3.59)
Tobin's Q × Ceo Age -0.001
(-4.35)
Cash Flow × Ceo Age -0.001
(-2.77)
NegOwnership 0.001 0.001
(1.00) (1.09)
NegOwnershipL5 0.002
(0.47)
NegOwnershipG5 0.001
(0.75)
Tobin's Q × NegOwnership -0.001 -0.001
(-2.88) (-2.71)
Tobin's Q × NegOwnershipL5 -0.003
(-1.95)
Tobin's Q × NegOwnershipG5 -0.001
(-1.01)
Cash Flow × NegOwnership 0.000
(0.43)
Cash Flow × NegOwnershipL5 -0.005 0.000
(-2.25) (0.20)
Cash Flow × NegOwnershipG5 0.001 0.000
(1.44) (0.43)
N 6 ,848 6, 848 6 ,768 6 ,768 6 ,848
2
R 0.694 0.696 0.697 0.697 0.695

32
Table 3
Regression of Investment on Cash Flow and Tobin's Q and Misvaluation
The sample covers firms from COMPUSTAT from 1993 to 2004. NegOwnership is - percentage of shares owned by
CEO. Dependent variable is Investment, COMPUSTAT 128. CEO Age and NegOwnership are without winsorising.
Cash flow is the sum of earnings before extraordinary items, item 18, and depreciation, item 18. Both investment and
cash flow is deflated by capital, which is net property, plant and equipment , item 8, at the beginning of the fiscal year.
Tobin’s Q is the market value of asset divided by the book value of asset. Market Value of asset is the sum of the book
value of asset and market value of equity minus the sum of the book value of common equity item 60 and balance sheet
deferred taxes item 74. Market value of equity is the product of data25 and data199. NegOwnership is - percentage of
shares owned by CEO. NegOwnershipL5 is -min(5,percentage of shares owned by manager). NegOwnershipG5 is -
max(0,percentage of shares owned by manager -5). The data definition is from Kaplan and Zingales 1997.
Misvaluation measure is defined as the ratio of market value of equity to the intrinsic value of the firm. We control for
firm age, firm size, asset tangibility, sales growth, and leverage, dummy for bond rating, dividend payout and financial
slack. Regressions are estimated with fixed firm effects and year effects.

33
Table 4
Regression of Investment on Cash Flow and Tobin's Q and Optimism
The sample covers firms from COMPUSTAT from 1993 to 2004. NegOwnership is - percentage of shares owned by
CEO. Dependent variable is Investment, COMPUSTAT 128. CEO Age and NegOwnership are without winsorising.
Cash flow is the sum of earnings before extraordinary items, item 18, and depreciation, item 18. Both investment and
cash flow is deflated by capital, which is net property, plant and equipment, item 8, at the beginning of the fiscal year.
Tobin’s Q is the market value of asset divided by the book value of asset. Market Value of asset is the sum of the book
value of asset and market value of equity minus the sum of the book value of common equity item 60 and balance sheet
deferred taxes item 74. Market value of equity is the product of data25 and data199. NegOwnership is - percentage of
shares owned by CEO. NegOwnershipL5 is -min(5,percentage of shares owned by manager). NegOwnershipG5 is -
max(0,percentage of shares owned by manager -5). The data definition is from Kaplan and Zingales 1997. Optimism is
defined as the ratio of inmonex to optionVal . Inmonex is Unexercised exercisable options and optionVal is the total
value of options in managerial compensation. Managerial optimism is a measure of undervaluation as perceived by the
manager We control for firm age, firm size, asset tangibility, sales growth, leverage, dummy for bond rating, dividend
payout and financial slack. Regressions are estimated with fixed firm effects and year effects.

Variables [1] [2] [3] [4] [5]
Tobin's Q 0.038 0.116 0.032 0.032 0.034
(11.94) (6.58) (9.04) (9.07) (8.52)
Cash Flow 0.139 0.209 0.141 0.139 0.141
(24.42) (7.32) (23.45) (20.23) (19.89)
Optimism x 10-3 0.006 0.285 0.002 0.002 0.001
(0.43) (2.79) (0.12) (0.12) (0.09)
Ceo Age 0.005
(4.72)
Tobin's Q x Ceo Age -0.001
(-4.46)
Cash Flow x Ceo Age -0.001
(-2.52)
NegOwnership 0.002 0.002
(1.02) (1.08)
NegOwnershipL5 -0.008
(-1.69)
NegOwnershipG5 0.005
(2.23)
Tobin's Q x NegOwnership -0.002 -0.002
(-3.10) (-3.05)
Tobin's Q x NegOwnershipL5 x 10-1 0.001
(0.06)
Tobin's Q x NegOwnershipG5 -0.003
(-2.80)
Cash Flow x NegOwnership 0.001
(1.28)
Cash Flow x NegOwnershipL5 x 10-1 -0.001 0.005
(-0.51) (0.17)
Cash Flow x NegOwnershipG5 0.001 0.001
(1.51) (1.23)
N 6 ,203 6 ,203 6 ,140 6 ,140 6 ,140
2
R 0.704 0.707 0.707 0.707 0.707

34
Table 5
Effect of corporate governance on myopia. Governance measure is Gomper,Ishii and
Metrick index

The variable GIMIndex is the Gompers, Metrick, Ishii governance index. Higher is GIMIndex, lower is corporate
governance. We divide our sample of firms into lower GIM firms and higher GIM firms. We define industry by 2
digits SIC code. For each firm, we calculate the median GIM index for that industry. If a firm is above (below) the
median industry GIM index, we call the firm high (low) GIM firm. Higher GIM Index firms have weaker share
holder’s rights and hence have weaker corporate governance. Dependent variable is Investment. Investment is
COMPUSTAT 128. Cash flow is the sum of earnings before extraordinary items, item 18, and depreciation, item 18.
Both investment and cash flow is deflated by capital, which is net property, plant and equipment , item 8, at the
beginning of the fiscal year. Tobin’s Q is the market value of asset divided by the book value of asset. Market Value of
asset is the sum of the book value of asset and market value of equity minus the sum of the book value of common
equity item 60 and balance sheet deferred taxes item 74. Market value of equity is the product of data25 and data199.
The data definition is from Kaplan and Zingales 1997.

Variables All Firms Low GIM Firms High GIM Firms
Tobin's Q 0.108 0.112 0.081
(5.91) (4.20) (3.53)
Cash Flow 0.245 0.112 0.198
(8.63) (6.02) (5.65)
Ceo Age 0.003 0.003 0.002
(4.01) (2.66) (2.25)
Tobin's Q x Ceo Age -0.001 -0.001 -0.001
(-2.26) (-2.38) (-2.03)
Cash Flow x Ceo Age -0.001 -0.002 -0.001
(-2.27) (-2.84) (-1.78)
Tobin's Q x Ceoage x GIMIndex x 10-2 -0.004
(-1.65)
CashFlow x Ceoage x GIMIndex x 10-2 -0.008
(-2.04)
N 6 ,796 2 ,809 3 ,987
2
R 0.639 0.622 0.649

35
Table 6
Effect of Reduction of Agency Problem between Shareholders and Manager on Managerial Myopia. Managerial Ownership captures
Agency Problem

Ownership is percentage of shares owned by CEO. OwnershipL5 is min(5,percentage of shares owned by manager). OwnershipG5 is max (0, percentage of shares owned by
manager -5). Investment is COMPUSTAT 128. Cash flow is the sum of earnings before extraordinary items, item 18, and depreciation, item 18. Both investment and cash flow is
deflated by capital, which is net property, plant and equipment, item 8, at the beginning of the fiscal year. Tobin’s Q is the market value of asset divided by the book value of asset.
Market Value of asset is the sum of the book value of asset and market value of equity minus the sum of the book value of common equity item 60 and balance sheet deferred taxes
item 74. Market value of equity is the product of data25 and data199. The data definition is from Kaplan and Zingales 1997. We control for firm age, firm size, asset tangibility,
sales growth, and leverage, dummy for bond rating, dividend payout and financial slack. Regressions are estimated with fixed firm effects and year effects.

Variables Top 3 Deciles Bottom 3 Deciles All Firms All Firms All Firms
Tobin's Q 0.123 0.087 0.107 0.106 0.111
(3.75) (3.42) (6.48) (6.44) (6.63)
Cash Flow 0.462 0.398 0.218 0.227 0.222
(6.93) (7.96) (7.88) (8.13) (7.91)
Ceo Age 0.004 0.003 0.003 0.003 0.003
(2.50) (2.75) (3.55) (3.5) (3.53)
Tobin's Q x Ceo Age -0.002 -0.001 -0.001 -0.001 -0.001
(-3.02) (-2.6) (-4.59) (-4.52) (-4.80)
Cash Flow x Ceo Age -0.005 -0.005 -0.001 -0.002 -0.001
(-4.49) (-5.12) (-2.50) (-3.02) (-2.69)
Tobin's Q x Ceo Age x Ownership x 10-3 0.01389 0.012
(2.47) (2.14)
Cash Flow x Ceo Age x Ownership x 10-3 -0.00554
(-0.62)
TobinQ x Ceo Age x OwnershipL5 x 10-3 0.048
(2.25)
TobinQ x Ceo Age x OwnershipG5 x 10-3 0.003
(0.42)
Cash Flow x Ceo Age x OwnershipL5 x 10-3 0.093 0.054
(2.31) (2.17)
Cash Flow x Ceo Age x OwnershipG5 x 10-3 -0.016 -0.009
(-1.62) (-0.82)
N 2 ,025 2 ,034 6 ,768 6 ,768 6 ,978
2
R 0.744 0.776 0.699 0.699 0.665

36
Table 7
Effect of Corporate Governance on Myopia. Pay Performance Sensitivity captures
Corporate Governance

CEO compensation is composed of three components: flow compensation, the change in the value of stock holding and
the change in the value of stock options. Flow compensation is easily calculated as TDC1, which is available from
ExecuComp. TDC1 is composed of salary, bonus, total value of stock options, long-term incentive payouts, other
annual compensation and all other, as is defined in ExecuComp manual. The change in the value of stock holding is
defined as the percentage of stocks held by the CEO at the beginning of the fiscal year multiplied by shareholder dollar
return. Total return to shareholders is reported in ExecuComp in percentages. The dollar return is defined as the
percentage total return multiplied by the market value of the firm at the beginning of the fiscal year. Once we have the
dollar return to shareholder, we can calculate the change in the value of stock holding. The change in the value of stock
options is a bit difficult to calculate. We calculate the value of old options as the sum of INMONEX and INMONUN.
INMONEX is the value of the unexercised exercisable options. INMONUN is the value of unexercised unexercisable
options. The new options are defined as BLK-VALU, which the value of new options granted in ExecuComp. Total
option value is the sum of old options and new options. Change in the option value is the value of the option in year t
minus the value of the option in year t-1. The total value of CEO's compensation package is defined as the sum of the
flow compensation, the change in the value of stock holding and the change in the value of stock options. The variance
of preceding five years stock returns is termed as variance and is used a proxy for stock's risk. We calculate CEO
tenure using BECAMECEO from ExecuComp, which gives us the date an individual has become the CEO. CEO tenure
acts a proxy for her abilities when we run pay performance sensitivity regressions.

The baseline regression, from Aggrawal and Samwick (1999) is

CEO_Compensation = β1Ret+ β2Ret*Tenure + β3*Variance + β *Size + ε

Ret is the total dollar return to the share holder. Variance is the variance of the preceding 5 year stock return of the firm.
Variance captures the risk of the stock. Tenure is proxy for CEO's ability. Size is defined as log of assets, data6. Size
captures the size effect, which is common in CEO compensation regression.

The coefficient beta1 is the pay performance sensitivity of CEO compensation. Higher is the pay performance
sensitivity, greater is the incentive to the CEO to boost the current share price and act myopically.

Firms are divided into deciles based on pay performance sensitivity.

Variables Top 3 deciles Bottom 3 deciles All Firms
Tobin's Q 0.168 0.035 0.078
(3.80) (0.63) (3.10)
Cash Flow 0.368 0.567 0.332
(3.54) (3.65) (5.03)
Ceo Age 0.007 0.000 0.003
(4.16) (0.11) (3.11)
Tobin's Q x Ceoage -0.002 0.000 -0.001
(-3.13) (0.34) (-1.64)
Cash Flow x Ceoage -0.004 -0.006 -0.003
(-2.18) (-2.12) (-2.64)
Tobin's Q x Ceoage x PayPeform x 10-3 -0.049
(-2.11)
CashFlow x Ceoage x PayPerform x 10-3 -0.21
(-4.37)
N 822 817 2 ,736
2
R 0.598 0.592 0.615

37
Table 8
Fixed Effect Regression of Investment on Cash Flow and Tobin's Q where investment and
cash flow are deflated by asset.

The sample covers firms from COMPUSTAT from 1993 to 2004. NegOwnership is - percentage of shares owned by
CEO. Dependent variable is Investment. Investment is COMPUSTAT 128. Cash flow is the sum of earnings before
extraordinary items, item 18, and depreciation, item 18. Both investment and cash flow is deflated by asset, which is
item 6, at the beginning of the fiscal year. Tobin’s Q is the market value of asset divided by the book value of asset.
Market Value of asset is the sum of the book value of asset and market value of equity minus the sum of the book value
of common equity item 60 and balance sheet deferred taxes item 74. Market value of equity is the product of data25
and data199. NegOwnershipL5 is -min(5,percentage of shares owned by manager). NegOwnershipG5 is -
max(0,percentage of shares owned by manager -5). CEO age and NegOwnership are without winsorising. The data
definition is from Kaplan and Zingales 1997. We control for firm age, firm size, asset tangibility, sales growth, and
leverage, dummy for bond rating, dividend payout and financial slack. Regressions are estimated with fixed firm
effects and year effects.

Variables [1] [2] [3] [4] [5]
Tobin's Q 0.011 0.031 0.010 0.010 0.009
(12.67) (6.20) (10.17) (9.99) (8.38)
Cash Flow 0.234 0.366 0.223 0.208 0.207
(16.10) (4.37) (14.03) (12.78) (11.98)
Ceo Age 0.001
(3.98)
Tobin's Q x Ceo Age x 10-1 -0.004
(-4.03)
Cash Flow x Ceo Age -0.002
(-1.58)
NegOwnership 0.001 0.001
(2.59) (2.21)
NegOwnershipL5 0.002
(1.99)
NegOwnershipG5 x 10-1 0.005
(1.11)
Tobin's Q x NegOwnership x 10-1 -0.002 -0.002
(-1.73) (-1.76)
Tobin's Q x NegOwnershipL5 -0.001
(-1.77)
Tobin's Q x NegOwnershipG5 x 10-2 -0.003
(-0.19)
Cash Flow x NegOwnership -0.006
(-2.86)
Cash Flow x NegOwnershipL5 -0.022 -0.017
(-4.92) (-2.4)
Cash Flow x NegOwnershipG5 -0.001 -0.002
(-0.49) (-0.87)
N 6 ,848 6 ,848 6 ,769 6 ,769 6 ,848
2
R 0.766 0.768 0.767 0.768 0.768

38
Table 9
Fixed Effect Regression of Investment on Cash Flow and Tobin's Q where investment is
COMPUSTAT data 8 plus research and development data46.

The sample covers firms from COMPUSTAT from 1993 to 2004. NegOwnership is - percentage of shares owned by
CEO. Dependent variable is Investment. Investment is COMPUSTAT data 8 plus research and development data46.
Cash flow is the sum of earnings before extraordinary items,item 18, and depreciation,item 18. Both investment and
cash flow is deflated by asset, which is item 6, at the beginning of the fiscal year. Tobin’s Q is the market value of asset
divided by the book value of asset. Market Value of asset is the sum of the book value of asset and market value of
equity minus the sum of the book value of common equity item 60 and balance sheet deferred taxes item 74. Market
value of equity is the product of data25 and data199. NegOwnershipL5 is -min(5,percentage of shares owned by
manager). NegOwnershipG5 is - max(0,percentage of shares owned by manager -5). CEO age and NegOwnership are
without winsorising. The data definition is from Kaplan and Zingales 1997. We control for firm age, firm size, asset
tangibility, sales growth, leverage, dummy for bond rating, dividend payout and financial slack. Regressions are
estimated with fixed firm effects and year effects.

Variables [1] [2] [3] [4] [5]
Tobin's Q 0.011 0.035 0.010 0.010 0.009
(11.50) (6.06) (9.25) (8.72) (7.36)
Cash Flow 0.231 0.333 0.229 0.208 0.199
(12.46) (3.22) (11.36) (10.20) (9.10)
Ceo Age 0.001
(2.98)
Tobin's Q x Ceo Age x 10-1 -0.004
(-4.12)
Cash Flow x Ceo Age -0.002
(-0.97)
NegOwnership 0.001 0.001
(2.02) (1.94)
NegOwnershipL5 0.004
(2.78)
NegOwnershipG5 x 10-2 0.001
(0.20)
Tobin's Q x NegOwnership x 10-1 -0.003 -0.003
(-1.83) (-2.21)
Tobin's Q x NegOwnershipL5 -0.001
(-1.68)
Tobin's Q x NegOwnershipG5 x 10-1 -0.002
(-0.80)
Cash Flow x NegOwnership -0.003
(-1.10)
Cash Flow x NegOwnershipL5 -0.028 -0.028
(-5.31) (-3.09)
Cash Flow x NegOwnershipG5 0.005 0.005
(1.81) (1.33)
N 4 ,273 4 ,273 4 ,221 4 ,221 4 ,273
2
R 0.824 0.826 0.826 0.827 0.826

39
Figure 1
Investment Tobin’s Q Sensitivity

The inverse capital demand function for a myopic manager D0 m D0 m will be steeper than the inverse capital demand
function for a non myopic manager D0 D0 .Suppose there is one unit increase in the growth opportunities. For the non
myopic manager, the capital demand function will shift outward from D0 D0 to D1 D1 . This leads to an increase in
equilibrium capital demanded from K0 to K1 . For the myopic manager, the capital demand function shift from
D0 m D0 m to D1m D1m . Equilibrium capital demanded increases from K0 to K2 . The investment Tobin’s Q
sensitivity for a non myopic manager is K0 K1 whereas the investment cash flow sensitivity for a myopic manager is
K0 K2 . This illustrates the reduction of investment Tobin’s Q sensitivity for myopic manager, the reduction given by
K1 K2 .

Cost of D1m
Capital D1
S(W0)
D0m
D0

Rf D1m
D0
D0m
K
W0 K0 K2 K1
K0 K2 K1

40
Figure 2
Investment Cash Flow Sensitivity

Suppose the wealth of the firm increases from W0 to W1 . Following Hubbard (1998) the inverse capital supply
function shifts outward from S (W0 ) to S (W1 ) . In case of a non myopic manager, the capital demand increases
from K0 to K1 (investment cash flow sensitivity) due to increase of firm’s internal funds from W0 to W1 . In case of
a myopic manager, the capital demand increases from K0 to K1m . Hence investment cash flow sensitivity of a
myopic manager is lower in magnitude compared to that of non myopic manager, the magnitude of reduction being
K1m K1

Cost of
Capital
S(W0)
D0m
S(W1)
D0

Rf
D0m D0

K
W0 W1 K0 K1

K1m