CEO Overconfidence and the Long-TermPerformance Following Unexpected R&D Increases

Sheng-Syan Chena, Keng-Yu Hoa, and Po-Hsin Ho∗,a

aDepartment of Finance, National Taiwan University, Taipei, Taiwan.

October 25, 2010

Abstract

In this paper, we examine the relationship between unexpected increase inresearch and development (R&D) expenditure and CEO overconfidence. Pre-vious studies show a positively significant market reaction to the increase inR&D expenditure both in the short run and long run. However, our empiricalresults show that the positive long-run stock performance is found only for firmswith non-overconfident CEOs. In addition, we also provide evidence that un-expected increase of R&D expenditure is a beneficial investment decision onlyfor firms without overconfident CEOs. We suggest that the empirical findingsmay be due to overestimation of future cash flow and overinvestment by over-confident CEOs. Our study indicates that overinvestment behavior often leadsto value-destroying R&D investment projects.

Keywords: R&D Expenditure, CEO overconfidence, Long-Run PerformanceJEL Classification: G00; G14; G30

∗Corresponding author.Tel: +886-2-33663834; E-mail address: d95723002@ntu.edu.tw; Fax: +886-2-23660764; Address:Department of Finance, National Taiwan University, No. 1, Sec. 4, Roosevelt Road, Taipei, 10617Taiwan.

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1 Introduction

Many studies have documented that how the overconfident CEOs affect the firms’

investment decisions. The story of CEO overconfidence stems from a prominent styl-

ized fact, the “better-than-average” effect, in the psychology literature. Prior research

(Larwood and Whittaker (1977); Svenson (1981); Alicke (1985)) suggest that people

tend to overestimate their wisdom or skill relative to the average. People are more

likely to attribute good outcomes to their actions and bad outcomes to bad luck or

external factors. Previous studies consider that attribute the evidence of overconfi-

dence to three main factors: the illusion of control, a high degree of commitment to

nice outcomes, and abstract reference points which make it hard to compare perfor-

mance between individuals (Weinstein (1980); Alicke, Klotz, Breitenbecher, Yurak,

and Vredenburg (1995)). First, a CEO tends to be overconfident when he believes

that the outcome is under his control. Second, a CEO more likely to be overconfi-

dent when he is highly committed to firm’s good performance because his personal

wealth varies with the company’s stock price and he invests his human capital in the

company. Lastly, a CEO overestimates his ability of choosing profitable investment

projects when the reference point of comparing his skill relative to the average is

abstract.

Using the overestimation of future cash flow as the source of managerial overconfi-

dence, Malmendier and Tate (2005) propose a simple model and the empirically show

that managerial overconfidence can cause corporate investment distortions. They find

that the sensitivity of investment to cash flow is strongest in the presence of overcon-

fidence. In addition, Malmendier and Tate (2008) point out that CEO overconfidence

can help to explain merger decisions. Overconfident CEOs overestimate their skill to

generate returns. As a result, they pursuit value-destroying mergers and overpay for

target companies. Malmendier and Tate (2008) also suggest that, in general, over-

confident CEOs are more likely to engage in mergers and acquisitions. They find

that the possibility of making an acquisition are 65% higher if the CEO is classified

as overconfident, the magnitude is larger if the merger is diversifying and does not

require external financing. The market reaction at merger announcement done by

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overconfident CEOs is significantly more negative than those by non-overconfident

CEOs.

Being different from Malmendier and Tate’s analytic framework, Ben-David, Gra-

ham, and Harvey (2007) provide a theoretic framework to analyze the relations be-

tween managerial overconfidence and corporate policies. However, they define the

CEO overconfidence as miscalibration. Based on their definitions, overconfident man-

agers who either underestimate the volatility of their firms’ future cash flows or use

lower discount rates than unbiased managers do. Nevertheless, they propose that

overconfident managers invest more than less confident managers. They empirically

use the narrowness of probability distributions for predicted future stock market re-

turns which are surveyed from hundreds of U.S. Chief Financial Officer (CFOs) as a

proxy for each respondent’s confidence, and the findings support their prediction.

Geol and Thakor (2009) also propose a theoretical model on the relations among

overconfidence, CEO selection, and corporate governance. In their paper, there are

three types of CEOs: excessively overconfident CEOs, moderate overconfident CEOs,

and excessively diffident CEOs. They predict that excessively overconfident CEOs

face a greater likelihood of forced turnover than CEOs with moderate overconfidence.

They further demonstrate excessively overconfident CEOs reduce firm value due to

overinvestment. Campbell, Gallmeyer, Johnson, Rutherford, and Stanley (2010) em-

pirically examine the prediction of Geol and Thakor (2009) and find strong support

to their model.

Other empirical studies focusing on the relevant research topics include Mal-

mendier, Tate, and Yan (2010) who examine the relation between overconfidence

and major financial decisions. In addition, Liu and Taffler (2008) study the relation

between overconfidence and M&A decisions, while Billett and Qian (2009) examine

the linkage between overconfidence and M&A frequencies. Finally, Hribar and Yang

(2010) examines whether overconfidence increases the issuance of overly optimistic

management earnings forecasts and greater earnings management.

Although, as mentioned above, there are many prior studies on various financial

decisions and CEO overconfidence, few studies investigate the related research issue

from R&D expenditures. To the best of our knowledge, the only two related studies

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are Galasso and Simco (2010) and Hirshleifer, Low, and Teoh (2010), who study

the relationship between CEO overconfidence and the innovation behaviour. They

find that overconfident CEOs are more likely to make their firms towards innovation.

They also examine relation between CEO overconfidence and patent citations. The

results suggest that overconfident CEOs obtain more patents and patent citations.

Our study, on the other hand, deviates from their research in two important ways.

First, our study investigates the relation between CEO overconfidence and the effect

of unexpected increase in R&D expenditure. Second, we examine the long-term

abnormal stock return and operating performance following unexpected increase of

R&D from the perspective of CEO overconfidence. Both Galasso and Simco (2010)

and Hirshleifer, Low, and Teoh (2010) do not examine the effect of overconfidence on

firm performance following unexpected R&D increase.

R&D decisions of overconfident CEOs are an interesting research issue at least

for three reasons. First, R&D increases represent a managerial decision made by

CEOs and Eberhart, Maxwell, and Siddique (2004) argue that R&D increases are

beneficial investment decisions in the long run. From this perspective, we wonder

whether CEOs with overconfidence could also make profitable R&D investments, since

previous studies point out that managerial overconfidence result in overinvestment

(Malmendier and Tate (2005; 2008); Ben-David, Graham, and Harvey (2007); Geol

and Thakor (2009)). We thus conjecture that only CEOs without overconfidence

could make beneficial R&D decisions.

Second, past research (Chan, Martin, Kensinger (1990); Zantout and Tsetsekos

(1994); Szewczyk, Tsetsekos, and Zantout (1996); Eberhart, Maxwell, and Siddique

(2004)) suggest that market reactions of increase in R&D expenditure are positively

significant. However, the market investor reactions at merger announcement by CEOs

with overconfidence are lower than firms without overconfident CEOs in Malmendier

and Tate (2008). Hence, based on previous discussion on CEO overconfidence, we

conjecture that investors could identify the overconfident level of CEOs, and only

firms with non-overconfident CEOs could earn long-term abnormal stock returns.

Third, although R&D investment is one type of firm’s investment decision, it has

unique features that are different from other long-term investments. The information

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contain of an R&D increase not only conveys tangible information but also reflects

intangible information about the prospect of future cash flow. Since overconfident

CEOs may overestimate future cash flow of their investment projects, it is intriguing to

further study R&D expenditure from the aspect of CEO overconfidence. Furthermore,

Simon and Houghton (2003) argue that CEO overconfidence is positively related to the

introduction of pioneering and risky products using survey data. They also indicate

that those products are less likely to achieve success. Because R&D expenditure may

provide a new technology development or create a new product, the influence of CEO

overconfidence might play an important role in the innovation decision of company.

We use various methodologies in detecting the long-term abnormal stock returns

and operating performance. For the abnormal stock returns, we find consistent evi-

dence that non-overconfident CEOs exhibit significantly positive long-term abnormal

stock returns following R&D increases. On the other hand, CEOs with overconfidence

could not earn significantly positive long-term abnormal stock returns following R&D

increases. For the aspect of operating performance, our findings indicate that R&D

investment are beneficial only when a firm’s CEO is not overconfident. In addition,

both the stock and operating performance results are robust for high-tech samples,

but weaker for low-tech samples.

Our research contributes the literature by combining CEO overconfidence and

R&D investment decisions. To the best of our knowledge, we are the first paper

to examine the CEO overconfidence from the aspect of unexpected R&D increase.

Second, comparing with previous research which indicates that investors could earn

positively abnormal stock returns in R&D increases decisions, we show that investor

could gain significantly abnormal stock returns for firms with non-overconfident CEOs

from the effect of increase in R&D expenditure. The phenomenons are due to overcon-

fident CEO overestimates the future prospect of investment project. Finally, we find

that R&D increases are beneficial investments only for firms without overconfident

CEOs.

The rest of the paper is organized as follows. In section 2, we describe the data

used in our paper. Section 3 introduces the methodology used in this paper. Section

4 analyzes the empirical results, and section 5 concludes the paper.

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2 Data

2.1 Measuring Overconfidence

Our measures of CEO overconfidence are the same as a series paper of Malmendier

and Tate (2005; 2008). They employ the panel data of the CEO’s personal portfolios

in executive options exercise to identify whether CEO is overconfidence. CEO obtains

the right to purchase their company’s share from executive options, and the exercise

price of the options are usually at the stock price on the grant date. The duration of

most executive options is ten years, and the vesting period in which the options are

unexercisable are usually four years.

Merton (1973) derives that investors should not exercise their options early be-

cause the time value of options is non-negative for European call options on non-

dividend-paying stocks. However, this logic may not apply to executive options,

because executive options are non-tradeable and CEOs cannot short sell their hold-

ings to hedge the idiosyncratic risk of their company’s stocks. In addition, CEOs

also highly exposed their wealth to company risk since a large part of their compen-

sation is equity-based and they invest their human capital in the firms. As a result,

risk-averse CEOs should exercise option grants early if the stock price is sufficiently

high. Lambert, Larcker, and Verrecchia (1991) propose a theoretical framework to

show that risk averse CEOs should exercise in-the-money executive stock options be-

fore expiration to avoid their exposure to company-specific risk. Hall and Murphy

(2002) further derive that overconfident CEOs delay exercise their option grants until

expiration, even when the underlying stock price exceeds rational exercise thresholds.

In our sample, CEOs persistently deny to exercise highly in-the-money vested op-

tions until the year of expiration. The interpretation of this delay exercise behaviour

is overconfidence, i.e., overestimation of the firm’s future returns. Other alternative

explanations of this behavior such as positive inside information, signaling, board

pressure, risk tolerance, and taxes have demonstrated fail to interpret the delay exer-

cise behavior in Malmendier and Tate (2008). They provide strong evidence that the

empirical results are consistent with fact that overconfident CEOs think that they

can make profitable merger and acquisition activities. We adopt the same criterions

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in Malmendier and Tate (2008) as the CEO overconfidence indicator and also name

the variable as Longholder. CEOs who, at least once during their tenure, keep an op-

tion until the year of expiration, even though the option is at least 40% in-the-money

during its final year. The exercise threshold of 40% is calibrated from the model of

Hall and Murphy (2002) which assumes a constant relative risk aversion (CRRA) of

three and 67% of his or her wealth in the company stock.1 We apply this measure as

a managerial fixed effect and it reflects that overconfidence is a persistent trait.

2.2 Sample Construction

The initial sample of our overconfident CEO dataset is identical in Malmendier and

Tate (2008),2 and this sample is constructed from Hall and Liebman (1998) and

Yermack (1995). The empirical sample covers 477 large publicly traded US firms from

the years 1980 to 1994. A firm must be included at least four times on the lists of the

largest U.S. companies constructed by Forbes magazine from 1984 to 1994. These

data are constructed by complete information on CEOs stock and option holdings

and provide an entirely detailed picture of the CEO’s portfolio rebalancing over his

tenure.

Due to the sample limitation of CEO overconfidence data, our research period cov-

ers from 1980 to 1994. The stock price data of our samples are obtained from Center

for Research on Security Prices (CRSP). We include only ordinary common equities

whose share type codes are “10” or “11” in CRSP to estimate the abnormal stock

return. Moreover, we retrieve the accounting data from COMPUSTAT database to

compute abnormal operating performance. We impose three major requirements fol-

lowing Eberhart, Maxwell, and Siddique (2004) to measure the unexpected increase

of R&D expenditures. First, an unexpected R&D increase is defined as a firm’s ra-

tio of R&D to assets increase. Second, we concentrate on firms whose R&D intensity

(defined as ratios of R&D to assets and ratios of R&D to sales) are greater than 5 per-

cent. Third, we only focus on an economically significant R&D increase. As a result,

1The particular choice of parameter values is not important for our results: according to Mal-mendier and Tate (2008), the median percentage in-the-money entering the final year for optionsheld to expiration is 253%.

2We are grateful to Professor Ulrike Malmendier who provides the CEO overconfidence data.

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the firm must also increases its dollar R&D at least 5 percent and its ratio of R&D

to assets at least 5 percent. Finally, we yield 54 overconfident CEOs with significant

R&D increase and 97 non-overconfident CEOs with significant R&D increase.

We use annual accounting data and follow Eberhart, Maxwell, and Siddique (2004)

which use a three-month lag to allow the market to be informed of the accounting

data. Hence, we begin measuring these returns from the fourth month following the

fiscal year-end in which the firm increases its R&D.3 The definition of all variables

used in this paper are described in the Appendix.

3 Methodology

Although there is still much debate on the empirical estimation of long-term abnormal

stock returns, there are two popular empirical methods on computing long-run abnor-

mal stock returns: buy-and-hold abnormal returns (BHARs hereafter) of the event

firm relative to a benchmark and calendar-time abnormal returns (CTARs hereafter)

using a factor model to estimate the risk adjusted returns.

Barber and Lyon (1997) show that the arithmetic summation of returns do not

precisely reflect the returns of investors. Furthermore, Lyon, Barber, and Tsai (1999)

argue that the calendar-time return method would bias in nonrandom sample. How-

ever, Fama (1998) argues that event-time returns exhibit a cross-sectional dependence

problem that causes the downward bias of standard error.

Additionally, there is also a debate regarding the use of value-weighted calendar-

time returns versus equal-weighted calendar-time returns. On one hand, Loughran

and Ritter (2000) argue that equal-weighted portfolio is better, since the mispricing is

more likely to occur with smaller firms. On the other hand, Fama (1998) documents

that value-weighted portfolio is more appropriate because it considers the total wealth

effects experienced by investors.

3In addition, in order to rule out the repeated event of R&D increase, we also examine the samplewithout overlapping. In other words, a firm can only be included in the sample once during ourresearch period.

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3.1 Buy-And-Hold Abnormal Return (BHAR)

We compute the one-, three-, and five-year BHARs for all sample firms with a three-

month lag following the unexpected R&D increase, respectively. For the ith sample

firm from month T1 to T 2, BHARi,t is expressed as follows:

BHARi,t =T2∏t=T1

(1 +Ri,t)−T2∏t=T1

(1 +Rb,t), (1)

where Ri,t is the return of the sample firm in event month t, and Rb,t is the return of

the benchmark over the same period. The test statistics used is the following:

t = BHAR×√n/σ(BHAR), (2)

where BHAR is the average across firms’ BHARs, σ(BHAR) is the cross-sectional

standard deviation of BHARs for all sample firms, and n is the number of firms.

Barber and Lyon (1997) document that the control firms approach eliminate the

new listing bias, rebalancing bias, and the skewness problem. Hence, we use the

matching firm approach as the benchmark. Following Barber and Lyon (1997), we

first identify all firms with a market value of equity within 30% of the market value

of equity of the sample firm at the beginning of the year in which R&D increase

significantly. From this set of firms, we choose the firm which has the closest book-

to-market equity ratio to the sample firm.

3.2 Calendar-Time Abnormal Return (CTAR)

Since Fama (1998) point out that the problem of cross-sectional dependence of return

is severe when the sample event dates are close, we use a calendar-time approach that

could mitigate such bias.

We use a Fama-French three-factor model and estimate the average monthly ab-

normal returns, αi , by using the following equation:

Rpt −Rft = αi + βi(Rmt −Rft) + siSMBt + hiHMLt + εpt, (3)

where Rpt is the monthly return on the equal- or value-weighted portfolio in calendar

month t (where a sample stock is included if month t is within the 60-month period

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following its R&D increase) , Rft is the 1-month treasury bill return, Rmt is the

CRSP value-weighted market index return, SMBt is the difference in the returns on

the value-weighted portfolios of small and big stocks, and HMLt is the difference in

the returns on the value-weighted portfolios of high and low book-to-market stocks.

We also use the Carhart four-factor4 model to measure the monthly abnormal re-

turn following R&D increase by estimating the intercept from the following equation:

Rpt −Rft = αi + βi(Rmt −Rft) + siSMBt + hiHMLt +miUMDt + εpt, (4)

where UMDt is the difference in the return on a value-weighted portfolio of high and

low momentum stocks. Additionally, we impose each calendar month to consist of at

least three observations in calculating the monthly average returns of the calendar-

time return series.

In addition, Berk, Green, and Naik (2000) argue that a firm’s systematic risk may

change because of an investment in R&D. To confirm this argument, we also estimate

each factor loading based on a rolling regression approach in Equation (3) and (4). For

instance, we use the first 60 months to estimate the factor loadings in the Equations

(3) and (4). We then estimate the abnormal return in the 61st month as the difference

between the actual portfolio return and the expected portfolio return. The expected

portfolio return is computed as the estimated factor loadings times their respective

factor returns in the 61st month. We repeat this step every month and then average

the time series of the abnormal returns. Finally, we use the volatility of the time

series abnormal returns to estimate the standard errors of their respective averages.

3.3 Operating Performance Measures

We compute the sample firm’s operating performance from the first year to the fifth

year following the year in which they unexpected increase R&D by an economically

significant amount. The abnormal operating performance is defined as a sample firm’s

raw operating performance minus its control firm’s operating performance. We first

screen control firms that do not have the same corporate event as the sample firms

4We obtain the data of Rm, Rf , SMB, HML, and UMD from Wharton Research Data Services,WRDS, website.

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in the event year. In this study, we use two different criteria to select the matched

firm. The first principle of the matched firm is based on the industry and pre-event

performance. We choose a group of control firms which are in the same two-digit SIC

code as the sample firm that do not unexpectedly significant increase its R&D during

the sample firm’s R&D increase year. From those screened firms, we select a firm as

the matched control firm that has the closest operating performance measures (OPMs

hereafter) with sample firm’s OPMs prior the sample firm’s R&D increase year.

For the second matching criterion, we form another group of control firms based

on the sample firm’s characteristics, i.e. size, book-to-market equity ratio, and mo-

mentum (defined as the prior 12 months returns). Ikenberry and Ramnath (2002) and

Eberhart, Maxwell, Siddique (2004) also use this matching criteria. In the beginning

of a sample firm’s R&D increase year, we choose a matched firm that the market

equity value is within 30% of the market equity value of the sample firm and then we

choose the jointly lowest value absolute of the difference in these characteristics. Fi-

nally, compare the relative operating performance improvements between the sample

firm and control firm.

4 Empirical Results

4.1 Descriptive Statistics

Panel A of Table 1 shows the sample selection procedure in this study. We finally

obtain 151 firm-year observations with 39 firms. There are 54 (15) firm-year (firm)

observations for overconfident CEOs and 97 (30) firm-year (firm) observations for

non-overconfident CEOs.

Panel B of Table 1 reports the descriptive statistics, and all variables in dollar

are adjusted in the 1994 dollars. On average, for the year in which sample firms

increase their R&D, firms with overconfident CEOs have $8,264 million in annual sales

and sample firms with non-overconfident CEOs have $3,648 million in annual sales.

The average level of book value of total assets and market value of common stock

for firms with overconfident CEOs are 7,142 million and 8,628 million, respectively.

The average level of book value of total assets and market value of common stock

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for firms with non-overconfident CEOs are 3,091 million, 4,045 million, respectively.

The average and median book-to-market equity ratio for firms with overconfident

CEOs are significantly greater than non-overconfident CEOs. We compute the book-

to-market equity as the ratio of book equity to market equity and apply the same

procedure in Daniel and Titman (1997) to define book equity. All the mean difference

tests of firm characteristics between overconfident CEOs and non-overconfident CEOs

are at least significant under 5% level. In addition, for all the variables, the median

test of the difference between overconfident CEOs and non-overconfident CEOs are

statistically significant at 1% level, except for median difference test of the book-to-

market equity which are significant under 5% level.

Moreover, the median difference tests of the R&D intensity measure and the per-

centage of increase in dollar R&D between overconfident CEOs and non-overconfident

CEOs are significant under 5% level. To sum up, based on the mean and median dif-

ference tests, Table 1 suggests that the characteristics of firms with and without

overconfident CEOs who unexpected increase R&D may be different. Accordingly, it

is important to control the firm characteristics in the following cross-sectional regres-

sion analysis.

[Insert Table 1 about here]

4.2 Buy-and-Hold Abnormal Returns

Table 2 reports the one-, three-, and five-year BHARs following unexpected signifi-

cant R&D increase,5 respectively. The median test results of BHARs in Table 2 are

similar to those of the mean test. Hence, we concentrate our discussion on the results

of the mean test. On average, firms with non-overconfident CEOs exhibit positive

abnormal stock returns following R&D increase. In Panel A, the result shows that

non-overconfident CEOs earn almost 14% (54% or 102%) for investing one (three or

five) years. Moreover, the positive BHARs for firms with non-overconfident CEOs are

5The results of non-overlapping samples are qualitatively similar with full samples. Moreover, wefurther examine our results by trimming and winsorization the 5th and 95th of our full samples andnon-overlapping samples, and the results are also qualitatively similar with the original full samples.For brevity, we do not report here.

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positively significant during all investment periods. On the other hand, the one- and

three-year BHARs of firms with overconfident CEOs do not significant differ from

zero, and the five-year BHARs for overconfident CEOs are even negatively signifi-

cant. Furthermore, for the full samples, the BHARs difference between firms with

non-overconfident CEOs and overconfident CEOs are positively significant. In other

words, the average BHARs of firms with non-overconfident CEOs are significantly

greater than firms with overconfident CEOs.

Although Eberhart, Maxwell, and Siddique (2004) suggest that R&D expenditure

usually conveys intangible information so that investors under-react their stock price,

our finding suggests that such argument exists only for non-overconfident CEOs. CEO

with overconfidence may feel that they are “better-than-average” and overestimate the

future cash flow after investing the R&D project. The R&D increase of overconfident

CEOs may suggest another form of overinvestment, and investors may identify that

overconfident CEOs overestimate future cash.

Furthermore, previous studies (Chan, Martin, and Kensinger (1990); Eberhart,

Maxwell, and Siddique (2004); Brown, Fazzari, and Petersen (2009)) point out that

R&D expenditure may have difference effects on high- and low-tech industries because

the attribute of R&D investment for high-tech industries are more complicated than

low-tech industries. Accordingly, we separate our samples into high- and low-tech

industry categories by Brown, Fazzari, and Petersen (2009)6 in Panel B and Panel

C of Table 2, respectively. In Panel B, all BHARs are positively significant for non-

overconfident CEOs and are insignificant different from zero for overconfident CEOs.

Except for one-year BHAR, the average BHAR difference between non-overconfident

CEOs and overconfident CEOs are also significantly positive at 1% level for three- and

five-year BHARs. For the low-tech firms in Panel C of Table 2, only one-year BHAR

for non-overconfident CEOs are positively significant. The BHARs for overconfident

CEOs are still insignificant different from zero. However, the average difference be-

tween non-overconfident CEOs and overconfident CEOs are still positively significant

for one- and five-year BHARs.

6Brown, Fazzari, and Petersen (2009) classifies the industries with SIC codes 283, 357, 366, 367,383, 384, and 737 as high-tech industries.

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The results for high-tech firms are consistent with that firms with non-overconfident

CEOs could earn significantly positive abnormal returns than firm with overconfident

CEOs following their R&D increase.

[Insert Table 2 about here]

Table 3 provides the empirical results from the cross-sectional regressions. We

examine the relationship between the one-, three-, and five-year BHAR and CEO

overconfidence after firm significantly increase their R&D, respectively. Except for

Model (3) of Table 3, θ1 in Model (1) and Model (5) indicate that the BHARs following

R&D increase for firms with overconfident CEOs are significantly lower than non-

overconfident CEOs after controlling other factors. Furthermore, for Model (2), Model

(4), and Model (6) of Table 3, we add high-tech industry dummies and the interaction

term between CEO overconfidence and high-tech industry. Contrast with the results

of θ1 in Model (1), Model (3), and Model (5) of Table 3, not only the coefficients of θ1

are not negatively significant anymore, but the coefficients of θ3 are not significant,

except for model (4). However, it does not mean that CEO overconfidence plays no

role in high-tech industry. In fact, we need to test the sum of θ1 and θ3 to examine

the importance of CEO overconfidence in high-tech industry. The bottom of Table

3 reports the results of the Wald test for θ1 + θ3 = 0. The results show that all the

statistics of the Wald test in Table 3 are negatively significant. These findings are

consistent with our previous results which indicate the BHARs of overconfident CEOs

are significantly lower than non-overconfident CEOs.

[Insert Table 3 about here]

To conclude, investors may underreact the information content of R&D increase

only for firms with non-overconfident CEOs. A possible explanation could be that

overconfident CEOs may invest in value-destroying projects resulting from their over-

estimation of future cash flow or underestimation of the risk from R&D investment.

However, this phenomenon is more pronounced for high-tech firms.

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4.3 Calendar-Time Abnormal Returns

Table 4 reports the long-term abnormal stock returns following R&D increase by

calendar-time approach.7 In Panel A of Table 4, for the full sample, we estimate

the risk adjusted abnormal returns using the Fama-French three-factor and Carhart

four-factor model. For both the equal- and value-weighted schemes, the intercepts

of non-overconfident CEOs are significantly positive with Fama-French three-factor

model (0.61 percent and 0.65 percent) and with the Carhart four-factor model (0.67

percent and 0.61 percent). However, the abnormal returns of overconfident CEOs

do not significantly differ from zero for Fama-French three-factor model and Carhart

four-factor model under equal- or value-weighted cases. Furthermore, the difference of

abnormal returns between non-overconfident CEOs and overconfident CEOs are posi-

tively significant for equal-weighted calendar-time portfolios when using Fama-French

three-factor model. In addition, when estimating by Carhart four-factor model, the

difference of abnormal returns between non-overconfident CEOs and overconfident

CEOs are positively significant for value-weighted calendar-time portfolios.

We report the rolling regression results in Panel B. With the Fama-French three-

factor model and the Carhart four-factor model, the differences of the equal- and

value-weighted abnormal returns between non-overconfident CEOs and overconfident

CEOs are all positively significant at least under 5 percent significance level. Fur-

thermore, the abnormal stock returns of value-weighted calendar time portfolio for

firms with overconfident CEOs are significantly negative for the Fama-French three

factor mode and Carhart four factor model. The results suggest that our findings are

robust when take into the consideration of the changes in risk of the portfolio over

time.

[Insert Table 4 about here]

Table 5 provides the subsample tests of abnormal stock returns for calendar time

portfolio. Our sample firms are divided into high- and low-tech firms according to

the definition in Brown, Fazzari, and Petersen (2009). With the high-tech samples,

7We also estimate the abnormal return by the purged factor portfolios which eliminate the samplefirms and non-overlap samples, and the results are qualitatively similar.

15

we obtain positively significant abnormal return estimates across all categories for

non-overconfident CEOs. The abnormal returns computed from the Carhart four-

factor model for overconfident CEOs become positively significant with equal- and

value-weighted return. The abnormal return difference between non-overconfident

CEOs and overconfident CEOs are significantly positive with value weighting. For

the low-tech firms, however, both the equal- and value-weighted abnormal returns are

insignificantly different from zero, using the Fama-French three factor model or the

Carhart four-factor model, for non-overconfident and overconfident CEOs. Therefore,

the abnormal return difference between non-overconfident CEOs and overconfident

CEOs are insignificantly different from zero for low-tech firms. In summary, the

results of calendar-time abnormal returns are consistent with BHARs.

[Insert Table 5 about here]

4.4 Operating Performance

We report the abnormal operating performance of our sample firms in Table 6.8 The

empirical results of median tests of abnormal operating performance are similar to

the results of mean tests. Hence, we only concentrate the results on the mean tests.

In panel A of Table 6, we show the changes in the abnormal operating performance of

OPM1 following R&D increase. The results based on the industry and performance

matching firm criterion, on average, reveal that firms with non-overconfident CEOs

significantly improve their operating performance than their matched firm. This

finding is consistent with previous literature that R&D investments benefit company’s

operating performance in the long run.

On the other hand, overconfident CEOs do not operate better than their bench-

mark following their R&D increase. This result consists with our inference that

overconfident CEOs exhibit overinvestment. As a result, their abnormal operating

performance reveal flat. These results may suggest that R&D is a beneficial invest-

ment only when firm’s CEO is not overconfident. In addition, we compare the relative

8In order to avoiding the extreme value affect out results, we also compute the abnormal operatingperformance by trimming and winsorization the 5th and the 95th of our smaples, and the results aresimilar. Also, we estimate the abnormal operating performance for non-overlapping samples, andthe results are still qualitatively similar to the full samples.

16

changes in abnormal operating performance for non-overconfident CEOs with over-

confident CEOs and the difference between them are significantly positive each year.

Similarly, non-overconfident CEOs also outperform their benchmark following the

R&D increase year when we use the characteristics as the matching firm criteria. For

overconfident CEOs, their abnormal operating performance are still insignificantly dif-

ferent from zero. Being consistent with the industry and performance match method,

the difference of abnormal operating performance between CEOs with overconfidence

and without overconfidence are significantly positive except for the first year following

significantly increase in R&D.

In Panel B of Table 6, we report the changes in the abnormal operating per-

formance of OPM2 following R&D increase. On average, the results of industry

and performance match also reveal that non-overconfident CEOs significantly im-

prove their profit margin following R&D increase. Although CEOs with overconfi-

dence also significant improve their operating performance than their control firm

after the third year following R&D increase, the magnitude of abnormal operat-

ing performance for firms with non-overconfident CEOs are greater than firms with

overconfident CEOs. Therefore, the difference of abnormal operating performance

between non-overconfident CEOs and overconfident CEOs are still significantly pos-

itive. The results are qualitatively similar to the results by using performance and

industry matched firm criterion when choosing the characteristic as the alternative

matched method. The difference of abnormal operating performance between non-

overconfident CEOs and overconfident CEOs are still significantly positive after the

second year following the R&D increase.

The implication of Table 6 suggests that R&D increase may benefit firms only

when their CEOs are not overconfident. Eberhart, Maxwell, and Siddique (2004)

point out that, in general, R&D investment are beneficial. We further examine this

argument from the viewpoint of CEO overconfidence. When considering CEO over-

confidence, R&D investment may benefit firms whose CEO are not overconfidence.

Otherwise, the R&D investments may be inefficiency if CEO is overconfident.

[Insert Table 6 about here]

17

Table 7 presents the subsample tests for the abnormal operating performance.9

For the high-tech firms in Panel A of Table 7, the result of abnormal operating

performance for non-overconfident CEOs are significantly positive and overconfident

CEOs are insignificantly different from zero. In addition, the difference between them

are also significantly positive from the second year following R&D increase both for the

results of industry and performance match method and characteristic match method.

For the low-tech sample in Panel B of Table 7, although the abnormal operat-

ing performance of OPM1 for overconfident CEOs are still insignificantly different

from zero, the abnormal OPM1 for non-overconfident CEOs are only significantly

positive in the first two year following R&D increase for industry and performance

match. Additionally, the difference for OPM1 between non-overconfident CEOs and

overconfident CEOs are significantly positive only in the first three years following

R&D increase. The results of abnormal operating performance of OPM2 for non-

overconfident CEOs are significantly positive in the second, fourth, and fifth year fol-

lowing R&D increase. The difference of abnormal OPM2 between non-overconfident

CEOs and overconfident CEOs are still significantly positive every year except for the

third year following R&D increase.

For the case of characteristic matching firm approach, however, the non-overconfident

CEOs even perform significantly worse than their benchmark for OPM1 in the third

and fourth year following significantly increase in R&D. Besides, the non-overconfident

CEOs also significantly underperform their benchmark for OPM2 in the third year

following R&D increase. However, the difference of abnormal operating performance

between non-overconfident CEOs and overconfident CEOs are insignificantly different

from zero each year. To summarize, the results in Table 6 are also consistent with our

previous results that the effect of R&D increases are significantly positive in high-tech

industries, and Eberhart, Maxwell, and Siddique (2004) also obtain similar empirical

evidence.

[Insert Table 7 about here]

9For the sake of brevity, we only report the results of mean test. The results of median test aresimilar to the results of mean tests.

18

Table 8 provides the cross-sectional regression of long-term abnormal operating

performance. We further examine the relationship between the five-year abnormal

operating performance and CEO overconfidence. Except for Model (5) of Table 8,

θ1 in Model (1) and Model (3) suggest that the abnormal operating performance

following significantly increase in R&D for overconfident CEOs are significantly lower

than non-overconfident CEOs. The result is consistent with our previous empirical

finding.

For Model (2) and Model (4) the results of Wald test in the bottom of Table 8

are negatively significant except for Model (6), although the coefficients of θ1 are not

positively significant anymore. To sum up, strongly reinforcing evidence about the

effect of CEO overconfidence on R&D expenditure are provided by Table 8 that over-

confidence is associated with a substantially lower abnormal operating performance

following unexpected significantly R&D increase, especially in high-tech industries.

[Insert Table 8 about here]

5 Conclusion

In this paper, we investigate the relationship between unexpected R&D increase and

CEO overconfidence. Previous studies document that R&D increase is a beneficial in-

vestment decisions that could improve a firm’s operating performance following R&D

increase. In addition, investors usually underreact to intangible information, such as

the prospect of future cash flow of R&D investment. Therefore, the long-term abnor-

mal stock returns following R&D increase are significantly positive. CEOs may feel

themselves “better-than-average” and they often result in the overestimation of the

future return from their investment or underestimation the likelihood of failure. Since

CEO overconfidence often leads to overinvestment behavior or raise the investment-

cash flow sensitivity, we examine whether the unexpected R&D increase made by

overconfident CEOs still benefit for firms and whether investor exhibit underreaction

for firms with overconfident CEOs following unexpected R&D increase.

We examine both the long-term abnormal stock and operating performance by

varies methodologies. The results show that investors earn abnormal stock returns

19

for firms with non-overconfident CEOs following R&D increase. In addition, the long-

term abnormal stock returns for non-overconfident CEOs are significantly larger than

overconfident CEOs. We also find consistently strong evidence that R&D increase is a

beneficial investment decisions only for firms with non-overconfident CEOs. In other

words, the long-term abnormal operating performance for non-overconfident CEOs

are significantly greater than overconfident CEOs. Cross-sectional regression analyses

also show that overconfidence is associated with a substantially lower abnormal stock

and operating performance following unexpected significantly R&D increase. Further-

more, our findings are stronger for high-tech industries because R&D expenditures

are usually complicated and essential for high-tech industries.

Our results contribute to the link between behavior corporate finance and R&D

investment decisions. A large of recent studies investigates how CEO overconfidence

affects a firm’s corporate decisions. Past research demonstrates that increases in R&D

expenditure are beneficial for shareholders and firms. Our work complements these

gaps by using option based overconfidence measure to deeply examine the impact

of R&D increase. Our finding also provide empirically evidence to support previous

research which propose overconfident manager are more like to be the pioneer to

introduce risky products and these products are less likely to achieve success. Since

R&D expenditure may bring a new technology development or create a new product,

the effect of increase in R&D expenditure for overconfident CEOs may be inefficiency.

20

Appendix: Variable Definitions

Variables Definitions

Panel A: CEO Overconfidence Measure and Characteristics*

Longholder Dummy variable: 1 if the CEO at some point during his tenure heldan option grants until the last year before expiration, in case thatthe option grants was at least 40% in-the-money entering its lastyear

CEO Stock Ownership Percentage of common stocks owned by CEO and his immediatefamily

CEO Vested Options CEO’s holdings of options that are exercisable within six monthsdivided by the common shares outstanding, and multiplied by ten

Panel B: Firm Characteristics

Market Capitalization Fiscal year end price (Compustat Item 25) multiplied by outstandingshares (Compustat Item 199)

Tobin q Market value of assets to book value of assets (Compustat Item 6)

Market Value of Assets Book value of assets (Compustat Item 6), minus the book value ofequity plus the market value of common equity (Compustat Item25 × Compustat Item 199)

Book Value of CommonEquity

Stockholder’s equity (Compustat Item 216) plus any deferred tax(Compustat Item 74) and any investment tax credit (CompustatItem 208), minus any preferred stock

Preferred Stock Redemption value (Compustat Item 56) if it is available, otherwiseuse liquidating value (Compustat Item 10) if it is available, and ifnot available use carrying value (Compustat Item 130)

Book-to-Market equity Book value of common equity to market value of common equity

Sales Growth Sum of sales (Compustat Item 12) minus prior year sales and dividedby prior year sales

Cash Ratio of cash and short-term Investments (Compustat Item 1) tobook assets

PPE/Emp Ratio of property, plant, and equipment (Compustat Item 7) to theemployees (Compustat Item 29)

Panel C: R&D Intensity Measure

R&D/Assets R&D (Compustat Item 46) divided by assets (Compustat Item 6)

R&D/Sales R&D divided by sales

Panel D: Profit Margin Measure

OPM1 EBIT (Compustat Item 178) divided by sales

OPM2 Sum of EBIT and after-tax R&D (taxes (Compustat Item 16) totaxable income (Compustat Item 170) multiplied by R&D) dividedby sales

* The data in Panel A are provided by Professor Ulrike Malmendier.

21

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24

Tab

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mb

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aveLongh

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445

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mb

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aveLongh

older

an

du

nex

pec

ted

sign

ifica

ntl

yin

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&D

151

39

Pan

elB

:S

um

mary

Sta

tist

ics

Non

-over

con

fid

ent

CE

Os

(N=

97)

Over

con

fid

ent

CE

Os

(N=

54)

Diff

eren

ce

Mea

nM

edia

nS

td.

Dev

.M

ean

Med

ian

Std

.D

ev.

Mea

nM

edia

n

Sale

s($

MM

)3,6

48.0

81,2

12.9

38,0

95.6

98,2

64.1

55,5

42.0

011,9

55.5

40.0

133**

<.0

001***

Tota

lA

sset

s($

MM

)3,0

91.0

71,2

85.9

25,7

68.7

87,1

41.9

25,1

51.7

09,4

53.2

60.0

054***

<.0

001***

Mark

etC

ap

italiza

tion

($M

M)

4,0

45.1

22,1

02.6

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71.7

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26.6

98,9

03.9

20.0

011***

<.0

001***

Book-t

o-m

ark

eteq

uit

y0.4

00.3

60.2

30.4

80.4

60.2

60.0

482**

0.0

486**

R&

Din

ten

sity

mea

sure

(%)

R&

D/A

sset

s9.5

88.7

74.2

28.5

07.2

33.5

50.1

113

0.0

177**

R&

D/S

ale

s12.1

78.7

614.0

37.3

96.5

82.9

00.0

016***

0.0

014***

Incr

ease

ind

ollar

R&

D(%

)20.7

712.7

227.5

112.4

610.0

410.1

90.0

087***

0.0

048***

Th

esy

mb

ols

***,

**,

*in

dic

ate

stati

stic

al

sign

ifica

nce

at

the

1,

5,

10

per

cent

level

s,re

spec

tivel

y.

25

Table 2: BHARs following R&D Increase

This table provides one-, three-, and five-year BHARs following sample firms’ R&D increase, respectively. For theith sample firm from month T1 to T2, BHARi,t express as follows:

BHARi,t =

T2∏t=T1

(1 +Ri,t)−T2∏t=T1

(1 +Rb,t),

where Ri,tis the return of the sample firm in event month t, and Rb,t is the return of the benchmark over the sameperiod. The test statistics used is the following:

t = BHAR×√n/σ(BHAR),

where BHAR is the average across firms’ BHARs, σ(BHAR) is the cross-sectional standard deviation of BHARsfor all sample firms, and n is the number of firms. For each event firm, we compute corresponding one-, three-and five-year BHAR following R&D increase year with a three month lag. We use control firm approach as thebenchmark. We first identify all firms with a market value of equity within 30% of the market value of equity of thesample firm in the year of R&D increase. From this set of firms, we choose the firm which has the closest book-to-market equity ratio to the sample firm. Then, we compute and report the mean and median BHARs of firms withoverconfident CEOs and firms with non-overconfident CEOs, respectively. Non-OC CEOs refers to the firms withnon-overconfident CEOs, and OC CEOs refers to the firms with overconfident CEOs. We use the Kruskal-Wallistest to test the median difference. Firms are classified into high-tech and low-tech firms using the definition inBrown, Fazzari, and Peterson (2009).

Longholder1-Year BHAR 3-Year BHAR 5-Year BHAR

N Mean Median N Mean Median N Mean Median

Panel A: Full Sample

Non-OC CEOs 97 0.1445** 0.1494*** 97 0.5431*** 0.3016*** 97 1.0202*** 0.4264**OC CEOs 54 −0.0218 −0.0533 54 −0.1568 −0.1631 54 −0.3277* −0.4739*Difference 0.1663** 0.2070** 0.6999*** 0.4647*** 1.3478*** 0.9003***

Panel B: High-Tech Sample

Non-OC CEOs 77 0.1381** 0.1580** 77 0.6464*** 0.3318*** 77 1.2134*** 0.4331**OC CEOs 39 −0.0043 −0.0434 39 −0.1387 −0.1073 39 −0.2371 −0.0369Difference 0.1424 0.2014* 0.7851*** 0.4391*** 1.4505*** 0.4700**

Panel C: Low-Tech Sample

Non-OC CEOs 20 0.1691** 0.1069** 20 0.1451 0.2412 20 0.2761 0.3359OC CEOs 15 −0.0673 −0.1144 15 −0.2040 −0.2800 15 −0.5633 −1.1538Difference 0.2364** 0.2213** 0.3491 0.5212 0.8394* 1.4897**

The symbols ***, **, * indicate statistical significance at the 1, 5, 10 percent levels, respectively.

26

Table 3: Cross-sectional Regression of BHARs

In this table, we estimate the equation as follows:

BHARit = θ0 + θ1OCit + xit−1β + εit

where BHARit refers to the 1-, 3-, and 5-year BHARs following R&D significantly increases for firm i in yeart. OCit refers to the CEO overconfidence indicator, Longholder, for firm i in year t, and xit−1 is a vector ofcontrol variables of firm i in year t − 1. The definitions of control variables, x, are described in Appendix.In addition, we measure the following equation by adding the high-tech indicator as the explanatory variableand its interaction term between CEO overconfidence indicator:

BHARit = θ0 + θ1OCit + θ2HTit + θ3OCit ×HTit + xit−1β + εit

where BHARit refers to the 1-, 3-, and 5-year BHARs following R&D significantly increases for firm i inyear t. OCit refers to the CEO overconfidence indicator, Longholder, for firm i in year t. HTit refers tothe high-tech industries indicator, and xit−1 is a vector of control variables of firm i in year t− 1. Standarderrors are clustered at firm level, heteroskedasticity consistent, and reported in the parentheses. The bottompresents the results of Wald test for θ1 + θ3 = 0, and P -values are reported in the parentheses.

1-Year BHAR 3-Year BHAR 5-Year BHAR

(1) (2) (3) (4) (5) (6)

Intercept −0.814 −0.978 −2.067 −2.655 −8.275 −9.646(−1.47) (−1.71)* (−1.49) (−1.81)* (−2.32)** (−2.52)**

Longholder −0.205 −0.034 −0.532 0.385 −1.060 0.806(−1.72)* (−0.18) (−1.64) (0.72) (−1.80)* (0.66)

High Tech 0.267 0.778 1.861(1.25) (1.30) (1.50)

Longholder×High-Tech −0.228 −1.236 −2.216(−0.98) (−1.87)* (−1.60)

Size 0.000 0.000 0.000 0.000 0.000 0.000(−1.08) (−1.20) (−1.10) (−1.31) (−0.55) (−0.69)

BM 0.789 0.740 1.649 1.446 2.953 2.509(1.37) (1.34) (1.38) (1.29) (1.27) (1.13)

Tobin q 0.009 0.010 0.381 0.370 −0.154 −0.170(0.08) (0.08) (1.16) (1.13) (−0.46) (−0.51)

R&D/Asset 0.773 −0.065 −3.738 −5.500 −2.789 −8.091(0.49) (−0.04) (−0.80) (−1.39) (−0.27) (−0.89)

ROA 3.115 3.158 3.983 4.859 15.436 13.646(2.41)** (2.54)** (1.08) (1.38) (2.32)** (2.29)**

Sales Growth 0.162 0.139 0.086 −0.091 1.163 1.144(0.32) (0.28) (0.09) (−0.09) (0.62) (0.56)

Cash 0.727 0.826 1.776 2.081 3.664 5.659(1.07) (1.29) (0.87) (1.11) (0.76) (1.21)

PPE/Emp −0.002 0.000 −0.004 0.000 0.001 0.004(−0.56) (−0.11) (−0.40) (−0.04) (0.04) (0.26)

Stock Ownership 0.585 0.334 6.703 5.666 29.233 25.910(0.59) (0.32) (3.00)*** (2.31)** (6.26)*** (6.13)***

Vested Options 3.950 2.312 5.431 0.873 44.802 14.388(0.45) (0.24) (0.20) (0.03) (0.98) (0.29)

(Vested Options)2 −91.419 −72.609 −120.193 −52.363 −390.702 124.745(−0.62) (−0.46) (−0.28) (−0.12) (−0.61) (0.16)

Year Effects Yes Yes Yes Yes Yes YesSIC 2-digit Effects Yes Yes Yes Yes Yes Yes

Obs 136 136 136 136 136 136Adjusted R2 0.095 0.094 0.213 0.231 0.264 0.323

Wald Tests: θ1 + θ3 = 0 −0.262 −0.852 −1.410P -value (0.070)* (0.033)** (0.028)**

The symbols ***, **, * indicate statistical significance at the 1, 5, 10 percent levels, respectively.

27

Tab

le4:

Lon

g-T

erm

Abnor

mal

Ret

urn

sfo

rC

alen

dar

-Tim

eP

ortf

olio

Th

ista

ble

pro

vid

esab

norm

al

stock

retu

rns

for

the

sam

ple

incr

easi

ng

thei

rR

&D

from

1980

to1994.

We

use

the

Fam

a-F

ren

chth

ree-

fact

or

mod

elto

esti

mate

the

ab

norm

al

retu

rnby

the

follow

ing

equ

ati

on

:

Rpt−Rft

=αi

+βi(Rmt−Rft)

+s iSMBt

+hiHMLt

+ε pt

wh

ereRpt

isth

em

onth

lyre

turn

on

the

equ

al-

or

valu

e-w

eighte

dp

ort

folio

inca

len

dar

montht

(wh

ere

asa

mp

lest

ock

isin

clu

ded

ifm

ontht

isw

ith

inth

e60-m

onth

per

iod

follow

ing

its

R&

Din

crea

se)

,Rft

isth

e1-m

onth

trea

sury

bill

retu

rn,Rmt

isth

eC

RS

Pvalu

e-w

eighte

dm

ark

etin

dex

retu

rn,SMBt

isth

ed

iffer

ence

inth

ere

turn

son

the

valu

e-w

eighte

dp

ort

folios

of

small

an

db

igst

ock

s,an

dHMLt

isth

ed

iffer

ence

inth

ere

turn

son

the

valu

e-w

eighte

dp

ort

folios

of

hig

han

dlo

wb

ook-t

o-m

ark

etst

ock

s.W

eals

ou

seth

eC

arh

art

four-

fact

or

mod

elto

mea

sure

the

month

lyab

norm

al

retu

rnfo

llow

ing

R&

Din

crea

seby

esti

mati

ng

the

inte

rcep

tfr

om

the

follow

ing

equ

ati

on

:

Rpt−Rft

=αi

+βi(Rmt−Rft)

+s iSMBt

+hiHMLt

+miUMDt

+ε pt,

wh

ereUMDt

isth

ed

iffer

ence

inth

ere

turn

on

avalu

e-w

eighte

dp

ort

folio

of

hig

han

dlo

wm

om

entu

mst

ock

s.A

dd

itio

nally,

we

imp

ose

that

each

cale

nd

ar

month

at

least

have

thre

eob

serv

ati

on

s.In

pan

elB

,w

eu

seth

efi

rst

60

month

sto

esti

mate

the

fact

or

load

ings

for

equ

ati

on

(3)

an

d(4

).T

hen

,w

ees

tim

ate

the

ab

norm

al

retu

rnin

month

61

as

the

diff

eren

ceb

etw

een

the

act

ual

port

folio

retu

rnan

dth

eex

pec

ted

port

folio

retu

rn.

Th

eex

pec

ted

port

folio

retu

rnd

efin

edas

the

fact

or

load

ings

esti

mate

dover

the

pre

vio

us

60

month

sti

mes

thei

rre

spec

tive

month

61

fact

or

retu

rns.

We

can

rep

lica

teth

isst

epev

ery

month

,an

dth

enaver

age

the

tim

ese

ries

of

thes

eab

norm

al

retu

rnan

dfa

ctor

load

ing

esti

mate

s.N

on

-OC

CE

Os

refe

rsto

the

firm

sw

ith

non

-over

con

fid

ent

CE

Os,

an

dO

CC

EO

sre

fers

toth

efi

rms

wit

hover

con

fid

ent

CE

Os.

Inp

are

nth

eses

are

New

ey-W

est

(1987)

ad

just

edfo

rse

rial

corr

elati

on

an

dh

eter

osc

edast

icit

yt-

stati

stic

s.

Lon

gh

old

erF

am

a-F

ren

chT

hre

e-F

act

or

Mod

elC

arh

art

Fou

r-F

act

or

Mod

el

αb

sh

αb

sh

m

Pan

elA

:F

ull

Sam

ple

Equ

al

Non

-OC

CE

Os

0.0

061***

1.0

972***

0.2

705***

-0.5

160***

0.0

067***

1.1

008***

0.2

566***

-0.5

273***

-0.0

797

Wei

ght

(3.2

5)

(19.8

2)

(2.9

2)

(-4.5

5)

(3.3

5)

(20.4

8)

(2.7

4)

(-4.4

4)

(-1.0

6)

OC

CE

Os

0.0

004

1.1

951***

0.1

454

-0.2

672*

0.0

023

1.2

067***

0.0

996

-0.3

046**

-0.2

628***

(0.1

5)

(17.0

9)

(1.3

1)

(-1.8

3)

(0.8

4)

(18.2

4)

(0.9

4)

(-2.0

8)

(-2.9

3)

Diff

eren

ce0.0

057**

-0.0

979

0.1

251

-0.2

488*

0.0

044

-0.1

060

0.1

570

-0.2

227*

0.1

831*

(2.1

3)

(-1.5

3)

(1.2

2)

(-1.9

0)

(1.6

0)

(-1.6

5)

(1.5

4)

(-1.7

0)

(1.9

4)

Valu

eN

on

-OC

CE

Os

0.0

065***

0.9

987***

-0.1

447*

-0.6

719***

0.0

061***

0.9

965***

-0.1

358*

-0.6

646***

0.0

509

Wei

ght

(3.8

5)

(19.3

7)

(-1.9

4)

(-6.7

2)

(3.4

2)

(19.0

6)

(-1.7

3)

(-6.5

2)

(0.8

4)

OC

CE

Os

0.0

001

1.0

305***

-0.3

117***

-0.4

790***

0.0

005

1.0

325***

-0.3

196***

-0.4

854***

-0.0

453

(0.0

7)

(17.1

6)

(-3.1

5)

(-4.8

1)

(0.2

2)

(16.9

4)

(-3.1

5)

(-4.9

5)

(-0.5

9)

Diff

eren

ce0.0

063***

-0.0

318

0.1

670

-0.1

929

0.0

056**

-0.0

360

0.1

838

-0.1

792

0.0

962

(2.6

7)

(-0.4

3)

(1.4

4)

(-1.4

6)

(2.3

5)

(-0.4

8)

(1.5

2)

(-1.3

5)

(1.0

7)

28

Tab

le4

Con

tinued

Lon

gh

old

erF

am

a-F

ren

chT

hre

e-F

act

or

Mod

elC

arh

art

Fou

r-F

act

or

Mod

el

αb

sh

αb

sh

m

Pan

elB

:R

ollin

gR

egre

ssio

nM

eth

od

Equ

al

Non

-OC

CE

Os

0.0

048*

0.0

079***

1.0

845***

-0.5

768***

0.0

066**

0.0

090***

1.0

959***

-0.6

198***

-0.1

591***

Wei

ght

(1.8

5)

(75.2

2)

(193.9

9)

(-21.1

3)

(2.5

0)

(64.0

0)

(195.4

9)

(-20.9

6)

(-16.2

7)

OC

CE

Os

-0.0

032

0.0

003

1.1

048***

-0.3

152***

-0.0

016

0.0

019***

1.1

193***

-0.3

509***

-0.2

079***

(-1.0

7)

(1.0

8)

(119.0

1)

(-9.5

3)

(-0.5

4)

(6.6

2)

(124.8

2)

(-10.1

8)

(-19.6

0)

Diff

eren

ce0.0

080**

0.0

075***

-0.0

203*

-0.2

616***

0.0

082**

0.0

071***

-0.0

234**

-0.2

689***

0.0

487***

(2.0

3)

(23.2

4)

(-1.8

8)

(-6.1

0)

(2.0

7)

(22.3

6)

(-2.2

1)

(-5.9

2)

(3.3

8)

Valu

eN

on

-OC

CE

Os

0.0

044*

0.0

068***

1.0

124***

-0.6

948***

0.0

042*

0.0

067***

1.0

118***

-0.6

898***

0.0

108**

Wei

ght

(1.9

1)

(50.9

4)

(299.9

3)

(-30.6

7)

(1.7

9)

(45.4

4)

(294.0

2)

(-30.2

9)

(2.4

4)

OC

CE

Os

-0.0

041*

-0.0

002

0.9

559***

-0.5

910***

-0.0

043*

-0.0

000

0.9

566***

-0.5

806***

-0.0

016

(-1.8

5)

(-0.5

6)

(223.0

3)

(-34.7

4)

(-1.9

3)

(-0.0

9)

(216.3

0)

(-33.7

4)

(-0.2

0)

Diff

eren

ce0.0

085***

0.0

070***

0.0

565***

-0.1

037***

0.0

085***

0.0

068***

0.0

552***

-0.1

092***

0.0

124

(2.6

6)

(18.1

7)

(10.3

5)

(-3.6

6)

(2.6

3)

(18.1

9)

(9.8

5)

(-3.8

3)

(1.3

4)

Th

esy

mb

ols

***,

**,

*in

dic

ate

stati

stic

al

sign

ifica

nce

at

the

1,

5,

10

per

cent

level

s,re

spec

tivel

y.

29

Tab

le5:

Subsa

mple

Tes

tsof

Lon

g-T

erm

Abnor

mal

Ret

urn

sfo

rC

alen

dar

-Tim

eP

ortf

olio

Th

ista

ble

pro

vid

esab

nor

mal

stock

retu

rns

for

the

sam

ple

incr

easi

ng

thei

rR

&D

from

1980

to1994.

The

sam

ple

are

div

ided

into

hig

h-t

ech

and

low

-tec

hfi

rms

usi

ng

the

defi

nit

ion

inB

row

n,

Fazz

ari

,an

dP

eter

son

(2009).

Th

eα

refe

rsto

the

ab

norm

al

retu

rnm

easu

re.

See

Tab

le3

for

ad

etai

led

des

crip

tion

of

the

test

pro

ced

ure

s.In

pare

nth

eses

are

New

ey-W

est

(1987)

ad

just

edfo

rse

rial

corr

elat

ion

and

het

eros

ced

asti

cityt-

stat

isti

cs.

Lon

gh

old

erF

am

a-F

ren

chT

hre

e-F

act

or

Mod

elC

arh

art

Fou

r-F

act

or

Mod

el

αb

sh

αb

sh

m

Pan

elA

:H

igh

-Tec

hS

am

ple

Equ

al

Non

-OC

CE

Os

0.0

096***

1.0

970***

0.3

442**

-0.6

777***

0.0

103***

1.1

012***

0.3

318**

-0.6

948***

-0.1

074

Wei

ght

(3.8

8)

(14.0

7)

(2.1

5)

(-4.2

7)

(3.9

7)

(14.8

2)

(2.0

8)

(-4.1

2)

(-1.0

7)

OC

CE

Os

0.0

038

1.1

970***

0.2

450

-0.5

164***

0.0

064*

1.2

114***

0.2

025

-0.5

751***

-0.3

697***

(1.0

1)

(13.0

7)

(1.5

4)

(-2.6

8)

(1.7

2)

(14.7

3)

(1.4

1)

(-2.8

9)

(-3.5

1)

Diff

eren

ce0.0

058

-0.0

999

0.0

991

-0.1

613

0.0

039

-0.1

102

0.1

293

-0.1

197

0.2

623**

(1.5

2)

(-1.2

5)

(0.5

8)

(-0.8

5)

(1.0

0)

(-1.3

6)

(0.7

5)

(-0.6

2)

(2.3

5)

Valu

eN

on

-OC

CE

Os

0.0

100***

1.0

075***

-0.2

625**

-0.9

416***

0.0

102***

1.0

088***

-0.2

665**

-0.9

471***

-0.0

350

Wei

ght

(4.7

5)

(16.8

9)

(-2.3

7)

(-7.9

3)

(4.5

9)

(17.1

8)

(-2.4

3)

(-7.8

1)

(-0.4

7)

OC

CE

Os

0.0

036

1.0

183***

-0.3

451**

-0.7

283***

0.0

045*

1.0

232***

-0.3

594***

-0.7

481***

-0.1

243

(1.4

6)

(14.2

4)

(-2.5

9)

(-5.4

7)

(1.7

4)

(14.4

9)

(-2.6

6)

(-5.8

2)

(-1.5

6)

Diff

eren

ce0.0

064**

-0.0

109

0.0

826

-0.2

133

0.0

057**

-0.0

143

0.0

928

-0.1

991

0.0

893

(2.5

0)

(-0.1

5)

(0.5

2)

(-1.4

5)

(2.1

1)

(-0.1

9)

(0.5

8)

(-1.3

6)

(0.9

7)

Pan

elB

:L

ow

-Tec

hS

am

ple

Equ

al

Non

-OC

CE

Os

-0.0

028

1.1

981***

-0.1

156

-0.0

528

0.0

011

1.1

509***

-0.2

675*

-0.1

636

-0.4

591***

Wei

ght

(-0.9

0)

(10.9

6)

(-0.7

2)

(-0.2

3)

(0.3

7)

(11.0

8)

(-1.8

4)

(-0.8

0)

(-2.9

6)

OC

CE

Os

-0.0

035

1.1

307***

0.2

145

0.2

953

-0.0

029

1.1

224***

0.1

878

0.2

759

-0.0

808

(-0.9

3)

(9.1

5)

(1.0

5)

(1.2

9)

(-0.7

5)

(8.8

8)

(0.9

8)

(1.2

6)

(-0.4

9)

Diff

eren

ce0.0

008

0.0

674

-0.3

301

-0.3

481

0.0

039

0.0

285

-0.4

553**

-0.4

394

-0.3

784*

(0.1

7)

(0.4

0)

(-1.4

7)

(-1.2

2)

(0.9

1)

(0.1

9)

(-2.1

8)

(-1.6

)(-

1.8

9)

Valu

eN

on

-OC

CE

Os

-0.0

027

1.1

236***

-0.5

588***

0.1

164

-0.0

032

1.1

295***

-0.5

396***

0.1

304

0.0

581

Wei

ght

(-0.6

6)

(7.6

7)

(-3.0

8)

(0.4

4)

(-0.6

8)

(7.9

9)

(-2.9

2)

(0.4

8)

(0.2

1)

OC

CE

Os

-0.0

026

0.9

466***

-0.1

200

-0.0

094

-0.0

029

0.9

495***

-0.1

108

-0.0

027

0.0

278

(-0.6

6)

(7.2

8)

(-0.6

9)

(-0.0

4)

(-0.6

7)

(7.0

5)

(-0.6

5)

(-0.0

1)

(0.1

6)

Diff

eren

ce-0

.0001

0.1

769

-0.4

388*

0.1

257

-0.0

003

0.1

801

-0.4

287*

0.1

330

0.0

303

(-0.0

1)

(0.8

5)

(-1.9

4)

(0.3

5)

(-0.0

5)

(0.8

5)

(-1.8

9)

(0.3

6)

(0.0

9)

30

Table 6: Long-Term Abnormal Operating Performance

We compute sample firm’s changes in operating performance for five years following the year in which theyunexpected increase R&D by an economically significant amount. The definition of OPM measure aredescribed in Appendix. We measure abnormal operating performance minus its matched firm’s operatingperformance. We choose matched firms that do not have the same corporate event as the sample in theevent year. We select the a group of control firms, in the same two-digit SIC code as the sample firm, thatdo not unexpectedly significant increase its R&D during the sample firm’s R&D increase year. From thosescreened firms, we choose a firm as the matched firm that has the closest OPM with sample firm’s OPM priorthe sample firm’s R&D increase year. We also create another group of matched firms based on the samplefirm’s characteristics such as size, book-to-market ratio, and momentum. In the beginning of a sample firm’sR&D increase year, we choose a matched firm that the market equity value is within 30% of the marketequity value of the sample firm and then we choose the jointly lowest absolute value of the difference in thecharacteristics. Non-OC CEOs refers to the firms with non-overconfident CEOs, and OC CEOs refers to thefirms with overconfident CEOs. We use the Kruskal-Wallis test to test the median difference.

Year LongholderIndustry and Performance Match Characteristic Match

N Mean Median N Mean Median

Panel A: Changes in OPM1

Non-OC CEOs 96 0.0376*** 0.0119** 96 0.0013 0.0043-1 to +1 OC CEOs 53 −0.0004 −0.0023 53 −0.0210* −0.0093*

Difference 0.0380** 0.0143** 0.0223 0.0136*

Non-OC CEOs 95 0.0719*** 0.0279*** 95 0.0495 0.0047-1 to +2 OC CEOs 52 −0.0006 −0.0092 52 −0.0233** −0.0123*

Difference 0.0725*** 0.0370*** 0.0728** 0.0170*

Non-OC CEOs 93 0.0719*** 0.0401*** 95 0.0379** −0.0032-1 to +3 OC CEOs 52 0.0168 0.0161 52 −0.0120 −0.0013

Difference 0.0552** 0.0240* 0.0499** −0.0020

Non-OC CEOs 92 0.0885*** 0.0538*** 94 0.0555*** 0.0184**-1 to +4 OC CEOs 52 0.0230 0.0154 52 −0.0127 −0.0069

Difference 0.0656** 0.0384** 0.0682*** 0.0253**

Non-OC CEOs 91 0.0962*** 0.0392*** 93 0.0993*** 0.0249***-1 to +5 OC CEOs 52 0.0211 0.0248 52 −0.0082 0.0117

Difference 0.0751** 0.0144* 0.1075*** 0.0132**

Panel B: Changes in OPM2

Non-OC CEOs 96 0.0347** 0.0105** 96 0.0036 0.0035-1 to +1 OC CEOs 53 −0.0043 0.0096 53 −0.0232 −0.0099

Difference 0.0390 0.0009 0.0268 0.0134*

Non-OC CEOs 95 0.0542*** 0.0387*** 95 0.0435 0.0126-1 to +2 OC CEOs 52 0.0140 0.0334* 52 −0.0271* −0.0098

Difference 0.0402* 0.0053 0.0706* 0.0224

Non-OC CEOs 95 0.1694* 0.0381*** 95 0.0371* 0.0100-1 to +3 OC CEOs 52 0.0487* 0.0340** 52 −0.0108 0.0040

Difference 0.1207 0.0041 0.0478* 0.0060

Non-OC CEOs 94 0.1505*** 0.0835*** 94 0.0660*** 0.0323***-1 to +4 OC CEOs 52 0.0375* 0.0293* 52 −0.0124 −0.0076

Difference 0.1130** 0.0541** 0.0784*** 0.0399**

Non-OC CEOs 90 0.0937*** 0.0981*** 93 0.1065*** 0.0445***-1 to +5 OC CEOs 52 0.0394* 0.0432** 52 −0.0065 0.0152

Difference 0.0543* 0.0549 0.1129*** 0.0293**

The symbols ***, **, * indicate statistical significance at the 1, 5, 10 percent levels, respectively.

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Table 7: Subsample Tests for Long-Term Abnormal Operating Performance

This table reports the abnormal operating performance for the sample increasing their R&D from1980 to 1994. The sample are divided into high-tech and low-tech firms using the definition inBrown, Fazzari, and Peterson (2009). See Table 5 for a detailed description of the test procedures.

Year Longholder

Industry and Performance Match Characteristic Match

OPM1 OPM2 OPM1 OPM2

N Mean N Mean N Mean N Mean

Panel A: High-Tech Sample

Non-OC CEOs 76 0.0323** 76 0.0331* 76 0.0077 76 0.0111-1 to +1 OC CEOs 38 0.0051 38 0.0057 38 −0.0250 38 −0.0285

Difference 0.0272 0.0273 0.0327 0.0396

Non-OC CEOs 75 0.0732*** 75 0.0551*** 75 0.0686* 75 0.0584-1 to +2 OC CEOs 37 0.0083 37 0.0231 37 −0.0284* 37 −0.0339*

Difference 0.0649** 0.0320 0.0970** 0.0922**

Non-OC CEOs 73 0.0828*** 75 0.2097* 75 0.0577*** 75 0.0561**-1 to +3 OC CEOs 37 0.0290 37 0.0372 37 −0.0179 37 −0.0167

Difference 0.0538* 0.1725 0.0755*** 0.0728**

Non-OC CEOs 72 0.0858*** 74 0.1680*** 74 0.0778*** 74 0.0895***-1 to +4 OC CEOs 37 0.0287 37 0.0529* 37 −0.0206 37 −0.0199

Difference 0.0572* 0.1152* 0.0984*** 0.1094***

Non-OC CEOs 71 0.1053*** 70 0.1004*** 73 0.1259*** 73 0.1340***-1 to +5 OC CEOs 37 0.0266 37 0.0548* 37 −0.0191 37 −0.0161

Difference 0.0787** 0.0456 0.1450*** 0.1501***

Panel B: Low-Tech Sample

Non-OC CEOs 20 0.0579* 20 0.0406 20 −0.0230 20 −0.0249-1 to +1 OC CEOs 15 −0.0143 15 −0.0298 15 −0.0110 15 −0.0099

Difference 0.0722** 0.0704** −0.0119 −0.0150

Non-OC CEOs 20 0.0670* 20 0.0506* 20 −0.0220 20 −0.0125-1 to +2 OC CEOs 15 −0.0227* 15 −0.0085 15 −0.0107 15 −0.0105

Difference 0.0897** 0.0591* −0.0114 −0.0020

Non-OC CEOs 20 0.0322 20 0.0183 20 −0.0363* 20 −0.0345*-1 to +3 OC CEOs 15 −0.0134 15 0.0772 15 0.0023 15 0.0037

Difference 0.0456* −0.0589 −0.0386 −0.0383

Non-OC CEOs 20 0.0983 20 0.0855* 20 −0.0270* 20 −0.0207-1 to +4 OC CEOs 15 0.0090 15 −0.0004 15 0.0067 15 0.0062

Difference 0.0893 0.0859* −0.0337 −0.0270

Non-OC CEOs 20 0.0636 20 0.0703** 20 0.0022 20 0.0060-1 to +5 OC CEOs 15 0.0074 15 0.0015 15 0.0188 15 0.0173

Difference 0.0562 0.0688* −0.0166 −0.0113

The symbols ***, **, * indicate statistical significance at the 1, 5, 10 percent levels, respectively.

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Table 8: Cross-Sectional Regression of Long-Term Abnormal Operating Performance

In this table, we estimate the equation as follows:

OPMit = θ0 + θ1OCit + xit−1β + εit

where OPMit refers to the five-year abnormal operating performance following R&D significantly increases for firm i inyear t. OCit refers to the CEO overconfidence indicator, Longholder, for firm i in year t, and xit−1 is a vector of controlvariables of firm i in year t− 1. The definitions of control variables, x, are described in Appendix. In addition, we measurethe following equation by adding the high-tech indicator as the explanatory variable and its interaction term between CEOoverconfidence indicator:

OPMit = θ0 + θ1OCit + θ2HTit + θ3OCit ×HTit + xit−1β + εit

where OPMit refers to the five-year abnormal operating performance following R&D significantly increases for firm i inyear t. OCit refers to the CEO overconfidence indicator, Longholder, for firm i in year t. HTit refers to the high-techindustries indicator, and xit−1 is a vector of control variables of firm i in year t − 1. Standard errors are clustered atfirm level, heteroskedasticity consistent, and reported in the parentheses. The bottom presents the results of Wald test forθ1 + θ3 = 0, and P -values are reported in the parentheses.

Industry and Performance Match Characteristics Match

OPM1 OPM2 OPM1 OPM2

(1) (2) (3) (4) (5) (6) (7) (8)

Intercept −0.062 −0.147 −0.052 −0.084 −0.090 −0.340 −0.0661 −0.374(−0.31) (−0.68) (−0.31) (−0.49) (−0.50) (−1.11) (−0.33) (−1.30)

Longholder −0.070 −0.027 −0.057 −0.060 −0.079 0.092 −0.082 0.081(−2.07)** (−0.47) (−1.71)* (−1.12) (−1.58) (1.07) (−1.71)* (0.87)

High Tech 0.090 0.043 0.274 0.293(1.20) (0.66) (1.91)* (2.04)**

Longholder×High-Tech −0.060 0.003 −0.244 −0.225(−0.81) (0.04) (−1.76)* (−1.51)

Tobin q 0.009 0.010 0.029 0.030 0.084 0.071 0.069 0.086(0.42) (0.44) (1.12) (1.14) (2.40)** (1.95)* (1.80)* (2.66)**

R&D/Asset −0.153 −0.408 0.004 −0.160 −1.002 −1.467 −0.774 −1.788(−0.34) (−0.76) (0.01) (−0.30) (−1.41) (−2.58)** (−1.12) (−3.09)***

ROA 0.235 0.203 −0.198 −0.263 −0.992 −0.685 −0.672 −1.056(0.60) (0.51) (−0.47) (−0.59) (−1.90)* (−1.45) (−1.38) (−2.01)*

Sales Growth −0.110 −0.106 0.000 0.009 0.034 0.052 0.050 0.042(−1.52) (−1.52) (0.00) (0.07) (0.16) (0.27) (0.25) (0.20)

Cash 0.087 0.127 0.173 0.187 0.178 0.388 0.264 0.308(0.47) (0.68) (1.00) (1.05) (0.63) (1.31) (0.91) (1.07)

PPE/Emp 0.000 0.001 0.000 0.000 −0.001 0.000 −0.001 0.000(0.27) (0.66) (−0.15) (0.07) (−1.12) (0.03) (−0.79) (−0.14)

Stock Ownership 0.157 0.093 −0.025 −0.042 0.181 −0.165 0.063 −0.042(0.51) (0.30) (−0.08) (−0.12) (0.45) (−0.41) (0.17) (−0.10)

Vested Options 2.435 1.976 2.460 2.211 7.420 5.626 6.901 6.046(0.93) (0.70) (0.65) (0.57) (1.37) (1.13) (1.32) (1.18)

(Vested Options)2 −36.208 −29.684 −65.129 −62.168 −114.973 −83.176 −102.889 −94.651(−0.86) (−0.66) (−1.08) (−1.01) (−1.67) (−1.27) (−1.51) (−1.43)

Year Effects Yes Yes Yes Yes Yes Yes Yes YesSIC 2-digit Effects Yes Yes Yes Yes Yes Yes Yes Yes

Obs 128 128 127 127 130 130 130 130

Adjusted R2 0.3331 0.3386 0.1532 0.1386 0.1846 0.2157 0.1758 0.2225

Wald Tests: θ1 + θ3 = 0 −0.087 −0.057 −0.152 −0.144P -value (0.04)** (0.14) (0.06)* (0.09)*

The symbols ***, **, * indicate statistical significance at the 1, 5, 10 percent levels, respectively.

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