CEO Overconfidence and the Long-Term Performance Following

Introduction Data Methodology Empirical Results Conclusion

CEO Overconfidence and the Long-Term PerformanceFollowing Unexpected R&D Increases

Sheng-Syan Chen Keng-Yu Ho Po-Hsin Ho

Department of Finance, National Taiwan University

December 11, 2010NTU International Conference on Finance

Chen, Ho, and Ho CEO Overconfidence and R&D Increases

mailto:[email protected]


CEO Overconfidence

The story of CEO overconfidence stems from the “better-than-average” effect in the psychology literature, which suggests thatpeople tend to overestimate their wisdom or skill relative to theaverage.

Previous studies attribute the evidence of overconfidence to three mainfactors.

The illusion of control.

A high degree of commitment to nice outcomes.

Abstract reference points.



Previous Literature on CEO Overconfidence

Malmendier and Tate (2005) use the overestimation of future cash flow asthe source of managerial overconfidence and show that managerialoverconfidence cause corporate investment distortions.

The sensitivity of investment to cash flow is strongest in the presence ofoverconfidence.

Malmendier and Tate (2008) argue that CEO overconfidence can help toexplain merger decisions because overconfident CEOs overestimate their skillto generate returns.

Overconfidence CEOs pursuit value-destroying mergers.

The market reactions at merger announcement done by overconfidentCEOs are more negative than those by non-overconfident CEOs.



Previous Literature on CEO Overconfidence

Ben-David et al. (2007) provide another model to analyze the relationsbetween managerial overconfidence and corporate policies.

Goel and Thakor (2009) propose a theoretical model on the relationsamong overconfidence, CEO selection, and corporate governance.

Excessively overconfident CEOs face a greater likelihood of forcedturnover than CEOs with moderate overconfidence.

Excessively overconfident CEOs reduce firm value due tooverinvestment.

Campell et al. (2010) empirically examine the prediction of Geol andThakor (2009) and find strong support to their model.



Previous Literature on CEO Overconfidence and R&D

Galasso and Simco (2010) and Hirshleifer et al. (2010) both study therelationship between CEO overconfidence and innovation behaviour.

Our study deviates from theirs in two ways.

Investigate the relation between CEO overconfidence and the effect ofunexpected increases in R&D expenditure.

Examine the long-term abnormal stock return and operatingperformance following R&D increases.



Motivations and Contributions

R&D decision is essential and vital for a firm’s growth. Eberhart et al.(2004) argue that R&D increases improve firms’ long-term operatingperformance.

Given that prior studies argue that managerial overconfidence oftenresults in overinvestment, could overconfident CEOs still makeprofitable R&D investments?

Previous studies suggest that the market reaction to increases in R&Dexpenditure is significantly positive. It motivates us to investigatewhether firms with overconfident CEOs could earn long-run abnormalstock returns following R&D increases.



Motivations and Contributions

The information contain of R&D increases not only conveys tangibleinformation (PP&E) but also reflects intangible information about theprospect of future cash flow, such as a new technology development ora new product.

Overconfident CEOs may overestimate the future cash flow of theirinvestment projects.

Simon and Houghton (2003) argue that CEO overconfidence ispositively related to the introduction of pioneering and risky productsand those products are less likely to achieve success.



Summary of Empirical Results

To the best of our knowledge, this study is the first to examine theCEO overconfidence from the aspect of unexpected R&D increases.

Comparing with previous research which indicates that investors couldearn positively abnormal stock returns in R&D increases, we show thatinvestors could gain significantly abnormal stock returns only fromfirms with non-overconfident CEOs. The phenomenon may be due tooverconfident CEOs overestimating the future prospect of investmentproject.

By calculating the abnormal operating performance, we find that R&Dincreases are beneficial investments only for firms withoutoverconfident CEOs.



Overconfidence Measure

The CEO overconfidence measure is identical to that in Malmendierand Tate (2008).

477 largest publicly traded US firms from 1980 to 1994.

A firm must be included at least four times on the lists of the largestU.S. companies constructed by Forbes magazine from 1980 to 1994.

CEO overconfidence indicator, Longholder.

A CEO who, at least once during his/her tenure, keeps an option untilthe year of expiration, even though the option is at least 40%in-the-money entering its final year (Hall and Murphy (2002)).



Data

Stock price data are obtained from CRSP and the accounting data areretrieved from Compustat.

Unexpected increases in R&D.

We concentrate on firms whose R&D intensity (ratios of R&D to assetsand ratios of R&D to sales) are greater than 5%.

In addition, we focus on economoically significant R&D increases.Therefore, a firm must increase its dollar amount of R&D by at least5% and its R&D to assets for more than 5%.

Finally, we yield 54 overconfident CEOs with significant R&D increasesand 97 non-overconfident CEOs with significant R&D increases.



Buy-and-Hold Abnormal Returns (BHARs)

Compute the one-, three-, and five-year BHARs for all sample firmswith a three-month lag following the unexpected R&D increases,respectively. For the ith sample firm, its BHAR from month T1 to T2

is expressed as follows:

BHARi =

T2∏

t=T1

(1 + Ri ,t) −T2∏

t=T1

(1 + Rb,t),

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.



Calendar-Time Abnormal Returns (CTARs)

Use the Fama-French three-factor model to measure the monthlyabnormal return following R&D increases by estimating the followingequation:

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

Use the Carhart four-factor model to measure the monthly abnormalreturn following R&D increases by estimating the following equation:

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



Rolling Regression of CTARs

Berk, Green, and Naik (2004) argue that a firm’s systematic risk maychange because of an investment in R&D.

Estimate each factor loading based on a rolling regression approach inthe Fama-French three-factor and Carhart four-factor models.

Use the first 60 months to estimate the factor loadings in the twomodels.

Then estimate the abnormal return in the 61st month as the differencebetween the actual portfolio return and the expected portfolio return.

Repeat the above steps each month and construct a time-series of theabnormal return for statistical tests.



Operating Performance Measure

Compute the sample firm’s operating performance from the first yearto the fifth year following the year in which they unexpectedly increaseR&D expenditure.

OPM1: EBIT/Sales.

OPM2: (EBIT+After-Tax R&D)/Sales.

Matching firm criteria.

Industry and performance match.

Characteristics match.

Compare the relative operating performance improvements betweenthe sample firm and control firm.



Summary Statistics

Table 1: Descriptive and Summary Statistics

This table provides sample selection procedure in Panel A, and Panel B shows the summary statistics for the sample of overconfident CEOs andnon-overconfident CEOs with (unexpected and economically significant) research and development increases. The sample period covers from 1980 to1994. Longholder is a binary variable where 1 refers that the CEO at some point during his tenure held an option grants until the last year beforeexpiration, in case that the option grants was at least 40% in-the-money entering its last year. The variables sales, total assets, market capitalization,and book-to-market equity are measured as the beginning of the sample firm’s R&D increase year, and are adjusted by the CPI to reflect 1994dollars. The variable definition of sales, total assets, market capitalization, book-to-market equity are described in Appendix. The R&D intensityratio is also measured as of the beginning of the R&D increase year. The percentage increase in dollar R&D is measured over the R&D increase year.T -test (Kruskal-Wallis test) are employed to test for difference between the means (medians) for the firms with overconfident CEOs and firms withnon-overconfident CEOs and the P -value are reported in the last two columns of Panel B.

Panel A: Sample Selection Procedure

Firm-year Firm

Number of observations in Compustat from 1980 to 1994 148,973 18,804Number of observations have unexpected significantly increase R&D 5,247 2,190Number of observations have Longholder 5,392 445Number of observations have Longholder and unexpected significantly increase R&D 151 39

Panel B: Summary Statistics

Non-overconfident CEOs (N=97) Overconfident CEOs (N=54) Difference

Mean Median Std. Dev. Mean Median Std. Dev. Mean Median

Sales ($MM) 3, 648.08 1, 212.93 8, 095.69 8, 264.15 5, 542.00 11, 955.54 0.0133** <.0001***

Total Assets ($MM) 3, 091.07 1, 285.92 5, 768.78 7, 141.92 5, 151.70 9, 453.26 0.0054*** <.0001***

Market Capitalization ($MM) 4, 045.12 2, 102.66 5, 871.72 8, 627.98 6, 026.69 8, 903.92 0.0011*** <.0001***

Book-to-market equity 0.40 0.36 0.23 0.48 0.46 0.26 0.0482** 0.0486**

R&D intensity measure (%)

R&D/Assets 9.58 8.77 4.22 8.50 7.23 3.55 0.1113 0.0177**

R&D/Sales 12.17 8.76 14.03 7.39 6.58 2.90 0.0016*** 0.0014***

Increase in dollar R&D (%) 20.77 12.72 27.51 12.46 10.04 10.19 0.0087*** 0.0048***

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

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The mean and median difference tests suggest that the characteristicsof firms with and without overconfident CEOs may be different.Therefore, it is important to control the firm characteristics in thefollowing cross-sectional analysis.



CEO Overconfidence and BHARs

For the full sample, the average BHAR for firms with non-overconfidentCEOs are significantly greater than those with overconfident CEOs.

Investors may not underreact the information content of R&D increasesfor overconfident CEOs, because they may invest in value-destroyingR&D projects through overestimating of future cash flow.

This evidence is more pronounced for high-tech firms and is weaker forlow-tech firms.

The results of cross-sectional regression are also consistent with theunivariate results.



BHARs following R&D Increases

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**


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CEO Overconfidence and CTARs

For the full sample, the difference between the abnormal returns ofnon-overconfident and overconfident CEOs is significantly positive,except for the equal-weighted case using the Carhart four-factor model.

For high-tech firms, the difference between the value-weightedabnormal returns of non-overconfident and overconfident CEOs arepositively significant.



CTARs following R&D increasesTable 4: Long-Term Abnormal Returns for Calendar-Time Portfolio

LongholderFama-French Three-Factor Model Carhart Four-Factor Model

α b s h α b s h m

Panel A: Full Sample

Equal Non-OC CEOs 0.0061*** 1.0972*** 0.2705*** -0.5160*** 0.0067*** 1.1008*** 0.2566*** -0.5273*** -0.0797Weight (3.25) (19.82) (2.92) (-4.55) (3.35) (20.48) (2.74) (-4.44) (-1.06)

OC CEOs 0.0004 1.1951*** 0.1454 -0.2672* 0.0023 1.2067*** 0.0996 -0.3046** -0.2628***(0.15) (17.09) (1.31) (-1.83) (0.84) (18.24) (0.94) (-2.08) (-2.93)

Difference 0.0057** -0.0979 0.1251 -0.2488* 0.0044 -0.1060 0.1570 -0.2227* 0.1831*(2.13) (-1.53) (1.22) (-1.90) (1.60) (-1.65) (1.54) (-1.70) (1.94)

Value Non-OC CEOs 0.0065*** 0.9987*** -0.1447* -0.6719*** 0.0061*** 0.9965*** -0.1358* -0.6646*** 0.0509Weight (3.85) (19.37) (-1.94) (-6.72) (3.42) (19.06) (-1.73) (-6.52) (0.84)

OC CEOs 0.0001 1.0305*** -0.3117*** -0.4790*** 0.0005 1.0325*** -0.3196*** -0.4854*** -0.0453(0.07) (17.16) (-3.15) (-4.81) (0.22) (16.94) (-3.15) (-4.95) (-0.59)

Difference 0.0063*** -0.0318 0.1670 -0.1929 0.0056** -0.0360 0.1838 -0.1792 0.0962(2.67) (-0.43) (1.44) (-1.46) (2.35) (-0.48) (1.52) (-1.35) (1.07)

Panel B: Rolling Regression Method

Equal Non-OC CEOs 0.0048* 0.0079*** 1.0845*** -0.5768*** 0.0066** 0.0090*** 1.0959*** -0.6198*** -0.1591***Weight (1.85) (75.22) (193.99) (-21.13) (2.50) (64.00) (195.49) (-20.96) (-16.27)

OC CEOs -0.0032 0.0003 1.1048*** -0.3152*** -0.0016 0.0019*** 1.1193*** -0.3509*** -0.2079***(-1.07) (1.08) (119.01) (-9.53) (-0.54) (6.62) (124.82) (-10.18) (-19.60)

Difference 0.0080** 0.0075*** -0.0203* -0.2616*** 0.0082** 0.0071*** -0.0234** -0.2689*** 0.0487***(2.03) (23.24) (-1.88) (-6.10) (2.07) (22.36) (-2.21) (-5.92) (3.38)

Value Non-OC CEOs 0.0044* 0.0068*** 1.0124*** -0.6948*** 0.0042* 0.0067*** 1.0118*** -0.6898*** 0.0108**Weight (1.91) (50.94) (299.93) (-30.67) (1.79) (45.44) (294.02) (-30.29) (2.44)

OC CEOs -0.0041* -0.0002 0.9559*** -0.5910*** -0.0043* -0.0000 0.9566*** -0.5806*** -0.0016(-1.85) (-0.56) (223.03) (-34.74) (-1.93) (-0.09) (216.30) (-33.74) (-0.20)

Difference 0.0085*** 0.0070*** 0.0565*** -0.1037*** 0.0085*** 0.0068*** 0.0552*** -0.1092*** 0.0124(2.66) (18.17) (10.35) (-3.66) (2.63) (18.19) (9.85) (-3.83) (1.34)

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Subsample Tests of CTARs

Table 5: Subsample Tests of Long-Term Abnormal Returns for Calendar-Time Portfolio

This table provides abnormal stock returns for the sample increasing their R&D from 1980 to 1994. The sample are divided intohigh-tech and low-tech firms using the definition in Brown, Fazzari, and Peterson (2009). The α refers to the abnormal returnmeasure. See Table 4 for a detailed description of the test procedures. In parentheses are Newey-West (1987) adjusted for serialcorrelation and heteroscedasticity t-statistics.

LongholderFama-French Three-Factor Model Carhart Four-Factor Model

α b s h α b s h m

Panel A: High-Tech Sample

Equal Non-OC CEOs 0.0096*** 1.0970*** 0.3442** -0.6777*** 0.0103*** 1.1012*** 0.3318** -0.6948*** -0.1074Weight (3.88) (14.07) (2.15) (-4.27) (3.97) (14.82) (2.08) (-4.12) (-1.07)

OC CEOs 0.0038 1.1970*** 0.2450 -0.5164*** 0.0064* 1.2114*** 0.2025 -0.5751*** -0.3697***(1.01) (13.07) (1.54) (-2.68) (1.72) (14.73) (1.41) (-2.89) (-3.51)

Difference 0.0058 -0.0999 0.0991 -0.1613 0.0039 -0.1102 0.1293 -0.1197 0.2623**(1.52) (-1.25) (0.58) (-0.85) (1.00) (-1.36) (0.75) (-0.62) (2.35)

Value Non-OC CEOs 0.0100*** 1.0075*** -0.2625** -0.9416*** 0.0102*** 1.0088*** -0.2665** -0.9471*** -0.0350Weight (4.75) (16.89) (-2.37) (-7.93) (4.59) (17.18) (-2.43) (-7.81) (-0.47)

OC CEOs 0.0036 1.0183*** -0.3451** -0.7283*** 0.0045* 1.0232*** -0.3594*** -0.7481*** -0.1243(1.46) (14.24) (-2.59) (-5.47) (1.74) (14.49) (-2.66) (-5.82) (-1.56)

Difference 0.0064** -0.0109 0.0826 -0.2133 0.0057** -0.0143 0.0928 -0.1991 0.0893(2.50) (-0.15) (0.52) (-1.45) (2.11) (-0.19) (0.58) (-1.36) (0.97)

Panel B: Low-Tech Sample

Equal Non-OC CEOs -0.0028 1.1981*** -0.1156 -0.0528 0.0011 1.1509*** -0.2675* -0.1636 -0.4591***Weight (-0.90) (10.96) (-0.72) (-0.23) (0.37) (11.08) (-1.84) (-0.80) (-2.96)

OC CEOs -0.0035 1.1307*** 0.2145 0.2953 -0.0029 1.1224*** 0.1878 0.2759 -0.0808(-0.93) (9.15) (1.05) (1.29) (-0.75) (8.88) (0.98) (1.26) (-0.49)

Difference 0.0008 0.0674 -0.3301 -0.3481 0.0039 0.0285 -0.4553** -0.4394 -0.3784*(0.17) (0.40) (-1.47) (-1.22) (0.91) (0.19) (-2.18) (-1.6) (-1.89)

Value Non-OC CEOs -0.0027 1.1236*** -0.5588*** 0.1164 -0.0032 1.1295*** -0.5396*** 0.1304 0.0581Weight (-0.66) (7.67) (-3.08) (0.44) (-0.68) (7.99) (-2.92) (0.48) (0.21)

OC CEOs -0.0026 0.9466*** -0.1200 -0.0094 -0.0029 0.9495*** -0.1108 -0.0027 0.0278(-0.66) (7.28) (-0.69) (-0.04) (-0.67) (7.05) (-0.65) (-0.01) (0.16)

Difference -0.0001 0.1769 -0.4388* 0.1257 -0.0003 0.1801 -0.4287* 0.1330 0.0303(-0.01) (0.85) (-1.94) (0.35) (-0.05) (0.85) (-1.89) (0.36) (0.09)

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Long-Term Abnormal Operating Performance

The results show a significantly positive difference between theabnormal operating performance of non-overconfident andoverconfident CEOs. R&D expenditures may benefit firms withnon-overconfident CEOs.

For the high-tech firms, the difference between abnormal operatingperformance of overconfident and non-overconfident CEOs arepositively significant.

The mixed results for low-tech firms are consistent with Eberhart et al.(2004) and may be linked to the relative importance of R&Dexpenditure under different firm types.

The results of cross-sectional regression are consistent with theunivariate results.




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 definitions 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**


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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 definitions 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 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**


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High-Tech Sample of Long-Term Abnormal OperatingPerformance

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***


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


32



Low-Tech Sample of Long-Term Abnormal OperatingPerformance

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 samples are divided into high-tech and low-tech firms using the definition inBrown, Fazzari, and Peterson (2009). See Table 6 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


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


32



Conclusion

In this paper, we investigate the relationship between unexpected R&Dincreases and CEO overconfidence. Both the long-term abnormal stockand operating performance are calculated by varies methodologies.

The results show that investors earn positive abnormal stock returnsfor firms with non-overconfident CEOs following R&D increases. Wealso find strong evidence that R&D increases are beneficial investmentdecisions only for firms with non-overconfident CEOs.

The benefit of intangible information of R&D increases may bemitigated for overconfident CEOs, because they overestimate futurecash flow and tend to overinvestment.

Our findings are stronger for high-tech firms, since R&D expendituresmay be more crucial for high-tech industries.


Documents

CEO Overconfidence and the Long-Term Performance Following