Aggressive R&D investments in pre-IPO years: the signalling explanation

Abstract
Relying upon the signalling theory in the IPO literature, we hypothesize that companies arelikely to make an aggressive investment in R&D activities in pre-IPO years to signal future
growth potential to the IPO market. We focus on abnormal R&D expenditures in pre-IPO years
as a proxy for the signal. Based on a sample of IPOs during 1980-2016, we find the following.
First, companies invest more aggressively in R&D activities in pre-IPO years. Second,
investors price the abnormal R&D spending in pre-IPO years into the IPO first-day market
value and post-IPO stock returns. Third, the abnormal R&D activities generate more patents
and patents with higher impact, which appear to be a channel through which companies growin sales and market value in post-IPO years. Collectively, our findings indicate that “goodprivate companies” make aggressive R&D spending in pre-IPO years as a credible signal of ahigh-growth potential, which successfully generates innovative outputs and sales growth in the
post-IPO period. Our evidence sheds light on the real benefits of abnormal R&D activities
before going public but contrasts to the view that companies use earnings management
opportunistically in the IPO market.
Keywords: Abnormal R&D expenditures; Initial public offerings; Signaling; Innovation;
GrowthData availability: Data are available from sources identified in the text.

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1.

Introduction
Information differences between companies and investors in the capital market often lead tothe information or “lemons” problem (Healy and Palepu 2001). Analyzing the role of signals
within the IPO process, Leland and Pyle (1977) show how companies with favorable prospects
and higher possibilities of success (“good companies”) should always send clear signals to themarket when going public to separate themselves from bad ones in the IPO market due to highinformation asymmetry (for example, the owner keeping control of a significant percentage of thecompany shares). If no signal is sent to the market, significant asymmetric information will resultin adverse selection in the IPO market. To be reliable, a signal should be prohibitively too costly
to be mimicked by “bad companies” and rational investors can clearly infer the value of the signal
and price it accordingly in the IPO market. The signaling theory helps explain some anomalies in
the IPO literature. In particular, Fan (2007) finds that good companies use ownership retention
and discretionary accruals as joint signals in the IPO market to distinguish themselves from bad
ones.1 Allen and Faulhaber (1989) design a model showing that better-informed companies use
IPO underpricing to signal their quality to investors.
Prior IPO studies show that companies invest more aggressively prior to IPO (Pagano et al.
1998) and primarily use IPO proceeds to repay debt due to the aggressive investments (Leone etal. 2007). Since most R&D expenditures are not allowed to be capitalized under the current
GAAP, investing in R&D activities is conceptually costly to IPO companies as it leads to lower
reported earnings before going public. This cost can be recouped by companies with favorable
prospects (“good companies”) in the future when companies experience increased growth andgenerate more innovation outputs. As such, “good companies” are likely to invest in R&D

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Earlier studies examining earnings management of IPOs show that companies are likely to opportunistically manage
accruals in the IPO market (e.g., Teoh et al. 1998a, 1998b). Recent studies, however, cast doubt on the opportunism
explanation for accruals management in the IPO year and provide corroborating evidence that companies report moreconservatively in pre-IPO years (e.g., Fan 2007; Ball and Shivakumar 2008; Cecchini et al. 2012; Armstrong et al.
2016).
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activities more aggressively in pre-IPO years, so that the cost is prohibitively too high to be
mimicked by “bad companies”. With such costly signal, rational investors will realize only “goodcompanies” can bear this cost and thus price the signal accordingly. The literature of IPO
valuation provides evidence that investors are likely to price R&D expenditures as an importantvaluation input during the IPO process (e.g., Hand 2003; Guo et al. 2006; Aggarwal et al. 2009).
The two streams of studies on the signaling and the IPO valuation of R&D investments
motivates us to examine the following two research questions. First, relying upon the signallingtheory, we examine if private companies engage in abnormally high R&D investments before
going public to signal their quality of future growth in the IPO market. Second, we investigate if
such aggressive R&D investments credibly signal the IPO market by testing on the first-day
market value upon IPOs and post-IPO stock returns as well as future innovation outputs andgrowth in the post-IPO period.
To test our research questions, we focus on discretionary R&D expenditures rather thanactual R&D expenditures that prior IPO studies frequently use, because the discretionary portion
beyond the normal level of R&D expenditures represents managers’ incentives for signaling in the
IPO market.2 We refer those firms with positive discretionary R&D expenditures to those of
making aggressive R&D expenditures for signaling. By linking the discretionary R&D spendingof IPO firms to the market pricing and future generation of innovation outputs, we shed additional
light on IPO firm managers’ incentives to signal their quality, opportunistically mimic other goodcompanies, or opportunistically manage earnings. The abnormal (or discretionary) R&D

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While the literature of real earnings management generally adopts discretionary R&D expenditures as one of themeasures of real earnings management, prior studies document mixed evidence on the opportunistic use of R&D
expenditures by managers. Some provide evidence that companies are likely to cut R&D expenditures to meet theearnings benchmark in the secondary market (See, e.g., Roychowdhury 2006; Cohen and Zarowin 2010; Zang 2012).
Others argue that the cut of R&D expenditures to meet the earnings benchmark is not opportunistic but signaling
superior future earnings (Gunny 2010) or correcting stock underpricing (Fang and Fu 2018). Nevertheless, althoughseveral studies have examined the existence of real earnings management (e.g., cutting R&D expenditures) in thesecondary market, few pay attention to the IPO setting. Moreover, while prior studies focus on negative discretionary
R&D expenditures for opportunistic earnings management, in contrast, our study focuses on positive discretionary
R&D expenditures as a signaling proxy for aggressive R&D investments.
3expenditures are calculated as the difference between actual R&D expenditures and the expected(i.e., normal) level of R&D expenditures of IPO firms in pre-IPO years. The expected level of
R&D expenditures is calculated from the model in Canace et al. (2018).3 We compare abnormal
R&D expenditures between pre-and post-IPO years and also between IPOs and non-IPO
companies matched by earnings performance (Kothari et al. 2005) and a propensity score by firmsize, age and sales growth as Armstrong et al. (2016) propose.4
It is theoretically unclear whether investors recognize “abnormal” R&D expenditures
beyond the normal level of R&D spending in pre-IPO years as a credible signal and price it
accordingly. Our study provides empirical evidence on this important query. Our univariate andmultivariate analyses indicate that private companies are likely to aggressively invest in R&D
activities before going public. In specific, IPO companies have significantly larger amounts ofabnormal (discretionary) R&D expenditures in pre-IPO year (Year -1) and the IPO year (Year 0).
They are significantly greater than those of the matched non-IPO firms.5 In contrast, abnormal
R&D expenditures in post-IPO years are generally insignificant and decrease monotonically.
Next, we investigate three economic consequences of abnormal R&D spending in pre-IPO
years. Firstly, we examine whether the market recognizes the abnormal R&D expenditures as asignal in the IPO process by testing their effects on the IPO first-day market value and post-IPO
stock returns. We find that abnormal R&D expenditures in pre-IPO years are significantly andpositively associated with the IPO first-day market value and the post-IPO short- and long-run
stock returns (from one month to 36 months). The evidence is consistent with the signaling

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While the model in Canace et al. (2018) provides reliable estimation of abnormal R&D expenditure, there is anotherwidely-used model implemented by Roychowdhury (2006) in the literature of real earnings management to estimate
abnormal discretionary expenditures. To ensure our inference does not depend on a model choice, we redo theestimation procedure using the Roychowdhury model. The results are qualitatively the same as we discuss in detail in
Section 6.1.
4 We provide another comprehensive measure of abnormal R&D expenditures by adopting a similar propensity scorematching by earnings, firm size, age and sales growth. We discuss details in Section 3.
5
We analyze abnormal R&D expenditures both in Year -1 and Year 0 (the IPO year), as companies are likely tocontinue investing in R&D activities up to the IPO date. In addition, most prior IPO studies focus on Year 0 due to asmall sample size available in Year -1.
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explanation of aggressive R&D spending. Investors clearly utilize the abnormal R&D
expenditures in pre-IPO years as an “extra” valuation input beyond the normal level of R&D
expenditures and price it accordingly upon the IPO and in the post-IPO years.
Secondly, we examine the real effects of aggressive R&D investments on future growth.
Following Aggarwal et al. (2009) who argue that R&D investments can proxy for growth
opportunities, we predict and test whether abnormal R&D expenditures in pre-IPO years are
significantly and positively associated with post-IPO growth. We measure firm growth by sales
growth and a price-to-sales ratio in post-IPO years, respectively. Consistent with our prediction,
we find that abnormal R&D expenditures in pre-IPO years are significantly and positively
associated with future sales growth and the fiscal-year-end price-to-sales ratios for Year 1 throughYear 3 after the IPO.
Thirdly, we examine whether the abnormal R&D spending prior to the IPO promotes
innovation outputs in the post-IPO years. Prior studies provide evidence that R&D investments
are important innovation input activities that lead to future patent output (e.g., Bereskin et al.
2018). We capture the innovation outcomes in terms of the quantity and quality of granted patents
and measure them by the number of successfully registered (i.e., approved) patents and the total
number of forward citations received by all successful patent registrations, respectively. We
expect that high discretionary R&D expenditures before going public increase the total number ofregistered patents and the total forward citations for Year 1 through Year 3 following the IPO.
Consistent with the expectation, we find significantly positive association of abnormal R&D
expenditures of Year -1 and Year 0 with the total number of registered patents and the total
number of forward citations in Year 1 to Year 3 after the IPO. We consider that innovation
outputs are a channel through which IPO companies effectively grow in sales and market values
in the post-IPO period. Our results are robust to a choice of estimation models, measurement ofabnormal R&D expenditures, matching techniques, and controlling for the normal level of R&D

5and capital expenditures. Collectively, our results indicate that “good” private companies makeabnormally high R&D spending in pre-IPO years as a credible signal of their quality and it
actually generates innovative outputs and helps them grow in the post-IPO period.
Our study makes several contributions to the IPO literature of signalling, real activities
management, and innovation. First, it contributes to the signaling literature in the IPO market byproviding evidence that “good private companies” with high growth potentials are likely to use
abnormally high investments in R&D activities before going public as a credible signal todistinguish themselves from other low-growth companies within the IPO process. The signal ofabnormal R&D spending carries value of predicting post-IPO growth opportunities and isaccordingly priced by market participants upon the IPO and after the IPOs.
Second, it contributes to the literature of real activities management in the IPO market.
Utilizing the important R&D measure of real activities management, we discover the significant
magnitudes of discretionary R&D expenditures in pre-IPO years that increase the IPO first-day
market value and post-IPO stock returns. Such positive market responses are consistent with ourfindings that pre-IPO abnormal R&D spending actually promotes innovation outputs and sales
growth in post-IPO years. Collectively, these findings contrast to the view that companies arelikely to use earnings management opportunistically in the IPO process. This evidence isparticularly important as it corroborates recent findings that companies are likely to reportearnings more conservatively in the IPO market (e.g., Fan 2007; Ball and Shivakumar 2008;
Cecchini et al. 2012; Armstrong et al. 2016).
Third, our study extends the prior studies that document the association between R&D
investments and innovation outputs (e.g., Bereskin et al. 2018) to the IPO market. We provide
evidence that aggressive R&D expenditures of private companies before going public help
generate successful patents outputs and forward citations, and help them grow operational andmarket performance in the post-IPO years. As such, successful innovation outputs appear to be a6channel through which companies grow after the IPO. Our result is in alignment with a recent
evidence that private companies with more disruptive technology (e.g., the technologicaldisruptiveness of patent) are more likely to exit through IPOs than via acquisitions (Bowen et al.
2018).
The remainder of this paper is organized as follows. Section 2 reviews the signalling
literature in the IPO setting and develops research hypotheses. Section 3 discusses research design
and Section 4 describes sample. Section 5 provides empirical results and Section 6 conducts
robustness checks. Section 7 concludes the paper.

2.

Literature Review and Hypothesis Development
Significant information asymmetries often exist between insiders (e.g., managers) andoutside investors in the capital market, which will result in the “lemons” problem (Healy andPalepu 2001). To attempt to solve such a problem, Leland and Pyle (1977) design a model toanalyze the signals within the IPO process. The authors argue that when going public, a privatecompany with favourable prospects and higher possibilities of success (“good companies”) should
send a signal to the market about the value of the firm (for example, retaining part of its equity).
They argue that a reliable signal should be prohibitively too costly to be mimicked by “badcompanies” and rational investors realize only “good companies” are able to bear this cost andcan correctly infer firm value from the signal in the IPO market.
The signalling theory helps explain some anomalies in the IPO literature. For example,
Allen and Faulhaber (1989) design a model to explain the anomaly of IPO underpricing. They
argue that companies with good future perspectives should signal their quality by underpricing
their IPOs, as investors know that only “good companies” can recoup the cost of this signal from
subsequent issues. Similarly, Fan (2007) designs a model and provides empirical evidence that7

“good companies” use discretionary accruals and ownership retention as joint signals to themarket to distinguish themselves from bad ones.
In this study, we extend prior studies by exploring the motivation and economic
consequences of aggressive R&D investments of private companies before going public as asignal of firm value in the IPO market. Relying upon the signalling literature, we predict thatR&D investments of private companies are a reliable signal in the IPO market as follows. First,
and perhaps more important, the investment in R&D activities is conceptually costly to privatecompanies before going public. Under the current GAAP in the U.S., companies are not allowed
to capitalize most R&D expenditures (SFAS No. 2). Instead, most spending on R&D activities has
to be recognized as current expenses in the income statement (FASB 1974).6 As a result, thosecompanies that invest more aggressively in R&D activities are likely to face earnings decrease
and consequentially increase in the cost of capital. Only companies with favourable prospects(“good companies”) are likely to recoup this cost in the future when the economic benefits of
R&D investment (such as increases in innovation outputs and operational and market growth)
start to materialize. In contrast, “bad companies” that invest in R&D activities aggressively toimitate “good companies” in the IPO market are likely to suffer from this cost without thepossibility of recouping in the future. To separate themselves from bad ones, “good companies”
have an incentive to aggressively invest in R&D activities to a level that the cost is prohibitively
too high for “bad companies” to mimic. In equilibrium, rational investors realize that only “goodcompanies” can bear the cost of aggressive R&D investments to such a high level. In this sense,
abnormally high R&D expenditures can be viewed as a signal to reduce the adverse selection in
the IPO market.
Second, literature provides evidence that investors take R&D investments as an importantvalue driver in the IPO market. For instance, Hand (2003) finds that investors value negative book6

The one exception is software development costs, which can be capitalized under SFAS No. 86.
8values in the IPO market as they indicate investments in R&D and other intangibles over the years
prior to the IPO. Similarly, Guo et al. (2006) indicate that pre-IPO R&D expenditures are
significantly and positively correlated with both initial underpricing and long-term performance.
Aggarwal et al. (2009) find that the level of pre-IPO R&D expenditures is significantly andpositively correlated with the IPO offer value and the first-day market value as it is priced as
growth opportunity in the IPO market. In this sense, investors are likely to infer firm value from
R&D investments accordingly in the IPO market. As a result, good private companies will have astrong incentive to invest more aggressively in R&D activities before going public to signal future
growth potential to investors in the IPO market. Different from prior studies, the objective of ourstudy is to examine the implications of aggressive R&D expenditures before going public and thus
focuses on the discretionary portion of R&D expenditures to measure aggressive R&D
investments beyond the normal level.
Since R&D investments are part of the corporate decision procedure, managers often haveconsiderable discretion in deciding the timing and amounts of R&D investments.7 During the IPO
process, it is difficult for investors to judge the appropriateness of the amounts of R&D
investments of private companies in pre-IPO years due to the lack of other sources ofcorroborative information available in the IPO market. As such, we expect that private companies
have an exceptional opportunity to invest aggressively in R&D activities to signal their quality in
the IPO market. In this sense, we expect that good companies with favourable future perspectives
are likely to make aggressive R&D investments in pre-IPO years as a signal for their future
growth opportunities and to separate themselves from others in the IPO markets. Thus, wedevelop our first hypothesis as follows (in an alternative form):
H1: Ceteris paribus, companies are likely to invest aggressively in R&D activities in preIPO years.

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For example, prior studies in the literature of real earnings management provide evidence that companies havediscretion to opportunistically reduce R&D investment to avoid reporting losses (e.g., Roychowdhury 2006).
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In an equilibrium of signalling, rational investors can realize that only good companies
could bear the cost of the signal to a prohibitively high level and value the signal accordingly.
Relying upon this theory, we expect that aggressive R&D expenditures in pre-IPO years signal
future growth opportunities and, as such, are significantly and positively correlated with the IPO
first-day market value and post-IPO stock returns. Prior studies provide some evidence that thelevel of R&D expenditures in pre-IPO years is significantly and positively associated with the IPO
first-day and long-term equity values (e.g., Guo et al. 2006; Aggarwal et al. 2009). Unlike thosestudies, we focus on abnormal (i.e., discretionary) R&D spending beyond the normal level of
R&D expenditures as a proxy for signalling, as we aim to understand the managers’ motivation
for signalling in the IPO market and its real effects on the IPO markets and on post-IPO
performance.
It is theoretically unclear whether investors recognize “abnormal” R&D expenditures
beyond the normal level of R&D spending in pre-IPO years as a credible signal and price it
accordingly. As such, an examination of the associations between pre-IPO abnormal R&D
expenditures and the IPO first-day and long-term equity values can provide corroborating
evidence to support our signalling explanation for the aggressive R&D investments in pre-IPO
years. While the IPO first-day market value can provide evidence on investors’ immediate pricing
of abnormal pre-IPO R&D expenditures, post-IPO stock returns will allow us to examine how
investors price the value of abnormal R&D investments in pre-IPO years in a longer run following
the IPO. Correspondingly, we state our second hypothesis as follows (in an alternative form):
H2: Ceteris paribus, abnormal R&D expenditures in pre-IPO years are positively associatedwith the IPO first-day market value and post-IPO stock returns.
Our third hypothesis is developed to examine the real benefits of abnormal R&D
expenditures made before going public. As indicated in the signalling theory, only companies
with good future perspectives can bear the cost of signalling in the IPO market. Thus, we
postulate that aggressive investment in R&D activities in pre-IPO years can generate significant
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economic benefits for issuers in post-IPO years. Aggarwal et al. (2009) provide evidence that
actual R&D expenditures proxy for company’s growth opportunities in the future. Bereskin et al.
(2018) find that R&D expenditures are important innovation input activities that can lead tosubsequent innovation output such as patent registrations (quantity) and citations (quality). If
aggressive R&D expenditures in the pre-IPO years are perceived as a credible signal for the future
growth potential in the IPO market, we postulate that the abnormal R&D spending is likely todeliver innovation output and operating and market growth in the post-IPO years. We develop ourthird hypothesis as follows (in an alternative form):
H3a: Ceteris paribus, abnormal R&D expenditures in pre-IPO years are positively
associated with post-IPO growth.
H3b: Ceteris paribus, abnormal R&D expenditures in pre-IPO years are positively
associated with post-IPO patent outputs.
The above three hypotheses complement each other and shed lights on managers’
motivation of aggressive R&D investments in pre-IPO year and their economic benefits on equity
values and operating performance in the years following the IPO.

3.

Research Design

3.1. Abnormal R&D expenditures
We use abnormal R&D expenditures to capture the signalling effect of aggressive R&D
investments in pre-IPO years and test Hypothesis 1 (H1). The abnormal R&D expenditures (ARD)
are measured as the difference between R&D expenditures and the expected level of R&D
expenditures of IPO firms in pre-IPO years. The expected (i.e., normal) level of R&D
expenditures (NRD) is estimated from the coefficient estimates of the following model11

implemented by Canace et al. (2018) on all other non-IPO firms in the same 2-digit SIC industries
in the same fiscal year but excluding firms within ±3 years of their IPO offerings: 8
RD = 0 + 1CAPX + 2Lag(RD) + 3RD_IND + 4LEV + 5Lag(CASH)+ 6SIZE + 7SGA + 8ΔSALE + 9DIV + 10TSTK + 11ACQ + ε(1)

The dependent variable RD is the R&D expenses scaled by average total assets. We deflate
all of the following independent variables by average total assets (except SIZE). CAPX is thecapital expenditures. Lag(RD) is the lagged R&D expenses. RD_IND is the industry average
R&D. LEV is the total debt. Lag(CASH) is the sum of the lagged cash and short-term investments.
SIZE is the natural logarithm of average total assets. SGA is the selling, general and administrative
expenses. ΔSALE is the year-to-year change in net sales. DIV is the total dividends paid. TSTK is
the amount of stock repurchases. ACQ is the amount of acquisitions. All variables in Eq. (1) anddata sources are defined in Appendix A.9
While the model in Canace et al. (2018) produces reliable estimation of abnormal R&D
expenditures in the non-IPO setting, it may possibly produce noisy estimates when applying to the
IPO setting if the non-random sample characteristics of IPO firms are not properly addressed. To
mitigate this concern, we adjust abnormal R&D expenditures based on a performance-matchedapproach, as implemented in Kothari et al. (2005). Specifically, we first match each IPO firm inour sample with a non-IPO firm from the same 2-digit SIC industries in the same fiscal year withthe closest return on assets (ROA) (i.e., earnings before extraordinary items and discontinued
operations scaled by average total assets). Then, we estimate abnormal R&D expenditures forboth the IPO firm-year observation and its matched counterpart using the Canace et al. model.

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The model in Canace et al. (2018) provides reliable estimation of abnormal R&D expenditures. However, to makesure our inference is not sensitive to alternative model choices, we use another well-known model implemented byRoychowdhury (2006) to estimate abnormal discretionary expenditures. We discuss the results in Section 6.1.
9
The original model in Canace et al. (2018) includes four additional variables THRESH, MB, CFOBRD and FINCF.
We drop THRESH as our study is not utilizing the opportunistic earnings management explanation. We drop MB as it
is not applicable to IPO companies since market value doesn’t exist prior to the IPO. We drop CFOBRD and FINCF
to make sure our sample period can start from 1980 as cash flow statement data are only available after July 1987. To
ensure that our results are not sensitive to missing of these variables, we redo our tests by adding back these variablesin the model for the period of 1990 to 2016. Untabulated results show that our inferences are qualitatively similar.
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The performance-matched abnormal R&D expenditures (PM_ARD) are the differences between
the abnormal R&D expenditures of the two matched firms.10
Alternate to adjusting abnormal R&D expenditures using earnings performance, Armstrong
et al. (2016) propose a propensity score matching by firm size, age and sales growth. Thus, weestimate a propensity score matched abnormal R&D (SAG_ARD) following their study. The
estimation procedure proceeds as follows: first, we estimate the probability of an IPO from alogistic regression function of firm size, age and sales growth. A predicted probability is derived
as a propensity score. Second, we match each IPO company to non-IPOs in the same 2-digit SIC
industries in the same fiscal year. Third, one non-IPO is selected per an IPO by minimizing thesquared values of the differences of propensity scores in year-industry matched IPOs and nonIPOs. To accommodate both earnings performance, firm size, age and growth characteristics of
IPO companies, we provide another comprehensive measure of abnormal R&D expenditures byadopting a similar propensity score matching by earnings, firm size, age and sales growth(ROA_SAG_ARD).
While estimating abnormal R&D expenditures of IPO companies from a pool of non-IPO
companies can provide direct measures, it can potentially cause biased estimate that leads tobiased inference. Ball and Shivakumar (2008) point out that estimation of any measure from nonIPO coefficients to IPO sample might be biased. To mitigate this concern, we implement a
multivariate regression model based on the Canace et al. model following Ball and Shivakumar(2008) approach by pooling together IPO and non-IPO companies. The model is presented asfollows:
RD = 0 + 1CAPX + 2Lag(RD) + 3RD_IND + 4LEV + 5Lag(CASH) + 6SIZE +7SGA + 8ΔSALE + 9DIV + 10TSTK + 11ACQ + 12IPO + 13IPO×CAPX +14IPO×Lag(RD) + 15IPO×RD_IND + 16IPO×LEV + 17IPO×Lag(CASH) +10

While performance-matched approach can reduce the noise of estimating abnormal R&D expenditures from the
Canace et al. model in the IPO setting, we note different interpretation of the results: if the estimated performancematched abnormal R&D expenditures are not different from zero, the implication is not that R&D expenditures are notaggressively invested, but rather that R&D expenditures are not reported any more significantly than in publicly tradedfirms with similar earnings performance.
1318IPO×SIZE + 19IPO×SGA + 20IPO×ΔSALE + 21IPO×DIV + 22IPO×TSTK +23IPO×ACQ + Year and industry fixed effects + ε(2)
The variable of interest is IPO which is a dummy variable that equals 1 for IPOs and 0
otherwise. We include year and industry fixed effects. All other variables are the same as in Eq.
(1) and defined in Appendix A. Hypothesis 1 (H1) predicts that the coefficient of IPO (α12) in preIPO years is significantly positive.
While pooled OLS regression in Eq. (2) reduces the concern of applying coefficientestimation from non-IPO sample to IPO sample, it may induce another problem. While the IPO
sample is based on one-year data, non-IPO sample covers time series. To reduce the potential bias
due to sample size differences, we rerun Eq. (2) by using IPOs and their propensity score matched
non-IPO counterparts. We expect the coefficient estimated for IPO (α12) in pre-IPO years to besignificantly positive.

3.2. IPO first-day market value and post-IPO stock returns
To examine H2 on the association between pre-IPO abnormal R&D expenditures and the
IPO first-day market value, we estimate the following equation:
L(MV) = 0 + 1L(ARD) + 2L(NRD) + 3L(CAPX) + 4L(IBBRD) +5L(IBBRD)×NegIBBRD + 6L(CEQ) + 7L(CEQ)×NegCEQ + 8SIZE + 9AGE +10PreUW+ 11RETENTION + 12BIGN11 + 13PriceUpdate + Year and industry fixed
effects + ε(3)The dependent variable L(MV) is the natural logarithm of MV, which is the IPO first-day
closing price multiplied by the number of shares outstanding immediately after the IPO. We use
total IPO first-day market value (rather than first-day market price per share) to remove thearbitrary effect of the number of shares issued following Aggarwal et al. (2009), who suggest that
underwriters somewhat arbitrarily partition total equity value into price per share and shares

11

Big N auditors include Arthur Andersen, Ernst & Young, Deloitte & Touche, KPMG and PricewaterhouseCoopers.
14

offered.12 Beatty et al. (2000) also indicate that the explanatory power of the IPO valuation modelsignificantly increases when they use total IPO value compared with when they use offer price pershare as the dependent variable. We apply natural logarithm (L) transformation13 to both
dependent variable and financial statement variables14 to alleviate the non-normality andheteroskedasticity problems following Aggarwal et al. (2009).
Hypothesis 2 (H2) predicts that the coefficient of L(ARD), 1, to be significantly positive.
We include L(NRD) to control for the normal level of pre-IPO R&D expenditures as they are
priced by investors as an important valuation component of IPO first-day market value. Prior
studies have found asymmetries in the relations between IPO equity value and positive andnegative earnings. In particular, Aggarwal et al. (2009) find that positive earnings are positively
associated with IPO equity value and negative earnings are negatively associated with IPO equity
value, suggesting that negative earnings are a proxy for growth opportunities and that such growth
options are a significant component of IPO firm value. We therefore break up earnings before
R&D expenditures (IBBRD) into positive and negative (NegIBBRD) components using a dummy
variable. Prior research also finds that negative book values of equity may have different
valuation implications from positive book values. Negative book values may indicate investments
in R&D and other intangibles over the years prior to the IPO and thus may be valued by the stock

12

In general, prior studies use several different dependent variables in IPO valuation. For example, Kim and Ritter(1999) and Purnanandam and Swaninathan (2004) recommend using the offer price or first-day closing price pershare deflated by earnings per share as dependent variables. Klein (1996) and Bartov et al. (2002) recommend using
the offer price or first-day closing price per share as dependent variables. As Aggarwal et al. (2009) argue, however,all candidates for dependent variables in the specification of an IPO valuation model in the literature have their own
limitations. For example, the offer price or first-day closing price per share deflated by earnings per share leads to theelimination of firms with negative earnings and reduces the generalizability of the findings. The offer price or firstday closing price per share is deficient on theoretical and empirical grounds given the fact that the true economic
variable being priced in the IPO process is total value of equity. Underwriters somewhat arbitrarily partition total
equity value into price per share and shares offered.
13
L(MV) = loge(1 + MV) when MV ≥ 0 and L(MV) = -loge(1 – MV) when MV < 0.
14
As ARD and NRD are estimated from Eq. (1) with the scaling treatment of average total assets, to be consistent withother financial statement variables in Eq. (3), we multiple these two variables by average total assets before applying
the natural logarithm transformation.
15

market (Hand 2003). Therefore, similar to the treatment of earnings, we separate positive bookvalue of equity (CEQ) from negative ones using a dummy variable NegCEQ.
Following prior studies, we control for capital expenditures (CAPX), as companies are likely
to shift between R&D expenditures and capital expenditures (see, for example, Canace et al.
2018) and firm size (SIZE) (see, for example, Beatty et al. 2000). We also include three
nonfinancial variables – underwriter prestige15 (PreUW), ownership retention (RETENTION), andauditor type (BIGN) – as additional control variables. Prior studies find that these variables serve
as signals of an IPO’s quality and are important determinants of the IPO’s realized value (see, forexample, Leland and Pyle 1977; Beatty and Ritter 1986; Klein 1996; Carter et al. 1998; Fan 2007;
Aggarwal et al. 2009). Finally, we control for PriceUpdate, measuring the position of the final
offer price relative to the expected price at the time the prospectus was filed. Prior studies findthat this variable is an important predictor for IPOs’ first-day stock price performance (see, forexample, Benveniste and Spindt 1989; Hanley 1993). Other variables are defined as before.
Detailed definitions of variables are given in Appendix A.
Next, we examine the associations between pre-IPO abnormal R&D expenditures and postIPO stock returns for the short-run and the long-run windows. If investors are rational to price thesignaling of abnormal R&D expenditures made in pre-IPO years, they value the company
accordingly. As such, we expect abnormal R&D expenditures are positively associated with postIPO stock returns. We estimate various post-IPO short-run and long-run abnormal returns using acalendar regression approach, as implemented in Armstrong et al. (2016). This approach has two

15

Following Aggarwal et al. (2009), we define prestigious underwriters as underwriters whose reputation rank isgreater or equal to 8, where underwriter reputation rank is obtained from Jay Ritter’s website(http://bear.warrington.ufl.edu/ritter/ipodata.htm). We acknowledge that the sign of prestigious underwriter variable
may change across different time periods, especially given the adverse impact of the 2008 financial crisis on theprestige of investment banks in the U.S. To address this concern, we construct a dummy variable CRISIS that equals 1
if the IPO offer date is between January 2008 and December 2016, and 0 otherwise and include it as well as its
interaction term with PreUW in Eq. (3) to redo the test. Untabulated result provides no significant evidence that theassociation between underwriter prestige and the IPO first-day market value differs between the pre- and post-2008
financial crisis periods.
16

advantages. First, it avoids problems inherent in using long run buy-and-hold returns. For
example, the severe skewness of the distribution of buy-and-hold returns is no longer an issue, as
this approach uses monthly returns (Fama 1998). Furthermore, the test statistics will have moredesirable statistical properties (Mitchell and Stafford 2000). Second, it adjusts for risk in a morecomprehensive manner (relative to an explicit asset-pricing model) than does simply subtracting
the return of a benchmark index. In specific, we estimate the following model:(Ri,t – Rf,t) = 0 + 1ARD + 2SIZE + 3AGE + 4PreUW + 5RETENTION + 6BIGN +7IR + 8(Rm,t – Rf,t) + 9SMB + 10HML + 11UMD + Year and industry fixed
effects + ε(4)where Ri,t – Rf,t is monthly risk-free excess returns over the subsequent 1-, 3-, 6-, 12-, 24-, and 36month period starting from the month following the IPO month. We implement firm specific risk
factors as in Eq. (3) and Fama and French (1993) three factors (Rm,t – Rf,t, SMB and HML) and a
momentum factor (UMD) to control for risk associated premiums. We also control for initial
return (IR), measuring the position of the first-day closing price relative to the offer price, as priorstudies find that IPOs with higher initial returns are generally associated with lower long-term
stock returns (IPO anomalies) (e.g., Loughran and Ritter 1995). Hypothesis 2 (H2) predicts thecoefficient of ARD, 1, to be significantly positive.

3.3. The real benefits of pre-IPO abnormal R&D expenditures
To test Hypothesis H3a, we first examine the association between pre-IPO abnormal R&D
expenditures and post-IPO growth measures by estimating the following equation:
FSGR/FPS = 0 + 1ARD + 2NRD + Year and industry fixed effects + ε(5)The dependent variable FSGR is post-IPO sales growth. The alternative dependent variable FPS ispost-IPO fiscal year-end price-to-sales ratio. All variables are defined in Appendix A. H3apredicts 1 to be significantly positive.

17

To test Hypothesis H3b, we examine the association between pre-IPO abnormal R&D
expenditures and post-IPO patent output by estimating the following equation:
L(COUNTS/CITES) = 0 + 1L(ARD) + 2L(NRD) + 3L(CAPX) + Year and industry
fixed effects + ε(6)Following Bereskin et al. (2018), we construct two measures for post-IPO patent output,
including patent counts (COUNTS), defined as the number of successful (i.e., approved) patentapplications for Year 1 – Year 3, and patent citations (CITES), defined as the total number offorward citations received by all successful patent applications for Year 1 – Year 3. We control
for the normal level of R&D expenditures (NRD) and capital expenditures (CAPX) following
Canace et al. (2018). We apply natural logarithm transformation to both dependent andindependent variables to alleviate the non-normality and heteroskedasticity problems. Allvariables are defined in Appendix A. To support H3b, we expect 1 to be significantly positive.

4.

Sample and Data
The initial sample includes U.S. IPOs that are extracted from the Securities Data

Corporation (SDC) database from 1980 to 2016. Following prior literature, we exclude IPOs (1)that are unavailable in the Centre for Research on Security Prices (CRSP) database, (2) that are
non-ordinary or common shares,16 and (3) that are filed by regulated utility firms (SIC codes in
the range 4900-4999) and financial services, insurance and real estate firms (SIC codes in therange 6000-6999). This selection procedure leads to a final sample of 6,706 IPOs. Table 1
summarizes the effects of the sample-selection criteria on the sample size.
(INSERT TABLE 1 ABOUT HERE)
Table 2 reports distribution statistics of our final IPO sample. Panel A shows the distributionof IPOs over issuing years. Consistent with prior IPO studies, we observe a cluster of IPOs during

16

We do not rely on SDC classification alone for identifying IPOs of ordinary shares, because SDC occasionallyidentifies ADRs as ordinary shares. We independently verify the share type using CRSP share code (not equal to 11).
18

the 1990s. In specific, 3,737 firms (or 56%) of our total sample issued IPOs during 1990–1999.
This is not surprising given that the stock market roared during this period. As the stock market
value dropped significantly in early 2000, so did the number of IPOs. IPO activities somewhat
resumed in 2004 before another recession hit the market in late 2007 due to the subprime
mortgage crisis. IPO activities came back again after 2013.
Panel B reports the distribution of IPOs across industries classified by two-digit SIC codes.
We find that IPOs take place across different industries. The prime industry is business services,which accounts for about 21% of our sample, followed by chemicals and allied products;
electronic and other electrical equipment and components; measuring, analysing, and controlling
instruments, photographic, medical and optical goods, and watches and clocks; Industrial andcommercial machinery and computer equipment. These industries, taken together, comprise morethan 50% of our sample.
(INSERT TABLE 2 ABOUT HERE)
We collect IPO offer price, preliminary filing price range, primary and secondary shares
offered in the IPOs and the name of the leading underwriter(s) from SDC17 or hand collect themfrom IPO prospectuses available on SEC’s website.18 We obtain IPO founding dates andunderwriters’ reputation rankings from Jay Ritter’s website.19 We extract the post-IPO stock price,
returns and the number of shares outstanding following the IPO from CRSP. We rely on
COMPUSTAT for financial statement data. We retrieve the patent records of all U.S. public firms
from the NBER patent database originally developed by Hall et al. (2005) and updated by Lai etal. (2011).20 The data set includes information on filing and grant dates, patents and associated17

We also correct for some SDC errors based on the file from Jay Ritter’s website on
https://site.warrington.ufl.edu/ritter/files/2015/04/SDC_corrections.pdf
18
https://www.sec.gov/edgar/searchedgar/companysearch.html.
19
http://bear.warrington.ufl.edu/ritter/ipodata.htm.
20
The original NBER patent database covers patent data for 1976-2006 and is available from the following website
https://sites.google.com/site/patentdataproject/Home. The updated patent database covers patent data for 1975-2010
and is available from the following website https://dataverse.harvard.edu/dataverse/patent.
19

assignees (i.e., companies), COMPUSTAT-matched identifiers (GVKEY) and the citations madeand received by each patent until the end of 2010, for all patents approved (granted) by the
USPTO from 1975 to 2010.

5.

Empirical results

5.1. Abnormal R&D expenditures
We start univariate analyses by examining the magnitudes of abnormal R&D expenditures(ARD) estimated in Eq. (1) from Year -1 to Year 3 around the IPO year (Year 0) to provide acomplete picture of annual changes of ARD from pre- to post-IPO years. We concentrate on Year-1, because this is the most recent year prior to the IPO that ARD can signal company quality to
the market.21 Similarly, it is expected that companies continue to signal until the IPO date and, assuch, we are also interested in ARD in Year 0 (together with Year -1 as pre-IPO years). This also
helps make our results directly comparable to prior IPO studies as they analyze the IPO year
(Year 0) due to the lack of data availability in pre-IPO years.
In Panel A of Table 3, to mitigate the effect of outliers, we report the medians of ARD and
use the Wilcoxon test to examine whether they are statistically different from zero. It indicatesthat the results are qualitatively consistent across various measures of unmatched ARD andmatched ARD (PM_ARD, SAG_ARD, ROA_SAG_ARD). The unmatched ARD are significantly
positive in Year -1 and Year 0 with the largest magnitudes (1.4% of total assets) in Year -1 anddecrease monotonically from Year -1 to Year 3. Similarly, all of the matched ARD are
significantly positive in Year -1 and Year 0 again with the largest magnitudes in Year -1 (2.0% of
total assets for the case of PM_ARD and 1.9% for both SAG_ARD and ROA_SAG_ARD).
However, they are mostly insignificant in Year 1 and Year 2 and are significantly negative in

21

Ideally, we hope to track the annual changes of abnormal R&D expenditures from Year -3 to Year 3, as privatecompanies are likely to use abnormal R&D expenditures as the signal earlier than Year -1 (e.g., Year -3 and Year -2).
However, financial statement data for years prior to Year -1 are always missing in COMPUSTAT.
20

Year 3. In sum, the results of univariate analyses are consistent with our hypothesis thatcompanies invest aggressively in R&D activities in pre-IPO years (H1). Panel B of Table 3
exhibits correlation coefficients among ARD measures in Year -1. Our variable of interest, ARD,
is positively correlated with R&D expenditures (Pearson correlation coef. = 0.776 and Spearman’s
correlation coef. = 0.556) but has low correlation coefficients with NRD (0.228 and 0.237
respectively). The Pearson correlation coefficient between ROA_SAG_ARD and RD is only 0.385,
suggesting that our matched ARD measures do not simply capture actual R&D expenditures thatprior studies frequently use.
(INSERT TABLE 3 ABOUT HERE)
Next, we conduct multivariate regression analysis using Eq. (2) to compare abnormal R&D
expenditures of the IPOs with those of non-IPOs for Year -1 through Year 3 on an unmatchedpool basis. Following Ball and Shivakumar (2008), we implement our analysis based on the
Canace et al. model and pooling IPO and non-IPO companies together. Table 4 reports theresults.22 The variable of interest is IPO. The coefficients of IPO in Year -1 and Year 0 are bothsignificantly positive (P < 0.01) with the magnitude in Year -1 (0.092) which is more than threetimes that in Year 0 (0.028). In contrast, the coefficients of IPO in the post-IPO years (Year 1 toYear 3) are all negative and insignificant. Thus, the results are consistent with the univariate test
results reported in Table 3, supporting H1.
In terms of control variables, all control variables are significantly associated with R&D
expenditures and the signs are consistent with Canace et al. (2018). We find that CAPX, Lag(RD),RD_IND, Lag(CASH), SIZE, SGA, ΔSALE, and ACQ are all significantly and positively associatedwith R&D expenditures. We also find that LEV, DIV, and TSTK are significantly and negatively
correlated with R&D expenditures. Adjusted R2 across all test years are consistently high (around

22 For brevity, estimated coefficients of interaction terms of IPO and other variables in Eq. (2) are not presented inTables 4 and 5.
2180%), suggesting that the Canace et al. (2018) model provides a powerful estimation of abnormal
R&D expenditures.
(INSERT TABLE 4 ABOUT HERE)
Table 5 presents the results of running multivariate analysis in Eq. (2) to compare abnormal
R&D expenditures of the IPOs with PSM matched non-IPOs by earnings, size, age and sales
growth in the same 2-digit SIC industries for Year -1 to Year 3. The results are qualitatively
similar to those in Table 4. The coefficients of IPO in Year -1 and Year 0 are significantly
positive (P < 0.05 and P < 0.01, respectively) with the magnitude in Year -1 (0.045) more thandouble that in Year 0 (0.021). In contrast, the coefficients of IPO in Year 1 and Year 2 are
insignificant, and the coefficient of IPO in Year 3 is significantly negative at the 10% level. The
results on control variables are generally similar to those reported in Table 4. Adjusted R2 acrossall test years are consistently high (79% to 82%) although sample size is smaller. In sum, both
univariate and multivariate analyses in Tables 3 - 5 provide corroborating evidence thatcompanies are likely to invest aggressively in R&D activities in pre-IPO years. In the next
section, we examine whether abnormal R&D spending in pre-IPO years serves as a credible signal
in the IPO market in the first-day and post-IPO years.
(INSERT TABLE 5 ABOUT HERE)

5.2. The Effect of abnormal R&D expenditures on IPO first-day market value and post-IPO
stock returns
We first examine the association between ARD in Year -1 and the IPO first-day market
value as in Eq. (3). Table 6 presents the results. Column (1) reports the results only with abnormal
and normal R&D variables, L(ARD) and L(NRD). Column (2) reports the results after controllingfor capital expenditure (L(CAPX)). Column (3) reports the results after further controlling forearnings (L(IBBRD) and L(IBBRD)×NegIBBRD) and book value of equity (L(CEQ) and22

L(CEQ)×NegCEQ). Column (4) reports the results with additional controls on firm size (SIZE),
auditor type (BIGN), underwriter prestige (PreUW) and ownership retention (RETENTION).
Column (5) adds PriceUpdate as an additional control.
The coefficients of L(ARD) are significantly positive across all five specifications (p <0.01), which are consistent with the signaling explanation of aggressive R&D spending in thefirst-day market value and supportive of H2. Next turning to control variables, the coefficients on
positive and negative earnings and book value of equity are all statistically significant with thepredicted signs. The coefficients of the normal R&D expenditures (L(NRD)), capital expenditures(L(CAPX)), firm size (SIZE), underwriter prestige (PreUW), ownership retention (RETENTION),
auditor type (BIGN) and PriceUpdate are all significantly and positively associated with IPO firstday market value, whereas firm age (AGE) is significantly negative. Our findings of theassociations between control variables and the IPO first-day market value are consistent with priorstudies.
(INSERT TABLE 6 ABOUT HERE)
Next, we examine the associations between abnormal R&D expenditures and post-IPO
stock returns covering various short- and long-windows from one month up to three years (1-, 3-,
6-, 12-, 24-, and 36 months) following the IPO month as in Eq. (4). Columns (1) – (6) of Table 7
report the results across different windows, respectively. We find consistently and significantly
positive associations between ARD in Year -1 and post-IPO stock returns across various windows
up to three years.23 Collectively, the results indicate that investors value IPO firms according to
pre-IPO abnormal R&D expenditures in post-IPO years, consistent with our signalling
explanation and supporting H2.

23

We also examine the association between ARD in Year 0 and future stock returns across various windows
beginning at the end of the fourth month following the fiscal year-end of Year 0. The result is qualitatively similar.
23

Among the control variables, there are positive and significant coefficients of PreUW acrossall regression models, suggesting that prestigious underwriters play an important role insupporting post-IPO stock returns. We also find a significantly negative coefficient of IR,
suggesting that IPOs with higher initial returns are generally associated with lower long-term
stock returns (IPO anomalies) (e.g., Loughran and Ritter 1995). All Fama-French factors and themomentum factor are statistically significant, as expected. Finally, the intercept, or alpha, isnegative across all regression models, indicating that, on average, newly public companies earn
negative abnormal returns in the 36 months following their IPOs, consistent with the IPO
underperformance literature.
(INSERT TABLE 7 ABOUT HERE)
Taken together, Tables 6 and 7 present the results of examining the consequences of preIPO R&D investments on the IPO first-day pricing and post-IPO stock returns. They provide
systematic evidence of investors pricing the aggressive R&D investments in pre-IPO years. Thisevidence is consistent with the signaling theory that managers send a credible signal to the market
through pre-IPO R&D investments and investors recognize it as an important value driver, leading
to higher IPO first-day price and long-term equity values. It would be possible that the aggressive
R&D expenditures made in pre-IPO year successfully generate innovation products and sales
growth in the post-IPO years, which contributes to the higher long-term equity value. The next
section investigates this possibility.

5.3. Economic benefits of aggressive R&D investments in pre-IPO years
We first examine the real effects of aggressive R&D investment in pre-IPO years on
operational and market performance in the post-IPO years. We measure the performance based onsales growth and price-to-sales ratios and implement Eq. (5). Panel A of Table 8 reports theresults of post-IPO sales growth. Columns (1) – (4) report the results of the associations between

24

abnormal R&D expenditure in Year -1 and sales growth for each of Year 1 to Year 3 relative toYear -1 (FSGR_Y1, FSGR_Y2, FSGR_Y3). Columns (5) – (8) report the results of the associations
between abnormal R&D expenditures in Year 0 and sales growth for each of Year 1 to Year 3
relative to Year 0. They provide systematic evidence that abnormal R&D expenditures in pre-IPO
years are significantly and positively associated with post-IPO sales growth in each of the three
years following the IPO, supporting H3a. The coefficients of the normal level of R&D
expenditures (NRD) are significantly positive in most periods.
Panel B of Table 8 reports the results of post-IPO price-to-sales ratios. We utilize post-IPO
fiscal year-end price-to-sales ratios to proxy for company’s growth opportunities. Columns (1) –(3) report the associations between abnormal R&D expenditures in Year -1 and the price-to-sales
ratio at the fiscal year end of Year 1 – Year 3 (FPS_Y1, FPS_Y2, FPS_Y3). Columns (4) – (6)report the results for abnormal R&D expenditures in Year 0. They indicate that abnormal pre-IPO
R&D expenditures are significantly and positively correlated with post-IPO price-to-sales ratios infive of six specifications, supporting H3a. The coefficients of NRD remain significantly positive
across all models.
(INSERT TABLE 8 ABOUT HERE)
It would be plausible that the aggressive R&D investments in pre-IPO period successfully
generate patents after IPOs, which help companies to grow in the post-IPO period. As such, we
consider that innovation outputs are a channel through which IPOs grow in sales and market
values in the post-IPO period. In this section, we examine the associations between pre-IPO
abnormal R&D expenditures and future patent outputs after IPOs, as in Eq. (6). Table 9 reports
the results. In Columns (1) – (4), we report the results on the association between the total numberof patents registered from Year 1 to Year 3 (L(COUNTS)) and abnormal R&D expenditures inYear -1 and Year 0, respectively. We report the results based on total patent citations (L(CITES))
in Columns (5) – (8). We find that, after controlling for the normal level of R&D expenditures,25

abnormal R&D expenditures are significantly and positively associated with both the quantity andquality of patents in post-IPO years in seven of the eight regressions, supporting H3b. We alsofind that capital expenditures (L(CAPX)) are significantly and positively correlated with the total
patent number (L(COUNTS)) and the total patent citations (L(CITES)), suggesting that capital
expenditures are also predictive of future patent outputs.
(INSERT TABLE 9 ABOUT HERE)

6.

Robustness Checks

6.1. Comparing future growth measures between IPO companies and PSM matched non-IPO
companies by performance (ROA), size, age and sales growth
In this section, we compare future growth measures between IPO companies and PSM
matched non-IPO companies by performance (ROA), size, age and sales growth and test whether
pre-IPO abnormal R&D expenditures of IPO companies “signal” greater than those of PSM
matched control companies. Specifically, we estimate the following equation:
FSGR/FPS = 0 + 1IPO + 2IPO×ARD + 3IPO×NRD + 4ARD + 5NRD + Year andindustry fixed effects + ε(7)
The dependent variables include sales growth (FSGR) and price-to-sales (FPS) ratios. To beconsistent with the signaling theory, we expect that 2 is significantly positive. Table 10 reports
the results from estimating Eq. (7). Columns (1) – (6) estimate by pooling together the IPO
companies of Year -1 and their PSM matched control companies. Columns (7) – (12) estimate for
IPO companies of Year 0 and their PSM matched control companies. They show that thecoefficients on IPO are significantly positive across ten out of twelve columns (p < 0.01),
indicating that IPO companies are associated with significantly higher growth than the PSM
matched non-IPO companies after controlling for R&D expenditures. More importantly, thecoefficients on IPO×ARD are significantly positive for eight of twelve cases after controlling forthe normal level of R&D expenditures. In contrast, the coefficients for non-IPO counterparts
26

(ARD) are significantly positive only for two cases. These results are consistent with our
expectation that abnormal R&D expenditures in pre-IPO period credibly “signal” future growth
opportunities, to a greater extent, relative to the PSM matched non-IPO companies.
(INSERT TABLE 10 ABOUT HERE)

6.2. Comparing future patent outputs between IPO companies and PSM matched non-IPO
companies by performance (ROA), size, age and sales growth
In this section, we compare future patent outputs between IPO companies and PSM matched
non-IPO companies by performance (ROA), size, age and sales growth and test whether pre-IPO
abnormal R&D expenditures of IPO companies “signal” greater than those of PSM matched
control companies. Specifically, we estimate the following equation:
L(COUNTS/CITES) = 0 + 1IPO + 2IPO×L(ARD) + 3IPO×L(NRD) +4IPO×L(CAPX) + 5L(ARD) + 6L(NRD) + 7L(CAPX) + Year andindustry fixed effects + ε(8)
To be consistent with the signaling theory, we expect that 2 is significantly positive. Table
10 reports the results. Columns (1) and (2) estimate Eq. (8) by pooling together the IPO
companies of Year -1 and their PSM matched control companies. Columns (3) and (4) estimate
Eq. (7) for IPO companies of Year 0. They show that the coefficients on IPO are significantly
positive across all four columns (p < 0.01), suggesting that IPO companies are associated withsignificantly more patent quantity and higher patent quality than the PSM matched non-IPO
companies, after controlling for R&D and capital expenditures. Furthermore, the coefficients on
IPO×L(ARD) are significantly positive for Year -1 in the first two columns and positive but
insignificant for Year 0 in the last two columns, after controlling for the normal level of R&D andcapital expenditures. Overall, these results are consistent with our expectation that abnormal R&D
expenditures in pre-IPO period are “signaling” greater of future patent number and citations for
IPO companies than for the PSM matched non-IPO companies.

27

(INSERT TABLE 11 ABOUT HERE)

6.3. Estimating abnormal R&D expenditures using the model in Roychowdbury (2006)
In the literature of real earnings management, Roychowdhury (2006) implements a model toestimate abnormal discretionary expenditures (including R&D expenditures) by regressing
discretionary expenditures on lagged sales. To make sure our main findings are not sensitive tothe choice of the estimation model, we re-estimate abnormal R&D expenditures using the modelin Roychowdhury (2006) and repeat our empirical tests. The multivariate regression model bypooling together IPO and non-IPO companies is presented as follows:
RD = 0 + 1Lag(SALE) + 2IPO + 3IPO×Lag(SALE) + Year and industry fixed effects+ε(9)Table 12 reports the univariate and multivariate analyses of the magnitudes of abnormal
R&D expenditures across the five years around the IPO. Panel A shows that abnormal R&D
expenditures are significantly positive in pre-IPO years and the magnitudes decrease
monotonically from Year -1 to Year 3 (0.085 to 0.023 for the performance-matched ARD),
consistent with our main results in Table 3. However, the Roychowdhury model produces
significantly non-negative abnormal R&D expenditures even for post-IPO years but withsignificantly smaller magnitudes. Panel B of Table 12 replicates the tests in Table 5 by using Eq.
(9) and produces consistent results, indicating that that IPO companies invest more aggressively in
R&D activities in pre-IPO years relative to control companies. It is interesting to note that the
explanatory powers of regressions using the Roychowdhury model in Panel B of Table 12 (lessthan 15% of adjusted R2) are substantially lower than those of our main estimation models in
Table 5 (about 80% of adjusted R2). It might suggest that the power of different estimation models
for abnormal R&D expenditures varies between IPO firms and non-IPO firms.
(INSERT TABLE 12 ABOUT HERE)

28

7.

Conclusions
The signaling theory in the IPO literature indicates that “good companies” with favorable

prospects are likely to send signals to the market within the process of IPO to separate themselvesfrom bad ones. Signaling can help reduce adverse selection in the IPO market where information
asymmetry is high. To be reliable, the signal should be potentially too costly to be imitated by“bad companies” and rational investors can readily infer value inputs from these signals in themarket. In this paper, we argue that private companies with high growth potential are likely to use
aggressive R&D investments in pre-IPO years as a signal of their quality in the IPO market. We
use abnormal R&D expenditures in pre-IPO years (Year -1 and Year 0) as a proxy for the signal
and implement various tests to examine the magnitudes of abnormal R&D expenditures forcompanies across years around the IPO. We also test the effects of abnormal R&D expenditures in
pre-IPO years on the IPO first-day pricing and the post-IPO stock returns. Finally, we examine theeconomic benefits of abnormal R&D expenditures in pre-IPO years by linking them to future
growth and patent outputs after IPOs.
The results from these tests are consistently strong and support our hypothesis of signalingfor IPO firms. We report the following evidence. First, private companies invest moreaggressively in R&D activities than their non-IPO counterparts before going public. Second,
investors price pre-IPO abnormal R&D expenditure accordingly in valuing IPOs at the issuance aswell as in post-IPO stock returns. Third, abnormal R&D expenditures in pre-IPO years generatesignificant economic benefits for issuers in the post-IPO years. Specifically, companies that invest
more aggressively in pre-IPO R&D activities experience higher growth in sales and market values
and more patent outputs and citations in the post-IPO years. We consider that effective innovation
outputs of those IPO companies are a channel through which IPOs enjoy growth in operation andmarket valuation in the post-IPO years. Our results are robust to alternative measure of abnormal
R&D expenditures, various matching techniques, and a series of tests on managers’ decisions on

29

R&D expenditures, market pricing, innovation outputs, and future growth. Collectively, our study
provides consistent and corroborating empirical evidence that companies effectively use
aggressive R&D investments in pre-IPO years to signal their high quality in the IPO market.
Our findings are important to the literature. Contributing to the literature of signaling, we
provide reliable evidence on the use of abnormal R&D expenditures beyond the normal level as acredible signal of high quality to separate themselves from low-growth companies in the IPO
market where information asymmetry is high. Contributing to the literature on real activities
management of IPOs, we provide evidence that companies more aggressively invest in R&D
activities in pre-IPO years which help generate innovation outputs and grow after the IPO. These
results contrast the opportunism view of earnings management through cutting R&D expenditures
around IPOs in some of earlier studies, but are consistent with more recent findings that IPO
companies report earnings more conservatively in pre-IPO years (e.g., Fan 2007; Ball andShivakumar 2008; Cecchini et al. 2012; Armstrong et al. 2016). Our findings are in alignment
with a recent evidence that private companies with more technological disruptiveness of patent
exit through IPOs than via acquisitions (Bowen et al. 2018). Lastly, our study adds to the literateof innovation by providing evidence that abnormal R&D investments in pre-IPO years credibly
signal future innovative products and growth opportunities.

30

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33

APPENDIX A: Variable Definition
ATA
RD
CAPX
Lag(RD)
RD_INDLEV
Lag(CASH)
SIZE
SGA
ΔSALE
DIV
TSTK
ACQ
AGE
ARD
NRD
ROA
PM_ARDSAG_ARDROA_SAG_ARD
L(MV)
L(ARD)
L(NRD)
L(CAPX)
L(IBBRD)

NegIBBRD
L(CEQ)
NegCEQPreUW
RETENTION

Average total assets (COMPUSTAT item AT).
Research and development expense (COMPUSTAT item XRD) scaled by
ATA.
Capital expenditures (COMPUSTAT item CAPX) scaled by ATA.
Lag of research and development expense (COMPUSTAT item XRD)
scaled by ATA.
Industry average RD (industry is defined at the two-digit SIC code level).
Sum of book value of short-term debt (COMPUSTAT item DLC) and longterm debt (COMPUSTAT item DLTT) scaled by ATA.
Lag of the sum of cash and short-term investments (COMPUSTAT item
CHE) scaled by ATA.
The natural logarithm of ATA.
Selling, general and administrative expense (COMPUSTAT item XSGA)(net of R&D expense) scaled by ATA.
Year-to-year change in net sales (COMPUSTAT item SALE) scaled by
ATA.
Total dividends paid (COMPUSTAT item DIV) scaled by ATA.
The dollar amount of stock which has been reacquired and placed into
treasury (COMPUSTAT item TSTK) scaled by ATA.
The dollar amount of acquisitions (COMPUSTAT item ACQ) scaled by
ATA.
The natural logarithm of 1 plus the difference between the founding year of
the company and the IPO year. Founding years are obtained from Jay
Ritter’s website (http://bear.warrington.ufl.edu/ritter/ipodata.htm).
Abnormal level of RD.
Estimated normal level of RD.
Earnings before extraordinary items and discontinued operations(COMPUSTAT item IB) scaled by ATA.
Performance matched ARD.
Size, age and sales growth matched ARD.
ROA, size, age and sales growth matched ARD.
The natural logarithm of (IPO first-day closing price × number of shares
outstanding immediately after the IPO).
The natural logarithm of (ARD × ATA).
The natural logarithm of (NRD × ATA).
The natural logarithm of (CAPX × ATA).
The natural logarithm of net income before R&D expenditures, calculated
as the sum of earnings before extraordinary items and discontinued
operations (COMPUSTAT item IB) and R&D expenditures (COMPUSTAT
item XRD).
1 if L(IBBRD) is negative and 0 otherwise.
The natural logarithm of book value of equity (COMPUSTAT item CEQ).
1 if L(CEQ) is negative and 0 otherwise.
1 if the underwriter reputation rank is greater or equal to 8, and 0 otherwise.
Underwriter reputation rank is obtained from Jay Ritter’s website(http://bear.warrington.ufl.edu/ritter/ipodata.htm).
(Number of shares outstanding immediately after IPO – number of primary& secondary shares offered in IPO) / number of shares outstanding
34

BIGN
PriceUpdate
IR
Ri
Rf
Rm
SMB
HML
UMD
FSGR
FPS
L(COUNTS)
L(CITES)

immediately after IPO.
1 if the auditor is a Big N auditing firm and 0 otherwise.
(IPO offer price – median value of IPO filing price range) / median value of
IPO filing price range.
(IPO first-day market price – IPO offer price) / IPO offer price.
Monthly return on IPO firm i.
Monthly 30-day T-bill yield.
Monthly return on the value-weighted CRSP index.
Monthly return on small firms minus the monthly return on large firms.
Monthly return on high book-to-market stocks minus the monthly return on
low book-to-market stocks.
Monthly return on high prior return portfolios minus the monthly return on
low prior return portfolios.
Sales growth for Year 1 – Year 3 relative to Year -1 or Year 0.
Fiscal year-end price-to-sales ratio for Year 1 – Year 3.
The natural logarithm of number of successful (i.e., approved) patentapplications for Year 1 – Year 3.
The natural logarithm of number of forward citations received by all
successful patent applications for Year 1 – Year 3.

35

Table 1
Sample Selection Process
Total number of IPOs during 1980-2016 in the U.S. market
9,983
Less:
No matching firms in CRSP database(41)
Non-ordinary or common shares(1,898)Regulated utilities, Financial services, insurance, and real estate firms(1,338)Domestic IPOs by non-regulated and non-financial firms listed on NYSE,
6,706
NASDAQ, or AMEXThis table presents the sample selection process for IPOs during 1980-2016. Regulated firms are
firms with SIC codes in the range 4900-4999. Financial services, insurance, and real estate firms
are firms with SIC codes in the range 6000-6999. Non-ordinary/common shares issues are
identified based on CRSP share code (not equal to 11).

36

Table 2
Distribution of IPOs by Year and Industry
Panel A: Distribution by issuing-year
Issuing Year
Number of IPOs
1980
37
1981
77
1982
39
1983
231
1984
93
1985
124
1986
270
1987
222
1988
102
1989
103
1990
104
1991
265
1992
385
1993
495
1994
412
1995
449
1996
597
1997
395
1998
227
1999
408
2000
316
2001
61
2002
52
2003
50
2004
134
2005
122
2006
116
2007
122
2008
15
2009
37
2010
68
2011
63
2012
80
2013
119
2014
158
2015
91
2016
67
Total
6,706

37

Percent (%)
0.6
1.1
0.6
3.4
1.4
1.8
4.0
3.3
1.5
1.5
1.6
4.0
5.7
7.4
6.1
6.7
8.9
5.9
3.4
6.1
4.7
0.9
0.8
0.7
2.0
1.8
1.7
1.8
0.2
0.6
1.0
0.9
1.2
1.8
2.4
1.4
1.0
100.0

Table 2 (Continued)
Distribution of IPOs by Year and Industry
Panel B: Distribution by industry (SIC code)
IndustryTwo-digit
SIC code73
28

Number of
IPOs
1,395
729

Percent
(%)
20.8
10.9

Business services
Chemicals and allied productsElectronic and other electrical equipment and
36
552
8.2
components, except computer equipment
Measuring, analyzing, and controlling instruments;
Photographic, medical and optical goods; Watches and
38
486
7.2
clocksIndustrial and commercial machinery and computer
35
459
6.8
equipment
Communications
48
265
4.0
Engineering, accounting, research, management, and
87
218
3.3
related services
Health services
80
215
3.2
Miscellaneous retail
59
210
3.1
Wholesale trade-durable goods
50
197
2.9
Eating and drinking places
58
153
2.3
Oil and gas extraction
13
147
2.2
Transportation equipment
37
106
1.6
Food and kindred products
20
104
1.6
Miscellaneous manufacturing industries
39
89
1.3
Wholesale trade-non-durable goods
51
86
1.3
Printing, Publishing, and Allied Industries
27
75
1.1
Apparel and other finished products made from fabrics
23
75
1.1
and similar materials
Primary metal industries
33
70
1.0
Apparel and accessory stores
56
70
1.0
Motion pictures
78
68
1.0
Amusement and recreation services
79
68
1.0
Others (each one < 1% of total IPO sample)
869
13.0
Total
6,706
100
This table presents the distribution of IPOs from 1980 to 2016 by year and industry. Panel A
presents the time distribution. Panel B presents the industry distribution.

38

Table 3
Univariate analysis of abnormal R&D expenditures from Year -1 to Year 3 around IPO
Panel A: Median values of abnormal R&D expenditures from Year -1 to Year 3
Year
-1
0
1
2
3
N
1,194
2,769
2,766
2,528
2,262
ARD
0.014***
0.010***-0.002***-0.003***-0.005***
PM_ARD0.020***
0.015***
0.002***
0.001-0.003***
SAG_ARD
0.019***
0.016***
0.000
0.002
-0.003**
ROA_SAG_ARD
0.019***
0.016***
0.001
0.002
-0.003***
Panel B: Correlation matrix (Pearson above and Spearman below) among R&D expenditure
measures for Year -1
RD
NRD
ARD
PM_ARD SAG_ARD ROA_SAG_ARD
RD
0.776*** 0.791*** 0.702*** 0.384***
0.385***
NRD
0.909***
0.228*** 0.210*** 0.086***
0.086***
ARD
0.556*** 0.237***
0.879*** 0.683***0.683***
PM_ARD
0.456*** 0.208*** 0.743***0.507***
0.507***SAG_ARD
0.468*** 0.199*** 0.795*** 0.594***
1.000***ROA_SAG_ARD 0.469*** 0.199*** 0.796*** 0.593*** 0.997***
This table presents univariate analysis of abnormal R&D expenditures from Year -1 to Year 3
around IPOs. Panel A presents the median values of abnormal R&D expenditures for the period
from Year -1 to Year 3 around IPOs. Panel B presents the correlation matrix among R&D
expenditures, normal R&D expenditures and abnormal R&D expenditures for Year -1. ARD is theabnormal R&D expenditures estimated from the model in Canace et al. (2018). PM_ARD is theperformance (ROA) matched abnormal R&D expenditures calculated following the approach in
Kothari et al. (2005). SAG_ARD is the size, age and sales growth matched abnormal R&D
expenditures using the propensity score matching procedure as in Armstrong et al. (2016).
ROA_SAG_ARD is the performance (ROA), size, age and sales growth matched abnormal R&D
expenditures using the propensity score matching procedure as in Armstrong et al. (2016).
Detailed variable definitions can be found in Appendix A. Wilcoxon test is used to examine
whether the median value is statistically different from zero. *, ** and *** denote statisticalsignificance at the 10%, 5% and 1% levels, respectively.

39

Table 4
Multivariate analysis of comparing R&D expenditures of IPO companies and non-IPO
companies from Year -1 to Year 3 around IPO (test for H1)
0
1
2
3
Year
-1
-0.014-0.014-0.014-0.015Intercept-0.017(-1.45)(-1.45)(-1.37)(-1.46)(-1.63)0.028-0.008-0.011-0.011IPO
0.092(2.89)***(-0.85)(-1.13)(-1.19)(4.61)***
CAPX

0.127
0.125
0.126
0.126
0.126
(12.01)***(12.16)*** (12.20)*** (12.21)*** (12.21)***
Lag(RD)
0.606
0.609
0.608
0.602
0.606
(56.52)***(58.17)*** (58.16)*** (57.80)*** (57.94)***
RD_IND
0.014
0.013
0.013
0.013
0.013
(5.22)***(5.10)***(5.20)*** (5.08)*** (5.29)***LEV-0.013-0.013-0.013-0.014-0.014(-5.18)***(-5.47)***(-5.45)*** (-5.57)*** (-5.46)***
Lag(CASH)
0.065
0.064
0.064
0.064
0.064
(21.69)***(22.00)*** (21.95)*** (22.12)*** (22.05)***
SIZE
0.002
0.002
0.002
0.002
0.002
(8.70)***(8.31)***(8.44)*** (8.12)*** (8.32)***
SGA
0.093
0.090
0.090
0.090
0.090
(23.62)***(23.78)*** (23.79)*** (23.74)*** (23.75)***
ΔSALE
0.012
0.012
0.012
0.012
0.012
(6.91)***(7.34)***(7.37)*** (7.29)*** (7.34)***DIV-0.084-0.082-0.089-0.088-0.087(-3.96)***(-4.05)***(-4.26)*** (-4.24)*** (-4.19)***TSTK-0.025-0.025-0.025-0.025-0.025(-6.27)***(-6.36)***(-6.31)*** (-6.31)*** (-6.30)***
ACQ
0.044
0.043
0.043
0.043
0.043
(7.94)***(7.99)***(7.98)*** (7.89)*** (7.91)***
Yes
Yes
Yes
Yes
Year fixed effects
Yes
Yes
Yes
Yes
Yes
Industry fixed effects
Yes
79,376
79,371
79,127
78,838
N
77,7250.77990.77930.77890.8203
Adj. R2
0.7847This table presents the multivariate OLS regression results of R&D expenditures on a set ofvariables in the model of Canace et al. (2018) by pooling together all IPO companies and non-IPO
companies from 1980 to 2016, as proposed and implemented in Ball and Shivakumar (2008). IPO
is a dummy variable that equals 1 for IPO companies and 0 otherwise. Detailed definitions forother variables can be found in Appendix A. For brevity, estimated coefficients of interaction
terms of IPO and other variables in Eq. (2) are not presented in the table. T-statistics appear inparentheses and are calculated based on standard errors clustered by companies. Year fixed effectand industry fixed effect based on 2-digit SIC code are included but not reported. *, **, and ***represent significance levels of 0.10, 0.05, and 0.01 respectively (two-tailed).
40

Table 5
Multivariate analysis of comparing R&D expenditures between IPO companies and PSM
matched non-IPO companies by performance (ROA), size, age and sales growth from Year 1 to Year 3 around IPO (test for H1)
0
1
2
3
Year
-1
-0.024-0.016-0.022-0.007
Intercept-0.022(-3.01)***(-0.33)(-1.56)(-0.40)(-0.28)
0.021
0.002
0.003
-0.020IPO
0.045(2.92)***(0.29)(0.27)(-1.75)*(2.25)**-0.002-0.036
0.016
CAPX
0.330
-0.048(-0.06)(-1.07)(0.34)(4.09)***(-2.02)**
0.673
0.494
0.470
Lag(RD)
0.701
0.649(25.92)***(53.22)*** (52.83)*** (39.04)*** (32.93)***0.003-0.021
0.002
RD_IND-0.024-0.032(0.21)(-1.51)(0.06)(-1.67)*(-2.62)***-0.007-0.004-0.012
LEV
0.011
0.007
(-1.88)*(-0.88) (-2.88)***(1.77)*(1.76)*
0.038
0.044-0.054Lag(CASH)
0.036
0.028
(4.66)*** (4.65)*** (-4.04)***(1.66)*(3.09)***
0.002
0.004
0.002
SIZE
0.001
0.002
(3.78)*** (4.94)***(1.67)(0.73)(2.99)***
0.098
0.138
0.148
SGA
0.095
0.086
(9.22)***(19.22)*** (21.53)*** (28.10)*** (34.78)***
0.005
0.007
0.003
ΔSALE
0.033
0.016
(1.37)(1.71)*(0.58)(5.31)***(6.15)***-0.069-0.1370.008
DIV-0.408-0.005(-1.88)* (-2.93)***(0.12)(-7.79)***(-0.26)-0.004-0.010
0.058
TSTK
0.061
0.001
(-0.32)(-0.67)(3.07)***(2.05)**(0.04)
0.056
0.016
0.000
ACQ
0.175
0.081
(1.89)*(0.46)(0.00)(3.02)***(2.57)**
Yes
Yes
Yes
Yes
Year fixed effects
Yes
Yes
Yes
Yes
Yes
Industry fixed effects
Yes
5,296
5,418
5,048
4,566
N
2,168
2
0.79560.80480.79430.8129Adj. R
0.8225This table presents the multivariate OLS regression results of R&D expenditures on a set of variables in themodel of Canace et al. (2018) by pooling together all IPO companies and their matched non-IPO
companies from 1980 to 2016 by performance (ROA), size, age and sales growth using a propensity scorematching procedure as proposed and implemented in Armstrong et al. (2016). IPO is a dummy variable
that equals 1 for IPO companies and 0 otherwise. Detailed definitions for other variables can be found in
Appendix A. For brevity, estimated coefficients for interaction terms of IPO and other variables in Eq. (2)are not presented in the table. T-statistics appear in parentheses. Year fixed effect and industry fixed effectbased on 2-digit SIC code are included but not reported. *, **, and *** represent significance levels of
0.10, 0.05, and 0.01 respectively (two-tailed).
41

Table 6
Abnormal R&D expenditures of Year -1 and IPO first-day market value (test for H2)
Variables
Intercept

(1)
3.824
(15.22)***0.067(3.25)***
0.421(9.25)***

(2)
3.271
(15.51)***
0.065(3.42)***
0.231(5.86)***
0.391(12.06)***

(5)
2.826
(14.04)***L(ARD)
0.028(2.22)**
L(NRD)
0.068(3.31)***
L(CAPX)
0.140(4.41)***
L(IBBRD)
0.094(4.31)***
L(IBBRD)×NegIBBRD-0.172(-3.85)***
L(CEQ)
0.011(0.53)
L(CEQ)×NegCEQ-0.029(-0.78)SIZE
0.233(8.40)***
AGE-0.107(-4.00)***
PreUW
0.394
(7.32)***RETENTION
1.973(14.95)***
BIGN
0.377
(4.93)***
PriceUpdate
1.552(17.29)***Year fixed effects
Yes
Yes
Yes
Yes
Yes
Industry fixed effects
Yes
Yes
Yes
Yes
Yes
N
1,156
1,156
1,156
1,156
1,156
2
Adj. R
0.41770.5101
0.53340.69440.7859This table presents results from estimating Eq. (3). Detailed variable definitions can be found in
Appendix A. T-statistics appear in parentheses and are calculated based on standard errors
clustered by IPO year-quarter. Year fixed effect and industry fixed effect based on 2-digit SIC
code are included but not reported. *, **, and *** represent significance levels of 0.10, 0.05, and0.01 respectively (two-tailed).

42

(3)
2.844
(11.82)***0.050(2.74)***
0.139(3.71)***
0.269(8.95)***
0.131(4.30)***-0.283(-4.81)***
0.132(4.51)***-0.260(-4.78)***

(4)
2.784
(14.23)***0.031(2.03)**
0.063(2.55)**
0.171(5.06)***
0.102(3.96)***-0.191(-3.82)***0.029(1.15)-0.056(-1.22)
0.199(6.60)***-0.158(-5.19)***0.457(7.56)***
2.034(14.93)***0.414(4.66)***

Table 7
Abnormal R&D expenditures of Year -1 and Post-IPO short- and long-window returns (testfor H2)
Variables
Intercept
ARD
SIZE
AGE
PreUW
RETENTION
BIGN
IR
Rm,t – Rf,t
SMB
HML
UMD
Year fixed effectIndustry fixed effectN
Adj. R2(1)
RET_1M-0.251(-3.96)***0.083(1.77)*
0.000(0.05)
0.016(1.53)
0.056(2.31)**
0.042(1.29)
0.042(2.08)**-0.053(-2.74)***
1.315(6.94)***
1.015(3.27)***-1.845(-4.25)***0.051(0.19)
Yes
Yes
1,138
0.2055(2)
RET_3M-0.082(-1.93)*0.092(3.33)***-0.005(-1.30)0.008(1.30)
0.041(3.04)***
0.029(1.33)
0.021(1.53)-0.019(-1.61)
1.412(11.89)***1.221(5.65)***-1.333(-6.66)***0.008(0.06)
Yes
Yes
3,468
0.2069(3)
RET_6M-0.103(-3.89)***0.063(3.29)***-0.002(-0.93)0.007(1.80)*
0.021(2.72)***
0.034(2.17)**
0.001(0.08)-0.015(-2.61)***
1.325(13.40)***1.127(6.90)***-1.036(-8.01)***
0.062(0.64)
Yes
Yes
6,935
0.1800(4)
RET_12M-0.054(-3.56)***0.029(2.18)**-0.000(-0.12)0.006(2.33)**
0.016(3.25)***
0.021(2.08)**
0.001(0.14)-0.011(-2.00)**1.385(19.18)***1.015(6.87)***-1.041(-10.20)***-0.118(-1.63)
Yes
Yes
13,8180.1874(5)
RET_24M-0.014(-1.41)0.020(2.08)**-0.002(-1.30)0.006(2.69)***
0.015(3.61)***-0.001(-0.10)0.003(0.51)-0.010(-3.02)***
1.384(22.39)***1.117(13.12)***-1.008(-12.86)***-0.295(-6.07)***
Yes
Yes
26,7620.1745(6)
RET_36M-0.021(-2.14)**0.018(2.15)**-0.001(-1.25)0.004(1.91)*
0.013(3.89)***
0.002(0.27)
0.010(2.20)**-0.010(-3.94)***1.369(26.56)***
1.142(19.22)***-0.945(-13.34)***-0.393(-11.60)***Yes
Yes
37,8440.1642This table presents results from estimating Eq. (4). Columns (1)-(6) present results for post-IPO
stock returns covering different windows of 1 month (RET_1M), 3 months (RET_3M), 6 months(RET_6M), 12 months (RET_12M), 24 months (RET_24M), and 36 months (RET_36M). Detailed
variable definitions can be found in Appendix A. T-statistics appear in parentheses and are
calculated based on standard errors clustered by IPO year-quarter. Year fixed effect and industry
fixed effect based on 2-digit SIC code are included but not reported. *, **, and *** represent
significance levels of 0.10, 0.05, and 0.01 respectively (two-tailed).

43

Table 8
Abnormal R&D expenditures of Year -1 (Year 0) and Sales Growth and Price-to-Sales ratios of post-IPO years (test for H3a)
Panel A: Future sales growth
Variables
Intercept
ARD_Year -1(1)
FSGR_Y1-0.916(-1.00)7.390(2.54)**(2)
FSGR_Y2-1.506(-1.27)8.377(2.03)**(3)
FSGR_Y3-1.729(-1.31)7.772(1.80)*

ARD_Year 0
NRD
Industry fixed effectYear fixed effectN
Adj. R2

3.731(1.76)*
Yes
Yes
733
0.11596.306(1.84)*
Yes
Yes
733
0.07227.866(2.00)**
Yes
Yes
733
0.0421

44(4)
FSGR_Y1
0.491(2.34)**(5)
FSGR_Y2
0.843(2.36)**(6)
FSGR_Y3
1.206(2.39)**

1.627(2.62)***
0.482(1.12)
Yes
Yes
1,966
0.09792.415(2.50)**
1.184(1.73)*
Yes
Yes
1,966
0.05173.282(2.44)**
2.338(2.41)**
Yes
Yes
1,966
0.0333

Table 8 (Continued)
Abnormal R&D expenditures of Year -1 (Year 0) and Sales Growth and Price-to-Sales ratios of post-IPO years
Panel B: Future price-to-sales ratio
Variables
Intercept
ARD_Year -1(1)
FPS_Y13.684(0.80)
3.846(0.53)(2)
FPS_Y24.024(1.49)
8.236(1.86)*(3)
FPS_Y35.784(2.93)***
6.120(1.73)*

ARD_Year 0
NRDYear fixed effectIndustry fixed effectN
Adj. R2

19.489(3.76)***
Yes
Yes
733
0.07918.024(2.87)***
Yes
Yes
733
0.0916

6.374(3.04)***
Yes
Yes
733
0.1279(4)
FPS_Y1
12.823(2.48)**(5)
FPS_Y2
10.783(3.21)***(6)
FPS_Y313.113(3.79)***

28.359(3.49)***
29.490(3.56)***
Yes
Yes
1,966
0.096519.126(3.82)***
11.242(2.76)***
Yes
Yes
1,966
0.117816.039(3.44)***
7.183(2.31)**
Yes
Yes
1,966
0.1033This table presents results from estimating Eq. (5). Panel A presents results for future sales growth. Columns (1)-(3) present results for the
associations between abnormal R&D expenditures of Year -1 and post-IPO sales growth. Columns (4)-(6) present results for the associations
between abnormal R&D expenditures of Year 0 and post-IPO sales growth. Panel B presents results for future price-to-sales ratios. Column (1)(3) present results for the associations between abnormal R&D expenditures of Year -1 and post-IPO fiscal year end price-to-sales ratios.
Column (4)-(6) present results for the associations between abnormal R&D expenditures of Year 0 and post-IPO fiscal year end price-to-sales
ratios. Detailed variable definitions can be found in Appendix A. T-statistics appear in parentheses and are calculated based on standard errors
clustered by IPO year-quarter. Year fixed effect and industry fixed effect based on 2-digit SIC code are included but not reported. *, **, and ***represent significance levels of 0.10, 0.05, and 0.01 respectively (two-tailed).

45

Table 9
Abnormal R&D expenditures of Year -1 (Year 0) and patent output of post-IPO years (test for H3b)
Variables
Intercept
L(ARD)_Year -1(1)
L(COUNTS)-0.778(-1.45)0.094(1.93)*(2)
L(COUNTS)-1.252(-2.25)**0.100(1.85)*

L(ARD)_Year 0
L(NRD)

0.380(5.19)***

L(CAPX)Year fixed effects
Industry fixed effects
N
Adj. R-Sq

Yes
Yes
335
0.23210.074(1.15)
0.435(6.16)***
Yes
Yes
335
0.3002(3)
L(COUNTS)-0.109(-0.35)(4)L(COUNTS)-0.706(-2.19)**0.163(5.08)***
0.535(11.43)***0.172(5.46)***
0.320(5.72)***
0.276(6.15)***
Yes
Yes
1,147
0.2582

Yes
Yes
1,147
0.2309(5)
L(CITES)-0.165(-0.22)0.099(1.54)

0.430(5.10)***

Yes
Yes
380
0.2063(6)
L(CITES)-0.618(-0.75)0.113(1.88)*

0.089(0.94)
0.467(4.42)***
Yes
Yes
380
0.2426(7)
L(CITES)-0.881(-1.89)*(8)
L(CITES)
1.222(2.90)***

0.182(4.07)***
0.601(8.15)***

0.194(4.24)***
0.307(3.55)***
0.354(5.55)***
Yes
Yes
1,206
0.1509

Yes
Yes
1,206
0.1419This table presents results from estimating Eq. (6). Columns (1)-(4) present results for the associations between abnormal R&D expenditures ofYear -1 (Year 0) and total number of patents registered from Year 1 to Year 3. Columns (5)-(8) present results or the associations between
abnormal R&D expenditures of Year -1 (Year 0) and total patent citations from Year 1 to Year 3. Detailed variable definitions can be found in
Appendix A. T-statistics appear in parentheses and are calculated based on standard errors clustered by IPO year-quarter. Year fixed effect andindustry fixed effect based on 2-digit SIC code are included but not reported. *, **, and *** represent significance levels of 0.10, 0.05, and 0.01
respectively (two-tailed).

46

Table 10
Sensitivity Test: Multivariate analysis of comparing Sales Growth and Price-to-Sales ratios of post-IPO years between IPO companies
and PSM matched non-IPO companies by performance (ROA), size, age and sales growth of Year -1 and Year 0
Variables(1)
FSGR_Y1(2)
FSGR_Y2(3)
FSGR_Y3(4)
FPS_Y1(5)
FPS_Y2(6)
FPS_Y3

Intercept-0.493-1.024-1.530

3.353

3.542

1.908

0.340

0.057

-0.047-0.009

2.334

3.043

(-0.59)(-0.59)(-0.60)(0.26)(0.83)(0.84)(0.47)(0.56)(-0.36)(-0.01)(1.32)(3.11)***

0.694

1.028

1.300

3.378

-2.231

2.463

0.415

0.598

0.752

2.633

0.422

1.715

(9.27)***(6.62)***(5.78)***(2.98)***(-1.91)*(0.71)(4.30)***

2.495

4.204

3.783

-10.966-83.7566.962(3.08)***(2.50)**(1.59)(-0.87)(-6.68)***(0.87)
25.06923.332IPO
IPO×ARD_Year -1

(7)
(8)
(9)
(10)
(11)
(12)
FSGR_Y1 FSGR_Y2 FSGR_Y3 FPS_Y1 FPS_Y2 FPS_Y3(3.66)*** (13.54)*** (13.64)*** (13.46)*** (6.84)***

IPO×ARD_Year 0

1.397

2.126

2.253

16.043(3.65)*** (3.89)*** (3.23)*** (3.36)*** (3.33)*** (4.63)***
IPO×NRD

0.692

1.995

2.747

8.525

-9.611-18.048

0.087

0.166

0.279

1.712

-0.161-3.485(2.02)**(2.81)***(2.67)***(1.60)(-1.82)* (-5.68)***(0.45)(0.60)(0.79)(0.71)(-0.04)(-1.34)-0.959-0.808-0.70945.64894.2025.140-0.417-0.884-0.892

5.363-12.122-8.293(-1.33)(-0.54)(-0.34)(4.05)***(8.42)***(0.71)(-1.39)(-2.07)**(-1.64)(1.44)(-2.03)** (-2.10)**

0.500

0.686

0.803

23.94125.08225.810

0.163

0.283

0.340

10.29710.755(1.81)*(1.20)(0.97)(5.57)***(5.93)*** (10.08)***(1.34)(1.64)(1.54)(6.80)*** (4.48)*** (7.86)***

Year fixed effects

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Industry fixed effects

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

N

1,973

1,976

1,977

1,957

1,824

1,615

4,700

4,705

4,707

4,592

4,175

3,762

0.24680.19580.15040.20540.18220.14070.15140.12480.11190.10490.11750.0802ARD
NRD

2Adj. R

12.807This table presents the multivariate OLS regression results from estimating Eq. (7) by pooling together all IPO companies and their matched non-IPO
companies from 1980 to 2016 by performance (ROA), size, age and sales growth using a propensity score matching procedure as proposed and implemented
in Armstrong et al. (2016). IPO is a dummy variable that equals 1 for IPO companies and 0 otherwise. The dependent variables include sales growth (FSGR)
and price-to-sales (FPS) ratios. Detailed definitions for other variables can be found in Appendix A. T-statistics appear in parentheses. Year fixed effect andindustry fixed effect based on 2-digit SIC code are included but not reported. *, **, and *** represent significance levels of 0.10, 0.05, and 0.01 respectively(two-tailed).
47

Table 11
Sensitivity Test: Multivariate analysis of comparing patent output of post-IPO years
between IPO companies and PSM matched non-IPO companies by performance (ROA),
size, age and sales growth of Year -1 and Year 0
Variables
Intercept
IPO
IPO×L(ARD)_Year -1(1)
L(COUNTS)-2.107(-3.63)***0.562(2.88)***
0.144(2.60)***(2)
L(CITES)-1.608(-2.49)**1.023(3.77)***
0.243(3.23)***

IPO×L(ARD)_Year 0
IPO×L(NRD)
L(ARD)
L(NRD)
L(CAPX)Year fixed effects
Industry fixed effects
N
Adj. R2-0.282(-3.13)***-0.271(-4.13)***-0.032(-0.99)0.229(5.18)***
0.620(13.90)***
Yes
Yes
546
0.6897-0.114(-2.54)**0.139(2.22)**
0.797(12.92)***
Yes
Yes
595
0.5863(3)
L(COUNTS)-0.845(-0.66)0.264(2.95)***(4)
L(CITES)-1.895(-1.10)1.081(8.11)***

0.036(1.08)-0.156(-4.04)***0.148(7.49)***
0.293(11.44)***0.516(20.78)***
Yes
Yes
1,756
0.59530.065(1.31)-0.262(-4.60)***0.158(5.11)***
0.363(9.34)***
0.609(16.14)***
Yes
Yes
1,809
0.4308This table presents the multivariate OLS regression results from estimating Eq. (8) by pooling together all
IPO companies and their matched non-IPO companies from 1980 to 2016 by performance (ROA), size, age
and sales growth using a propensity score matching procedure as proposed and implemented in Armstrong
et al. (2016). IPO is a dummy variable that equals 1 for IPO companies and 0 otherwise. Detailed
definitions for other variables can be found in Appendix A. T-statistics appear in parentheses. Year fixed
effect and industry fixed effect based on 2-digit SIC code are included but not reported. *, **, and ***represent significance levels of 0.10, 0.05, and 0.01 respectively (two-tailed).

48

Table 12
Alternative measure of abnormal R&D expenditures using the model in Roychowdhury(2006): Univariate and Multivariate analyses for years around IPO
Panel A: Univariate analysis
Year
-1
0
1
2
3
N
1,274
1,262
1,178
1,032
879
ARD
0.150***
0.094***
0.072***
0.066***
0.058***
PM_ARD
0.085***
0.053***
0.037***
0.035***
0.023***SAG_ARD
0.252***
0.158***
0.114***
0.116***
0.107***ROA_SAG_ARD
0.252***
0.165***
0.117***
0.119***
0.110***
Panel B: Multivariate analysis by pooling together all IPO companies and their PSM
matched non-IPO counterparts by performance (ROA), size, age and sales growth
0
1
2
3
Year
-1
0.117
0.123
0.127
0.158
Intercept-0.019(8.98)***(1.19)(4.66)*** (4.62)***(-0.11)
0.096
0.088
0.070
0.062
IPO
0.255
(12.59)*** (10.79)*** (7.36)*** (4.89)***(13.07)***
0.034
0.050
0.038
0.065
Lag(SALE)
0.074(8.61)***(11.38)*** (7.48)*** (10.21)***(7.14)***
Yes
Yes
Yes
Yes
Year fixed effects
Yes
Yes
Yes
Yes
Yes
Industry fixed effects
Yes
5,296
5,418
5,048
4,566
N
2,168
0.12540.11690.09910.1179Adj. R-Sq
0.1441This table presents results from estimating abnormal R&D expenditures using the model proposed byRoychowdhury (2006). Panel A presents the magnitudes of estimated unmatched and matched abnormal
R&D expenditures for years around IPO. Panel B presents the multivariate OLS regression results fromestimating Eq. (9) by pooling together all IPO companies and their non-IPO counterparts matched by
performance (ROA), size, age and sales growth using the propensity score matching technique as
implemented in Armstrong et al. (2016). Detailed variable definitions can be found in Appendix A. For
brevity, estimated coefficient of the interaction term IPO×Lag(SALE) is not presented in the table. Tstatistics appear in parentheses. Year fixed effect and industry fixed effect based on 2-digit SIC code areincluded but not reported. *, **, and *** represent significance levels of 0.10, 0.05, and 0.01 respectively(two-tailed).

49