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What Follows Innovation Awards?
R&D 100 Awards, Product Segmentation, and Stock Returns
Po-Hsuan Hsu* Yiming Yang† Tong Zhou‡
January 12, 2018
We thank Hyun Joong Im, Shigang Li, Chi-Yang Tsou, Yi Wu, and seminar participants in Peking University HSBC
Business School and Sun Yat-Sen University for their constructive comments.
* Faculty of Business and Economics, University of Hong Kong, Hong Kong. Email: [email protected]. † Faculty of Business and Economics, University of Hong Kong, Hong Kong. Email: [email protected]. ‡ Lingnan College, Sun Yat-Sen University, China. Email: [email protected].
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What Follows Innovation Awards?
R&D 100 Awards, Product Segmentation, and Stock Returns
January 12, 2018
Abstract: This paper connects product market segmentation to asset pricing through a
prestigious award for technology breakthroughs in product inventions: the R&D 100
Award. We argue that award-winning events have asset pricing implications because
award-winning firms can promote their products to the high-end market, which
increases profitability in a procyclical way and results in higher systematic risk. We
find that, compared with their matched industry peers, award-winning firms are
associated with higher future gross profit margins and net sales. Moreover, award-
winning firms outperform their matched industry peers by 3% in annual return. To
explain this intriguing pattern, we develop an investment-based model to connect
growth opportunities, R&D investments, product awards, and stock returns. The model
also suggests that the award-return relation increases with R&D investments and
aggregate consumption, which receive empirical support.
Key words: Innovation Award, Product Segmentation, Growth Opportunity, Stock
Returns
JEL classification: E23, G12, L22, O31
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1. Introduction
Product awards are a specific titles or marks of recognition granted to products and
producers in honor of particular achievements or unique features of such products.
These awards, especially prestigious ones, serve as important indicators of the quality
and merits (in various dimensions) of firms and products and are highly regarded by
industry professionals and communities. Thus, product awards likely attract market
attentions, enhance company image, and help firms differentiate their products from
competitors. Award-winning firms consider prestigious awards as cornerstones of
company reputations and list them on the webpages of company history and honors to
highlight such achievements and legacies.1 The asset pricing implications of product
awards, however, are underexplored in the finance literature to our knowledge and thus
call for further investigation.
In this paper, we focus on the R&D 100 Award, which is granted by R&D Magazine
since 1965 to honor great R&D pioneers and their revolutionary ideas in science and
technology. It is often known as “Oscar of Innovation” based on its prestige and long
history.2 Over past five decades, R&D Magazine announced the application process to
the public in spring or summer, and formed judge panels to select and grant awards to
the 100 most technologically significant new products and services (in any technology
area) that are incorporated into commercial products or services in the last year.3 The
winner is announced at the end of summer or fall. When a product is selected and
granted with the award, the producer is entitled to use the term and logo of “R&D 100
Award” in making announcements and in marketing and promotion of the product. The
1 For example, Goodyear and United Technologies list their awards on the webpages of company history
(https://corporate.goodyear.com/en-US/about/history.html and http://www.utrc.utc.com/our-history.html). 3M lists
their awards on the webpage of company profile and awards (http://solutions.3m.com/wps/portal/3M/en_US/3M-
Company/Information/Profile/Awards/ ). 2 The term “Oscar of Innovation” is also used by many award-winning firms and organizations including NASA
(https://technology.grc.nasa.gov/featurestory/rd100-press-release), Mercedes-Benz
(http://mercedesblog.com/mercedes-benznanoslide-technology/), Toyota
(https://www.toyota.com/usa/environmentreport2014/carbon.html), and Los Alamos National Lab
(http://www.lanl.gov/discover/news-release-archive/2016/November/11.15-rd-100-awards.php). 3 There is no further ranking within the 100 winning products and services. Applicants can be companies, individuals,
or non-profit institutes such as universities and national laboratories. Judge panels consist of outside experts with
experience in the areas they are judging, such professional consultants, university faculty members, and industrial
researchers. Judges must also be unbiased and must possess no conflicts of interest with any entries they may be
judging.
3
award thus offers recipient firms a chance to signal the novelty of their award-winning
products to buyers,4 to differentiate the products from other competitors’, and to charge
higher prices.
Intel’s marketing strategy for its award-winning product, Intel Core processor
family, is a prominent example for the marketing value of the R&D 100 Award. From
1994 to 2010, the Central Processing Unit (CPU) market is dominated by Intel, with
Pentium aiming for mid-to-high-end markets and Celeron for low-end markets.
However, the demand of the high-end computation-efficient markets, driven by work
stations and advanced electric game players, are expanding rapidly but unfilled. In 2011,
R&D Magazine announces the Intel Core processor family as a recipient of the R&D
100 Award. In the same year, Intel targets its Core processors to the mid-to-high end,
moving the Pentium to the entry level and bumping the Celeron to low end. 5 Intel
treats the reception of R&D 100 Award as a key determinant of their successful
marketing of the new high-end products, as it lists this accomplishment on their website
of the Core processors and poses its case on the R&D 100 Award website as a successful
example connecting this award to product commercialization.
We argue that winning the R&D 100 Award has asset pricing implications because
awarded firms gain access to more growth opportunities because the positive image and
credit associated with the award enable these firms to market their products on the high
end of the market. Such a market position likely increases award-winning firms’
profitability; in fact, it also makes these firms’ profits more procyclical, which results
in higher systematic risk. To examine the asset pricing implications of the award, we
collect total 5144 award records that were granted to U.S. public firms in the sample
period 1965 to 2014: these awards were granted to 601 unique firms.6 Due to rarity of
4 Some most recent studies have started to use R&D 100 Award to measure firms’ technological breakthroughs (e.g.
Lev and Sougiannis (1996); Ait‐Sahalia, Parker, and Yogo (2004); Verhoeven, Bakker, and Veugelers (2016); and
Chen et al. (2017)). 5 The winning announcement of 2010 Intel Core processor family is unveiled in the webpage of R&D100 in 2011
(https://www.rd100conference.com/awards/winners-finalists/926/next-generation-processors-enhance-graphics-
speed/), and Intel also advertises their winning award on the webpage of company
newsroom (https://newsroom.intel.com/chip-shots/chip-shot-intel-core-snags-rd-100-award/). 6 The average probability for a public firm to win one or more awards in a year is 0.5%. Awards can be
downloaded via https://www.rd100conference.com/awards/winners-finalists/year/2015/
4
the awards, we define a firm as an award-winner if it receives at least one award over
the past five years.
Since a firm that is able to create award-winning breakthroughs may be quite
different from most of the other firms, we construct a comparable benchmark to each
awarded firm following the methodology of Daniel et al. (1997). Specifically, we
identify an unawarded firm as a comparable benchmark to the awarded firm if it falls
in the same quintile of market capitalization, in the same quintile of book-to-market
ratio, in the same quintile of momentum, and in the same 12-industry classification by
Fama and French (1995) by the same year end. Our summary statistics suggests that
the realized awarded firms and the benchmarked unawarded firms are highly
comparable in many dimensions, including R&D expenses.
Our empirical tests provide empirical evidence consistent with the notion that
award-winning firms perform better in profitability through access to high-end product
markets. We find that, compared with their benchmarked counterparts, award-winning
firms generate higher gross profit margins and higher net sales, spend higher SG&A
costs and advertising expenditures, hold less cash, and invest more in R&D and physical
capital over the next five-year horizon.
The aforementioned tests are only suggestive and subject to endogeneity problem,
though, as a firm with more growth opportunities and potential will endogenously
choose to invest more in R&D, and therefore have a higher chance to win the award
and continue to have more growth opportunities in the future. To address this concern
and further deliver asset pricing implications of the award outcome, we build an
investment-based model that formulates the relations among growth opportunities,
R&D investments, and award outcomes. Specifically, we assume that a firm is endowed
with a certain number of growth opportunities (such as talents, initial market positions,
and brand images) to commercialize its products in high-end markets, which generate
more procyclical profits than the low-end markets. However, the profits of these high-
end markets can be realized only if the firm wins the award. Winning an award requires
the firm to invest in R&D – higher R&D investment leads to higher award-winning
5
probability and access to high-end markets. The model suggests that in equilibrium, a
firm endowed with more growth opportunities indeed invests more in R&D and
therefore have a higher chance to win the award. This relation is supported in our
empirical analysis.
Three testable model implications are derived from our model. As we assume that
both the profits of high-end markets and the profits of low-end markets are procyclical
to aggregate consumption risk, awarded firms with more growth opportunities have
higher future exposure to systematic consumption risks than unawarded firms. Since
awarded firms are riskier, in equilibrium awarded firms demand higher expected stock
returns than unawarded firms (Proposition 1). Proposition 2 posits that the risk premium
of awarded firms is higher in higher R&D investment subgroup, as in equilibrium the
intensity of R&D investment reflects the number of growth opportunities for each firm.
Lastly, the risk premium of awarded firms varies with aggregate consumption.
Proposition 3 proposes that the risk premium of awarded firms is higher during periods
of high aggregate consumption.
We implement portfolio sorting to examine the award-return relation and find
supportive evidence for Proposition 1. We form an awarded portfolio that takes equal
positions in all awarded firms that win at least one award from year t−4 to year t, and
hold this portfolio from July of year t+1 and June of year t+2.7 We also form an
unawarded portfolio that takes equal positions in all benchmarked unawarded firms,
and hold this portfolio from July of year t+1 and June of year t+2. Lastly, we construct
an awarded-minus-unawarded (AMU) portfolio by going long in the awarded portfolio
and going short in the unawarded portfolio, and hold it from July of year t+1 and June
of year t+2. The return from the AMU portfolio ranges from 0.19% to 0.32% per month
under different factor models (but all statistically significant at the 5% level).
We document that the risk premium of awarded firms differs in subgroups of R&D
investments and subperiods of aggregate consumption, which supports Propositions 2
7 We use equal-weighted stock returns for two reasons: first, the number of awarded firms is not big; and second,
the awarded firms and the benchmarked unawarded firms are constructed to be highly comparable in firm size.
6
and 3. Using two-way sequential portfolio sorting, we document that the monthly return
of the AMU portfolio ranges from 0.47% to 0.78% in the high R&D investment
subgroup and ranges from -0.13% to 14% in the low R&D investment subgroup. We
also find that the monthly return of the AMU portfolio mainly comes from the non-
crisis periods, which are defined by NBER and proxy for periods of high aggregate
consumption. This evidence supports our argument of growth opportunities and
consumption risk.
This study is related to Ait‐Sahalia, Parker, and Yogo (2004), which uses the
import data of 70 French luxury good manufacturers to identify luxury consumption
and shows that the cross-section of stock returns can be explained by the loadings on
luxury consumption growth. Different from their approach that estimates stocks’
exposure to aggregate luxury consumption and associated risk, we propose to directly
use the R&D 100 Award to measure firm-level access to high-end markets and to sort
portfolios.
Our paper also adds to the stream of asset pricing literature on product market
competition. Hou and Robinson (2006) focus on concentration of firm sales within
industry and document that higher competition leads to lower stock returns. Hoberg and
Phillips (2012) use text analysis to define product market rivals and find that product
uniqueness does not have any explanatory power on future stock returns. We also focus
on product market development but from a completely different angle – while they
study the existent structure of product markets (e.g., “red seas”), we pay more attention
to the asset pricing implications of growth opportunities in new product markets (e.g.,
“blue seas”). Growth opportunities are key drivers of firm risks and asset prices and
therefore more value-relevant.
Our study also provides new evidence to the literature on technological innovation
in finance. Prior studies investigate the relation between asset prices and the dynamics
of technological innovation.8 Different from prior studies based on R&D and patent
8 These studies include Pakes (1985), Lev and Sougiannis (1996), Greenwood, Hercowitz, and Krusell (2001), Berk,
Green, and Naik (1999), Deng, Lev, and Narin (1999), Chan, Lakonishok, and Sougiannis (2001), Hobijn and
Jovanovic (2001), Bloom and Reenen (2002), Gomes, Kogan, and Zhang (2003), Laitner and Stolyarov (2003),
7
data, our paper focuses one important channel through which technological innovation
influences equity pricing – product awards and high-end market demand. Moreover, we
provide evidence to confirm that R&D investments, innovative products, and asset
prices are interlinked.
The rest of the paper is organized as follows. Section 2 provides suggestive
evidence examining the difference in future product-market performance between
awarded firms and unawarded firms. In Section 3, we present a tractable model that
explains the relations among growth opportunities, R&D investments, award-winning
outcomes, and asset prices. We test our model implications in Section 4. Section 5
concludes.
2. Awards and Future Firm Operations
In this section, we investigate the relation between a firm’s receipt of the R&D 100
Award and its future product-market performance. To do so, we manually collect the
full list of products receiving the “R&D 100 Award” published by R&D Magazine from
1965 to 2014. We further match these products to their developers as U.S. public
companies if these firms are listed as the co-developers of the awarded products. In our
sample of 2014, the 100 awarded products are co-developed by 88 unique firms, among
them 30 unique public firms. From our full sample from 1965 to 2014, we end up with
5144 award-winning records granted to 601 unique U.S. public firms.
Figure 1 Panel A illustrates the distribution of awarding outcomes across the Fama-
French 12 industries (Fama and French, 1995). The four industries winning the most
R&D 100 awards in our sample are Durables (8%), Manufacturing (31%), Chemicals,
(12%) and Business Equipment (32%). Panel B further implies that even in these
industries dominating the awards, the awarding outcomes vary significantly over time.
[Figure 1 here.]
Kogan (2004), Carlson, Fisher, and Giammarino (2004), Zhang (2005), Aguerrevere (2009), Hsu (2009), Pastor and
Veronesi (2009), Abel and Eberly (2011), Papanikolaou (2011), Garleanu, Panageas, and Yu (2012), Lin (2012), Ai
and Kiku (2013), Kogan and Papanikolaou (2014), Kogan et al. (2017), and , Zhou (2017) among others.
8
Since we argue that an awarded product may grant a firm with the access to the
high-end markets and therefore generate higher product value and profit margins in the
long term, we identify a firm as awarded by year t if it receives at least one award in
the previous five years from year t-4 to t due to the rarity of the award. In our sample,
total 1862 firm-year observations are “awarded firms”.
To compare the operational performance of awarded firms with that of unawarded
firms, we first construct a comparable benchmark to each awarded firm following the
methodology of Daniel et al. (1997). Specifically, we identify an unawarded firm as a
comparable benchmark to the awarded firm if it falls in the same quintile of market
capitalization, in the same quintile of book-to-market ratio, in the same quintile of
momentum, and in the same 12-industry classification according to Fama and French
(1995) by the end of year t. As we compare awarded firms with their counterparts in
the same industry, our results are immune to industry heterogeneity. Our stock
transaction data is extracted from the Center for Research in Security Prices (CRSP)
database and the accounting data is from Compustat.
Table 1 Panel A shows that, on an average year, we identify 57 awarded firms and
258 benchmarked unawarded firms. The ratio of awarded firms over benchmarked
unawarded firms is stable around 0.24 with the time-series standard deviation of 0.07,
but this ratio is largely dispersed across industries with the mean of 0.51 and the
standard deviation of 0.49. Panel B compares the median of the unawarded group with
the median of the awarded group and confirms that our benchmarked unawarded firms
are highly comparable to the awarded firms, in terms of market capitalization ($945m
vs. $954m), book-to-market ratio (0.59 vs. 0.62), momentum (9.64% vs. 8.83%), total
assets ($706m vs. $827m), R&D expenses ($16m vs. $30m), R&D-to-total asset ratio
(4% vs. 6%), sales ($749m vs. $928m), profit margins (5% vs. 5%), and cash holdings
(6% vs. 6%).
[Table 1 here.]
Then we examine whether the awarded firms outperform their benchmarked
counterparts in product markets over the next five-year horizon. To do so, we consider
9
four direct measures of product market operations: gross profit margin (the difference
between sales and the cost of the goods sold divided by the sales), net sales, costs of
selling, general, and administration (SG&A), and advertising expenditures. These four
measures are scaled by total assets to eliminate firm size effect. Under the notion that
awarded firms have more access to high-end product markets, we expect that the future
gross profit margin, net sales, SG&A costs, and advertising expenditures of the awarded
firms are higher than those of their unawarded benchmarks. We also consider three
variables that are related to firms’ product market operations: R&D expenditures scaled
by total assets, capital expenditures by total assets, and cash holdings (cash and short-
term investments divided by sales). Facing higher growth opportunities, the awarded
firms are expected to spend more cash in R&D investment and capital expenditure.
Table 2 presents the results when we regress one of the seven above-mentioned
variables averaged across year t+1 to t+5 as dependent variable on a dummy indicating
whether the firm is awarded at year t (from year t-4 to t) or not. We also control for the
following variables in year t to mitigate any bias caused by the ex-ante heterogeneity
in firm characteristics: market capitalization, book-to-market ratio, stock price
momentum, year fixed effects, and industry fixed effects.
Panels A-D suggest that the awarded firms are expected to have higher gross profit
margins, higher net sales, higher SG&A costs, and higher advertising expenditures than
their unawarded benchmarks by 1%-2%, 4%-5%, 1%-2%, and 0.2%, respectively.
These coefficients are all statistically significant at the 10% level and vary only little
under different sets of control variables. Such outperformance in product markets is
considerable and translated into 4-8% of the sample mean of gross profit margin, 4-5%
of the sample mean of net sales, 5-10% of the sample mean of SG&A costs, and 10%
of the sample mean of advertising expenditures.9 We also observe in Panels E-G that
awarded firms invest significantly more in R&D and physical capital and hold
significantly less cash in the next five years. In sum, the empirical evidence in Table 2
9 The sample means of gross profit margin, net sales, SG&A costs, and advertising expenditures are 27%, 108%,
22%, and 2%, respectively.
10
supports our argument that awarded firms have higher growth opportunities in product
markets than their benchmarked unawarded peers.
[Table 2 here.]
3. Model and Asset Pricing Implications
In the previous section, we document that firms winning the awards have more
growth opportunities in high-end product markets, as indicated by their higher gross
profit margins, higher net sales, higher SG&A costs, and higher advertising
expenditures in the future. One major concern of our empirical analysis in the last
section is related to endogeneity – a firm with higher growth opportunities will
endogenously choose to invest more in R&D, and therefore have a higher chance to
receive the award and continue to have higher growth opportunities in the future. To
address this concern and further deliver asset pricing implications of the award outcome,
we build an investment-based model that takes into account the relations among growth
opportunities, R&D investments, award outcomes, and stock returns.
3.1 Model setup
Only one firm operates in three periods, t, t+1, and t+2, in the economy. Its
stochastic operating profit (𝐹𝜏) in each period is given by the following equation:
𝐹𝜏 = 𝐿𝜏 + 𝐷𝜏 ∙ 𝑛𝐻𝜏, (1)
where 𝐿𝜏 is the stochastic profit generated from the low-end products and 𝐻𝜏 is the
stochastic profit generated from the high-end products. The parameter (constant across
all periods) 𝑛 captures the number of growth opportunities (talents, initial market
positions, and brand images) in high-end markets. We assume that the focal firm can
access the high-end product markets only if it is recognized by the award. Therefore,
𝐷𝜏 takes the value of zero when the firm is not awarded, and takes the value of one
when it is awarded.
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At Date t, the firm maximizes its firm value and invests a lump-sum amount 𝐼 to
develop innovative products. To model the relation between R&D investments and
award outcome, we assume that the magnitude of 𝐼 determines the probability of
receiving the award (denoted by 𝜋𝐴) at Date t+1. The relation between 𝜋𝐴 and 𝐼 is
further assumed to follow two inequalities: 𝑑𝜋𝐴 𝑑𝐼⁄ > 0 and 𝑑2𝜋𝐴 𝑑𝐼2⁄ > 0 . The
economic interpretation of these two inequalities is that on the one hand, as the firm
increases its intensity of R&D, its probability of achieving novel design is higher; on
the other hand, the marginal benefit of investment diminishes.
𝐿𝜏 and 𝐻𝜏 are assumed to comove with the same aggregate consumption shock,
i.e.,
𝐿𝜏 = 𝐶𝜏𝜃𝐿 , and (2)
𝐻𝜏 = 𝐶𝜏𝜃𝐻 . (3)
where 𝜃𝐿 and 𝜃𝐻 are the degrees of cyclicality of low-end products and high-end
product, respectively. Following Ait‐Sahalia, Parker, and Yogo (2004), we assume
𝜃𝐻 > 𝜃𝐿. Under this inequality, the profits of high-end products are more cyclical to
aggregate consumption than the profits of low-end products. In other words, given the
same positive (negative) shock to aggregate consumption, the profits of high-end goods
increase (decrease) by a larger amount than the profits of low-end goods.
The process of aggregate consumption 𝐶𝜏 is given by the following geometric
Brownian motion:
ln 𝐶𝜏+1 = ln𝐶𝜏 + 𝜇 + 𝜎𝜀𝜏+1, (4)
where 𝜀𝜏+1~𝑁(0,1) is the market-wide demand shock, 𝜇 is the parameter of the drift
term, and 𝜎 is the parameter of the variance term. Correspondingly, the stochastic
discount factor 𝑀𝜏 is assumed to follow a geometric Brownian motion, i.e.,
ln𝑀𝜏+1 = ln𝑀𝜏 − 𝛾 − 𝜅𝜀𝜏+1, (5)
where 𝛾 is a parameter of the risk-free rate and 𝜅 is a parameter of the variance.
Restrictions are assumed on these parameters to ensure the existence of risk-neutral
12
pricing: 𝛾 + 𝜅𝜎 − 1
2𝜅2 > 0.
3.2 Solution and comparative statics
To derive the optimal investment 𝐼 at Date t, we solve the firm value in backward
induction. The value of an awarded firm at Date t+1 is:
𝑉𝑡+1𝐴 = 𝐸𝑡+1 [
𝑀𝑡+2
𝑀𝑡+1(𝐿𝑡+2 + 𝑛𝐻𝑡+2)], (6)
and the value of a unawarded firm at Date t+1 is:
𝑉𝑡+1𝑈 = 𝐸𝑡+1 [
𝑀𝑡+2
𝑀𝑡+1(𝐿𝑡+2)]. (7)
The maximization problem of the firm at Date t is given below:
max𝐼
{𝐿𝑡 − 𝐼 + 𝐸𝑡 [𝑀𝑡+1
𝑀𝑡(𝐿𝑡+1 + 𝜋𝐴𝑉𝑡+1
𝐴 + (1 − 𝜋𝐴)𝑉𝑡+1𝑈 )]}.
Re-arranging the maximization problem with Equations (2)-(7), the first-order
condition can be solved in the following equation:
𝑛𝑑𝜋𝐴
𝑑𝐼𝐸𝑡+1 [
𝑀𝑡+2
𝑀𝑡𝐻𝑡+2] = 1. (8)
This first-order condition in Equation (8) implies that the firm will invest so that
the marginal benefit of R&D investments, which is calculated as the increment of award
probability multiplied by the present value of the increased firm value, equals to the
marginal cost of investment, which is assumed to be one and constant. Taking derivative
with respect to 𝑛 on both sides of Equation (8), we can derive the following inequality:
𝜕2𝜋𝐴𝜕𝐼∗𝜕𝑛
< 0.
Following the chain rule of derivative, we obtain:
𝜕𝐼∗
𝜕𝑛=
𝜕2𝜋𝐴
𝜕𝐼∗𝜕𝑛/𝜕2𝜋𝐴
𝜕𝐼∗2> 0, (9)
which leads to the following Lemma 1.
Lemma 1 The firm’s R&D investments in innovative products increase with the
13
number of growth opportunities in high-end markets, i.e., 𝜕𝐼∗ 𝜕𝑛⁄ > 0.
Remark 1 The mechanism is that, given the R&D investments and thereby the
probability of being awarded, the present value of being awarded is higher when the
number of growth opportunities in high-end markets is larger, and therefore, the firm
with more growth opportunities invests more at the first place. Lemma 1 is consistent
with previous studies documenting the relation between R&D investments and growth
opportunities, such as Berk, Green, and Naik (1999), Carlson, Fisher, and Giammarino
(2004), Garleanu, Panageas, and Yu (2012), and Ai and Kiku (2013), among others.
After solving the optimal investment, we can derive the ex-dividend firm value at
Date t as:
𝑉𝑡 = 𝐸𝑡 [𝑀𝑡+1
𝑀𝑡𝐿𝑡+1 +
𝑀𝑡+1
𝑀𝑡(𝜋𝐴𝑉𝑡+1
𝐴 + (1 − 𝜋𝐴)𝑉𝑡+1𝑈 )].
The expected stock return at Date t is given by the following equation:
𝑟𝑡 ≡𝐸𝑡[𝑉𝑡+1+𝐿𝑡+1]
𝑉𝑡. (10)
Re-organizing Equation (10) with Equations (2)-(7), we can show that 𝑟𝑡 increases in
𝑛, i.e.,
𝜕𝑟𝑡𝜕𝑛
> 0.
Therefore, we obtain the following inequality and Lemma 2 following the chain
rule of derivative:
𝜕𝑟𝑡𝜕𝐼∗
=𝜕𝑟𝑡𝜕𝑛
/𝜕𝐼∗
𝜕𝑛 > 0.
Lemma 2 The expected stock return of the firm increases with its R&D investments in
innovative products, i.e., 𝜕𝑟𝑡 𝜕𝐼∗⁄ > 0.
Remark 2 Lemma 2 is true under our assumption of 𝜃𝐻 > 𝜃𝐿 and confirms that the
mechanism of the positive relation between R&D investments and expected stock return
is that a firm with higher R&D investments is the one with more growth opportunities,
which have higher exposure to aggregate consumption shock, and therefore, its firm
14
value loads more on the aggregate consumption shock and generates higher expected
stock return. Lemma 2 is consistent with the evidence documented in Lev and
Sougiannis (1996) and Chan, Lakonishok, and Sougiannis (2001) showing that R&D
expenditure is a positive return predictor.
Next, we turn to the discussion on the effect of awards on stock returns. The net-
of-announcement-return stock return of the unawarded firm at Date t+1 is given as:
𝑟𝑡+1𝑈 =
𝐿𝑡+2
𝑉𝑡+1𝑈 ,
and the net-of-announcement-return stock return of the awarded firm at Date t+1 is
given as:
𝑟𝑡+1𝐴 =
𝐿𝑡+2 + 𝑛𝐻𝑡+2
𝑉𝑡+1𝑁 .
Following the asset pricing literature, the firm’s exposure to systematic (consumption)
risk is defined as the inverse of the ratio of its covariance between the (log) return and
the stochastic discount rate over the variance of the discount rate. Therefore, we can
derive the systematic risk exposures of the unawarded firm and the awarded firm in the
following two equations10, respectively:
𝛽𝑡+1𝑈 = −
𝐶𝑜𝑣𝑡+1[ln(𝑀𝑡+2𝑀𝑡+1
),ln𝑟𝑡+1𝑁 ]
𝑉𝑎𝑟𝑡+1[ln(𝑀𝑡+2𝑀𝑡+1
)]= 𝜃𝐿 ∙
𝜎
𝜅, and (11)
𝛽𝑡+1𝐴 = −
𝐶𝑜𝑣𝑡+1[ln(𝑀𝑡+2𝑀𝑡+1
),ln𝑟𝑡+1𝐴 ]
𝑉𝑎𝑟𝑡+1[ln(𝑀𝑡+2𝑀𝑡+1
)]=
𝜃𝐿∙𝐶𝑡+1𝜃𝐿 𝜇𝜃𝐿+𝜃𝐻∙𝑛∙𝐶𝑡+1
𝜃𝐻𝜇𝜃𝐻
𝐶𝑡+1𝜃𝐿 𝜇𝜃𝐿+𝑛∙𝐶𝑡+1
𝜃𝐻𝜇𝜃𝐻∙𝜎
𝜅. (12)
From Equations (11) and (12), we obtain:
𝛽𝑡+1𝐴 > 𝛽𝑡+1
𝑈 ,
which leads to the following Lemma 3.
Lemma 3 The systematic risk exposure of the awarded firm is higher than that of the
unawarded firm, i.e., 𝛽𝑡+1𝐴 > 𝛽𝑡+1
𝑈 .
Remark 3 As we assume that the profits of high-end markets are more procyclical than
10 Equation (12) can be derived by applying Taylor expansion to 𝑟𝑡+1
𝐴 around 𝜀𝑡+2 = 0.
15
the profits of low-end markets, the awarded firms with more growth opportunities have
higher exposure to market-wide risks. Previous literature, such as Berk, Green, and
Naik (1999), Carlson, Fisher, and Giammarino (2004), Aguerrevere (2009), and
Garleanu, Panageas, and Yu (2012), among others, has documented that growth
options are riskier than assets in place. It is noteworthy that 𝛽𝑡+1𝐴 increases with 𝑛
(and thereby 𝐼∗ ) and 𝐶𝑡+1 but 𝛽𝑡+1𝑈 is uncorrelated with these two variables.
Therefore, Lemma 3 is consistent with Propositions 2 and 3 in that the systematic risk
exposure and expected stock return of awarded firms not only increases with R&D
investments but also are procyclical to aggregate consumption.
The expected stock return of the firm at Date t+1 contingent on not receiving the
award can be derived as:
𝐸𝑡+1[𝑟𝑡+1𝑈 ] = exp{𝛾 + 𝜃𝐿𝜎𝜅 −
1
2𝜅2}. (13)
The expected stock return of the firm at Date t+1 contingent on receiving the award can
be written as:
𝐸𝑡+1[𝑟𝑡+1𝐴 ] = 𝑟𝑡+1
𝑈 [1 +Ψ(𝑛)
1+Ψ(𝑛)(exp{(𝜃𝐻 − 𝜃𝐿)𝜎𝜅} − 1)], (14)
where Ψ(𝑛) is defined as the following function of 𝑛:
Ψ(𝑛) = 𝑛𝐶𝑡+1𝜃𝐻−𝜃𝐿 exp {((𝜃𝐻 − 𝜃𝐿)𝜇 +
1
2(𝜃𝐻
2 − 𝜃𝐿2)𝜎2 − (𝜃𝐻 − 𝜃𝐿)𝜎𝜅)} > 0.
(15)
Note that Ψ(𝑛) is increasing in 𝑛 and 𝐶𝑡+1.
From Equations (13) and (14), we can derive that the awarded-minus-unawarded
return spread (𝑟𝑡+1𝐴𝑀𝑈), which is the difference between the expected stock return of an
awarded firm (𝑟𝑡+1𝐴 ) and the expected stock return of an unawarded firm (𝑟𝑡+1
𝑈 ) (also
known as the risk premium of awarded firms):
𝑟𝑡+1𝐴𝑀𝑁 ≡ 𝑟𝑡+1
𝐴 − 𝑟𝑡+1𝑁 = 𝑟𝑡+1
𝑁 Ψ(𝑛)
1+Ψ(𝑛)(𝑒(𝜃𝐿−𝜃𝐵)𝜎𝜅 − 1) > 0. (16)
Inequality (16) leads to Proposition 1.
16
Proposition 1 The expected stock return of the awarded firm is higher than that of the
unawarded firm, i.e., 𝑟𝑡+1𝐴𝑀𝑈 > 0.
Remark 4 Awarded firms are expected to have higher growth opportunities to high-
end (riskier) market. Therefore, their operational profits are more procyclical to
aggregate consumption and higher expected stock returns are required.
We can also show that the risk premium of awarded firms increases with R&D
investments. Since Ψ(𝑛) is an increasing function of 𝑛 (Equation (15)), we can
derive the following inequality (17):
𝜕𝑟𝑡+1𝐴𝑀𝑈
𝜕𝑛> 0. (17)
Therefore, combining Inequalities (9) and (17) and applying the chain rule of
derivative, we can obtain:
𝜕𝑟𝑡+1𝐴𝑀𝑈
𝜕𝐼∗=𝜕𝑟𝑡+1
𝐴𝑀𝑈
𝜕𝑛
𝜕𝐼∗
𝜕𝑛⁄ > 0.
The above inequality can be interpreted into the following Proposition 2.
Proposition 2 The risk premium of the awarded firm is higher when the firm’s R&D
investment is higher, i.e., 𝜕𝑟𝑡+1𝐴𝑀𝑈 𝜕𝐼∗⁄ > 0.
Remark 5 Lemma 1 proposes that firms endowed with more growth opportunities
endogenously choose to invest more on in R&D. Thus, the intensity of R&D investment
is a proxy for the number of growth opportunities. Under the notion that the awarded
firms are entitled access to potential high-end markets whereas the unawarded firms
are not, the awards realize more growth opportunities, bring higher additional
systematic risks (Lemma 3), and demand higher risk premium for firms with higher
R&D investments.
The risk premium of awarded firms varies with aggregate consumption. To show
this, we derive the partial derivative of 𝑟𝑡+1𝐴𝑀𝑈 with respect to 𝐶𝑡+1. As indicated in
Equation (15), Ψ(𝑛) is an increasing function of 𝐶𝑡+1 . Thus, we can derive the
following inequality from Equation (16):
17
𝜕𝑟𝑡+1𝐴𝑀𝑈
𝜕𝐶𝑡+1> 0. (18)
Inequality (18) can be summarized in the following Proposition 3.
Proposition 3 The risk premium of the awarded firm is procyclical to aggregate
consumption is higher, i.e., 𝜕𝑟𝑡+1𝐴𝑀𝑈 𝜕𝐶𝑡+1⁄ > 0.
Remark 6 When the aggregate consumption follows the geometric Brownian motion,
the expected growth rate and volatility of consumption are higher if the current
consumption is higher. Therefore, the awarded firms, which have higher exposure to
new high-end product markets and thereby higher risk exposure (Lemma 3), should
demand higher risk premium in periods of higher consumption.
4. Empirical Tests
In the previous two sections, we empirically document the positive impact of the R&D
100 Award on future product market performance and theoretically derive the
corresponding asset pricing implications. Three hypotheses are developed: awarded
firms have higher expected stock returns than unawarded firms (Proposition 1), the
award-return relation is more pronounced for firms with higher R&D investments
(Proposition 2), and the award-return relation is more pronounced during periods of
high aggregate consumption (Proposition 3). In this section, we implement direct
empirical tests to the model.
4.1 R&D investments and the probability of winning awards
The key assumption of our model is that higher R&D investment leads to higher
probability of being awarded. We test this assumption by running the following logit or
probit regressions with the whole universe of public firms: the dependent variable is a
dummy variable indicating whether the focal firm receives the award in year t+1 or not,
and the key explanatory variable of interest is the average R&D investment, which
denotes the arithmetic average of R&D expenditure scaled by total assets during the
18
previous five years from t-4 to t. Other firm characteristics by the end of year t are also
controlled, such as market capitalization, book-to-market ratio, and stock price
momentum.
Table 3 shows that the average R&D investment in the previous five years
positively predicts awarding outcome in the next year. This coefficient is statistically
significant at the 1% level in different model specifications and no matter we include
other control variables or not. Its magnitude is economically meaningful – for example,
the coefficient of 1.96 under logit regression and 0.77 under probit regression are
translated into a marginal effect of 3%. Given that the sample standard deviation of
R&D investment is 10%, a one-standard-deviation increase in R&D investment leads
to a 0.3% increase in awarding probability, which is huge relative to the average
awarding probability of 0.5% among public firms.
[Table 3 here.]
4.2 Awards and expected stock returns
In our model, we propose that firms recognized by innovation awards outperform their
unawarded peers in the stock returns (Proposition 1). We conduct the portfolio sorting
analysis to test this model implication. Specifically, at the end of year t from 1969 to
2013, we consider a firm to be an awarded firm if it receives at least one award in the
previous five years (year t−4 to year t). Using the same methodology in Section 2, we
build the comparison benchmark of each awarded firm as the subgroup of unawarded
firms of similar size, book-to-market ratio, and momentum in the same industry. We
form an awarded portfolio that takes equal positions in all awarded firms that win at
least one award from year t−4 to year t, and hold this portfolio from July of year t+1
and June of year t+2.11 We also form an unawarded portfolio that takes equal positions
in all benchmarked unawarded firms, and hold this portfolio from July of year t+1 and
June of year t+2. To compare the difference in expected stock returns of awarded firms
11 We use equal-weighted stock returns for two reasons: first, the number of awarded firms is not big; and second,
the awarded firms and the benchmarked unawarded firms are constructed to be highly comparable in firm size.
19
and their benchmarked unawarded firms, we construct an awarded-minus-unawarded
(AMU) portfolio by going long in the awarded portfolio and going short in the
benchmarked unawarded portfolio, and hold it over the next twelve months from July
of year t+1 and June of year t+2.12 Since the awarded firms and the benchmarked
unawarded firms are already highly comparable in firm characteristics (as shown in
Table 1), we compare the equally weighted average stock returns of awarded firms with
those of their unawarded counterparts.
Table 4 reports the average monthly excess returns of the awarded portfolio and
the benchmarked unawarded portfolio, in which monthly excess return is monthly stock
return in excess of the one-month Treasury bill rate. In the first column, we document
that the awarded portfolio on average have higher expected return than the
benchmarked unawarded portfolio – the average excess return of the awarded portfolio
is 1.1% while the average excess return of the benchmarked unawarded portfolio is
0.84%. This return difference is positive, about 0.26% per month, and statistically
significant at the 1% level, which supports Proposition 1.
Although our awarded firms and benchmarked unawarded firms are matched in
the same industry and with similar size, book-to-market ratio, and momentum, we
further control for their exposure to the size factor (SMB) and the value factor (HML)
in the three-factor model (FF3) of Fama and French (1993), the momentum factor
(UMD) in the four-factor model (FF4) of Carhart (1997), the profitability factor (RMW)
and the investment factor (CMA) in the five-factor model (FF5) of Fama and French
(2015) and the six-factor model (FF6) of Fama and French (2017), and the R&D factor
by Chan, Lakonishok, and Sougiannis (2001). The results show that the perhaps
different exposure to these risk factors cannot explain the risk premium of awarded
firms. For instance, when we control for the Fama-French three factors, the monthly
risk premium of the awarded portfolio is 0.29% and the monthly risk premium of the
benchmarked unawarded portfolio is zero. Such a difference is about 0.28% per month,
12 We use a six-month lag to form portfolios in order to make out results comparable to priors studies such as Fama
and French (1993) and Fama and French (2015).
20
even higher than the one when we do not control for any risk factor, and statistically
significant at the 1% level. The risk premium of the awarded portfolio is still significant
at the 5% level when we control for R&D factor in addition to the Fama-French six
factors, even though its economic magnitude is 0.2% and a little lower.
[Table 4 here.]
Figure 2 implies that the performance of our long-short strategy based on awarding
outcomes is robust in our sample period from 1970 to 2014. Besides year 2000
accompanied with a significant shoot-up, the performance of our strategy is stable over
different time periods. Moreover, our long-short strategy does not experience large
draw-backs during the crisis periods, such as 1987 and 2008.
[Figure 3 here.]
We further explore the long-term performance of our long-short strategy up to a
five-year horizon. Specifically, we trace the cumulative returns of investments in both
the awarded portfolio and the benchmarked unawarded portfolio from July of year t
(the 1st month since the portfolio is formed) to June of year t+5 (the 60th and last
portfolio month). Figure 3 shows that the curves illustrating the cumulative returns of
both the awarded portfolio and the benchmarked unawarded portfolio are upward
sloping during the 60-portfolio-month window. Further comparison implies that the
performance of the awarded portfolio is persistently superior to that of benchmarked
unawarded portfolio – the cumulative return of the former portfolio is always higher
than that of the latter portfolio, and by the end of the 60th portfolio month, the average
cumulative return of the awarded portfolio reaches 185% while the average cumulative
return of the benchmarked unawarded portfolio reaches 175%. This long-lasting
outperformance of the awarded portfolio supports our model implication that the
awarded firms are exposed to higher long-term systematic risks due to access to more
growth opportunities.
Generally speaking, consistent with the model, we empirically document that the
awarded firms have higher expected stock returns than their benchmarked counterparts.
21
Such an award-return relation last up to five years, which probably reflects the higher
long-term risks of the awarded firms rather than any behavioral bias or market frictions.
[Figure 3 here.]
4.3 Award-return relation and R&D investments
The second prediction of our model is that the award-return relation is more pronounced
for firms with higher R&D investments (Proposition 2). To test this model implication,
we perform two-way sequential portfolio sorting analysis to examine the risk premium
of awarded firms across different subgroups of R&D intensity.13 Specifically, at the
end of year t from 1969 to 2013, we consider a firm to be an awarded firm if it receives
at least one award in the previous five years. These awarded firms with non-missing
R&D values are stratified into three subgroups based on the 30th and 70th percentiles of
R&D expenditure scaled by total assets at the same fiscal year end. Following the
similar methodology in Section 2, we identify a subgroup of unawarded firms of each
awarded firm if these firms are of similar size, book-to-market ratio, momentum, and
similar R&D expenditure. To compare the difference in expected stock returns of
awarded firms and their benchmarked unawarded firms in different subgroups of R&D
investments, we construct an awarded-minus-unawarded (AMU) portfolio by going
long in the awarded portfolio and going short in the benchmarked unawarded portfolio
in that R&D subgroup, and hold it over the next twelve months from July of year t+1
and June of year t+2. The equally weighted average stock returns of awarded firms are
compared with those of their unawarded counterparts, since the two types of firms are
already highly comparable in firm size.
Table 5 Panel A reports the average monthly excess returns of the awarded
portfolio and the benchmarked unawarded portfolio for both subgroups of high and low
13 Sequential sorting fits our theoretical setting – we argue that endogenously R&D investment predicts the
probability of being awarded and the award-return relation strengthens with R&D investments. In data, we find
consistent correlation between R&D investments and awarding outcomes. As the results, if we employ the standard
independent sort procedure, the underlying correlation might lead to suboptimal performance and poor
diversification (Lambert, Fays, and Hubner, 2016; Lambert and Hubner, 2013).
22
R&D investments. We document that the risk premium of awarded firms differs
significantly in different subgroup of R&D investments. For instance, the average
monthly excess stock return of the awarded portfolio is 1.56% and that of the
benchmarked unawarded portfolio is 0.96%. The difference is 0.6% per month and
statistically significant at the 1% level. However, the risk premium of awarded firms is
not significant in the low R&D investments subgroup.
We also control for their exposure to a bunch of systematic risk factors as in
Section 4.2 to relieve the concern that the risk premium of awarded firms is driven by
the distinct characteristics of the awarded firms and thereby higher exposure to certain
risk factors. The results show that under whatever factor models, the risk premium of
awarded firms is significant in the high R&D investments subgroup while it remains
insignificant in the low R&D investment subgroup. For example, the “abnormal” return
of the awarded portfolio relative to the unawarded portfolio is 0.58% under the Fama-
French three factor model and 0.55% under the Fama-French six factor model – it
appears the controlling these risk factors brings very little change to our estimation of
the risk premium of awarded firms in the high R&D investment subgroup. However,
such a premium remains statistically and economically significant in all factor models
we consider. In sum, our empirical evidence is strongly supportive to the model
prediction that the award-return relation is more pronounced among R&D-intensive
firms.
[Table 5 here.]
4.4 Award-return relation and aggregate consumption
Our final model prediction is that the award-return relation is more pronounced
during periods of high aggregate consumption (Proposition 3). To test this model
implication, we use the NBER recession (crisis) periods to proxy for periods of low
aggregate consumption, and use NBER expansion (non-crisis) periods to proxy for
periods of high aggregate consumption. From our full sample from 1970 to 2014, we
23
end up with 13 years of recessions and 32 years of expansions.
Table 5 Panel B reports the risk premium of awarded firms during recession and
expansion periods. We find that this risk premium mainly comes from the expansion
periods. For example, during the expansion periods, the risk premium of awarded firms
is 0.32% per month. It remains significant and does not change much under different
factor models. However, the magnitude of the award-return relation is much weaker
during recession periods, only 0.13% per month, and it is statistically insignificant.
Therefore, this evidence implies that the award-return relation is procyclical to
aggregate consumption and supports our argument of growth opportunities through
product market segmentation.
5. Concluding Remarks
In this paper, we study the asset pricing implications of product market
segmentation by focusing on a prestigious award for innovative products: the R&D 100
Award. This award is published by R&D Magazine and receives significant publicity
over fifty years. We first document suggestive evidence confirming that a firm
recognized by the award is more likely to perform better in product markets over the
next five-year horizon, which confirms the positive effect of the R&D 100 Award on
product market segmentation.
We then develop an investment-based asset pricing model, which takes into
account the relations among growth opportunities, R&D investments, award outcomes,
and stock prices, and propose three model implications. Empirical evidence supports
these implications: i) the monthly equal-weighted stock returns of awarded firms
outperform their benchmarked counterparts by 0.19% to 0.32%; ii) the difference in
monthly returns between awarded and unawarded firms in the high R&D investment
subgroup is 0.47% to 0.78% higher than that in the low R&D investment subgroup; and
iii) the difference in monthly returns between awarded and unawarded firms is
significantly higher in high-aggregate-consumption periods. Our results are robust to
24
industry heterogeneity, to different methods to construct awarded and benchmarked
unawarded firms, and in different factor models.
Overall, our findings collectively support a role of product awards in asset pricing:
product awards enable firms to commercialize its growth opportunities in the high-end
markets, and such a shift in product market segmentation leads to higher systematic risk
that requires higher expected stock returns as risk premium.
25
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27
Tables
Table 1: Summary Statistics of Industrial Design Awards
We manually collect awarding information of R&D 100 Award, one of the most prestigious international design competitions. The original sampling period for R&D
100 Award ranges from 1965 to 2014. By matching Standard Industrial Classification (SIC) code in the Center for Research in Security Prices (CRSP), we merge data
of awardees with all other public firms in the United States. We restrict to stocks with Fama-French 12-industries (FF12) classification. We also adopt the characteristic-
based sorting method proposed by Daniel, Grinblatt, Titman, and Wermers (DGTW) (1997) to identify comparable benchmark firms to the awarded firms - matched
unawarded firms are selected if they are in the same FF12 industry and the same quintiles of market capitalization, book-to-market ratio, and momentum by the end of
each fiscal year. Considering that the effect of the award on the firm may last for several years, we further define a firm as awarded if it receives at least one award in
the previous five years. After applying the same approach of data processing, the sampling interval for matched R&D 100 Award is 1969 - 2014. Panel A summarizes
the basic statistics of five-year matched sample. Awarded #, Unawarded #, Total # report the average of annual count of firms in each group. Annual Awarded/Unawarded
and Annual Awarded/Unawarded by Industry report the statistics of the ratio of awardees to non-awardees. Panel B compares the median values of indicators between
awarded and unawarded group. Data are collected from matched firms’ annual balance sheets and income statements. Several characteristics are considered: Market
Capitalization (Size, in billions), Book-to-market Ratio (B/M), Momentum (MOM, in percentages), Total Assets (in millions), R&D-to-total asset ratio (R&D Intensity),
Net Sales (Sales, in in millions), Profit Margins, which is defined as net income divided by net sales, and Cash Holdings, which is computed as cash and short-term
investments scalded by net sales.
Panel A: Five-year Computed Measures
Mean Median Std dev. Mean Median Std dev.
57 258 315 0.24 0.23 0.07 0.51 0.33 0.49
Panel B: Characteristics of Awarded Firms vs. Unawarded Firms
Group Market Cap B/M Momentum Total Assets R&D Intensity Sales Profit Margins Cash Holdings
Unawarded 944.87 0.59 9.64 705.87 0.04 749.16 0.05 0.06
Awarded 954.21 0.62 8.83 827.37 0.06 927.61 0.05 0.06
Awarded # Unawarded # Total #Annual Awarded/Unawarded Annual Awarded/Unawarded by Industry
28
Table 2: Future Product Market Performances and Awarding Outcomes
By the end of fiscal year t, we identify awarded firms and their unawarded matched counterparts following the method elaborated in Table 1. From Panel A to Panel G,
we respectively examine the impacts of awarding outcome on seven measures of future product market performance: Gross Profit Margin, which is a profitability ratio
calculated by the difference between sales and the cost of the goods sold (COGS) divided by the sales; Net Sales (Sales), Costs of Selling, General, and Administration
(SG&A), Advertising Expenditures, R&D Expenditures, and Capital Expenditures (Capex) are all scaled by total assets; Cash Holdings, which is a liquidity ratio
defined as cash and short-term investments divided by sales. For each variable, we consider the arithmetic average of its future five-year values from year t+1 to t+5
as dependent variable, and use the corresponding lagged value in year t as independent variable. We employ different specifications of firm-level panel regressions to
test the award influence by further considering the following control variables at year t: market capitalization (Size, in hundred trillions); book-to-market ratio (B/M);
12-month momentum factor with one month reversal (MOM). Numbers without parentheses report parameter estimates, and numbers with parentheses show robust t-
values clustered by industry and year. ***, **, and * indicate the significance level of 1%, 5%, and 10%, respectively.
29
(Table 2 continued)
Panel A: Gross Profit Margin
Gross Profit Margin (1) (2) (3) (4) (5) (6) (7) (8)
0.01* 0.01** 0.02*** 0.02*** 0.01** 0.02*** 0.02*** 0.03***
(1.71) (2.30) (3.73) (4.25) (2.35) (2.93) (4.29) (4.93)
0.10*** 0.09*** 0.08*** 0.08*** 0.09*** 0.08*** 0.08*** 0.07***
(13.87) (13.59) (11.28) (11.26) (12.39) (11.86) (10.50) (10.32)
---- ---- ---- ---- -0.04** -0.02 -0.02 0.01
---- ---- ---- ---- (-2.50) (-0.94) (-1.23) (0.33)
---- ---- ---- ---- -0.05*** -0.06*** -0.03*** -0.05***
---- ---- ---- ---- (-9.72) (-12.54) (-5.36) (-8.34)
---- ---- ---- ---- 0.00 0.00 -0.00 -0.00
---- ---- ---- ---- (0.14) (0.06) (-0.55) (-0.36)
Observations 2,729 2,729 2,636 2,636 2,689 2,689 2,599 2,599
R-squared 0.07 0.11 0.28 0.31 0.10 0.15 0.29 0.33
Year FE NO YES NO YES NO YES NO YES
Industry FE NO NO YES YES NO NO YES YES
Momentum (MOM)
Awarded
Lagged Gross Profit Margin
Market Capitalization (Size)
Book-to-Market Ratio (B/M)
30
(Table 2 continued)
Panel B: Sales
Sales/Total Assets (1) (2) (3) (4) (5) (6) (7) (8)
0.04*** 0.05*** 0.04*** 0.05*** 0.04*** 0.04*** 0.04*** 0.04***
(3.02) (4.06) (4.09) (5.05) (3.36) (3.96) (4.08) (4.73)
0.73*** 0.69*** 0.66*** 0.63*** 0.72*** 0.70*** 0.66*** 0.64***
(38.15) (36.02) (38.94) (39.00) (36.62) (34.87) (39.92) (38.48)
---- ---- ---- ---- -0.06** -0.01 -0.04* 0.01
---- ---- ---- ---- (-2.12) (-1.78) (-0.38) (0.75)
---- ---- ---- ---- 0.08*** 0.06*** 0.05*** 0.03***
---- ---- ---- ---- (5.60) (5.70) (4.01) (3.12)
---- ---- ---- ---- -0.05*** -0.03** -0.04*** -0.02
---- ---- ---- ---- (-3.24) (-2.27) (-3.09) (-1.55)
Observations 3,032 3,032 2,943 2,943 2,987 2,987 2,900 2,900
R-squared 0.60 0.65 0.67 0.70 0.62 0.62 0.68 0.68
Year FE NO YES NO YES NO YES NO YES
Industry FE NO NO YES YES NO NO YES YES
Momentum (MOM)
Lagged Sales/Total Assets
Market Capitalization (Size)
Book-to-Market Ratio (B/M)
Awarded
31
(Table 2 continued)
Panel C: Costs of Selling, General, and Administration
SG&A/Total Assets (1) (2) (3) (4) (5) (6) (7) (8)
0.01* 0.02*** 0.02*** 0.02*** 0.02** 0.02*** 0.02*** 0.02***
(1.84) (3.12) (3.29) (3.95) (2.42) (3.25) (3.46) (4.14)
0.70*** 0.73*** 0.67*** 0.69*** 0.72*** 0.73*** 0.68*** 0.69***
(24.34) (24.16) (18.48) (20.82) (26.22) (24.61) (19.78) (20.57)
---- ---- ---- ---- -0.07*** -0.02** -0.03** 0.01
---- ---- ---- ---- (-3.90) (-2.31) (-2.53) (1.00)
---- ---- ---- ---- 0.02*** -0.00 0.01*** -0.01*
---- ---- ---- ---- (3.33) (-0.07) (3.47) (-1.85)
---- ---- ---- ---- -0.02** -0.02*** -0.01 -0.01
---- ---- ---- ---- (-2.31) (-3.07) (-1.37) (-1.58)
Observations 2,510 2,510 2,427 2,427 2,471 2,471 2,391 2,391
R-squared 0.57 0.57 0.65 0.70 0.60 0.60 0.66 0.71
Year FE NO YES NO YES NO YES NO YES
Industry FE NO NO YES YES NO NO YES YES
Awarded
Momentum (MOM)
Lagged SG&A/Total Assets
Market Capitalization (Size)
Book-to-Market Ratio (B/M)
32
(Table 2 continued)
Panel D: Advertising Expenditures
Advertising/Total Assets (1) (2) (3) (4) (5) (6) (7) (8)
0.002** 0.002*** 0.002* 0.002** 0.002* 0.002* 0.002** 0.002**
(2.28) (2.76) (1.90) (2.20) (2.72) (2.72) (2.03) (2.23)
0.82*** 0.82*** 0.82*** 0.82*** 0.81*** 0.81*** 0.82*** 0.82***
(19.39) (18.62) (18.37) (17.74) (19.08) (17.98) (17.57) (16.93)
---- ---- ---- ---- -0.00** -0.00 -0.00 0.00
---- ---- ---- ---- (-2.48) (-0.82) (-0.33) (1.16)
---- ---- ---- ---- 0.00* 0.00 0.00 -0.00
---- ---- ---- ---- (1.93) (0.69) (1.12) (-0.13)
---- ---- ---- ---- -0.00* -0.00** 0.00 0.00
---- ---- ---- ---- (-1.77) (-2.18) (0.12) (0.11)
Observations 1,078 1,077 1,005 1,005 1,059 1,058 986 986
R-squared 0.79 0.80 0.84 0.84 0.78 0.78 0.83 0.83
Year FE NO YES NO YES NO YES NO YES
Industry FE NO NO YES YES NO NO YES YES
Awarded
Momentum (MOM)
Lagged Advertising/Total Assets
Market Capitalization (Size)
Book-to-Market Ratio (B/M)
33
(Table 2 continued)
Panel E: R&D Expenditures
R&D/Total Assets (1) (2) (3) (4) (5) (6) (7) (8)
0.005*** 0.005*** 0.006*** 0.006*** 0.005*** 0.005*** 0.006*** 0.006***
(3.20) (2.98) (4.16) (4.14) (3.24) (3.02) (4.03) (4.02)
0.68*** 0.71*** 0.60*** 0.61*** 0.69*** 0.71*** 0.60*** 0.60***
(6.46) (6.29) (4.56) (4.63) (6.22) (6.18) (4.37) (4.43)
---- ---- ---- ---- -0.00** -0.00 -0.00 0.00
---- ---- ---- ---- (-2.05) (-0.36) (-1.34) (0.67)
---- ---- ---- ---- 0.00 -0.00 0.00 -0.00
---- ---- ---- ---- (0.91) (-0.36) (0.09) (-1.33)
---- ---- ---- ---- -0.00 -0.00 -0.00 -0.00
---- ---- ---- ---- (-0.68) (-0.55) (-0.16) (-0.21)
Observations 1,244 1,244 1,187 1,187 1,239 1,239 1,182 1,182
R-squared 0.64 0.65 0.73 0.74 0.64 0.65 0.73 0.75
Year FE NO YES NO YES NO YES NO YES
Industry FE NO NO YES YES NO NO YES YES
Momentum (MOM)
Awarded
Lagged R&D/Total Assets
Market Capitalization (Size)
Book-to-Market Ratio (B/M)
34
(Table 2 continued)
Panel F: Capital Expenditures
Capex/Total Assets (1) (2) (3) (4) (5) (6) (7) (8)
0.006*** 0.007*** 0.005*** 0.006*** 0.007*** 0.007*** 0.005*** 0.006***
(4.84) (6.36) (3.94) (5.24) (5.48) (6.39) (4.32) (12.16)
0.42*** 0.39*** 0.35*** 0.33*** 0.43*** 0.39*** 0.35*** 0.34***
(13.96) (13.96) (13.14) (12.45) (14.24) (13.76) (13.36) (12.16)
---- ---- ---- ---- -0.00* 0.01*** -0.01** 0.00
---- ---- ---- ---- (-1.95) (2.71) (-2.13) (1.47)
---- ---- ---- ---- 0.01*** -0.00 0.01*** 0.00**
---- ---- ---- ---- (4.27) (-0.52) (5.83) (2.22)
---- ---- ---- ---- 0.00 0.00* -0.00 0.00
---- ---- ---- ---- (1.42) (1.75) (-1.18) (0.30)
Observations 2,989 2,989 2,899 2,899 2,944 2,944 2,856 2,856
R-squared 0.33 0.43 0.48 0.54 0.35 0.44 0.50 0.55
Year FE NO YES NO YES NO YES NO YES
Industry FE NO NO YES YES NO NO YES YES
Momentum (MOM)
Lagged Capex/Total Assets
Market Capitalization (Size)
Book-to-Market Ratio (B/M)
Awarded
35
(Table 2 continued)
Panel G: Cash Holdings
Cash Holdings (1) (2) (3) (4) (5) (6) (7) (8)
-0.08** -0.08*** -0.06*** -0.06*** -0.08*** -0.08*** -0.06*** -0.06***
(-2.59) (-3.18) (-3.08) (-3.83) (-2.73) (-3.12) (-3.07) (-3.62)
0.11*** 0.10*** 0.10*** 0.10*** 0.11*** 0.10*** 0.10*** 0.10***
(4.64) (4.84) (5.53) (5.79) (4.78) (4.95) (5.56) (5.76)
---- ---- ---- ---- -0.02 -0.12*** 0.04 -0.02
---- ---- ---- ---- (-0.63) (-3.05) (1.53) (-0.67)
---- ---- ---- ---- -0.06 0.01 -0.04 0.01
---- ---- ---- ---- (-1.18) (0.10) (-0.62) (0.07)
---- ---- ---- ---- -0.01 -0.01 0.01 0.01
---- ---- ---- ---- (-0.24) (-0.22) (0.88) (0.96)
Observations 3,026 3,026 2,936 2,936 2,983 2,983 2,895 2,895
R-squared 0.06 0.08 0.13 0.14 0.06 0.08 0.13 0.15
Year FE NO YES NO YES NO YES NO YES
Industry FE NO NO YES YES NO NO YES YES
Momentum (MOM)
Lagged Cash Holdings
Market Capitalization (Size)
Book-to-Market Ratio (B/M)
Awarded
36
Table 3: R&D Investments and the Probability of Winning Awards
We include the whole universe of public firms and run logistic or probit regressions to test the relation between R&D investments and the future probability of being
awarded. The dependent variable is a dummy variable indicating whether the focal firm receives the award in year t+1, and the key explanatory variable of interest is
the Average R&D Investment, which denotes the arithmetic average of R&D expenditure scaled by total assets during the previous five years from t-4 to t. Other firm
characteristics are also controlled at year t: market capitalization (Size, in hundred millions); book-to-market ratio (B/M); 12-month momentum factor with one month
reversal (MOM). Numbers without parentheses report parameter estimates, and numbers with parentheses show robust t-values. ***, **, and * indicate the significance
level of 1%, 5%, and 10%, respectively.
(1) (2) (1) (2)
0.63*** 0.85*** 0.33*** 0.43***
(6.32) (5.26) (6.87) (5.63）
---- 1.38*** ---- 0.73***
---- (18.55) ---- (20.13)
---- -0.37*** ---- -0.12***
---- (-6.81) ---- (-6.38)
---- -0.04 ---- -0.01
---- (-0.87) ---- (-0.86)
-5.35*** -4.85*** -2.60*** -2.44***
(-198.69) (-98.27) (-278.86) (-136.79)
Observations 295,042 189,946 295,042 189,946
R-squared 0.00 0.02 0.00 0.03
Log Likelihood -8977.10 -7218.48 -8972.22 -7180.72
AwardedLogistic
Average R&D Investment
Market Capitalization (Size)
Book-to-Market Ratio (B/M)
Momentum (MOM)
Constant
Probit
37
Table 4: One-Way Portfolio Sorting
By the end of fiscal year t-1, we identify awarded firms and their unawarded matched counterparts following the method elaborated in Table 1. We then construct an
awarded-minus-unawarded (AMU) portfolio by going long in the awarded portfolio and going short in the unawarded portfolio, and allow it to perform from July of
year t to June of year t+1. We compute and compare the equally-weighted monthly returns for both the awarded portfolio and the unawarded portfolio, since the firms
in the awarded portfolio are highly comparable with the firms in the unawarded portfolio in firm size. Specifically, to compute the monthly return of the awarded
portfolio, we average the monthly stock returns of the awarded firms, and to calculate the monthly return of the (matched) unawarded portfolio, we first average the
monthly stock returns of the unawarded firms matched to the same awarded firm and then average these matched monthly returns across all awarded firms. To test the
statistical significance of abnormal returns adjusted for the exposure to risk factors, we regress the monthly returns in excess of risk-free interest rate (Excess return)
on the following risk factors. The capital asset pricing model (CAPM) simply considers the market factor (MKT). The three-factor model of Fama and French (1993)
(FF3) includes MKT, the size factor (SMB), and the value factor (HML). Carhart (1997) four-factor model (FF4) takes into account MKT, SMB, HML, and the
momentum factor (UMD). The five-factor extension in Fama and French (2015) (FF5) captures the patterns of MKT, SMB, HML, the profitability factor (RMW), and
the investment factor (CMA). Fama and French (2017) six-factor model (FF6) uses MKT, SMB, HML, RMW, CMA, and UMD. Besides, we also extend the FF3, FF5,
and FF6 models by adding an R&D factor, where this extra factor is constructed following Chan, Lakonishok, and Sougiannis (2001). Numbers without parentheses
report parameter estimate of excess return and their corresponding alphas, and numbers with parentheses show t-values. ***, **, and * indicate the significance level
of 1%, 5%, and 10%, respectively. All estimates are in percentage.
Excess return CAPM FF3 FF4 FF5 FF6FF3
+R&D factor
FF5
+R&D factor
FF6
+R&D factor
1.10*** 0.38*** 0.29*** 0.37*** 0.35*** 0.41*** 0.26*** 0.30*** 0.34***
(3.89) (2.72) (2.79) (3.53) (3.33) (3.89) (2.70) (2.99) (3.43)
0.84*** 0.12 0.00 0.18** 0.03 0.17** -0.01 0.01 0.14*
(3.09) (1.03) (0.06) (2.52) (0.39) (2.32) (-0.14) (0.01) (1.93)
0.26*** 0.26*** 0.28*** 0.19** 0.32*** 0.24** 0.27*** 0.29*** 0.20**
(2.86) (2.88) (3.08) (2.05) (3.36) (2.58) (2.99) (3.16) (2.24)
Awarded
Unawarded
Awarded-Unawarded
38
Table 5: Two-Way Portfolio Sorting
In Panel A, we conduct sequential sort to examine the awarded-minus-unawarded spread across different subgroups of R&D investment. By the end of fiscal year t-1,
we identify awarded firms and their unawarded matched counterparts following the method elaborated in Table 1. In both awarded and unawarded groups, firms with
non-missing value of R&D investments are further sorted into three subgroups based on its tertiles (e.g., we separate firms into low, medium, and high group using the
30th and 70th percentiles) in year t-1. We consider the R&D expense over total assets as an indicator of R&D investments. In Panel B, we examine the return spread of
awarded-minus-unawarded portfolio in crisis and non-crisis periods. The recessions are defined by the National Bureau of Economic Research (NBER) to proxy for
periods of low aggregate consumption, which covers a period from the peak to the trough of business cycle. For each subgroup, we construct an awarded-minus-
unawarded (AMU) portfolio by going long in the awarded portfolio and going short in the unawarded portfolio, and allow it to perform from July of year t to June of
year t+1. We compute and compare the equally-weighted monthly returns for both the awarded portfolio and the unawarded portfolio, since the firms in the awarded
portfolio are highly comparable with the firms in the unawarded portfolio in firm size. To test the statistical significance of abnormal returns adjusted for the exposure
to risk factors, we regress the monthly returns in excess of risk-free interest rate (Excess return) on the following risk factors. The capital asset pricing model (CAPM)
simply considers the market factor (MKT). The three-factor model of Fama and French (1993) (FF3) includes MKT, the size factor (SMB), and the value factor (HML).
The five-factor extension in Fama and French (2015) (FF5) captures the patterns of MKT, SMB, HML, the profitability factor (RMW), and the investment factor
(CMA). Fama and French (2017) six-factor model (FF6) uses MKT, SMB, HML, RMW, CMA, and UMD. Numbers without parentheses report parameter estimate of
excess return and their corresponding alphas, and numbers with parentheses show t-values. ***, **, and * indicate the significance level of 1%, 5%, and 10%,
respectively. All estimates are in percentage.
39
(Table 5 continued)
Panel A: High vs. Low R&D Investments
Excess return CAPM FF3 FF5 FF6
0.91*** 1.01*** 0.10 0.14 0.07 -0.07 -0.13
(3.30) (3.73) (0.67) (0.94) (0.52) (-0.46) (-0.88)
0.96** 1.56*** 0.60*** 0.60** 0.58** 0.71*** 0.55**
(2.45) (3.61) (2.60) (2.57) (2.48) (2.92) (2.29)
0.04 0.55 0.51* 0.47* 0.51* 0.78*** 0.68**
(0.18) (1.62) (1.88) (1.71) (1.91) (2.84) (2.47)
Panel B: Non-crisis vs. Crisis Periods
Excess return CAPM α FF3 α FF5 α FF6 α
1.15** 1.28** 0.13 0.13 0.10 0.12 0.12
(2.08) (2.26) (0.86) (0.89) (0.64) (0.70) (0.71)
0.69** 1.01*** 0.32*** 0.32*** 0.39*** 0.41*** 0.29***
(2.28) (3.17) (2.83) (2.83) (3.41) (3.61) (2.61)Non-crisis
Crisis \ Award Unawarded AwardedAwarded-Unawarded
Crisis
AwardedAwarded-Unawarded
Low
High
High-Low
Xrd \ Award Unawarded
40
Figures
Figure 1: Industry Distribution of Awarding Outcome
This figure graphs the industry distribution and time-series variation of the awarding outcomes of the R&D 100 Award. The Fama-French 12 industries are defined in
the following twelve categories: (1) Consumer Non-Durables (NoDur); (2) Consumer Durables (Durbl); (3) Manufacturing (Manuf); (4) Energy (Enrgy); (5) Chemicals
(Chems); (6) Business Equipment (BusEq); (7) Telecom (Telcm); (8) Utilities (Utils); (9) Shops; (10) Health (Hlth); (11) Money ; (12) Other. The original sampling
period ranges from 1965 to 2014. Panel A exhibits the full-sample industry distribution of the awarding outcomes. The numbers imply the proportions (in percentages)
of awards received by these industries. Panel B separately displays the time-series line charts of annual awarding outcomes of total U.S. awardees and four selected
industries, which takes up the largest share. The vertical axis suggests the industry awarded share, where the percentage is computed as the number of awarded firms
in specific industry divided by the total number of awarded firms in the same year.
41
(Figure 1 continued)
Panel A: Average Awarding Outcomes of FF12 Industries
42
(Figure 1 continued)
Panel B: Annual Awarding Outcomes of Selected FF12 Industries
43
Figure 2: Performance of Long-short Strategy along Sample Years
The curve plots the total cumulative returns of our long-short strategy exploiting the awarded-minus-unawarded spread in Table 4 starting from July 1970 to June 2014.
The total cumulative return is presented in percentages and defined as: 𝑅𝑛 = (1 + 𝑟1) ∙ (1 + 𝑟2) ∙ ⋯ ∙ (1 + 𝑟𝑛). The overlay recession bands correspond to the U.S.
recession periods defined by the NBER. For example, the last gray bar starting from Dec 2007 to June 2009 denotes the “Great Recession” period.
44
Figure 3: Five-year Cumulative Performance by Portfolio Month
Panel A plots the cumulative returns of the investments in both the awarded and unawarded portfolios constructed in Table 4 over a five-year horizon, and Panel B
represents the total cumulative returns of long-short strategy over the same period. To trace the five-year portfolio performance, we set up the sequence of from M1 to
M60 to denote the portfolio month from July of year t (formation of the portfolios) to June of year t+5. The total cumulative return is defined as: 𝑅𝑛 = (1 + 𝑟1) ∙ (1 +
𝑟2) ∙ ⋯ ∙ (1 + 𝑟𝑛). Cumulative returns are presented in percentages.
