Article:
Japanese patent index and stock performance

The Journal of

Financial
Perspectives
EY Global Financial Services Institute			July 2014 | Volume 2 – Issue 2

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Editorial
EditorShahin Shojai
EY LLP
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Hong Kong University of Science and Technology

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Andreas RichterLudwig-Maximilians-Universitaet

Roland Füss
University of St. Gallen

Philip Rawlings
Queen Mary, University of London

Giampaolo Gabbi
SDA Bocconi

Roberta Romano
Yale Law School

Boris Groysberg
Harvard Business School

Hato Schmeiser
University of St. Gallen

Scott E. Harrington
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University of New South WalesPaul M. Healy
Harvard Business School

Paola Musile Tanzi
SDA Bocconi

Jun-Koo Kang
Nanyang Business School

Marno Verbeek
Erasmus University

Takao Kobayashi
Aoyama Gakuin University

Ingo Walter
New York University

Howard Kunreuther
The Wharton School

Bernard Yeung
National University of Singapore

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Executive summary
Japanese patent index and stock performanceby Takao Kobayashi, Professor of Finance, Aoyama Gakuin University, Yasuhiro Iwanaga,
Aoyama Gakuin University & Sumitomo-Mitsui Trust Bank, Limited and Hideaki Kudoh,
Nomura Asset Management Co., Ltd.

In the global economy, high technology serves as a source of competitive advantage
for Japanese companies. In Japan, there is a patent value indicator which is uniqueamong other patent value indicators developed in the U.S. The uniqueness lies in its
focus on measuring the exclusivity and technological competitiveness of each patent
using data based on the number of actions taken by third parties against the patent.
The construction of such a patent value metric became possible thanks to the Japanese
Government’s active disclosure of information on patent attacks. This paper is our
first attempt to study the relationship between technological competence and firm
performance using this technology indicator.

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Japanese patent index
and stock performance
Takao Kobayashi
Professor of Finance, Aoyama Gakuin University
Yasuhiro Iwanaga
Aoyama Gakuin University & Sumitomo-Mitsui Trust Bank, Limited
Hideaki Kudoh
Nomura Asset Management Co., Ltd.

Abstract
In the global economy, high technology serves as a source of competitive advantage
for Japanese companies. In Japan, there is a patent value indicator which is uniqueamong other patent value indicators developed in the U.S. The uniqueness lies in its
focus on measuring the exclusivity and technological competitiveness of each patent
using data based on the number of actions taken by third parties against the patent.
The construction of such a patent value metric became possible thanks to theJapanese Government’s active disclosure of information on patent attacks. This paper
is our first attempt to study the relationship between technological competence andfirm performance using this technology indicator. In particular, we demonstrate how thistechnology indicator may be used to forecast company stock performance. We construct
long/short strategies based on (1) the patent indicator, (2) R&D expenditure and (3)a combination of the two. The third strategy was the best performer. Combining R&D
expenditure, which is readily available in financial statements, with the patent indicatorenhanced portfolio expected return and reduced risk considerably. The best-performing
strategy generated an annual mean return of 11.50%, standard deviation of 9.25%, anda Sharpe ratio of 1.23. The return is not attributable to the Fama–French three factors.
Technology indicators should not work in some industry groups, hence, the result is evenmore striking given that the portfolio was constructed with a universe of stocks covering
all industry groups except financials.

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Japanese patent index and stock performance

1. Introduction
As the industrial economy has been supplanted by the knowledge economy, intellectual
property (IP) assets, such as patents, trademarks and copyrights, are gaining growing
importance in corporate value creation. In the global economy, high technology serves
as a source of competitiveness for Japanese companies. In particular, Japanese companies
account for at least 20% of all international patent applications in the world and produce
innovative products using patented technology as leverage. In the 2012–2013 edition of
the Global Competitiveness Report of the World Economic Forum, Japan is ranked second(next to Switzerland) in the “innovation and sophistication” factor of global competitiveness.1
Our basic interest is to find out whether the accumulation of these IP assets is sufficiently
incorporated in the stock market’s pricing of firms. If they are, information aboutcompanies’ R&D activities and/or the level of patent accumulation would not provide
arbitrage opportunities. If, on the contrary, the information predicts future stock returns
and serves as a useful signal for screening companies, the investment community needs
an easily accessible source of information on the value of each firm’s IP assets. Not only
does it satisfy the investors’ appetite for higher returns, but also promotes the efficiencyof the stock market. The latter means that a firm with highly valuable IP assets would be
able to raise equity capital at a lower cost. In Japan there is a patent value indicator, whichis unique among other patent value indicators developed in the U.S. In this paper, our focus
is mainly on this patent value index.
Thomas (2001) used a quantitative measure of patent assets based on the citation
frequency. The index is available via an online service called TechLine and provided by
Chi Research Inc. He showed that there is a strong relationship between this index
and stock market valuation. He further showed that by fitting a model to this relationship,
underpriced stocks relative to the model price tend to perform excellently and overpriced
stock perform poorly in the subsequent period. Cardoza et al. (2008) used anothermeasure called PatentRatings, which is provided by Ocean Tomo. This index includes anumber of qualitative measures for a patent, which they call the patent’s “economic value.”

1

The other factors are “basic requirements” and “efficiency enhancers.”

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Japanese patent index and stock performance

They constructed a portfolio of 300 stocks selected based on this patent index and used
it as a benchmark performance index of the technology sector of the U.S. economy.
They showed that this portfolio outperforms the S&P500 Index.
R&D expenditure carries information on R&D intensity. This information is readily available
in financial statements. Consequently, researches on the relationship between intangible
IP assets and stock market performance started with a focus on R&D expenditure.
The empirical results in this area are mixed. Titman et al. (2004) found that intensivenessin physical investment has a negative relationship with stock returns. The literature calls
it the “investment paradox.” These authors measure investment intensity by physicalinvestment relative to the amount of tangible assets. Li and Liu (2010) showed that therelationship between R&D intensity and stock return is similar. Namely, when R&D intensity
is measured by R&D investment-to-intangible asset ratio, R&D intensity has a negative
relationship with stock returns. In contrast, Chan et al. (2001) showed that R&D-intensive
firms earn higher returns. Their measure of R&D intensity used market capitalization as
the scale. Lev and Sougiannis (1996, 1999) and Chambers et al. (2002) obtained similar
results. In addition, Jung (2005) reported that R&D-intensive companies generate
exceptionally high positive returns in Japan.
We will examine the relationship between the value of patents (YK) and R&D expenditure
to future stock performance. In so doing, our objective is to distinguish between R&D
efficiency (or, R&D productivity) and R&D intensity, with the conjecture that they carrydisparate sets of information about IP assets. We measure “R&D efficiency” by the ratioof YK value to R&D expenditure. We measure “R&D intensity” by the ratio of R&D
expenditure to market capitalization. We will show that both measures have independent
predictability for future stock returns. We further show that combining these two measuressignificantly enhances investment performance.
The rest of the paper is organized as follows: section 2 illustrates how the YK value isconstructed. We also report some descriptive statistics of the YK value. In section 3,
we investigate the relationship between IP signals and stock returns in a portfolio context.
We construct portfolios of stocks using a different set of IP signals and compare theperformance of portfolios. In 3.1, we describe the data sources. In 3.2, we use the YKElectronic copy available at: https://ssrn.com/abstract=3079600

Japanese patent index and stock performancevalue scaled by market capitalization as the screening variable. In 3.3, we use YK value
scaled by R&D expenditure (i.e., R&D efficiency), and independently, R&D expenditurescaled by market capitalization (i.e., R&D intensity). In 3.4, we combine YK value and R&D
expenditure to make two-dimensional sorts of stocks. In 3.5, we compare results of ourportfolio strategies, and in 3.6, we report the industry groups in which our strategy works
well and the industry groups in which it works poorly. Section 4 summarizes the paper.
2. The YK value
In recent years, there have been many attempts to measure the value of patents. Chi
Research Inc.’s TechLine [see Thomas (2001)] and Ocean Tomo’s Patent Ratings [seeCardoza et al. (2008)] represent two of them.
TechLine’s measure is based on the following patent indicators:
1. Number of patents held by a company
2. Growth in the number of patents held by a company
3. Current impact index: the number of citations a company’s patents receive within otherparties’ patents issued in the most recent year
4. Scientific linkage: the number of references to scientific papers that a company’s
patents makes
5. Technology cycle time: the median age of patents that a company lists on the front
page of the company’s patent report
These are all quantitative measures that count the number of patents, number of citations,the age (obsolescence) of patents and so on. In addition to these indicators, Ocean Tomo’s
PatentRatings includes a qualitative measure, which they call the IPQ Score. Patent owners
must pay periodic fees to maintain their patents in force. By analyzing patent maintenancedata, Ocean Tomo constructs a score that predicts the probability that a patent will bemaintained or abandoned. They regard this score as a measure of the economic value ofeach patent.

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Japanese patent index and stock performance

The YK value that we use in this paper is derived from the model developed by Kudo &Associates,2 a leading intellectual property (IP) law firm in Japan. It is based on aqualitative value score that is assigned to each patent. The YK value of a company is
the sum of the YK values of all patents that a company has in force.
The uniqueness of the YK value lies in its focus on measuring the exclusivity andtechnological competitiveness of each patent using data based on actions taken against
the patent in the Japan Patent Office (JPO). If another party takes legal action against
a patent, it is viewed as proof that the patent is perceived as a threat by the firm’s
competitors. The greater the severity of the legal action, the greater the score given to
the value of the patent.
When an invention is submitted for patent registration, the JPO releases a “Publicationof unexamined application” to the general public. At this stage, some companies that
seek to prevent the approval of the patent can submit an “Offer of information” to provide
evidential documents to the JPO. When the application is under JPO’s examination, aparty that believes its own patent, or that of a third party’s, is being infringed can submit“Request for inspection” in the earlier stages, or “Objection” in the latter stages, of
examination. After the JPO has made a “Decision of grant,” a request of “Invalidation
trial” can be made. These actions are given scores. Actions taken in the latter stages
are allocated higher scores. The score also takes accounts of the number of parties
taking action.
The YK value of the individual patent n at time t is defined by(formula 1),
where i denotes an individual action, Pi is the score given to action i, Ni is the number ofparties who took action i, and ci is a decay factor reflecting the speed with which its
patents are expected to lose value or be replaced by superseding technologies. About 80%
of patent abolishment is due to unpaid patent maintenance fees and the rest is due to thelegal termination of the patent right (20 years). Very few patents are abandoned during
the first 4 years, and on average they are abandoned 13 to 19 years after the date they
were granted. The decay factor is computed by fitting a probabilistic model of technology
obsolescence to the data on patent abandonment and termination.
2

The other factors are “basic requirements” and “efficiency enhancers.”

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Japanese patent index and stock performance

The YK value of a company at time t is the sum of. The data has
been created by Kudo & Associates on a monthly basis since January 1988, and it covers
all listed firms in Japan. Information about third-party actions is available from a publicationby the National Center for Industrial Property Information and Training. The public releaseof detailed information about third-party actions against patents is peculiar to Japan.
Hence, the availability of the YK value adds uniqueness to the current research.
3. Returns to IP-driven strategies
If all the information about companies’ IP competitiveness is fully reflected in stock prices,
YK value would not provide any profitable opportunities for equity investors. The purposeof our analysis is to examine whether this is true. We demonstrate that the YK value
provides quite a useful signaling mechanism for screening stocks when constructing highly
performing equity portfolios.
3.1 Data sources
We include in our study all firms listed in the first section of the Tokyo Stock Exchange(TSE) except banks, securities companies, insurance companies, and other financials.
Stock prices and returns are from QUICK-Astra and accounting data are from the
Nikkei Economic Electronic Databank System (NEEDS).3 The YK value was provided by
Kudo & Associates.4 We computed the Fama–French three factors for all the stocks inour study using the method proposed by Kubota and Takehara (2007). The sample
period is between September 2002 and December 2012.
3.2 The default portfolios selected by YK/ME
Figure 1 shows the relationship between the YK value and market capitalization (later
denoted by ME, shorthand for market equity) for firms in the electric appliances industry.
The x and y axes are both in logarithmic scale. The correlation coefficient is 0.72.
Table 1 shows the correlation coefficient between these two variables for industries within
the 33 TSE industry classifications where the correlation coefficient is larger than 0.6.

We thank Quick Corp. for providing data on monthly stock returns for this research.
	We thank Mr. Ichiro Kudo of Kudo & Associates for enabling us to access the YK value database for thisresearch.

3
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Japanese patent index and stock performance

In Table 1, we also report the mean, the maximum value, and the crosssectional standard
deviation of the YK value in these industries. 5 As evident, the correlation is very high:larger firms tend to have larger YK values. This suggests that YK values should not be used
as a stand-alone measure for selecting stocks. We chose to scale the YK value by market
capitalization (YK/ME)6. Since it makes little sense to compare the patent competitivenessof a chemical company with that of an auto manufacturer, stocks’ YK/ME
comparisons were undertaken within each industry.
At the end of each month, stocks are separated into quintiles based on YK/ME and equalweight portfolios are formed in each quintile. We select stocks from each of the 33 TSE
industry classifications. Portfolio 1 is the portfolio of stocks from the highest YK/ME
quintile of each industry and portfolio 5 is the portfolio of stocks from the lowest YK/ME
quintile of each industry.
Panel A of Table 2 presents the properties of the quintile portfolios. BE/ME is book-tomarket (the ratio of book value of equity to market value of equity), E/P is the earnings
yield (the ratio of earnings per share to stock price), ROE is return on equity (the ratioof net income to book value of equity), and log(ME) is the log of market value of equity
measuring the size of the firm with ME denominated in yen.
One can see some “value-tilt” for higher YK/ME portfolios. Bookto- market is higher
and ROE is lower for higher YK/ME portfolios, and these relationships are monotone.
This should be a little surprising as growth firms, rather than value firms, tend to spend
more on R&D activities. We will scrutinize this observation later by segregating the effects
of YK/ME into R&D efficiency and R&D intensity.
Panel B reports the portfolio performance. Mean return is the time-series average ofthe annualized portfolio returns in percent. St.dev is the annualized standard deviationof the returns. Sharpe ratio is the mean excess return (over the risk-free rate) per unit

The minimum value is zero for most industries.
	We tested other measures of size, such as book value of assets, book value of equity, and sales as thescaling variable. Market capitalization was best among these variables.

5
6

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Japanese patent index and stock performancerisk (measured by the standard deviation), which is a standard risk-adjusted measure of
performance. We use the one-month T-bill rate for the risk-free rate. The column “H-L”
refers to the return of a hedge portfolio, which is long in portfolio 1 and short in
portfolio 5.
The highest YK/ME portfolio generates a mean return of 9.34% annually, while the lowest
YK/ME portfolio generates 1.52%. Consequently, the mean return on the H-L portfolio is7.81% annually. It has a t-statistic of 3.82, which shows that the mean return of the H-L
portfolio is significantly positive. The standard deviation of return is higher for higher YK/
ME quintiles. The monotonicity of the Sharpe ratio indicates that higher YK/ME quintile
portfolios yield higher risk-adjusted returns.
For each quintile portfolio and the H-L portfolio, we run a timeseries regression of monthly
MKT ++bb SMB
SMB ++bb HML
HML +
+ ff
excess return to the Fama–French three factors: RR - -rr ==aa ++bb MKT(formula 3), where, for each month t, Ri,t is the return of portfolio i, rf,t is the risk-free rate,
MKTt is the excess return of the market portfolio, SMBt is the Fama–French size factor (thereturn to being long in small ME stocks and short in big ME stocks), and HMLt is the Fama–
French value factor (the return to being long in high BE/ME stocks and short in low BE/ME
stocks). We created the Fama–French three factors for the Japanese stock market following
the procedure proposed by Kubota and Takehara (2007).7 The row labeled “FF3-alpha”
reports the estimate of the intercept term ai and its t-statistics (in parentheses). If any
portfolio has a significantly positive ai, it means that the portfolio has additional riskadjusted performance relative to its risk exposure to the Fama–French three factors.
i,ti,tf,tf,t

i i

i,MKT
i,MKT

tt

i,SMB
i,SMB

tt

i,HML
i,HML

tt

i,t
i,t

	The MKT factor was computed using (1) the cum-dividend monthly return series of TOPIX for the market
return and (2) the one-month T-bill rate for the risk-free rate. The risk-free rate is 0.2 percent during
our period of study, reflecting the fact that most of the years were under the “zero interest rate policy
regime” by Bank of Japan. TOPIX is the market index for the first section of the TSE.
Accordingly, we computed the SMB and HML factors on the same universe of stocks. The selectionof stocks for each portfolio was done annually, at the end of August. Small, big, high and low portfolios
were value-weighted portfolios in which weighting was based on the number of “free-float” shares(shares available for trading).

7

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Japanese patent index and stock performance

The FF3-alphas of the highest YK/ME portfolio and the H-L portfolio have t-statistics of
2.86 and 3.32, respectively. Hence, we can conclude that these two portfolios generate
additional risk-adjusted returns. This result confirms that YK/ME serves as a useful signal
for equity investment.
3.3 Portfolios selected using R&D efficiency and R&D intensity
Chan et al. (2001) showed that R&D expenditure, which is readily available in financial
statements, has some predictive powers for future stock returns. Specifically, highly R&Dintensive firms earn higher returns when R&D intensity is measured relative to the market
value of equity, R&D/ME. Since YK value and R&D expenditure are obtained from very
different sources, combining them may strengthen our results.
A variable that complements R&D intensity is the productivity of R&D activities, which wecall R&D efficiency. Noting that YK/ME can be decomposed to become: YK/ME = (YK/R&D)
× (R&D/ME) (formula 4), we will regard the first term, YK/R&D, as a measureof R&D efficiency.
Using these variables, we now investigate how the R&D efficiency measure, YK/R&D, andthe R&D intensity measure, R&D/ME, workindependently in selecting stocks for portfolios.
As in the previous case, selection is undertaken for each industry.
We start with YK/R&D. We repeat the same process as in Table 2 except that the R&D
efficiency, YK/R&D, is used for the screening signal. The results are reported in Table 3.
As can be seen from Panel A, there is no discernable difference across the YK/R&Dselected
quintile portfolios in terms of book-to-market, earnings yield, ROE, and size. Panel B
reports the portfolio performance. The highest YK/R&D portfolio generates a mean returnof 7.02% annually, while the lowest generates 3.79%. Consequently, the mean return on the
H-L portfolio is 3.23% annually. It has a t-statistic of 2.79, which shows that the mean
return is significantly positive. The standard deviation of returns is almost identical acrossthe quintile portfolios. The Sharpe ratio shows that higher YK/ME quintile portfolios yieldhigher risk-adjusted returns, but the relationship is not monotone.

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Japanese patent index and stock performance

The FF3-alphas of the highest YK/R&D portfolio and the H-L portfolio have t-statistics of
3.31 and 2.70, respectively. Hence, we can conclude that these two portfolios generate
additional risk-adjusted returns. This results confirm that R&D efficiency provides a usefulsignal for equity investment.
We repeat the same process except that the R&D intensity, R&D/ ME, is used for thescreening signal. The results are reported in Table 4. Panel A shows a certain degree of“value-tilt” for higher R&D/ME portfolios. Book-to-market is higher and ROE is lower for
higher R&D/ME portfolios; and these relationships are mostly monotone. We observed thisvalue-tilt for portfolios selected by YK/ME in Table 2. The bottom row shows that log(ME)is smallest for the highest R&D/ME portfolio. In Japan, most of the value firms are smaller
in size. Since we measure R&D intensity in terms of R&D expenditure per market
capitalization, the highest R&D/ME portfolio may have a majority of value firms.
Panel B reports the portfolio performance. The highest R&D/ME portfolio generates amean return of 10.13% annually, while the lowest generates 1.92%. Consequently, themean return on the H-L portfolio is 8.21% annually. It has a t-statistic of 2.73, whichshows that the mean return of the H-L portfolio is significantly positive. Unlike Panel B
of Table 3, the mean return increases monotonically for higher R&D/ME quintiles. On theother hand, the standard deviation of returns is also higher for higher R&D/ ME quintiles.
The Sharpe ratio shows that higher R&D/ME quintile portfolios yield higher risk-adjusted
return. FF3-alphas of the highest R&D/ME portfolio and the H-L portfolio have t-statistics
of 2.25 and 2.12, respectively. We can, therefore, conclude that these two portfolios
generate returns in excess of what can be expected by their exposures to the three risk
factors of Fama and French. This result confirms that R&D intensity also provides auseful signal for equity investment.
3.4 Two-dimensional selection
We now scrutinize the result of Table 3 that the mean returns and alphas are higher for
stocks with high R&D efficiency. We first sort stocks into quintiles based on their R&D
intensity. We then sort stocks into quintiles based on their R&D efficiency and average
across the R&D-intensity quintiles. This way we make sure that in each of the five

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Japanese patent index and stock performance

YK/R&D-selected portfolios stocks are spread evenly over R&D intensity.
We call these five portfolios “YK/R&Dselected portfolios controlling for R&D intensity.”At the end of each month, stocks are allocated into quintiles based on R&D/ME. Then,
within each R&D/ME quintile, we separate stocks into five subgroups based on YK/R&D.
This way we create 25 groups of stocks each month and in each group we form an equalweight portfolio. As before, we do the grouping of stocks within each of the 33 TSE
industry classifications. The results are reported in Table 5. Column “H-L” refers to thereturn of a hedge portfolio that is long portfolio 1 and short portfolio 5 for each
R&D/ME quintile.
For each of the 30 monthly time series of portfolio excess returns (for the 5×5 portfolios
and the 5 H-L por tfolios), we run a regression to the three Fama–French factors. The table
reports the estimate of the intercept term ai and its t-statistics (in parentheses).
In each R&D/ME quartile, the alpha is higher for higher YK/ R&D portfolios, although there
are some exceptions. As we conjectured, among firms with similar R&D intensity, firms
with higher R&D efficiency generate higher alphas. The rightmost column shows that theFF3-alpha of all H-L portfolios are positive, although some of the t-statistics are not large
enough to endorse statistical significance. The FF3-alpha is highest with 0.51 (t-statistic of2.20) for the highest YK/R&D portfolio belonging to the highest R&D/ME quintile (i.e., cell(1,1)). At the other end of the FF3-alpha spectrum is the lowest YK/R&D portfolio
belonging to the lowest R&D/ME quintile (i.e., cell (5,5)). Its FF3-alpha is –0.20 and its
t-statistic is –1.24.
In the bottom row are the FF3-alphas and the t-statistics for the YK/R&D-selected portfolios
controlling for R&D intensity. Again, the results indicate a monotone relationship between
R&D efficiency and risk-adjusted portfolio performance. In the H-L column, we see thatafter controlling for R&D intensity, the FF3-alpha of the H-L portfolio remains significant
with 0.25% per month with t-statistic of 2.43. This shows that the long/short portfolio produces
additional risk-adjusted performance relative to its risk exposure to the Fama–French
three factors.

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Japanese patent index and stock performance

In the same spirit we reexamine the results of Table 4, in which we saw that portfolios withhigher R&D intensity generate higher mean returns and alphas. At the end of each month,stocks are seperated into quintiles based on YK/R&D. Then, within each YK/R&D quintile,
we separate stocks into five subgroups based on R&D/ME. This way we create 25 groupsof stocks each month and in each group we form an equal-weight portfolio. As before, we
do the grouping of stocks within each of the 33 TSE industry classifications. This way wemake sure that in each of the five R&D/ME-selected portfolios stocks are spread evenly
over R&D efficiency. We call these five portfolios “R&D/ME-selected portfolios controllingfor R&D efficiency.” The results are reported in Table 6.
In each YK/R&D quintile, the alpha is higher for higher R&D/ME portfolios. This timethe monotonicity is violated only by one cell (cell (3,4)). Among firms with similar R &D
efficiency, more R&D intensive firms generate higher alphas. The rightmost column showsthat the FF3-alpha of all H-L portfolios are positive, The FF3-alpha is highest with 0.62
(t-statistic of 2.70) for the portfolio of most R&D-intensive firms in the highe st YK/R&D
quintile (cell (1,1)). At the other end of the FF3-alpha spectrum is the portfolio of the least
R&D-intensive firms in the lo west YK/R&D quintile (cell (5,5)). Its FF3-alpha is –0.12 and its
t-statistic is –0.82.
The bottom row reports the FF3-alphas and their t-statistics for the R&D/ME-selected
portfolios controlling for R&D efficiency. The results again indicate a monotone relationshipbetween R&D intensity and risk-adjusted portfolio performance. In the bottom H-L column
we see that after controlling for R&D efficiency, the FF3-alpha of the H-L portfolio remains
significant with 0.51% per month with t-statistic of 2.62. This shows that this long/shortportfolio produces additional risk-adjusted performance relative to its risk exposure to the
Fama–French three factors.
In conclusion, the results of Tables 5 and 6 suggest that the effects of R&D efficiency andR&D intensity are mutually independent. In addition, we can obtain returns more efficiently
by two-dimensional selections based on these two aspects of R&D activity.

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Japanese patent index and stock performance

3.5 Comparison of long/short strategies
We proposed that the best use of the YK value as a signal for stock selection might be in
combination with the data on firms’ R&D expenditure, which is readily available in financial
statements. It enables us to measure firms’ IP competitiveness in two dimensions: the R&D
efficiency (YK/R&D) and the R&D intensity (R&D/ME). We found in the control experimentsof Tables 5 and 6 that these two factors carry independent information.
Thus, our best candidate for a portfolio strategy is to take a long position in the cell (1,1)
portfolio and a short position in the cell (5,5) portfolio in either of the two-dimensional
selections of Tables 5 or 6.
Table 7 reports the mean return and standard deviation of these long/short strategies in
the first and the second column. Strategy 1 is the long/short strategy based on the twodimensional selection of Table 5, and strategy 2 is the long/short strategy based on thetwo-dimensional selection of Table 6. The t-statistics are shown in parentheses for testingwhether the mean return is significantly positive. We also report the Sharpe ratio.
Strategy 1 generates a mean return of 11.85% annually with a standard deviation of
10.23%. Strategy 2 generates a mean return of 11.50% annually with a standard deviationof 9.25%. Thus, strategy 1 has slightly higher mean return and higher risk than strategy 2.
The Sharpe ratios are 1.14 and 1.23, respectively.
To compare the performance of these strategies with our “default strategy” of selecting
stocks based on YK/ME, we separated stocks into 25 groups based on YK/ME andconstructed an equal-weight portfolio in each group. The procedure is identical to the oneused for Table 2 except that we now sort stocks into 25 groups rather than 5. We thentake a long position in the highest YK/ME portfolio and a short position in the lowest YK/
ME portfolio. This long/short portfolio has a mean return of 9.22% and standard deviationof 10.83%, yielding a Sharpe ratio of 0.84. The mean return is lower than strategy 1 andstrategy 2. The standard deviation is higher than strategy 1 and strategy 2. Thus, we
confirm a considerable performance improvement (increasing mean return andreducing risk) by decomposing IP competitiveness into two dimensions.

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Japanese patent index and stock performance

We also report the results for long/short portfolios based solely on R&D efficiency (YK/
R&D) or R&D intensity (R&D/ME). The procedure is again identical to the one we used forTables 3 and 4, respectively, except that we separated stocks into 25 groups.
The column YK/R&D is for stocks selected based on YK/R&D. The mean return is 4.46%
annually and standard deviation is 6.70%. The mean return is 11.38% annually andstandard deviation is 15.32%. The Sharpe ratios are 0.65 and 0.73, respectively. It issurprising that merely using R&D expenditure to construct a long/ short portfolio yields
an annual mean return of 11.38%, which is comparable to our strategies 1 and 2. On theother hand, its risk is considerably higher so that the Sharpe ratio is much lower than thetwo outperforming strategies.
3.6 The industry factor
The importance of R&D activities varies across industries. There should be industries inwhich our investment strategy based on IP signals work very well and industries in which
our strategy is not effective.
To answer this question, we examine how selections based on YK/ ME are useful for eachof the 33 TSE industry classifications. At the end of each month stocks are separated into
quintiles based on YK/ME and equal-weight portfolios are formed in each quintile.
Portfolio construction is done for each industry. The results are reported in Table 8.
Portfolio 1 is the portfolio of stocks in the highest YK/ME quintile and portfolio 5 is in thelowest YK/ME quintile. Mean return is the time-series average of the annualized
portfolio returns in percent. The rightmost column “H-L” refers to the return of a long/short portfolio which is long in portfolio 1 and shorts portfolio 5. The t-statistics for themean return of the H-L portfolio is shown in parentheses.

	For pharmaceuticals, the mean return of H-L portfolio was 2.37% with a t-statistic of 0.49.

8Electronic copy available at: https://ssrn.com/abstract=3079600

Japanese patent index and stock performance

Panel A lists industry groups with t-statistics greater than 2.0. chemicals, glass andceramics products, machinery, electric appliances, transportation equipment, andland transportation are the industry groups in which significant mean return can begenerated by the long/short strategy based on YK/ME.8 Panel B lists industries withnegative mean return on the H-L portfolio. fishery, agriculture and forestry, pulp andpaper, and oil and coal products are the industries in which the YK/ME signal does notseem to work, which is not surprising. In the foregoing analysis, the universe of stocks
covered all industries except financials. One can easily imagine our strategies working
much more strikingly if one is allowed to be selective in industries.
4. Conclusion
The analysis in this paper suggests that the accumulation of the IP assets of Japanesefirms is not sufficiently incorporated in the stock market’s pricing of firms. The YK value
provides useful signals to predict future stock returns. We constructed long/ shortstrategies based on (1) the YK value, (2) R&D expenditure, and (3) a combination of thetwo. The third strategy enhanced portfolio expected return and reduces risk considerably.
The bestperforming strategy generated an annual mean return of 11.50%, standard
deviation of 9.25%, and a Sharpe ratio of 1.23.
Thus, making the YK value available to the investment community will satisfy the investors’
aspirations for higher returns. But more importantly, it will promote the efficiency of theJapanese stock market. The latter means that a firm with highly valuable IP assets will
become able to raise equity capital at a lower cost. Constructing a technology-driven
index such as Ocean Tomo 300 for use as a benchmark index will serve the investmentcommunity and simultaneously contribute to enhancing the IP competitiveness ofthe economy.

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Japanese patent index and stock performance

References

Cardoza, K., C. Gray, and J. Barney, 2008, “Patents: a valuable indicator of stock performance,” The Journal of Investing 17, 68-74
Chambers, D., R. Jennings and R. Thompson, 2002, “Excess returns to R&Dintensive
firms,” Review of Accounting Studies 7, 133-158 Chan, L., J. Lakonishok, and T. Sougiannis, 2001, “Thestock market valuation of research and development expenditures,” Journal of Finance 56, 2431-2456
Fama, E. F. and K. R. French, 1993, “Common risk factors in the returns on stocks and bonds,” Journal of
Financial Economics 33, 3-56
Jung, E., 2005, “Stock performance of firms effecting intensive R&D e― xcess returns and R&D factor ,― ”
Securities Analysts Journal 43, 98-108 (in Japanese)
Kubota, K. and Takehara, H., 2007, “Re-examination of effectiveness of Fama- French factor model,”Modern Finance 22, 3-23 (in Japanese)Lev, B., and T. Sougiannis, 1996, “The capitalization, amortization and valuerelevance of R&D,” Journalof Accounting & Economics 21, 107-138
Lev, B., and T. Sougiannis, 1999, “Penetrating the book-to-market black box: the R&D effect,” Journal ofBusiness Finance and Accounting 26, 419-449
Liy, E. X. N., and L. X.L. Liuz, 2010, “Intangible assets and cross-sectional stock returns: evidence fromstructural estimation,” working paper
Thomas, P., 2001, “A relationship between technology indicators and stock market performance,”
Scientometrics, 51, 319—333
Titman, S., J. K. Wei, and F. Xie, 2004, “Capital investments and stock returns,”
Journal of Financial and Quantitative Analysis 39, 677-700

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Appendix
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APPENDIX: Japanese patent index and stock performance

Figure 1: The YK value and market capitalization (electric appliances)
100,000

10,000

YK value

1,000

100

10

1
1

10

100

1,000

10,000Market cap (¥ billion)

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APPENDIX: Japanese patent index and stock performance

Table 1: Descriptive statistics of YK value for selected industry groups
Maximum

Mean

St.devCorrelation withmarket cap

4,614

536

991

0.85

Land transportation

436

174

200

0.83

Rubber products

3,602

901

1,131

0.80

Textiles and apparels

7,853

943

2,006

0.75

Chemicals

16,407

1,231

2,447

0.73

Electric appliances

30,356

1,308

3,610

0.72

Pharmaceutical

2,181

314

459

0.70

Glass and ceramics products

3,663

594

868

0.69

Machinery

5,099

401

796

0.69

Transportation equipment

7,160

659

1,306

0.68

Metal products

2,708

310

631

0.67

Nonferrous metals

4,703

971

1,412

0.67

Precision instruments

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APPENDIX: Japanese patent index and stock performance

Table 2: Portfolios selected using YK/ME
Panel A: Properties of portfolios selected using YK/ME
1 (High)23

45 (Low)

YK/ME

0.207

0.071

0.037

0.017

0.005

BE/ME

1.242

1.095

1.067

1.036

0.989

E/P

0.088

0.092

0.092

0.092

0.097

ROE

0.070

0.089

0.107

0.119

0.126

Log(ME)

24.53725.05725.11125.10124.889Panel B: Return of portfolios selected using YK/ME
1 (High)23

45 (Low)

H–L

Mean

9.34

5.43

3.88

4.18

1.52

7.81 (3.82)

St.dev

21.22

20.10

19.24

18.68

18.50

Sharpe ratio

0.43

0.26

0.19

0.21

0.07

FF3–alpha0.45 (2.86)

0.20 (1.90)

0.06 (0.57)

0.17 (1.41)–0.08 (–0.77)

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0.52 (3.32)

APPENDIX: Japanese patent index and stock performance

Table 3: Portfolios selected using R&D efficiency (YK/R&D)
Panel A: Properties of portfolios selected using YK/R&D
1 (High)23

45 (Low)

YK/R&D

0.770

0.421

0.109

0.055

0.016

BE/ME

1.076

1.046

1.103

1.079

1.117

E/P

0.090

0.100

0.088

0.089

0.093

ROE

0.083

0.115

0.104

0.104

0.106

Log(ME)

24.61525.08325.20325.07924.715

Panel B: Return of portfolios selected using YK/R&D
1 (High)23

45 (Low)

H-L

Mean

7.02

4.01

4.56

4.90

3.79

3.23 (2.79)

St.dev

19.37

19.22

20.06

19.33

19.42

Sharpe ratio

0.35

0.20

0.22

0.24

0.18

FF3-alpha0.33 (3.31)

0.10 (1.01)

0.11 (0.90)

0.19 (1.56)

0.06 (0.57)

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0.26 (2.70)

APPENDIX: Japanese patent index and stock performance

Table 4: Portfolios selected using R&D intensity (R&D/ME)
Panel A: Properties of portfolios selected using R&D/ME
1 (High)23

45 (Low)

R&D/ME

0.125

0.061

0.039

0.025

0.012

BE/ME

1.337

1.132

1.045

1.027

0.890

E/P

0.076

0.095

0.098

0.094

0.096

ROE

0.064

0.094

0.091

0.117

0.145

Log(ME)

24.59224.96524.99424.98125.177

Panel B: Return of portfolios selected using R&D/ME
1 (High)23

45 (Low)

H-L

Mean

10.13

5.27

4.75

2.36

1.92

8.21 (2.73)

S.tdev

22.97

20.32

18.95

18.00

17.92

Sharpe ratio

0.43

0.25

0.24

0.12

0.10

FF3-alpha0.46 (2.25)

0.17 (1.41)

0.18 (1.71)–0.01 (-0.11)

0.00 (0.03)

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0.45 (2.12)

APPENDIX: Japanese patent index and stock performance

Table 5: FF3-alphas of portfolio selected using YK/R&D controlling for R&D intensity
Ranking on YK/R&D  
23

45 (Low)

H–L

1 (High)0.51 (2.20)

0.57 (2.17)

0.32 (1.28)

0.50 (1.97)

0.42 (1.65)

0.08 (0.31)

20.43 (2.29)

0.26 (1.49)

0.16 (0.88)

0.01 (0.06)

0.02 (0.15)

0.41 (2.03)

30.10 (0.68)

0.47 (3.08)

0.11 (0.64)

0.13 (0.79)

0.07 (0.44)

0.04 (0.17)

4

0.28 (1.77)–0.13 (–0.84)

0.05 (0.31)–0.14 (–0.94)–0.09 (–0.52)

0.36 (1.82)

5 (Low)

0.13 (0.74)

0.01 (0.05)

0.09 (0.50)–0.02 (–0.12)–0.20 (–1.24)

0.32 (1.55)

Control for R&D intensity

0.29 (2.54)

0.23 (2.23)

0.14 (1.24)

0.09 (0.80)

0.04 (0.40)

0.25 (2.43)

R&D/ME quintiles

1 (High)

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Table 6. FF3-alphas of portfolio selected by R&D/ME controlling for R&D efficiency
Ranking on R&D/ME
2

3

4

1 (High)0.62 (2.70)

0.53 (3.01)

0.45 (2.39)

20.36 (1.54)

0.23 (1.46)

0.09 (0.58)

3

0.41 (1.61)

0.09 (0.45)

40.33 (1.46)

5 (Low)

Control for R&D efficiency

YK/R&D quintiles

1 (High)

5 (Low)

H–L

0.12 (0.74)–0.06 (–0.35)

0.68 (2.36)

0.03 (0.18)–0.21 (–1.28)

0.57 (1.99)

0.07 (0.41)

0.09 (0.58)–0.06 (–0.41)

0.47 (1.62)

0.30 (1.64)

0.20 (1.04)

0.17 (1.05)–0.04 (–0.24)

0.37 (1.31)

0.33 (1.50)

0.26 (1.77)–0.11 (–0.64)–0.03 (–0.17)–0.12 (–0.82)

0.45 (1.87)

0.41 (2.26)

0.28 (2.33)

0.14 (1.20)

0.08 (0.69)–0.10 (–0.99)

0.51 (2.62)

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APPENDIX: Japanese patent index and stock performance

Table 7: Performance comparison of long/short strategies
Strategy 1

Strategy 2

YK/ME

YK/R&D

R&D/ME

Mean

11.85 (3.34)

11.50 (3.59)

9.22 (2.46)

4.46 (1.92)

11.38 (2.14)

St.dev

10.23

9.25

10.83

6.70

15.32

Sharpe ratio

1.14

1.23

0.84

0.65

0.73

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APPENDIX: Japanese patent index and stock performance

Table 8: Comparison of industry groups
Panel A: Industries in which YK/ME is significantly useful
1 (High)23

45 (Low)Chemicals

11.52%

Glass and ceramics products

16.11%

Machinery
Electric appliances

H-L

8.06%

5.79%

5.05%

2.92%

8.61% (3.20)

3.26%

16.62%

2.55%

0.38%

15.73% (2.11)

12.52%

10.10%

6.19%

4.74%

1.55%

10.97% (3.25)

13.80%

5.44%

6.32%

4.55%

0.81%

13.00% (3.25)

Transportation equipment

15.72%

8.86%

11.58%

9.73%

7.13%

8.59% (2.04)

Land transportation

18.93%

5.82%

2.88%

3.53%

0.17%

18.63% (2.02)

Panel B: Industries in which YK/ME is not useful

Fishery, agriculture and forestry

1 (High)23

45 (Low)

H-L–0.89%

6.54%

8.12%

–5.68%

6.64%–3.47%

Pulp and paper

2.13%

0.35%

–0.27%

7.72%

7.52%

–5.39%

Oil and coal products–5.60%

14.01%

0.67%

1.27%

5.88%

–1.70%

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