Thông tin tài liệu
ANALYSIS OF PATENT PORTFOLIO AND
FINANCIAL PERFORMANCE OF FIRMS
In Partial Fulfillment of the Requirements of the Degree of
MASTER OF INFORMATION TECHNOLOGY MANAGEMENT
In Computer Science and Engineer
By
Mr. Vu Ba Quang
ID: MITM03010
International University - Vietnam National University HCMC
May 2015
ANALYSIS OF PATENT PORTFOLIO AND
FINANCIAL PERFORMANCE OF FIRMS
In Partial Fulfillment of the Requirements of the Degree of
MASTER OF INFORMATION TECHNOLOGY MANAGEMENT
In Computer Science and Engineer
By
Mr. Vu Ba Quang
ID: MITM03010
International University - Vietnam National University HCMC
May 2015
Under the guidance and approval of the committee, and approved by all its members,
this thesis has been accepted in partial fulfillment of the requirements for the degree.
Approved:
---------------------------------------------Chairperson
-----------------------------------Committee member
---------------------------------------------Committee member
-----------------------------------Committee member
---------------------------------------------Committee member
-----------------------------------Committee member
Acknowledgements
First of all, I would like to express my deepest gratitude to my advisor, Dr.
Nguyen Hong Quang for his support and guidance throughout the research. His
valuable advices led me to the right way to complete the thesis.
During my time of studying at International University, I received lot of useful
knowledge and sharing as well as guidance from my professors and good support
from the Registrar Office. Therefore, I would also like to thank them.
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Plagiarism Statements
I would like to declare that, apart from the acknowledged references, this
thesis either does not use language, ideas, or other original material from anyone; or
has not been previously submitted to any other educational and research programs or
institutions. I fully understand that any writings in this thesis contradicted to the above
statement will automatically lead to the rejection from the MITM program at the
International University – Vietnam National University Ho Chi Minh City.
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Copyright Statement
This copy of the thesis has been supplied on condition that anyone who
consults it is understood to recognize that its copyright rests with its author and that
no quotation from the thesis and no information derived from it may be published
without the author’s prior consent.
© Vu Ba Quang / MITM03010 / 2012 – 2014
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Table of Contents
Acknowledgements .................................................................................................... i
Plagiarism Statements................................................................................................ ii
Copyright Statement ................................................................................................. iii
Table of Contents ..................................................................................................... iv
List of Tables........................................................................................................... vii
List of Figures ........................................................................................................ viii
Abstract .................................................................................................................... ix
Chapter One - Introduction ........................................................................................ 1
1. Motivation ......................................................................................................... 1
2. Problem Formulation ......................................................................................... 5
2.1. Unclear relationship between patent portfolio and its performance ............. 6
2.2. Unclear relationship between patent portfolio and financial performance. ... 7
2.3. Limited use of patent indicators to predict financial performance ............... 8
3. Objectives ......................................................................................................... 8
4. Scope................................................................................................................. 9
5. Thesis Structure ................................................................................................. 9
Chapter Two - Literature Review ............................................................................. 10
1. Background ..................................................................................................... 10
1.1. Patent and technology base of a company ................................................. 10
1.2. Financial performance. ............................................................................. 13
1.3. Correlation test. ........................................................................................ 16
2. Related Work .................................................................................................. 17
3. Comparative Analysis of Related Work ........................................................... 19
Chapter Three – Our Proposed Solutions ................................................................. 22
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1. Overview ......................................................................................................... 22
1.1. Methodology. ........................................................................................... 22
1.2. Building patent portfolio database ............................................................ 23
1.2.1. USPTO Patent data. ............................................................................ 24
1.2.2. Patent database from UC Berkeley. .................................................... 25
1.2.3. Integrate financial data with patent portfolio data …........................... 25
1.3. Patent portfolio indicators......................................................................... 28
2. Spearman correlation between patent portfolio and performance ..................... 30
2.1. Correlation calculation ............................................................................. 31
2.2. Correlation in yearly lags. ......................................................................... 38
2.2.1. Ability to create new technology ........................................................ 38
2.2.2. Innovation history .............................................................................. 39
2.2.3. Innovation rate.. ................................................................................. 40
2.2.4. R&D force.......................................................................................... 40
2.2.5. Summary ............................................................................................ 41
3. Spearman correlation between patent portfolio and financial performance ....... 41
3.1. Correlation calculation.............................................................................. 41
3.2. Correlation in yearly lags.......................................................................... 44
3.2.1. Ability to create new technology. ....................................................... 45
3.2.2. Ability to recognize and acquire existing technology. ......................... 46
3.2.3. Innovation history. ............................................................................. 47
3.2.4. Patent portfolio performance. ............................................................. 47
3.2.5. R&D force.......................................................................................... 48
3.2.6. Technology protection. ....................................................................... 49
3.2.7. Summary ............................................................................................ 49
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4. Artificial Neural Network to predict financial ratio .......................................... 50
4.1. Demonstration of using patent data to predict financial ratios ................... 50
4.1.1. Training.............................................................................................. 51
4.1.2. Network information .......................................................................... 51
4.1.3. Prediction by Observed Chart… ......................................................... 54
4.1.4. Summary ............................................................................................ 55
Chapter Four - Conclusion ....................................................................................... 56
1. Summary ......................................................................................................... 56
2. Future works ................................................................................................... 57
Appendix ................................................................................................................. 58
Appendix A: List of companies in our dataset...................................................... 58
Appendix B: Sample of patent assignment file..................................................... 58
Appendix C: Correlation between patent indicators with patent performance ....... 61
Appendix D: Correlation between patent indicators with financial performance .. 66
References ............................................................................................................... 71
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List of Tables
Table 1 Correlation result classification ................................................................... 16
Table 2 Methods used in recent researches and our study......................................... 19
Table 3 Indicators used in recent researches and our study ....................................... 19
Table 4 Example of different names of one company ............................................... 27
Table 5 Patent portfolio indicators ........................................................................... 28
Table 6 Correlation result with 2 year lag ................................................................ 34
Table 7 Correlation between inventor indicators with citation indicators.................. 36
Table 8 Correlation between claim indicators with citation indicators ...................... 37
Table 9 Correlation between patent portfolio and financial performance in 2 year lag
................................................................................................................................ 42
Table 10 Neural Network information ..................................................................... 51
Table 11 MLP Model summary ............................................................................... 53
Table 12 MLP Parameter estimates.......................................................................... 53
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List of Figures
Figure 1: Tangible versus intangible value of the S&P 500 companies....................... 2
Figure 2:. First page of patent “Method for node ranking in a linked” ...................... 11
Figure 3: Analysis steps ........................................................................................... 23
Figure 4: Our data preprocessing ............................................................................. 24
Figure 5: Correlation between the numbers of new patent with citation indicators ... 38
Figure 6: Correlation between the patent age standard deviation with citation
indicators ................................................................................................................. 39
Figure 7: Correlation between patent growth rate with citation indicators ................ 40
Figure 8: Correlation between the average of inventor indicators with citation
indicators ................................................................................................................. 41
Figure 9: Correlation between number of new patents and financial indicator .......... 46
Figure 10: Correlation between number of purchased patents and financial indicator46
Figure 11: Correlation between patent age standard deviation and financial indicator
................................................................................................................................ 47
Figure 12: Correlation between total citation and financial indicator ........................ 48
Figure 13: Correlation between number of inventor and financial indicator ............. 48
Figure 14: Correlation between number of claims and financial indicator ................ 49
Figure 15: Multi-layer perception network with EPS as output ................................ 52
Figure 16: Observed values versus predicted values ................................................. 55
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Abstract
Meaningful correlation between technological and financial performances is
important to management of technology and innovation. The technological
performance of a firm could be represented by its patent portfolio since the patented
inventions give their owners an exclusive right to exclude others from exploiting and
commercializing them on the market, which highly influences the financial
performance. In this thesis, a new approach is proposed to analyze such a correlation
between the technological and financial performances. Our contributions are threefold. First, our approach proposes that four patent-portfolio indicators highly
correlated to the technological performance of a firm include: patent age, patent
claims, the number of inventors, and the number of patents newly applied for or
purchased. Second, these four indicators give a strong correlation with financial
performance of a firm represented by price to earnings, earning per share, stock price
on the market and other four key financial indicators (liquidity, leverage, profitability,
and valuation ratios). Third, our analysis takes into account the yearly lags of the
technology-finance correlation that happen in reality. Our proposed approach adopts
Spearman correlation coefficient, artificial neuron network and financial ratio
analysis. We experimented on two kinds of datasets: (i) the technology datasets,
including USPTO patents and UC-Berkeley patent datasets, and (ii) the financial
datasets of NASDAQ, AMEX and NYSE stock markets. Such datasets include
322,095 patents from 259 companies specialized in computer technologies in the 35year period (1981 – 2013). Our research outcomes could benefit CEOs, investors and
other stakeholders to design better R&D strategies for increasing their technology
values or to find their investment opportunities.
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-x-
Chapter One - Introduction
Chapter 1 introduces the foundation of the thesis and provides a background
and the current problems and authors’ objectives to solve them.
1. Motivation
In this new era of technologies, modern companies are moving from
competing with others by decreasing prices or offering additional gifts to researching
and innovating new products and services which can help them to exploit for
commercial advantage. These products are results of practical application of
knowledge which is gained through research and development (R&D) activities.
Utilization of the knowledge during the research phase not only can be used to
introduce new products but also to improve existing ones or optimize processes to
save time and cost. The ultimate aim is to increase businesses’ values and market
share.
In technology industries such as computer hardware, software, and internet
which are evolving day by day due to continuous technological advancements,
businesses must constantly revise their product offerings in order to meet the demands
of consumers and stay competitive. R&D is one solution for them to achieve and
maintain their competitive positions. Taking mobile-phone industry as an example,
since the first iPhone was introduced in the United State, the industry has been
changing significantly and our phones have been evolved from the one with small
screen and keypad to a modern one with large and touchable screen. The top
manufacturers like Apple, Samsung, LG, and Sony...are investing their money in
developing new technologies and products to conquer the market.
Companies may possess many different types of assets including tangible and
intangible assets. Tangible assets can be real estate, office equipment, machines, cash
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and account receivable. These kinds of assets are quite easy to evaluate and often
included clearly on financial reports. However, beside tangible assets, companies also
possess a majority of different intangible assets which have real values and are
important to their success. These can be organizational ability, brand name, trade
mark, and patented technologies or processes.
Figure 1 shows the increasing of percentage of intangible asset of companies
in S&P 500. Its value reached 85% at the beginning of this year, an all-time high for
the years covered by the firm’s research, which extends back to 1975. “Within the last
quarter century, the market value of the S&P 500 companies has deviated greatly
from their book value. This ‘value gap’ indicates that physical and financial
accountable assets reflected on a company’s balance sheet comprises less than 20% of
the true value of the average firm” (James E. Malackowski, personal communication,
June 15, 2010).
Figure 1: Tangible versus intangible value of the S&P 500 companies
(http://www.oceantomo.com/intellectual-capital-equity)
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Not only increasing company assets, intangible assets also play an important
role to strengthen owner’s competitiveness and are considered as an early indicator of
stock price performance as Louis Basenese (2012), the founder of the Wall Street
Daily, said “While earnings growth remains a reliable indicator, we’d be well served
to add patent filings to our repertoire, too. It’s an even earlier indicator of stock price
performance.”
In a flat world today, being the first mover to introduce innovative products or
services to market is not enough to gain a competitive advantage. Companies have to
protect their ideas by applying for patents as a legal tool. This tool gives the owners
exclusive rights to solely exploit the patents or allow others to utilize by licensing.
R&D activities indicate that a company is making effort to gain new
advantageous and to make profit, which eventually reflect in the financial
performance measures. There have been already researches on patents as a measure
for technology bases as well as on how to evaluate patent value. However, research on
how the patent portfolio and financial performance is not very large and the utilization
of this correlation is not popular neither.
There are several studies have reported that there is a positive relationship
between innovation and firm performance. Hall et al. (2005), focus on patent citations
to explore whether they can be considered as a measure the market value. They
estimate Tobin’s q equations on the ratios of R&D to assets stocks, patents to R&D,
and citations to patents. And they find that each ratio significantly impacts market
value, with an extra citation per patent boosting market value by 3%. Another study of
Chang, Chen, and Huang (2012) calculated patent H index, current impact index (CII)
and essential patent index (EPI). Then they use panel fixed-effect model to verify if
these indices are positively associated with corporate performance which are
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represented by market value, sales and Return on Equity (ROE). The empirical results
of the fixed effect model indicated that patent H index and EPI were positively
associated with its market value, sales and ROE. That meant that the higher the patent
H index and EPI, the more was its market value, sales and ROE. In contrast, CII was
not positively associated with its market value, sales and ROE. Using data of 479
firms from 1990 to 1997 based on the DTI-Scoreboard, patent data from the “EPO
Worldwide Patent Statistical Database” (PATSTAT) and financial data from Standard
& Poor's COMPUSTAT Global and COMPUSTAT North America databases,
Neuhäusler, Frietsch, and Blind (2011) found that number of patent applications is not
a good predictor of firm performance while family size has a positive association with
Tobin’s q and ROI and average number of forward citations seems to affect market
value positively but not on ROI. Beside the studies of researchers, there are many
products to help users analyze patents data for their very specific purposes. One of
them is Patent Research and Analysis tool of Thomson Reutuers which provide
powerful analysis and visualization tools to gain greater insight. Another famous
patent research and analysis platform is Patent iNSIGHT Pro which includes
advanced text mining algorithms to bring out those insights in minutes which would
erstwhile take days for a researcher. However, most of the tools focus their strength
on patent data analysis to view technology trends, generate patent map report or
identify licensing, research or acquisition opportunities. Little of them can provide an
insight on how the patent portfolio correlates to financial figures or ratios.
In this thesis, the authors try to analyze the relationships between patent
portfolio and financial performance of firms to prove that such relationships do exists.
They will be the framework to build a prediction tools for normal users such as
investors to utilize the patent data in their decision making process.
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2. Problem Formulation
There are a large number of factors can affect the value and competitive
advantage of firms such as business strategy, human resources, market position,
products and services,…Nowadays, people tend to focus on a relatively new factor
which is innovative capacity because it allows the companies to implement new
products or improve existing ones to meet new requirements from customers as well
as to adapt with new market change. However, the successful completion of the
innovation process alone is not enough to secure the benefits gained from R&D. A
firm has to think of how to prevent other competitors to enter its market or mimic its
products and services. In other to do that, it must have protection mechanism provided
by government which patent is one of the most important instruments.
The thesis proposes to solve the problem of analyzing meaningful correlation
between patent portfolio and financial performance by examining the relationship
between indicators of number of patents, patent ages, inventors and patent protection
(patent claims) and patent performance (patent citation) and then between those
indicators and financial performance.
To assess company performance, financial ratio analysis method has been
conducted. It is a method to analyze at company’s financial statements to gain an
insight in financial position of a company in order to form the basis of all investment
decisions. If we find some relationships between patent information and financial
ratios, it means that we can also predict the financial health in the future by using
public patent data.
The following analysis tries to answer the question of how far the result of
R&D and the protection which patents bring can influence the financial performance
and market value of a firm. We will use patent data as a representative of technology
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base and market protection of a firm. Because the value of each patent is different, not
only number of patents in portfolio but also other indicators such as number of
forward citations, number of inventors are employed in the model. In other to
determine the health of a company, we will use stock price and financial ratios which
measure its liquidity, leverage, profitability, and valuation ratios. These ratios can
help to shed a light on how a company is performing in relation to key measures of
business success.
The data analysis comprises 3 problems as following:
1. Unclear relationship between patent portfolio and its performance.
2. Unclear relationship between patent portfolio and financial performance.
3. Limited use of patent portfolio to predict financial performance.
The technological performance of a firm could be represented by its patent
portfolio since the patented inventions give their owners an exclusive right to exclude
others from exploiting and commercializing them on the market, which highly
influences the financial performance
2.1. Unclear relationship between patent portfolio and its performance.
Patent portfolio is the result of R&D activities of companies. However, the
relationship between these indicators with patent performance is not very clear.
Knowing this relationship, the board of management can propose a strategic to
improve their portfolio performance and its value.
Citation to prior art is an indicator of the importance of the prior art to
subsequent inventions. The more citation a patent receives, the more significant it is
measured. In order to evaluate the efficiency of R&D activities, researchers and
management board usually use patent data. The innovation capabilities of companies
are often measured by some indicators. Basically, we have patent count as an outcome
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of R&D performance. However, this figure is somewhat noisy because not all patents
have the same value or technology strength. Some researchers suggest that among
various indicators, patent citation is one of the better to demonstrate patent portfolio’s
value and patent quality. Because when applicants submit new patents, they and
examiners must find and cite older patents which anticipate or be similar to the new
inventions. If we stand at the site of cited patents, these citations are forward citations.
If a patent is highly cited (i.e. cited in 5, 10, or more subsequent patents), then that
patent is likely to contain an important technology which later patents are built on.
Among patent indicators, there are some depends on R&D activities, R&D
team or strategy of companies such as number of patents, patent age, number of
inventors, and number of claims. These indicators are dependent on companies and
might not reflect the values of patent portfolio. Solving this problem may give us a
better understanding on how patent portfolio performance or value is related to the
portfolio characteristics.
2.2. Unclear relationship between patent portfolio and financial
performance. Companies spend money in R&D activities which is transformed into
new products, processes, and services in the future. The ultimate purpose of these
activities is to gain more revenue and benefit and that is also what most investors
want. To choose a stock to invest, they normally consider price and valuation or
evaluate financial health but little of them pay attention to the innovation aspect.
The value and performance of firms depend on a various factors such as
business strategy, human resources, market, products and services. In addition to this,
innovative ability is also very important because it allows firms to renew their
products and services to adapt with constantly changing market or to compete with
other companies. It is said that increased innovative capability can help to improve the
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competitive ability and as a result, leads to an increasing in company revenue and
value.
Calculating the correlation coefficients between patent portfolio figures and
financial ratios can provide us an insight to the relationship between the 2 sets of
information. Although the correlation does not prove that it is a cause-effect
relationship, knowing the trend of the patent portfolio figures which can be calculated
using public data can help to predict the trend of financial performance
2.3. Limited use of patent indicators to predict financial performance.
Solving problem 2 may give us the results that there are indicators have strong,
medium, weak or no relationship with financial performance. It means that we can use
indicators which strong, medium and even weak indicators to build a model to predict
future values of them. If we can do that, investors now have additional tool to help
them to make decision on choosing the right companies.
For average investors, it can be a challenge to select the right stocks on the
market to buy. There are some characteristics which they can pay attention to such as
business model, financial performance, dividend paid, and market trend (Alexander,
Raznick, & Bedigian, 2012) …However, little of them analyze patent data to invest
because the lack of an appropriate tool to give such information and help them to
make decision.
3. Objectives
The aim of our study is to solve above 3 problems. We will focus on
information technology firms which are listed on NASDAQ and NYSE stock markets
of USA because in the new “knowledge economy” era, they are among the fastest and
the most innovative companies and the way this industry has changed over the last
half century. Specifically, we test for the relationship among patent portfolio variables
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such as patent count, patent citations, patent age, citation age, and inventor and firm
financial positions.
4. Scope
The scope of this thesis focuses on analysis of patent portfolio and financial
performance of firms. First, we analyze the correlation of patent count, patent age,
patent claims, and inventor figures to the patent performance which can be indicated
by citations. Then we continue to analyze the correlation of those patent portfolio
indicators with financial ratios including liquidity, leverage, profitability and
valuation ratios. However, the analysis does not aim to explain a cause-and-effect
relationship between them because patent portfolio may not have a direct influence on
financial performance and we need further tools and analysis to explore it.
5. Thesis Structure
In chapter 1, we introduce the motivation of doing this thesis and reveal the
problems we solve together with thesis objectives and scope.
Chapter 2 is the literature review. This is where we get an overview on the
problems and gain a background. Related works of other researchers are presented
here. The main purpose of this part is to provide the meanings of patent portfolio
indicators and financial ratios which are used in the analysis as well as some related
prior arts.
Chapter 3 is where we discuss our solutions and results when we solve
problem 1, problem 2, and problem 3.
Finally, chapter 7 presents the conclusion of my thesis report and proposes
future work.
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Chapter Two - Literature Review
1. Background
1.1. Patent and technology base of a company. A patent, firstly, is a legal
tool having technical claim(s) which describes a technical invention such as a new
device, process, system, or method. According to The United States Patent and
Trademark Office (USPTO), an agency of the U.S. Department of Commerce, patent
for an invention is the grant of a property right to the inventor and the right conferred
by the patent grant is, in the language of the statute and of the grant itself, “the right to
exclude others from making, using, offering for sale, or selling” the invention in the
United States or “importing” the invention into the United States.
In order to register for a patent, inventor(s) normally start with filing an
application. The application should include description of the invention, the
implementation, and a collection of claims with the inventor(s) want to have. The
claims, which are one of the most important parts of patent, define, in technical terms,
the extent, i.e. the scope, of the protection conferred by a patent. This application then
is filed in the country where inventor(s) apply patent. After that, they have one year
decide whether they want to expand the application internationally (it is called patent
family). At the lasted 18 months after first filing, the patent application can be
published. It means that it is widely and freely accessible by anyone.
Figure 2 is an example of a patent named “METHOD FOR NODE RANKING
IN A LINKED DATABASE”. In the first page, there are patent number, publication
date, name, inventor(s), assignee(s), prior patents which it cites and the abstract.
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Figure 2:. First page of patent “Method for node ranking in a linked”
The patent application and patent publication include a header with show
name and address of the inventor(s), the assignee(s), the country of origin, filing date
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and the state of the art citations (citations which the inventor(s) or the of lawyer of
Patent Office make). In addition to this, there are the details of innovation so everyone
can access for free. All the patent data make up a very rich resource to study about
company technology development, technology strategic as well as result or R & D.
Moreover, the data are free and available for being analyzed.
The product cycle model places a foundation for the idea that technology can
drive the long-term development of market shares. This theory assumes a variety of
ability to exploit new technologies among different entities (Dosi, Pavitt, & Soete,
1991). In addition, it implies that a follower will need time and costs to imitate and
absorb new technology to apply for his products or services. These conditions mean
that innovative products will make monopoly of the market in a period of time before
the followers can catch up. Consequently, firms developing new products or services
using superior technology can take a large share of the market and gain more benefits
than others. To protect themselves from being imitated by other competitors, firms
usually public their technologies to apply for patents. Therefore, patent is one of the
most important intangible assets which can be related to financial performance.
This thesis aims to find out which of these indicators are related to financial
ratios and are applicable for the evaluation of a company value. Although total patents
in a portfolio is direct result of R&D activities, not all patents have the same
economic or technological value so only patent counts does not give us an accurate
view on firm’s technological basis. Therefore, many other indicators have been
calculated and proposed to asset many aspects of patent portfolio. Of course not all of
them have the same impacts on financial performance, some may have strong impact,
some may have slight impact or not at all.
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1.2. Financial performance. When people started to have share markets to
buy and sell securities of listed or unlisted companies, they also began to assess
company performance by analyzing financial ratio.
Ratio analysis is one of the most popular and widely used tools of financial
analysis. A ratio is a relation between two quantities. Although it is simple to
calculate a ratio, it may be complex to interpret the outcome. To be meaningful, a
ratio must refer to an economically important relation. For example, there is a direct
and crucial relation between an item’s sales price and its cost. Accordingly, the ratio
of cost of goods sold to sales is important.
Analysis of financial ratios can help stakeholder like creditors, investors,
regulator, or manager to find out the financial soundness of an organization. For
example, CEOs may look into financial ratio reports to get clues for their strategic
changes in business investment or financial activities. They also analyze competitors
to evaluate profitability and risk.
D’Amato (2010) proposed top 15 financial ratios for investors to consider.
Below are selected ratios to be used for the analysis:
Liquidity ratio: liquidity ratios indicate whether a company has the ability to
pay off short-term debt obligations (debts due to be paid within one year) as they fall
due. Generally, a higher value is desired as this indicates greater capacity to meet debt
obligations.
• f1 = Current Ratio: The Current ratio measures a company’s ability to repay
short-term liabilities such as accounts payable and current debt using short-term
assets such as cash, inventory and receivables. Another way to look at it would be the
value of a company’s current assets that will be converted to cash over the next
- 13 -
twelve months compared to the value of liabilities that will mature over the same
period.
= ݅ݐܽݎ ݐ݊݁ݎݎݑܥ
ݏݐ݁ݏݏܽ ݐ݊݁ݎݎݑܥ
ݏ݁݅ݐ݈ܾ݈݅݅ܽ݅ ݐ݊݁ݎݎݑܥ
• f2 = Cash balance to total liabilities (CBTL): this ratio shows a company’s
cash balance in relation to its total liabilities. Cash is the most liquid asset a business
has. A negative cash balance (caused by overdrafts) raises a warning signal and
failure to address such an issue will likely result in liquidity problems.
Lower risk firms typically have a higher value CBTL, because they have more
cash that can be used to pay suppliers, banks or any other party that has provided the
company with a product or service. Higher risk companies typically have a lower
value CBTL, which means the company’s ability to meet its debt obligations is
significantly hampered.
= ܮܶܤܥ
ݏܽܥℎ ܾ݈ܽܽ݊ܿ݁
ܶݏ݁݅ݐ݈ܾ݈݅݅ܽ݅ ݈ܽݐ
Leverage ratio: leverage ratios, also referred to as gearing ratios, measure the
extent to which a company utilizes debt to finance growth. Leverage ratios can
provide an indication of a company’s long-term solvency. Whilst most financial
experts will acknowledge that debt is a cheaper form of financing than equity, debt
carries risks and investors need to be aware of the extent of this risk.
• f3 = Debt to equity ratio (DE ratio): The debt to equity ratio provides an
indication of a company’s capital structure and whether the company is more reliant
on borrowings (debt) or shareholder capital (equity) to fund assets and activities.
= ݅ݐܽݎ ܧܦ
ܶ ݐܾ݁݀ ݈ܽݐ
ܵℎܽ݁ݎℎݕݐ݅ݑݍ݁ ’ݏݎ݈݁݀
Profitability ratio: this type of ratio measures a company’s performance and
provide an indication of its ability to generate profits. As profits are used to fund
- 14 -
business development and pay dividends to shareholders, a company’s profitability
and how efficient it is at generating profits is an important consideration for
shareholders
• f4 = Earnings per share (EPS): EPS ratio measures earnings in relation
to every share on issue. It is calculated by dividing the company’s net income by the
number of shares on issue.
= ܵܲܧ
ܰ݁ݏ ݊݉݉ܿ ݐ ݈ܾ݁ܽݐݑܾ݅ݎݐݐܽ ݁݉ܿ݊݅ ݐℎܽ݁ݎℎ ݏݎ݈݁݀
ܶݏ ݈ܽݐℎܽ݃݊݅݀݊ܽݐݏݐݑ ݏ݁ݎ
• f5 = Gross profit margin: this ratio tell us what percentage of a
company’s sales revenue would remain after deducting the cost of goods sold. This is
important as it helps to determine whether the company would still have enough funds
to cover operating expenses such as employee benefits, lease payments, advertising,
and so forth.
= ݊݅݃ݎܽ݉ ݐ݂݅ݎ ݏݏݎܩ
(݈ܵܽ݁ )݈݀ݏ ݏ݀݃ ݂ ݐݏܥ – ݏ
݈ܵܽ݁ ݔ ݏ100%
Valuation ratio: Ratios belong to this group are used to figure whether the
current share relation to its true value. Valuation ratios also help us assess if a
company is cheap or expensive relative to earnings, growth prospects and dividend
distributions
• f6 = Price to earnings ratio (PE): the price to earnings per share is a
valuation ratio of a company's current share price compared to its per-share earnings.
It is calculated as:
ܲ = ܧ
ܵ ݎ݁ ݁ݑ݈ܸܽ ݐ݁݇ݎܽܯℎܽ ݁ݎ
ܵ ݎ݁ ݏ݃݊݅݊ݎܽܧℎܽ)ܵܲܧ( ݁ݎ
Market value:
• f7 = stock price at the statistic date of selected companies
- 15 -
1.3. Correlation test. Correlation analysis is often utilized to find out the
relationship between 2 variables X and Y. It indicates the extent which 2 variable
fluctuate together. If variable X and variable Y increase or decrease in parallel, we
have a positive correlation. If variable X increases and variable Y decreases inversely,
we have a negative correlation.
Correlation coefficients can range from -1.00 to +1.00. Value -1.00 represents
perfect negative correlations while value +1.00 represents a perfect positive
correlation. The closer the coefficients are to +1.00 and -1.00, the greater the strength
of the relationship between variables is
We use Spearman’s correlation coefficient in this thesis. The formula used to
calculate its value is as following (Lovie, 1995):
6 ∑ ݀ଶ
ݎ௦ = 1 −
݊(݊ଶ − 1)
Where:
Σd2: the sum of the squared differences between the pairs of ranks
n: the number of pairs
In general, the higher the correlation coefficient is, the stronger the
relationship is. The following tables present classification of values of correlation
coefficients (Dancey & Reidy, 2004).
Table 1
Correlation result classification
Value of the Correlation Coefficient
Strength of Correlation
| rs | = 1
Perfect
0.7 = 0.2) regarding to figure 11. Rs between total patent age and stock
price has largest value at lag = 0 and continue to decrease when we increase the lag
time. It means that the market seems to response with changing in patent age in the
same year. EPS has a positive relation (rs in range of 0.2 to 0.24) with the 2 patent age
indicators over the 5 lag time and the values vary not too much.
0.25
Correlation value
0.2
Current ratio
0.15
CBTL
0.1
Debt to Equity
EPS
0.05
Gross Margin
0
0
1
2
4
Price To Earning
Price
-0.05
-0.1
3
Lag time
Figure 11: Correlation between patent age standard deviation and financial indicator
3.2.4. Patent portfolio performance. Looking at the 3 indicators of citations:
total new citation, internal citation and external citation, we find that they have
positive correlation with stock price and EPS like patent age. Figure 12 indicates that
the correlation between total citations/external citations with price hit highest values
(rs is in range 0.219 to 0.236) at lag = 2 and 3 years. EPS’s correlation values with
total and external citation indicators are greater than 0.2 at lag time = 2, 3, and 4 years
only. It indicates that the citation indicators are favorable to the earning of investors.
- 47 -
This finding is also agreed with previous studies that say patent citation implies the
economical benefit of organizations holding patents.
0.3
0.25
Current ratio
Correlation value
0.2
CBTL
0.15
Debt to Equity
EPS
0.1
Gross Margin
0.05
Price To Earning
0
Price
0
1
2
-0.05
3
4
Lag time
Figure 12: Correlation between total citation and financial indicator
3.2.5. R&D force. Figure 13 shows that the number of inventor has positive
correlation with stock price, EPS and also PE. The correlation with stock price has
maximum value at lag = 0 and continue to decrease when we increase the lag time. In
the mean time, correlation with EPS remains unchanged and correlation with PE is
stable in the first 3 lags and then decrease.
0.35
0.3
Current ratio
Correlation value
0.25
CBTL
0.2
Debt to Equity
0.15
EPS
0.1
Gross Margin
0.05
Price To Earning
0
-0.05 0
-0.1
1
2
3
4
Price
Lag time
Figure 13: Correlation between number of inventor and financial indicator
- 48 -
This scenario may suggest that higher number of inventors in firms has
practical implications to the stock market. Or we can say that the market can reflect
the changing in number of inventors which can represent the R & D intensity of an
organization.
3.2.6. Technology protection. The number of claims, which define the legal
scope of patents and define what can be protected by patent law, has positive
correlation with the stock price. In figure 14, all values are greater than 0.2 and reach
maximum (0.254) when the lag time is 1 year. Besides that, the average number of
claims also has positive relation with the gross margin ratio which no other patent
portfolio indicator correlates to.
0.3
0.25
Correlation value
0.2
Current ratio
CBTL
0.15
Debt to Equity
0.1
EPS
0.05
Gross Margin
Price To Earning
0
0
1
2
3
4
Price
-0.05
-0.1
Lag time
Figure 14: Correlation between number of claims and financial indicator
3.2.7. Summary. In summary, the analysis shows us that number of new
patents, total patent age increased each year, patent age standard deviation, number of
new overall/internal/external citations and inventors indicators has positive correlation
with 3 financial indicators: EPS, price to earnings (P/E), and stock price. These
indicators represent the benefit investors receive per share they have, confidence in
- 49 -
the future growth of earnings and the market value. Weak (but not too weak)
correlations tell us that the market has response to how companies innovate and
improve their technologies but it is not too much
4. Artificial Neural Network to predict financial ratio
We already found patent portfolio indicators having positive relationship with
financial indicators which represent earning and market value. For the prediction
model, we utilize the multi-layer perceptron (MLP) network model under SPSS v.20
statistical package. We decide that the relative number of cases assigned to the
training:tesing:holdout are 7:2:1. It means we assign 7/10 of the cases to training, 2/10
to testing, and 1/10 to holdout. For the MLP network we use the back propagation
(BP) algorithm. For the response function, we employ sigmoid transfer functions
because we expect output as continuous values. To prevent parameters having wide
range values prevail over the rest, we employ a normalization process which use
autoscaling approach.
4.1. Demonstration of using patent data to predict financial ratios. A
prediction tool will allow users to select how far in the future to calculate required
data. For example, with independent (input) variables in year T, the tool has options
to have predict dependent (output) variables of year T + 1, T + 2, T + 3,...For a
demonstration of using ANN to predict EPS, we train the network to predict EPS in
the next 3 years.
Input variables are patent indicators having correlation values with EPS equal
to or greater than 0.1. The inputs data are fed to the network through a file created by
Excel and imported into the SPSS. We select 1 hidden layer only for the network its
number of nodes is setup automatically to have a satisfactory model performance.
- 50 -
4.1.1. Training. In training phase, the network finds a set of weights between
the neurons that determine the global minimum of error function. We configure it to
use a gradient descent training algorithm which adjusts the weights to move down the
steepest slope of the error surface.
4.1.2. Network information. Table 10 displays information about neural
networks. Automatic architecture selection has chosen 8 units in the hidden layer
Table 10
Neural Network information
Network Information
1
p01: PatentCount
2
p02: PatentPurchasedCount
3
p05: PatentAgeAvg
4
p06: PatentAgeStandardDeviation
5
p07: CitationCount
6
p08: CitationInternalCount
7
p09: CitationExternalCount
8
p10: CitationAvg
9
p11: CitationStd
10
p15: InventorCount
11
p17: ClaimCount
12
p19: ClaimStd
Covariates
Input Layer
Number of Units
a
12
Rescaling Method for Covariates
Normalized
Number of Hidden Layers
Hidden Layer(s)
1
a
Number of Units in Hidden Layer 1
Activation Function
Dependent Variables
8
Sigmoid
1
f4: EPS
Number of Units
Output Layer
1
Rescaling Method for Scale Dependents
Normalized
Activation Function
Sigmoid
Error Function
Sum of Squares
a. Excluding the bias unit
- 51 -
Figure 15: Multi-layer perception network with EPS as output
- 52 -
Table 11 display information about the result of training and applying the MLP
network to the holdout sample to predict EPS at lag = 3 years. We have Sum of
Squares error to demonstrate the result of error function which the network minimized
when it is trained. The stopping rule we used is one consecutive step with no decrease
in error. The relative errors in training (0.978), testing (0.988) and holdout (0.982) are
not very different and it tell us that the model is not over-trained (the model adapts to
any data even noise).
Table 11
MLP Model summary
Model Summary
Sum of Squares Error
.785
Relative Error
.978
Training
Stopping Rule Used
1 consecutive step(s) with no decrease in error
Training Time
a
0:00:00.30
Sum of Squares Error
.678
Relative Error
.988
Relative Error
.982
Testing
Holdout
Dependent Variable: f4EPS
a. Error computations are based on the testing sample.
Table 12 gives us the parameter estimates. The predicted value of the network
is place in column titled output layer.
Table 12
MLP Parameter estimates
Predictor
Predicted
Hidden Layer 1
Output
Layer
H(1:1) H(1:2) H(1:3) H(1:4) H(1:5) H(1:6) H(1:7) H(1:8) EPS
(Bias)
.309
.047
-.363
.350
.419
.678
.308
-.319
# of new patents
.346
.560
.435
-.162
.362
-.339
.320
.233
Input Layer
- 53 -
Std of patent age
.094
-.030
-.138
.059
-.121
.086
-.179
-.010
# of new citations
.458
-.031
-.464
.266
-.028
-.077
.160
-.085
-.208
-.002
.129
-.063
-.306
.422
.425
.139
-.106
.560
-.445
.089
-.241
-.267
-.253
-.455
-.428
.254
-.413
-.317
-.051
-.178
.331
.087
.116
-.271
-.463
-.119
-.027
.471
.193
-.280
.330
.170
-.161
.135
-.134
.064
-.351
-.107
Avg of patent age
.169
.141
.128
.421
.417
.497
.146
-.174
Avg of citation
.314
.323
.055
-.446
.126
.311
-.496
.368
-.094
.473
-.465
.189
.103
.446
-.181
.117
.021
-.004
-.273
-.335
.097
.477
.122
-.397
# of new internal
citations
# of new external
citations
# of inventors
# of new claims
# of purchased
patents
Std of # of citations
Stf of # of claims
Hidden
Layer 1
(Bias)
.729
H(1:1)
-.345
H(1:2)
.429
H(1:3)
-.144
H(1:4)
-.157
H(1:5)
.212
H(1:6)
.318
H(1:7)
.399
H(1:8)
-.388
4.1.3. Prediction by Observed Chart. Figure 16 below, show that the
predicted-by-observed chart display scatter-plot of predicted values on the y-axis by
observed values on the x-axis for the combined training and testing samples. In order
to be considered to be performing well, the plots should be placed near the red line.
Because there are more plots which are far from that line, the chart show us that input
variables effect on output variable but only those variables may be not enough to
build a prediction model. This result is appropriate with real situation that the EPS
depends on many other factors such as the revenue, tax, net income, and number of
shares,…
- 54 -
Figure 16: Observed values versus predicted values
4.1.4. Summary. The multi-layer perceptron which is applied to predict EPS
using patent portfolio indicators show that the independent (input) variables can be
included in a tool to estimate financial ratios of a selected company. However, If we
use only these variables, the difference between predicted values and observed values
are too large when we use only these indicators. It is coincided with the real life
because financial performance is affected by many factors. Therefore, in order to
build a neural network having less error, we should find more independent variables
to include to the model to make it more robust.
- 55 -
Chapter Four - Conclusion
1. Summary
In this thesis, we propose methods and solve the problems of analyzing
meaningful relationship between patent portfolio and patent performance and between
patent portfolio and financial performance.
The result of first analysis shows us that total new patents each year, patent
growth rate, patent age increased each year, standard deviation of patent age, total
number and standard deviation of inventors, total number and average number of
claims have strong and moderate positive correlation with total number, internal and
external of citations per year at all lags in the research. As patent citation is
considered as a proxy of patent value and potentiality of bringing revenue to owner,
knowing this relationship can help companies to plan their R&D strategy better to
increase their patent value and future benefits.
This study suggested that companies focusing on providing products or
services to the market should maintain a good innovate rate to protect their future
profit and patent portfolio value. For those which belong to Computer Software:
Programming, Data Processing, or Electronic Data Processing, they may not need to
pay much attention to R&D as their income comes from outsourcing services. In
addition to this, they need to build a good R&D team and improve the collaboration
within the team to ensure patent quality. Only high quality patents containing useful
or disruptive technologies can attract other companies or the market to bring benefit
to owners.
The second analysis indicates that the ability to create new technology, to
recognize and acquire existing technology, patent portfolio performance, R&D force,
and technology protection correlate with market values of firms (Earnings per share,
- 56 -
Price to Earnings, and stock price) but not other financial ratios (liquidity ratios and
leverage ratios) at all lags.
Investors, CEO or other stakeholders who are interested in earnings or stock
price can take into account this finding together with other factors to decide which
companies to invest. If they want to have profit for a period from 1 to 4 years, they
should invest to companies having good innovation capability in current year and
some previous years and also have high average number of inventors per patent.
2. Future works
In the future, the thesis could be further developed in a number of approaches:
We
1.
Looking for other factors which associate with or affect financial
performance to build a robust prediction tools for commercial use.
2.
Extending the analysis to patent forward and backward citations to
calculate portfolio value
3.
Studying how companies exploit existing knowledge and explore new
knowledge and what the effects are
- 57 -
Appendix
Appendix A: List of companies in our dataset
List of 259 companies which we use for this analysis can be found here: Analysis data
Appendix B: Sample of patent assignment file
3625
888
20100924
N
19790305
4
NORTHERN TELECOM LIMITED
PATENT DEPT 265
P.O. BOX 3511 STATION C
OTTAWA ONTARIO CANADA K1Y 4H7
CHANGE OF NAME (SEE DOCUMENT FOR
DETAILS).
- 58 -
NORTHERN ELECTRIC COMPANY LIMITED
19760301
NORTHERN TELECOM LIMITED
NOT PROVIDED
US
05855408
X0
19771128
US
4149772
- 59 -
B1
19790417
OPTICAL FIBRE HAVING LOW
MODE DISPERSION
US
05517218
X0
US
3936602
B1
- 60 -
Appendix C: Correlation between patent indicators with patent performance
Lag = 0
p1: # of patents
rs
p2: #of purchased
rs
p13:
p14:
p12:
internal
external
p8: # of
p9: # of
citation
citation
citation
p7: # of
internal
external
growth
growth
growth
citation
citation
citation
rate
rate
rate
.744
.728
.717
.390
.396
.361
.300
.346
.286
.116
.146
.112
.097
.280
.079
-.074
.024
-.074
.425
.374
.413
.430
.275
.407
patents
p3: #of sold patents
rs
p4: patent growth
rs
rate
p5: avg of patent age
rs
.191
.187
.185
-.215
-.252
-.197
p6: patent age std
rs
.385
.456
.366
-.036
-.070
-.033
p15: # of inventors
rs
.671
.776
.648
.151
.076
.149
p16: avg of inventors
rs
.376
.354
.361
.158
.048
.147
p17: # of claims
rs
.744
.723
.716
.404
.406
.374
p18: avg number of
rs
.291
.252
.282
.105
.044
.103
.437
.413
.424
.157
.058
.154
p8: # of
p9: # of
p12:
p13:
p14:
p7: # of
internal
external
citation
internal
external
citation
citation
citation
growth
citation
citation
claims
p19: std of claims
rs
Lag = 1 year
- 61 -
rate
p1: # of patents
rs
p2: #of purchased
rs
patents
p3: #of sold patents
rs
p4: patent growth rate rs
p5: avg of patent age
rs
p6: patent age std
rs
p15: # of inventors
rs
p16: avg of inventors
rs
p17: # of claims
rs
p18: avg number of
rs
claims
p19: std of claims
growth
growth
rate
rate
.648
.732
.619
.258
.235
.242
.221
.299
.206
.013
.016
.013
.152
.252
.137
-.063
-.030
-.060
.403
.381
.390
.374
.189
.365
.147
.175
.141
-.229
-.227
-.213
.336
.430
.318
-.068
-.098
-.061
.638
.758
.616
.107
.040
.110
.363
.354
.347
.130
.031
.123
.651
.733
.622
.269
.248
.257
.286
.265
.275
.095
.034
.096
.415
.413
.402
.128
.045
.126
p13:
internal
citation
growth
rate
p14:
external
citation
growth
rate
rs
Lag = 2 years
p1: # of patents
rs
p2: #of purchased
rs
patents
p3: #of sold patents
rs
p4: patent growth
rs
p7: # of
citation
p8: # of
internal
citation
p9: # of
external
citation
p12:
citation
growth
rate
.618
.737
.593
.189
.131
.189
.179
.284
.164
-.024
-.041
-.018
.142
.232
.131
-.038
-.044
-.041
.421
.397
.409
.300
.138
.293
- 62 -
rate
p5: avg of patent age
rs
p6: patent age std
rs
p15: # of inventors
rs
p16: avg of inventors
rs
p17: # of claims
rs
p18: avg number of
rs
claims
p19: std of claims
.129
.172
.122
-.227
-.201
-.216
.315
.417
.297
-.083
-.105
-.075
.616
.741
.594
.067
.003
.072
.364
.358
.348
.107
.014
.096
.621
.742
.596
.198
.145
.193
.293
.283
.282
.065
.026
.059
.410
.421
.397
.089
.033
.088
p13:
p14:
p12:
internal
external
rs
Lag = 3 years
p1: # of patents
rs
p2: #of purchased
rs
patents
p8: # of
p9: # of
citation
citation
citation
p7: # of
internal
external
growth
growth
growth
citation
citation
citation
rate
rate
rate
.595
.735
.570
.135
.078
.131
.163
.277
.153
-.022
-.042
-.024
p3: #of sold patents
rs
.108
.218
.097
-.056
-.050
-.053
p4: patent growth rate
rs
.429
.403
.420
.227
.087
.220
p5: avg of patent age
rs
.114
.172
.107
-.222
-.177
-.212
p6: patent age std
rs
.295
.406
.278
-.093
-.105
-.084
p15: # of inventors
rs
.587
.720
.566
.028
-.028
.032
- 63 -
p16: avg of inventors
rs
.363
.358
.348
.065
-.005
.053
p17: # of claims
rs
.601
.738
.577
.148
.084
.145
p18: avg number of
rs
.296
.297
.283
.035
.013
.029
.402
.421
.387
.065
.015
.065
p13:
p14:
p12:
internal
external
claims
p19: std of claims
rs
Lag = 4 years
p1: # of patents
rs
p2: #of purchased
rs
patents
p8: # of
p9: # of
citation
citation
citation
p7: # of
internal
external
growth
growth
growth
citation
citation
citation
rate
rate
rate
.571
.725
.542
.089
.029
.096
.145
.268
.137
-.059
-.064
-.044
p3: #of sold patents
rs
.119
.215
.109
-.070
-.038
-.060
p4: patent growth rate
rs
.437
.415
.426
.173
.060
.169
p5: avg of patent age
rs
.103
.171
.095
-.227
-.164
-.215
p6: patent age std
rs
.278
.397
.260
-.106
-.098
-.095
p15: # of inventors
rs
.554
.696
.533
-.015
-.055
-.009
p16: avg of inventors
rs
.361
.358
.345
.028
-.019
.018
p17: # of claims
rs
.579
.730
.551
.098
.036
.106
p18: avg number of
rs
.298
.306
.285
.001
-.013
.000
.389
.420
.373
.024
.002
.026
claims
p19: std of claims
rs
- 64 -
- 65 -
Appendix D: Correlation between patent indicators with financial performance
Lag = 0
f4: EPS
f5:
gross
margin
f6: price
to
earnings
(P/E)
f7:
Price
.047
.141
.072
.173
.242
-.048
.063
.119
.051
.078
.152
f1:
current
ratio
f2:
CBTL
f3: debt
to
equity
.064
.018
-.047
p1: number of patents
rs
p2: number purchased
patents
p3: number of sold patents
rs
rs
-.078
-.079
.120
.084
-.030
.019
.060
p4: patent growth rate
rs
.028
.044
-.032
-.026
.110
.075
.145
p5: average of patent age
rs
-.031
-.006
.064
.164
-.067
.075
.007
p6: patent age standard
deviation
p7: number of citations
rs
.045
.012
.097
.226
-.009
.170
.173
rs
.033
.015
.062
.155
.038
.182
.211
p8: number of internal
citations
p9: number of external
citations
p10: average of citation per
patent
p11: standard deviation of
citation
p12: citation growth rate
rs
.045
-.025
.120
.198
.026
.175
.241
.030
.012
.059
.146
.033
.178
.199
-.014
.035
.007
.149
.071
.117
.062
-.027
.030
.026
.173
.110
.148
.106
.078
.037
-.043
-.029
.028
.054
.074
p13: internal citation growth
rate
p14: external citation growth
rate
p15: number of inventor
rs
.048
.013
-.004
.011
.010
.061
.093
.082
.032
-.034
-.038
.012
.043
.064
.006
-.049
.168
.278
-.008
.219
.299
p16: average of inventor per
patent
rs
-.096
-.037
.088
.098
.018
.077
.133
p17: number of new claims
rs
.062
.014
.050
.135
.073
.173
.237
p18: average number of
claims
p19: standard deviation of
number of claims
rs
-.070
.008
-.049
.045
.265
.083
.087
-.001
.029
-.012
.101
.184
.144
.143
f1:
current
ratio
f2:
CBTL
f3: debt
to
equity
f4: EPS
f5:
gross
margin
f6: price
to
earnings
(P/E)
f7:
Price
.060
.008
.104
.164
.068
.176
.259
rs
rs
rs
rs
rs
rs
rs
Lag =1 year
p1: number of patents
rs
- 66 -
p2: number purchased
patents
p3: number of sold patents
rs
-.043
-.038
.082
.129
.051
.092
.154
rs
-.093
-.087
.095
.093
-.039
.022
.071
p4: patent growth rate
rs
.005
.032
.020
-.026
.105
.080
.131
p5: average of patent age
p6: patent age standard
deviation
p7: number of citations
p8: number of internal
citations
p9: number of external
citations
p10: average of citation per
patent
p11: standard deviation of
citation
p12: citation growth rate
p13: internal citation growth
rate
p14: external citation growth
rate
p15: number of inventor
rs
-.018
.000
.066
.170
-.065
.076
.023
rs
.054
.008
.108
.226
-.019
.161
.174
rs
.022
.007
.098
.189
.041
.194
.221
rs
.037
-.032
.128
.208
.019
.174
.239
rs
.020
.004
.095
.178
.036
.186
.205
rs
-.017
.031
.028
.159
.073
.119
.056
rs
-.025
.021
.056
.184
.108
.148
.097
rs
.063
.058
-.028
-.044
.023
.054
.061
rs
.032
.008
.025
.009
.007
.063
.101
rs
.073
.053
-.018
-.046
.011
.050
.049
rs
.005
-.048
.173
.280
-.010
.214
.284
rs
-.096
-.029
.084
.096
.017
.080
.137
rs
.060
.007
.103
.159
.075
.170
.254
rs
-.078
.000
-.023
.052
.263
.090
.083
rs
.000
.031
.023
.113
.181
.149
.139
f1:
current
ratio
f2:
CBTL
f3: debt
to
equity
f4: EPS
f5:
gross
margin
f6: price
to
earnings
(P/E)
f7:
Price
p16: average of inventor per
patent
p17: number of new claims
p18: average number of
claims
p19: standard deviation of
number of claims
Lag = 2 years
p1: number of patents
p2: number purchased
patents
rs
.055
-.009
.115
.169
.055
.176
.251
rs
-.065
-.068
.095
.110
.040
.089
.141
p3: number of sold patents
rs
-.089
-.103
.104
.104
-.037
.026
.080
p4: patent growth rate
rs
-.025
.017
.022
-.014
.102
.085
.111
p5: average of patent age
rs
.004
-.001
.067
.168
-.072
.076
.045
rs
.066
.000
.110
.215
-.037
.152
.185
rs
.022
-.001
.109
.217
.039
.192
.234
p6: patent age standard
deviation
p7: number of citations
- 67 -
p8: number of internal
citations
p9: number of external
citations
p10: average of citation per
patent
p11: standard deviation of
citation
p12: citation growth rate
p13: internal citation growth
rate
p14: external citation growth
rate
p15: number of inventor
p16: average of inventor per
patent
p17: number of new claims
p18: average number of
claims
p19: standard deviation of
number of claims
rs
.035
-.034
.120
.217
.014
.180
.232
rs
.019
-.004
.110
.208
.035
.183
.219
rs
-.009
.027
.031
.165
.074
.120
.067
rs
-.011
.017
.062
.188
.107
.146
.108
rs
.053
.071
-.017
-.022
.025
.075
.064
rs
.008
-.001
.014
.017
.017
.082
.091
rs
.064
.069
-.009
-.028
.016
.066
.048
rs
.015
-.052
.172
.278
-.018
.211
.282
rs
-.091
-.033
.089
.105
.017
.084
.142
rs
.053
-.008
.109
.168
.064
.178
.250
rs
-.077
-.007
-.024
.082
.257
.112
.099
rs
.010
.021
.014
.134
.168
.165
.151
f1:
current
ratio
f2:
CBTL
f3: debt
to
equity
f4: EPS
f5:
gross
margin
Lag = 3 years
p1: number of patents
p2: number purchased
patents
rs
.051
-.023
.114
.187
.050
f6: price
to
earnings
(P/E)
.191
rs
-.072
-.058
.104
.113
.033
.078
.132
p3: number of sold patents
rs
-.076
-.103
.133
.097
-.043
.035
.063
p4: patent growth rate
rs
-.036
-.012
.035
-.005
.096
.084
.096
p5: average of patent age
rs
.029
.003
.067
.153
-.083
.060
.052
p6: patent age standard
deviation
rs
.081
.001
.110
.203
-.054
.134
.179
p7: number of citations
rs
.038
-.010
.113
.235
.032
.193
.236
rs
.047
-.034
.114
.227
.009
.172
.222
rs
.037
-.009
.112
.227
.030
.184
.221
rs
.007
.019
.035
.160
.070
.110
.072
rs
.007
.008
.069
.182
.099
.132
.112
rs
.050
.040
-.005
.009
.029
.099
.081
rs
.006
-.012
.028
.056
.024
.094
.096
p8: number of internal
citations
p9: number of external
citations
p10: average of citation per
patent
p11: standard deviation of
citation
p12: citation growth rate
p13: internal citation growth
- 68 -
f7:
Price
.245
rate
p14: external citation growth
rate
rs
.060
.041
.002
-.001
.017
.090
.066
p15: number of inventor
rs
.031
-.054
.168
.272
-.027
.195
.269
p16: average of inventor per
patent
rs
-.080
-.031
.093
.113
.018
.087
.149
rs
.049
-.023
.106
.188
.060
.195
.245
rs
-.071
-.018
-.033
.093
.246
.121
.111
rs
.025
.013
.001
.141
.154
.163
.156
f1:
current
ratio
f2:
CBTL
f3: debt
to
equity
f4: EPS
f5:
gross
margin
p17: number of new claims
p18: average number of
claims
p19: standard deviation of
number of claims
Lag = 4 years
p1: number of patents
p2: number purchased
patents
rs
.055
-.039
.125
.187
.043
f6: price
to
earnings
(P/E)
.162
rs
-.054
-.051
.113
.119
.025
.050
.114
p3: number of sold patents
rs
-.047
-.093
.115
.110
-.031
.047
.070
p4: patent growth rate
rs
-.027
-.015
.045
.025
.088
.084
.098
p5: average of patent age
rs
.048
-.001
.067
.145
-.092
.051
.041
p6: patent age standard
deviation
rs
.093
-.006
.108
.197
-.065
.117
.164
p7: number of citations
rs
.053
-.029
.117
.242
.027
.172
.218
rs
.059
-.034
.103
.232
.015
.154
.211
rs
.052
-.029
.117
.237
.027
.169
.205
rs
.019
.006
.044
.157
.071
.100
.062
rs
.020
-.003
.081
.179
.092
.113
.098
rs
.058
.035
-.006
.056
.047
.110
.081
rs
.011
-.018
.021
.079
.042
.106
.107
rs
.068
.033
.000
.052
.035
.106
.070
rs
.046
-.059
.167
.273
-.030
.171
.247
p16: average of inventor per
patent
rs
-.065
-.040
.095
.126
.020
.086
.155
p17: number of new claims
rs
.047
-.045
.117
.184
.056
.162
.229
p8: number of internal
citations
p9: number of external
citations
p10: average of citation per
patent
p11: standard deviation of
citation
p12: citation growth rate
p13: internal citation growth
rate
p14: external citation growth
rate
p15: number of inventor
- 69 -
f7:
Price
.229
p18: average number of
claims
p19: standard deviation of
number of claims
rs
-.057
-.033
-.022
.094
.231
.108
.103
rs
.040
.004
.004
.150
.143
.147
.152
- 70 -
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[...]... between patent portfolio and financial performance by examining the relationship between indicators of number of patents, patent ages, inventors and patent protection (patent claims) and patent performance (patent citation) and then between those indicators and financial performance To assess company performance, financial ratio analysis method has been conducted It is a method to analyze at company’s financial. .. thesis focuses on analysis of patent portfolio and financial performance of firms First, we analyze the correlation of patent count, patent age, patent claims, and inventor figures to the patent performance which can be indicated by citations Then we continue to analyze the correlation of those patent portfolio indicators with financial ratios including liquidity, leverage, profitability and valuation... Number of patents / R&D x investment - 19 - Number of citation / Number of x x x patents Number of patent applications x Granted patents x Opposed patents x Family size x Forward citations x Backward citations x Average number of inventors x x x x per patent Patent H index x Current impact index (CII) x Essential patent index (EPI) x Number of new purchased x patents Number of sold patents x Patent. .. important technology which later patents are built on Among patent indicators, there are some depends on R&D activities, R&D team or strategy of companies such as number of patents, patent age, number of inventors, and number of claims These indicators are dependent on companies and might not reflect the values of patent portfolio Solving this problem may give us a better understanding on how patent portfolio. .. correlation between patent portfolio and performance Spearman correlation between patent portfolio and financial performance The use of patent data to predict financial ratios Conclusion Figure 3: Analysis steps 1.2 Building patent portfolio database The patent data are taken from 2 main sources: one is the UPSTO weekly patent releases which are hosted by Google and another is research result of Balsmeier... influence the financial performance and market value of a firm We will use patent data as a representative of technology -5- base and market protection of a firm Because the value of each patent is different, not only number of patents in portfolio but also other indicators such as number of forward citations, number of inventors are employed in the model In other to determine the health of a company,... is one of the better to demonstrate patent portfolio s value and patent quality Because when applicants submit new patents, they and examiners must find and cite older patents which anticipate or be similar to the new inventions If we stand at the site of cited patents, these citations are forward citations If a patent is highly cited (i.e cited in 5, 10, or more subsequent patents), then that patent. .. bivariate relationship of patent and finance • Artificial Neural Network: used to explore the multivariate relationship of patent portfolio and finance Softwares: • Visual Studio 2012: used to build tools to process data and visualize the patent portfolio and finance indicators • IBM SPSS Statistics 20: used to conduct correlation test and ANN • Microsoft SQL Server 2012: store data for the analysis Our steps... Figure 2: First page of patent “Method for node ranking in a linked” The patent application and patent publication include a header with show name and address of the inventor(s), the assignee(s), the country of origin, filing date - 11 - and the state of the art citations (citations which the inventor(s) or the of lawyer of Patent Office make) In addition to this, there are the details of innovation so... NASDAQ and NYSE Extract patent Consolidate data assignment and calculate information indicator Consolidated Patent and Finance database Figure 4: Our data preprocessing 1.2.1 USPTO Patent data The United States Patent and Trademark Office (USPTO) is the federal agency for granting U.S patents and registering trademarks The USPTO advises the president of the United States, the secretary of commerce, and ... patents of previous years Patent p5 age Average of patent New Total patent age divided Age of the patent by number of patents portfolio Standard deviation of The spread of all patent age in patent. . .ANALYSIS OF PATENT PORTFOLIO AND FINANCIAL PERFORMANCE OF FIRMS In Partial Fulfillment of the Requirements of the Degree of MASTER OF INFORMATION TECHNOLOGY MANAGEMENT In Computer Science and. .. between indicators of number of patents, patent ages, inventors and patent protection (patent claims) and patent performance (patent citation) and then between those indicators and financial performance
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