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. -i- 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. - ii - 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 - iii - 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 - iv - 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 -v- 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 - vi - 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 - vii - 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 - viii - 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. - ix - -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 -1- 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) -2- 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 -3- 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. -4- 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 -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, 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 -6- 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 -7- 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 -8- 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. -9- 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. - 10 - 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 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. - 12 - 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 - References Alexander, A., Raznick, J., Bedigian, L. (2012). Six Rules to Follow When Picking Stocks. Benzinga Insights. Retrieved from http://www.forbes.com/sites/benzingainsights/2012/06/15/six-rules-tofollow-when-picking-stocks Balsmeier, B., Fierro, G., Fleming, L., Johnson, K., Kaulagi, A., Li, G.C., & Yeh, B. (2014). Weekly Disambiguations of US Patent Grants and Applications. University of California, Berkeley. Basenese, L. (2012). Patent Filings: The Next Great Leading Indicator. The Wall Street Daily. Chang, K. C., Chen, D. Z., & Huang, M. H. (2012). The relationships between the patent performance and corporation performance. Journal of Informetrics. 6:131–139. D’Amato, E. (2010). The Top 15 Financial Ratios. Australia: The Australian Shareholders’ Association. Dancey, C. P., & Reidy, J. (2004). Statistics without maths for psychology: Using SPSS for Windows (3rd ed.). Harlow, England: Pearson/Prentice Hall. Danies, G. P. (2007). Patent Citations and Licensing Value. (Unpublished master dissertation). Massachusetts Institute of Technology, Massachusetts. Dosi, G., Pavitt, K., & Soete, L. (1991). The Economics of Technical Change and International Trade. New York: New York University Press. Hall, B. H., Jaffe, A., & Trajtenberg, M. (2005). Market Value and Patent Citations. Rand Journal of Economics 36: 16-38. Jurczyk , P.,Lu, J. J., Xiong, L., Cragan, J. D., & Correa, A. (2008), FRIL: A Tool for Comparative Record Linkage, American Medical Informatics Association (AMIA) Annual Symposium Laerd Statistic, Spearman's Rank-Order Correlation, https://statistics.laerd.com/statistical-guides/spearmans-rank-ordercorrelation-statistical-guide-2.php Lovie, A. D. (1995), Who discovered Spearman's rank correlation?. British Journal of Mathematical and Statistical Psychology, 48: 255–269. doi: 10.1111/j.2044-8317.1995.tb01063. Neuhäusler, P., Frietsch, R., Schubert, T., & Blind, K. (2011) : Patents and the financial performance of firms – An analysis based on stock market data, Fraunhofer ISI discussion papers innovation systems and policy analysis. No. 28. - 71 - Nikulainen, T., Hermans, R., & Kulvik, M. (2008). Patent citations indicating present value of the Biotechnology business. International Journal of Innovation and Technology Management, Vol. 5, No. 3, 279-301. The Research Institute of the Finnish Economy. - 72 - [...]... 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