Search results for: related patent portfolios

Authors: Carol Anne Hargreaves

Abstract:

A key issue in stock investment is how to select representative features for stock selection. The objective of this paper is to firstly determine whether an automated stock investment system, using machine learning techniques, may be used to identify a portfolio of growth stocks that are highly likely to provide returns better than the stock market index. The second objective is to identify the technical features that best characterize whether a stock’s price is likely to go up and to identify the most important factors and their contribution to predicting the likelihood of the stock price going up. Unsupervised machine learning techniques, such as cluster analysis, were applied to the stock data to identify a cluster of stocks that was likely to go up in price – portfolio 1. Next, the principal component analysis technique was used to select stocks that were rated high on component one and component two – portfolio 2. Thirdly, a supervised machine learning technique, the logistic regression method, was used to select stocks with a high probability of their price going up – portfolio 3. The predictive models were validated with metrics such as, sensitivity (recall), specificity and overall accuracy for all models. All accuracy measures were above 70%. All portfolios outperformed the market by more than eight times. The top three stocks were selected for each of the three stock portfolios and traded in the market for one month. After one month the return for each stock portfolio was computed and compared with the stock market index returns. The returns for all three stock portfolios was 23.87% for the principal component analysis stock portfolio, 11.65% for the logistic regression portfolio and 8.88% for the K-means cluster portfolio while the stock market performance was 0.38%. This study confirms that an automated stock investment system using machine learning techniques can identify top performing stock portfolios that outperform the stock market.

Keywords: machine learning, stock market trading, logistic regression, cluster analysis, factor analysis, decision trees, neural networks, automated stock investment system

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Authors: Nikola Radovanovic, Petros Gkotsis, Mathieu Doussineau

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Aeronautics is regarded as a strategically important sector for European competitiveness. It was at the heart of European entrepreneurial development since the industry was born. Currently, the EU is the world leader in the production of civil aircraft, including helicopters, aircraft engines, parts, and components. It is recording a surplus in trade relating to aerospace products, which are exported all over the globe. Also, this industry shows above-average investments in research and development, as demonstrated in the patent activity in this area. The post-pandemic recovery of the industry will partly depend on the possibilities to streamline collaboration in further research and innovation activities. Aeronautics features as one of the often selected priority domains in smart specialisation, which represents the main regional and national approach in developing and implementing innovation policies in Europe. The basis for the selection of priority domains for smart specialisation lies in the mapping of innovative potential, with research and patent activities being among the key elements of this analysis. This research is aimed at identifying characteristics of the trends in research and patent activities in the regions and countries that base their competitiveness on the aeronautics sector. It is also aimed at determining the scope and patterns of collaborations in aeronautics between innovators from the European regions, focusing on revealing new technology areas that emerge from these collaborations. For this purpose, we developed a methodology based on desk research and the analysis of the PATSTAT patent database as well as the databases of R&I framework programmes.

Keywords: aeronautics, smart specialisation, innovation, research, regional policy

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Authors: Murat Sengoz, Mustafa Kemal Topcu

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This study aims to contribute to efforts of Turkey to increase research and development to overcome mid-income level trap by discussing regulations on patenting and licensing. Knowledge and technology transfer from universities to business world is attached great significance to increase innovation. Through literature survey, it is observed that the States accomplished to boost the economy and increase welfare by the Bayh-Dole Act enacted in 1980. Thus, this good practice is imitated by other nations to make technological developments. The Act allows universities to acquire patent right in research programs funded by government to increase technology transfer from universities whilst motivating real sector to use research pools in the universities. An act similar with Bayh-Dole could be beneficial to Turkey since efforts in Turkey are to promote research, development and innovation. Towards this end, the impact of Bayh-Dole Act on the patent system for universities in the Sates is deliberately examined, applicability in Turkey is discussed. However, it is conceded that success rate of applying Bayh-Dole Act in Turkey would be low once Turkey mainly differs from the States regarding social, economic and cultural traits.

Keywords: Bayh-Dole Act, knowledge transfer, license, patent, spin-off

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Authors: Zheng Miao, Dennis Fernandez

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CRISPR research has the potential to completely transform life science, agriculture, live-stock and the health care industry. The Intellectual Property derived from its research has raised significant attention in the academic as well as the biopharmaceutical industry culminating an urgent need for strategic IP protection. We review the rudimentary concepts and key competitors of CRISPR technologies as well as the paramount strategies for intellectual property protection. Further, we elaborate on prosecution issues related to CRISPR patents as well as possible solutions to various patent laws, interferences and litigation. Finally, we address how the bioinformatics of the CRISPR technology begs an inquiry into issues of privacy and a host of ethical concerns.

Keywords: bioinformatics, CRISPR, biotechnology, intellectual property

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Authors: Andrea Schiffauerova, Amnah Alzeyoudi

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This paper studies patterns of innovation within national constitutional context. Its objective is to examine national systems of innovation in biotechnology in six leading innovative countries: the US, Japan, Germany, the UK, France and Canada. The framework proposed for this purpose consists of specific factors considered critical for the development of national systems of innovation, which are industry size, innovative activities, area of specialization, industry structure, national policy, the level of government intervention, the stock of knowledge in universities and industries, knowledge transfer from universities to industry and country-specific conditions for start-ups. The paper then uses the framework to provide detailed cross-country comparisons while highlighting particular features of national institutional context which affect the creation and diffusion of scientific knowledge within the system. The study is primarily based on the extensive survey of literature and it is complemented by the quantitative analysis of the patent data extracted from the United States Patent and Trademark Office (USPTO). The empirical analysis provides numerous insights and greatly complements the data gained from the literature and other sources. The final cross-country comparative analysis identifies three patterns followed by the national innovation systems in the six countries. The proposed cross-country relative positioning analysis may help in drawing policy implications and strategies leading to the enhancement of national competitive advantage and innovation capabilities of nations.

Keywords: comparative analysis, framework, national systems of innovation, patent analysis, United States Patent and Trademark Office (USPTO)

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Authors: Xingyu Gao, Qiang Wu

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Patents play a crucial role in protecting innovation and intellectual property. Early prediction of the impact of artificial intelligence (AI) patents helps researchers and companies allocate resources and make better decisions. Understanding the key factors that influence patent impact can assist researchers in gaining a better understanding of the evolution of AI technology and innovation trends. Therefore, identifying highly impactful patents early and providing support for them holds immeasurable value in accelerating technological progress, reducing research and development costs, and mitigating market positioning risks. Despite the extensive research on AI patents, accurately predicting their early impact remains a challenge. Traditional methods often consider only single factors or simple combinations, failing to comprehensively and accurately reflect the actual impact of patents. This paper utilized the artificial intelligence patent database from the United States Patent and Trademark Office and the Len.org patent retrieval platform to obtain specific information on 35,708 AI patents. Using six machine learning models, namely Multiple Linear Regression, Random Forest Regression, XGBoost Regression, LightGBM Regression, Support Vector Machine Regression, and K-Nearest Neighbors Regression, and using early indicators of patents as features, the paper comprehensively predicted the impact of patents from three aspects: technical, social, and economic. These aspects include the technical leadership of patents, the number of citations they receive, and their shared value. The SHAP (Shapley Additive exPlanations) metric was used to explain the predictions of the best model, quantifying the contribution of each feature to the model's predictions. The experimental results on the AI patent dataset indicate that, for all three target variables, LightGBM regression shows the best predictive performance. Specifically, patent novelty has the greatest impact on predicting the technical impact of patents and has a positive effect. Additionally, the number of owners, the number of backward citations, and the number of independent claims are all crucial and have a positive influence on predicting technical impact. In predicting the social impact of patents, the number of applicants is considered the most critical input variable, but it has a negative impact on social impact. At the same time, the number of independent claims, the number of owners, and the number of backward citations are also important predictive factors, and they have a positive effect on social impact. For predicting the economic impact of patents, the number of independent claims is considered the most important factor and has a positive impact on economic impact. The number of owners, the number of sibling countries or regions, and the size of the extended patent family also have a positive influence on economic impact. The study primarily relies on data from the United States Patent and Trademark Office for artificial intelligence patents. Future research could consider more comprehensive data sources, including artificial intelligence patent data, from a global perspective. While the study takes into account various factors, there may still be other important features not considered. In the future, factors such as patent implementation and market applications may be considered as they could have an impact on the influence of patents.

Keywords: patent influence, interpretable machine learning, predictive models, SHAP

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Authors: M. Kalyan Raj, Vinay Umesh Rao, M. Sudhakar

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The work focuses on the development of a directly compressible controlled release co-processed excipient using melt granulation technique. Erodible wax matrix systems are fabricated in which three different types of waxes are co processed separately with Maize starch in different ratios by melt granulation. The resultant free flowing powder is characterized by FTIR, NMR, Mass spectrophotometer and gel permeation chromatography. Also, controlled release tablets of Aripiprazole were formulated and dissolution profile was compared with that of the target product profile given in Zysis patent (Patent no. 20100004262) for Aripiprazole once a week formulation.

Keywords: co-processing, hot melt extrusion, direct compression, maize starch, stearic acid, aripiprazole

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Authors: Wendy de Gomez

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Standardization is a critical element in the ability of a society to reduce uncertainty, subjectivity, misrepresentation, and interpretation while simultaneously contributing to innovation. Technological standardization is critical to codify specific operationalization through legal instruments that provide rules of development, expectation, and use. In the current emerging technology landscape Artificial Intelligence (AI) hardware as a general use technology has seen incredible growth as evidenced from AI technology patents between 2012 and 2018 in the United States Patent Trademark Office (USPTO) AI dataset. However, as outlined in the 2023 United States Government National Standards Strategy for Critical and Emerging Technology the codification through standardization of emerging technologies such as AI has not kept pace with its actual technological proliferation. This gap has the potential to cause significant divergent possibilities for the downstream outcomes of AI in both the short and long term. This original empirical research provides an overview of the standardization efforts around AI in different geographies and provides a background to standardization law. It quantifies the longitudinal trend of Artificial Intelligence hardware patents through the USPTO AI dataset. It seeks evidence of existing Standard Essential Patents from these AI hardware patents through a text analysis of the Statement of patent history and the Field of the invention of these patents in Patent Vector and examines their determination as a Standard Essential Patent and their inclusion in existing AI technology standards across the four main AI standards bodies- European Telecommunications Standards Institute (ETSI); International Telecommunication Union (ITU)/ Telecommunication Standardization Sector (-T); Institute of Electrical and Electronics Engineers (IEEE); and the International Organization for Standardization (ISO). Once the analysis is complete the paper will discuss both the theoretical and operational implications of F/Rand Licensing Agreements for the owners of these Standard Essential Patents in the United States Court and Administrative system. It will conclude with an evaluation of how Standard Setting Organizations (SSOs) can work with SEP owners more effectively through various forms of Intellectual Property mechanisms such as patent pools.

Keywords: patents, artifical intelligence, standards, F/Rand agreements

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Authors: Liubov Evstigneeva

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In this paper an attempt is made to establish causal nexuses between innovation and international trade in Russia. The topicality of this issue is determined by the necessity of choosing policy instruments for economic modernization and transition to innovative development. The vector auto regression (VAR) model and Granger test are applied for the Russian monthly data from 2005 until the second quartile of 2015. Both lagged import and export at the national level cause innovation, the latter starts to stimulate foreign trade since it is a remote lag. In comparison to aggregate data, the results by patent’s categories are more diverse. Importing technologies from foreign countries stimulates patent activity, while innovations created in Russia are only Granger causality for import to Commonwealth of Independent States.

Keywords: export, import, innovation, patents

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Authors: Abdulrahman Fahim M. Alsulami

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The current research seeks to explore the regulation of vaccine-related IP rights in light of the areas of divergence between the Trade-Related Aspects of Intellectual Property Rights (TRIPS) Agreement and investment treaties. The study is conducted in the context of the COVID-19 pandemic; therefore, it seems natural that a specific chapter is devoted to the examination of vaccine arrangements related to vaccine supplies. The chapter starts with the examination of a typical vaccine from the perspective of IP rights. It presents the distinctive features of vaccines as pharmaceutical products and investments, reviews the basics of their patent protection, reviews vaccines’ components, and discusses IPR protection of different components of vaccines. The subsection that focuses on vaccine development and licensing reviews vaccine development stages investigates differences between vaccine licensing in different countries and presents barriers to vaccine licensing. The third subsection, at the same time, introduces the existing arrangements related to COVID-19 vaccine supplies, including COVAX arrangements, international organizations’ assistance, and direct negotiations between governments and vaccine manufacturers.

Keywords: bilateral investment treaties, COVID-19 vaccine, IP rights, TRIPs agreement

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Authors: Karam Chand Gupta

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If an ellipse is to be drawn of given dimensions on a large ground, there is no formula, method or set of calculations & procedure available which will help in drawing an ellipse of given length and width on ground. Whenever a field engineer is to start the work of an ellipse-shaped structure like elliptical conference hall, screening chamber and pump chamber in disposal work etc., it is cumbersome for him to give demarcation of the structure on the big surface of the ground. No procedure is available, even in Google. A set of formulas with calculations has been made which helps the field engineer to draw an true and perfect ellipse of given length and width on the large ground very easily so as to start the construction work of elliptical structure. Based on these formulas a civil Engineering tool kit has been made with the help of which we can make perfect ellipse of desired dimensions on very large surface. The Patent of the tool kit has been filed in Intellectual Property India with Patent Filing Number: 201611026153 and Patent Application Filing Date: 30.07.2016. An App named ‘KC’s Mesh Formula’ has also been made to ease the calculation work. This can be downloaded from Play Store. After adopting these formulas and tool kit, a field engineer will not face difficulty in drawing ellipse on the ground to start the work.

Keywords: ellipse, elliptical structure, foci, string, wooden peg

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Authors: C. M. Priesnitz, G. Zanandrea, J. P. Fabris, S. L. Russo, M. E. Camargo

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This study aimed to the prospection the physiotherapy area technological production registered with the National Intellectual Property Institute (INPI) in Brazil, for understand the evolution of the technological production in the country over time and visualize the distribution this production request in Brazil. There was an evolution in the technology landscape, where the average annual deposits had an increase of 102%, from 3.14 before the year 2004 to 6,33 after this date. It was found differences in the distribution of the number the deposits requested to each Brazilian region, being that of the 132 request, 68,9% were from the southeast region. The international patent classification evaluated the request deposits, and the more found numbers were A61H and A63B. So even with an improved panorama of technology production, this should still have incentives since it is an important tool for the development of the country.

Keywords: distribution, evolution, patent, physiotherapy, technological prospecting

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Authors: Tarik Bazgour, Cedric Heuchenne, Danielle Sougne

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We examine time variation in the market beta of portfolios sorted on quality, liquidity level and liquidity beta characteristics across stock market phases. Using US stock market data for the period 1970-2010, we find, first, the US stock market was driven by four regimes. Second, during the crisis regime, low (high) quality, high (low) liquidity beta and illiquid (liquid) stocks exhibit an increase (a decrease) in their market betas. This finding is consistent with the flight-to-quality and liquidity phenomena. Third, we document the same pattern across stocks when the market volatility is low. We argue that, during low volatility times, investors shift their portfolios towards low quality and illiquid stocks to seek portfolio gains. The pattern observed in the tranquil regime can be, therefore, explained by a flight-to-low-quality and to illiquidity. Finally, our results reveal that liquidity level is more important than liquidity beta during the crisis regime.

Keywords: financial crises, quality, liquidity, liquidity risk, regime-switching models

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Authors: Chukwunonso Igboamalu, Daprim Ogaji

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Background: With the limited number of trained health care providers in Nigeria, patent and proprietary medicine vendors (PPMVs) are inevitable and highly needed especially in the rural areas for the supply of drugs in treating minor illnesses. These vendors serve as a crucial link between the healthcare system and the community, aiding in the distribution of medications and healthcare information, particularly in areas with limited hospital infrastructure. Objectives: The study set to measure the participants’ knowledge, attitude and patient referral practice and any association of their characteristics with patient referral. Methodology: This cross-sectional descriptive survey was conducted among PPMVs in Obio-Akpor LGA of Rivers State. Data was collected using a self-administered structured questionnaire and analysed using SPSS version 25. Results: The study showed that 18.3% had adequate knowledge, 62.4% had moderate knowledge and 19.2% had poor knowledge. Attitude was moderate among 73.4% of the study participants with only 13% showing adequate attitude. In reporting their referral practice, 34% showed poor referral practice, 58% reported moderate practice and only 8% showed adequate practice. Conclusion: Various facilitators as well as barriers to patient referral were highlighted by the respondents. This study indicated that while attitude and practice were moderate among respondents, the percentage of PPMVs with the adequate knowledge of patient referral was high. To enhance the effectiveness of patient referrals, addressing barriers to referral and promoting education and training for PPMVs are critical steps forward.

Keywords: knowledge, attitude, practice, barriers, facilitators, patent medicine vendor, referral

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Authors: Zahra Hatami, Hesham Ali, David Volkman

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Selecting a portfolio for investing is a crucial decision for individuals and legal entities. In the last two decades, with economic globalization, a stream of financial innovations has rushed to the aid of financial institutions. The importance of selecting stocks for the portfolio is always a challenging task for investors. This study aims to create a financial network to identify optimal portfolios using network centralities metrics. This research presents a community detection technique of superior stocks that can be described as an optimal stock portfolio to be used by investors. By using the advantages of a network and its property in extracted communities, a group of stocks was selected for each of the various time periods. The performance of the optimal portfolios compared to the famous index. Their Sharpe ratio was calculated in a timely manner to evaluate their profit for making decisions. The analysis shows that the selected potential portfolio from stocks with low centrality measurement can outperform the market; however, they have a lower Sharpe ratio than stocks with high centrality scores. In other words, stocks with low centralities could outperform the S&P500 yet have a lower Sharpe ratio than high central stocks.

Keywords: portfolio management performance, network analysis, centrality measurements, Sharpe ratio

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Authors: Sarkar Humayun Kabir

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This paper aims to investigate whether ethical investment instruments could contribute to stability in financial markets. In order to address the main issue, the study investigates the stability of return in seven conventional and Islamic equity markets of Asia, Europe and North America and in five major commodity markets starting from 1996 to June 2012. In addition, the study examines the unconditional correlation between returns of the assets under review to investigate portfolio diversification benefits of investors. Applying relevant methods, the study finds that investors may enjoy sustainable returns from their portfolios by investing in ethical financial instruments such as Islamic equities. In addition, it should be noted that most of the commodities, gold in particular, are either low or negatively correlated with equity returns. These results suggest that investors would be better off by investing in portfolios combining Islamic equities and commodities in general. The sustainable returns of ethical investments has important implications for the investors and markets since these investments can provide stable returns while the investors can avoid production of goods and services which believes to be harmful for human and the society as a whole.

Keywords: financial sustainability, ethical investment instruments, islamic equity, dynamic conditional correlation, conditional volatility

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Authors: Gideon Ndubuisi

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It is widely acknowledged that the quality of a country’s export matters more decisively than the quantity it exports. Hence, understanding the drivers of exported goods’ quality is a relevant policy question. Among other things, product quality upgrading is a considerable cost uncertainty venture that can be undertaken by an entrepreneur. Once a product is successfully upgraded, however, others can imitate the product, and hence, the returns to the pioneer entrepreneur are socialized. Along with this line, a government policy such as intellectual property rights (IPRs) protection which lessens the non-appropriability problem and incentivizes cost discovery investments becomes both a panacea in addressing the market failure and a sine qua non for an entrepreneur to engage in product quality upgrading. In addendum, product quality upgrading involves complex tasks which often require a lot of knowledge and technology sharing beyond the bounds of the firm thereby creating rooms for knowledge spillovers and imitations. Without an institution that protects upstream suppliers of knowledge and technology, technology masking occurs which bids up marginal production cost and product quality fall. Despite these clear associations between IPRs and product quality upgrading, the surging literature on the drivers of the quality of exported goods has proceeded almost in isolation of IPRs protection as a determinant. Consequently, the current study uses a difference-in-difference method to evaluate the effects of IPRs reforms on the quality of exported goods in 16 developing countries over the sample periods of 1984-2000. The study finds weak evidence that IPRs reforms increase the quality of all exported goods. When the industries are sorted into high and low-patent sensitive industries, however, we find strong indicative evidence that IPRs reform increases the quality of exported goods in high-patent sensitive sectors both in absolute terms and relative to the low-patent sensitive sectors in the post-reform period. We also obtain strong indicative evidence that it brought the quality of exported goods in the high-patent sensitive sectors closer to the quality frontier. Accounting for time-duration effects, these observed effects grow over time. The results are also largely consistent when we consider the sophistication and complexity of exported goods rather than just quality upgrades.

Keywords: exports, export quality, export sophistication, intellectual property rights

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Authors: Günther Schuh, Christina König, Toni Drescher

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One of the key factors for the competitiveness and market success of technology-driven companies is the timely provision of information about emerging technologies, changes in existing technologies, as well as relevant related changes in the market's structures and participants. Therefore, many companies conduct technology intelligence (TI) activities to ensure an early identification of appropriate technologies and other (weak) signals. One base activity of TI is technology monitoring, which is defined as the systematic tracking of developments within a specified topic of interest as well as related trends over a long period of time. Due to the very large number of dynamically changing parameters within the technological and the market environment of a company as well as their possible interdependencies, it is necessary to focus technology monitoring on specific indicators or other criteria, which are able to point out technological developments and market changes. In addition to the execution of a literature review on existing approaches, which mainly propose patent-based indicators, it is examined in this paper whether indicator systems from other branches such as risk management or economic research could be transferred to technology monitoring in order to enable an efficient and focused technology monitoring for companies.

Keywords: technology forecasting, technology indicator, technology intelligence, technology management, technology monitoring

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Authors: Chokri Slim

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The objective of this research is attempting to respond to this question: Is there a significant difference between the regression model and statistical learning models in predicting credit risk management? A Multiple Linear Regression (MLR) model was compared with neural networks including Multi-Layer Perceptron (MLP), and a Support vector regression (SVR). The population of this study includes 50 listed Banks in Tunis Stock Exchange (TSE) market from 2000 to 2016. Firstly, we show the factors that have significant effect on the quality of loan portfolios of banks in Tunisia. Secondly, it attempts to establish that the systematic use of objective techniques and methods designed to apprehend and assess risk when considering applications for granting credit, has a positive effect on the quality of loan portfolios of banks and their future collectability. Finally, we will try to show that the bank governance has an impact on the choice of methods and techniques for analyzing and measuring the risks inherent in the banking business, including the risk of non-repayment. The results of empirical tests confirm our claims.

Keywords: credit risk management, multiple linear regression, principal components analysis, artificial neural networks, support vector machines

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Authors: Lynmar M. Didal, Ramises G. Manzano Jr., Jacque Bon-Isaac C. Aboy

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This study analyzes the performance of global minimum variance and naive portfolios across different economic periods, using monthly stock returns from the Philippine Stock Exchange Index (PSEI), S&P 500, and Dow Jones Industrial Average (DOW). The performance is evaluated through the Sharpe ratio, Sortino ratio, Jensen’s Alpha, Treynor ratio, and Information ratio. Additionally, the study investigates the impact of short selling on portfolio performance. Six-time periods are defined for analysis, encompassing events such as the global financial crisis and the COVID-19 pandemic. Findings indicate that the Naive portfolio generally outperforms the GMV portfolio in the S&P 500, signifying higher returns with increased volatility. Conversely, in the PSEI and DOW, the GMV portfolio shows more efficient risk-adjusted returns. Short selling significantly impacts the GMV portfolio during mid-GFC and mid-COVID periods. The study offers insights for investors, suggesting the Naive portfolio for higher risk tolerance and the GMV portfolio as a conservative alternative.

Keywords: portfolio performance, global minimum variance, naïve portfolio, risk-adjusted metrics, short-selling

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Authors: Bakr Zade, Paolo Cervera

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With the aim of maximising return on innovation investments, organisations create central units to support successful implementation of innovation management initiatives. The support units–referred to in this research as innovation management offices (IMOs)–range from small teams of innovation management champions to fully resourced centres of excellence for innovation management. However, roles and responsibilities of IMOs vary in different organisations. This research investigates the different types of IMO in organisations, based on their different roles and responsibilities in supporting innovation management processes. The research uses grounded theory methodology to uncover an IMO taxonomy from emergent concepts during innovation management maturity assessment exercises in twelve organisations from the United Kingdom and the United Arab Emirates. The taxonomy distinguishes five types of IMO, based on their roles and responsibilities in supporting innovation management processes, from organisational strategic foresight to managing innovation management project portfolios. The IMO taxonomy addresses a gap in research into innovation management support in organisations and offers a practical framework that diverse organisations can appreciate and use in designing IMOs that are aligned with their innovation management visions and strategies.

Keywords: future foresight, future shaping, innovation management, innovation management office, portfolio management

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Authors: Juan David Sepulveda

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This article reflects the current stage of progress in the project “Determining technological trends in energy generation”. At first it was oriented towards finding out those trends by employing such tools as the scientometrics community had proved and accepted as effective for getting reliable results. Because a documented methodological guide for this purpose could not be found, the decision was made to reorient the scope and aim of this project, changing the degree of interest in pursuing the objectives. Therefore it was decided to propose and implement a novel guide from the elements and techniques found in the available literature. This article begins by explaining the elements and considerations taken into account when implementing and applying this methodology, and the tools that led to the implementation of a software application for patent revision. Univariate analysis helped recognize the technological leaders in the field of energy, and steered the way for a multivariate analysis of this sample, which allowed for a graphical description of the techniques of mature technologies, as well as the detection of emerging technologies. This article ends with a validation of the methodology as applied to the case of fuel cells.

Keywords: energy, technology mapping, patents, univariate analysis

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Authors: Ghita Benayad

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Asset allocation, which affects the long-term profitability of portfolios by distributing assets to fulfill a range of investment objectives, is the cornerstone of investment management in the dynamic and complicated world of financial markets. This paper offers a technique for optimizing strategic asset allocation with the goal of improving portfolio performance by addressing the inherent complexity and uncertainty of the market through the use of Principal Component Analysis (PCA) in a multi-objective modeling framework. The study's first section starts with a critical evaluation of conventional asset allocation techniques, highlighting how poorly they are able to capture the intricate relationships between assets and the volatile nature of the market. In order to overcome these challenges, the project suggests a PCA-driven methodology that isolates important characteristics influencing asset returns by decreasing the dimensionality of the investment universe. This decrease provides a stronger basis for asset allocation decisions by facilitating a clearer understanding of market structures and behaviors. Using a multi-objective optimization model, the project builds on this foundation by taking into account a number of performance metrics at once, including risk minimization, return maximization, and the accomplishment of predetermined investment goals like regulatory compliance or sustainability standards. This model provides a more comprehensive understanding of investor preferences and portfolio performance in comparison to conventional single-objective optimization techniques. While applying the PCA-driven multi-objective optimization model to historical market data, aiming to construct portfolios better under different market situations. As compared to portfolios produced from conventional asset allocation methodologies, the results show that portfolios optimized using the proposed method display improved risk-adjusted returns, more resilience to market downturns, and better alignment with specified investment objectives. The study also looks at the implications of this PCA technique for portfolio management, including the prospect that it might give investors a more advanced framework for navigating financial markets. The findings suggest that by combining PCA with multi-objective optimization, investors may obtain a more strategic and informed asset allocation that is responsive to both market conditions and individual investment preferences. In conclusion, this capstone project improves the field of financial engineering by creating a sophisticated asset allocation optimization model that integrates PCA with multi-objective optimization. In addition to raising concerns about the condition of asset allocation today, the proposed method of portfolio management opens up new avenues for research and application in the area of investment techniques.

Keywords: asset allocation, portfolio optimization, principle component analysis, multi-objective modelling, financial market

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Authors: Luciano Tarantino, Emanuele Balzano, Paolo Tarantino, Riccardo Aurelio Nasto, Aurelio Nasto

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Liver transplantation (OLT) is contraindicate in Portal Vein tumor Thrombosis (PVTT) from Hepatocellular Carcinoma at hepatic hilum(pH-HCC) Surgery,Thermal ablation and chemotherapy show poorer outcomes Electrochemotherapy (ECT) has been successfully used in patients with pH-HCC with PVTT. We report the results of ECT as downstaging aimed to definitive cure by OLT. F.P. 53 years HBV related Cirrhosis Child-Pugh B7 class; EGDS F2 aesophageal Varices. Diabetes. April 2016 : Enhanced Computed Tomography (CT) detected HCC(n.3 nodules in VII-VIII-VI;diameter range=25 cm) and PVTT of right portal vein. The patient was considered ineligible for OLT. May 2016: first ablation session with percutaneous Radiofrequency-ablation(RFA) of 3 HCC-nodules . August 2016: second ablation session with ECT of PVTT. CT october 2016: disappearance of PVTT and patent right portal vein. No intraparenchymal recurrence. CT march 2017: No recurrence in portal vein and in the left lobe. local recurrence in the VII-VIII segments. May 2017 : transarterial chemoembolization (TACE) of right lobe recurrences. CT October 2017: patent right portal vein. No recurrence. The patient was reconsidered for OLT. He underwent OLT in April 2018. At 36-months follow-up , no intrahepatic recurrence of HCC occurred. March 2021: enhanced CT and PET/CT detected a single small nodule (1.5 cm) uptaking tracer in the left upper pulmonary lobe, no hepatic recurrence . CT-guided FNB showed metastasis from HCC . June 2021: left lung upper lobectomy . At the current time the patient is alive and recurrence-free at 64 months follow-up. ECT Could be aneffective technique as pre-OLT dowstaging in HCC with PVTT.

Keywords: liver tumor ablation, interventional ultrasound, electrochemotherapy, liver transplantation

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Authors: Raúl González Muñoz, Essam Shehab, Martin Weinitzke, Chris Fowler, Paul Baguley

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Software applications have become crucial to the aerospace industry, providing a wide range of functionalities and capabilities used during the design, manufacturing and support of aircraft. However, as this criticality increases, so too does the risk for business operations when facing a software failure. Hence, there is a need for new methodologies to be developed to support aerospace companies in effectively managing their software portfolios, avoiding the hazards of business disruption and additional costs. This paper aims to provide a definition of operational software maturity, and how this can be used to assess software operational behaviour, as well as a view on the different aspects that drive software maturity within the aerospace industry. The key research question addressed is, how can operational software maturity monitoring assist the aerospace industry in effectively managing large software portfolios? This question has been addressed by conducting an in depth review of current literature, by working closely with aerospace professionals and by running an industry case study within a major aircraft manufacturer. The results are a software maturity model composed of a set of drivers and a prototype tool used for the testing and validation of the research findings. By utilising these methodologies to assess the operational maturity of software applications in aerospace, benefits in maintenance activities and operations disruption avoidance have been observed, supporting business cases for system improvement.

Keywords: aerospace, software lifecycle, software maintenance, software maturity

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Authors: Chung Danbi, Linton Oliver, Whang Yoon-Jae

Abstract:

Supermodularity, or complementarity, is a popular concept in economics which can characterize many objective functions such as utility, social welfare, and production functions. Further, supermodular dominance captures a preference for greater interdependence among inputs of those functions, and it can be applied to examine which input set would produce higher expected utility, social welfare, or production. Therefore, we propose and justify a consistent testing for a useful implication of supermodular dominance. We also conduct Monte Carlo simulations to explore the finite sample performance of our test, with critical values obtained from the recentered bootstrap method, with and without the selective recentering, and the subsampling method. Under various parameter settings, we confirmed that our test has reasonably good size and power performance. Finally, we apply our test to compare the geographic and distant knowledge spillover in terms of their effects on social welfare using the National Bureau of Economic Research (NBER) patent data. We expect localized citing to supermodularly dominate distant citing if the geographic knowledge spillover engenders greater social welfare than distant knowledge spillover. Taking subgroups based on firm and patent characteristics, we found that there is industry-wise and patent subclass-wise difference in the pattern of supermodular dominance between localized and distant citing. We also compare the results from analyzing different time periods to see if the development of Internet and communication technology has changed the pattern of the dominance. In addition, to appropriately deal with the sparse nature of the data, we apply high-dimensional methods to efficiently select relevant data.

Keywords: supermodularity, supermodular dominance, stochastic dominance, Monte Carlo simulation, bootstrap, subsampling

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Authors: Yosra Ben Mhamed

Abstract:

Most previous research that investigates the importance of earnings volatility for a firm’s value has focused on the effects of earnings volatility on the cost of capital. Many study illustrate that earnings volatility can reduce the firm’s value by enhancing the cost of capital. However, a few recent studies directly examine the relation between earnings volatility and subsequent earnings levels. In our study, we further explore the role of volatility in forecasting. Our study makes two primary contributions to the literature. First, taking into account the level of current firm’s performance, we provide causal theory to the link between volatility and earnings predictability. Nevertheless, previous studies testing the linearity of this relationship have not mentioned any underlying theory. Secondly, our study contributes to the vast body of fundamental analysis research that identifies a set of variables that improve valuation, by showing that earnings volatility affects the estimation of future earnings. Projections of earnings are used by valuation research and practice to derive estimates of firm value. Since we want to examine the impact of volatility on earnings predictability, we sort the sample into three portfolios according to the level of their earnings volatility in ascending order. For each quintile, we present the predictability coefficient. In a second test, each of these portfolios is, then, sorted into three further quintiles based on their level of current earnings. These yield nine quintiles. So we can observe whether volatility strongly predicts decreases on earnings predictability only for highest quintile of earnings. In general, we find that earnings volatility has an inverse relationship with earnings predictability. Our results also show that the sensibility of earnings predictability to ex-ante volatility is more pronounced among profitability firms. The findings are most consistent with overinvestment and persistence explanations.

Keywords: earnings volatility, earnings predictability, earnings persistence, current profitability

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Authors: Viviana Ventre, Giacomo Di Tollo, Roberta Martino

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The portfolio selection problem includes the evaluation of many criteria that are difficult to compare directly and is characterized by uncertain elements. The portfolio selection problem can be modeled as a group decision problem in which several experts are invited to present their assessment. In this context, it is important to study and analyze the process of reaching a consensus among group members. Indeed, due to the various diversities among experts, reaching consensus is not necessarily always simple and easily achievable. Moreover, the concept of consensus is accompanied by the concept of false consensus, which is particularly interesting in the dynamics of group decision-making processes. False consensus can alter the evaluation and selection phase of the alternative and is the consequence of the decision maker's inability to recognize that his preferences are conditioned by subjective structures. The present work aims to investigate the dynamics of consensus attainment in a group decision problem in which equivalent portfolios are proposed. In particular, the study aims to analyze the impact of the subjective structure of the decision-maker during the evaluation and selection phase of the alternatives. Therefore, the experimental framework is divided into three phases. In the first phase, experts are sent to evaluate the characteristics of all portfolios individually, without peer comparison, arriving independently at the selection of the preferred portfolio. The experts' evaluations are used to obtain individual Analytical Hierarchical Processes that define the weight that each expert gives to all criteria with respect to the proposed alternatives. This step provides insight into how the decision maker's decision process develops, step by step, from goal analysis to alternative selection. The second phase includes the description of the decision maker's state through Markov chains. In fact, the individual weights obtained in the first phase can be reviewed and described as transition weights from one state to another. Thus, with the construction of the individual transition matrices, the possible next state of the expert is determined from the individual weights at the end of the first phase. Finally, the experts meet, and the process of reaching consensus is analyzed by considering the single individual state obtained at the previous stage and the false consensus bias. The work contributes to the study of the impact of subjective structures, quantified through the Analytical Hierarchical Process, and how they combine with the false consensus bias in group decision-making dynamics and the consensus reaching process in problems involving the selection of equivalent portfolios.

Keywords: analytical hierarchical process, consensus building, false consensus effect, markov chains, portfolio selection problem

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Authors: Heeyong Noh, Seongryong Kang, Sungjoo Lee

Abstract:

As the world becomes increasingly interconnected, firms have tried to explore market opportunities not only in the domestic market but also abroad. In particular, transactions of intangible assets in the global market now take on great importance. Accordingly, technology transfer activities such as patent licensing, copyright transfer, or workforce trainings which are considered significant to leverage an organization’s internal capabilities, are occurring more frequently and briskly across the world than ever before. Though a number of studies have addressed the issues regarding technology transfer, most of them have focused on university-industry technology transfer. Of course, some have investigated international technology transfer phenomenon but used patent citations data as a proxy. In order to understand the phenomena more clearly, it would be necessary to collect and analyze data that can measure technology transfer activities between firms more directly. Therefore, this study aims to examine the patterns of international trade in technology by employing data about international technology in-licensing activities in Korean firms. We also investigate the effect of international technology in-licensing strategy on a firm’s innovation performance. The research findings are expected to help R&D managers understand how firms have absorbed technological knowledge from foreign firms in the form of licensing and further develop effective international collaboration strategies. In addition, significant implications can be offered for political decision-making regarding technology trade within increasing international interconnections.

Keywords: international technology trade, technology trade effect, technology transfer, R&D managers

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Authors: Vinayak Bassi, Rajpreet Singh

Abstract:

Over the years, with the emergence of sophisticated computers and algorithms, finance has been quantified using computational prowess. Asset valuation has been one of the key components of quantitative finance. In fact, it has become one of the embryonic steps in determining risk related to a portfolio, the main goal of quantitative finance. This study comprises a drawing comparison between valuation output generated by two stochastic dynamic models, namely Black-Scholes and Dupire’s bi-dimensionality model. Both of these models are formulated for computing the valuation function for a portfolio of European options using Monte Carlo simulation methods. Although Monte Carlo algorithms have a slower convergence rate than calculus-based simulation techniques (like FDM), they work quite effectively over high-dimensional dynamic models. A fidelity gap is analyzed between the static (historical) and stochastic inputs for a sample portfolio of underlying assets. In order to enhance the performance efficiency of the model, the study emphasized the use of variable reduction methods and customizing random number generators to implement parallelization. An attempt has been made to further implement the Dupire’s model on a GPU to achieve higher computational performance. Furthermore, ideas have been discussed around the performance enhancement and bottleneck identification related to the implementation of options-pricing models on GPUs.

Keywords: monte carlo, stochastic models, computational finance, parallel programming, scientific computing

Procedia PDF Downloads 160