Statistical Properties In Firms Large Scale Data
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Statistical Properties In Firms Large Scale Data
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Author : Atushi Ishikawa
language : en
Publisher:
Release Date : 2021
Statistical Properties In Firms Large Scale Data written by Atushi Ishikawa and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021 with categories.
This is the first book to provide a systematic description of statistical properties of large-scale financial data. Specifically, the power-law and log-normal distributions observed at a given time and their changes using time-reversal symmetry, quasi-time-reversal symmetry, Gibrat's law, and the non-Gibrat's property observed in a short-term period are derived here. The statistical properties observed over a long-term period, such as power-law and exponential growth, are also derived. These subjects have not been thoroughly discussed in the field of economics in the past, and this book is a compilation of the author's series of studies by reconstructing the data analyses published in 15 academic journals with new data. This book provides readers with a theoretical and empirical understanding of how the statistical properties observed in firms' large-scale data are related along the time axis. It is possible to expand this discussion to understand theoretically and empirically how the statistical properties observed among differing large-scale financial data are related. This possibility provides readers with an approach to microfoundations, an important issue that has been studied in economics for many years.
Statistical Properties In Firms Large Scale Data
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Author : Atushi Ishikawa
language : en
Publisher: Springer Nature
Release Date : 2021-06-25
Statistical Properties In Firms Large Scale Data written by Atushi Ishikawa and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-06-25 with Business & Economics categories.
This is the first book to provide a systematic description of statistical properties of large-scale financial data. Specifically, the power-law and log-normal distributions observed at a given time and their changes using time-reversal symmetry, quasi-time-reversal symmetry, Gibrat's law, and the non-Gibrat's property observed in a short-term period are derived here. The statistical properties observed over a long-term period, such as power-law and exponential growth, are also derived. These subjects have not been thoroughly discussed in the field of economics in the past, and this book is a compilation of the author's series of studies by reconstructing the data analyses published in 15 academic journals with new data. This book provides readers with a theoretical and empirical understanding of how the statistical properties observed in firms’ large-scale data are related along the time axis. It is possible to expand this discussion to understand theoretically and empirically how the statistical properties observed among differing large-scale financial data are related. This possibility provides readers with an approach to microfoundations, an important issue that has been studied in economics for many years.
Network Theory And Agent Based Modeling In Economics And Finance
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Author : Anindya S. Chakrabarti
language : en
Publisher: Springer Nature
Release Date : 2019-10-23
Network Theory And Agent Based Modeling In Economics And Finance written by Anindya S. Chakrabarti and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-10-23 with Business & Economics categories.
This book presents the latest findings on network theory and agent-based modeling of economic and financial phenomena. In this context, the economy is depicted as a complex system consisting of heterogeneous agents that interact through evolving networks; the aggregate behavior of the economy arises out of billions of small-scale interactions that take place via countless economic agents. The book focuses on analytical modeling, and on the econometric and statistical analysis of the properties emerging from microscopic interactions. In particular, it highlights the latest empirical and theoretical advances, helping readers understand economic and financial networks, as well as new work on modeling behavior using rich, agent-based frameworks. Innovatively, the book combines observational and theoretical insights in the form of networks and agent-based models, both of which have proved to be extremely valuable in understanding non-linear and evolving complex systems. Given its scope, the book will capture the interest of graduate students and researchers from various disciplines (e.g. economics, computer science, physics, and applied mathematics) whose work involves the domain of complexity theory.
Internet Economics Models Mechanisms And Management
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Author : Hans W. Gottinger
language : en
Publisher: Bentham Science Publishers
Release Date : 2017-08-28
Internet Economics Models Mechanisms And Management written by Hans W. Gottinger and has been published by Bentham Science Publishers this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-08-28 with Business & Economics categories.
The internet represents a rapidly evolving set of technologies which is central to the development of a modern economy. Internet Economics: Models, Mechanisms and Management integrates knowledge about internet service design with economic modelling principles (pricing, cost and service models). Chapters highlight specific applications of the internet such as service provisioning, cloud computing, commerce, business security, network externalities, social media and more recent developments such as the Internet of Things (IoT), the industrial internet, data analytics and the use of big data to bring value to commercial ventures. Therefore, readers will have a conceptual and practical framework for understanding the economics of internet infrastructure and service delivery.
The Federal Statistical System Its Vulnerability Matters More Than You Think
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Author : Kenneth Prewitt
language : en
Publisher: SAGE
Release Date : 2010-09
The Federal Statistical System Its Vulnerability Matters More Than You Think written by Kenneth Prewitt and has been published by SAGE this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010-09 with Political Science categories.
How do federal statistics strengthen our nation's science as well as its policy? In this latest volume of The ANNALS, leading academics, along with key federal officials, including the president's science advisor, the chief statistician of the U.S., the director of the Office of Management and Budget (OMB), the presidents of the National Academies, and the director of the Census Bureau address the argument that the statistics that the federal statistical system produces should be understood as constituting a scientific infrastructure for the empirical social sciences. Further, they see the current federal statistical system as "the best hope for bringing strong science to bear on new data sources" and "the best place to navigate unforeseen challenges in preserving the independence of statistical information from political interference." If federal statistics are the knowledge base from which policy problems and solutions emerge, it is imperative that we pay attention to the lessons they offer. Never before has this topic received this level of attention from such an array of contributors. A must read for all social scientists and policy-makers.
Big Data
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Author : Kiran Sood
language : en
Publisher: Emerald Group Publishing
Release Date : 2022-07-19
Big Data written by Kiran Sood and has been published by Emerald Group Publishing this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-07-19 with Business & Economics categories.
Striking a balance between the technical characteristics of the subject and the practical aspects of decision making, spanning from fraud analytics in claims management, to customer analytics, to risk analytics in solvency, the comprehensive coverage presented makes Big Data an invaluable resource for any insurance professional.
Big Data Of Complex Networks
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Author : Matthias Dehmer
language : en
Publisher: CRC Press
Release Date : 2016-08-19
Big Data Of Complex Networks written by Matthias Dehmer and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-08-19 with Computers categories.
Big Data of Complex Networks presents and explains the methods from the study of big data that can be used in analysing massive structural data sets, including both very large networks and sets of graphs. As well as applying statistical analysis techniques like sampling and bootstrapping in an interdisciplinary manner to produce novel techniques for analyzing massive amounts of data, this book also explores the possibilities offered by the special aspects such as computer memory in investigating large sets of complex networks. Intended for computer scientists, statisticians and mathematicians interested in the big data and networks, Big Data of Complex Networks is also a valuable tool for researchers in the fields of visualization, data analysis, computer vision and bioinformatics. Key features: Provides a complete discussion of both the hardware and software used to organize big data Describes a wide range of useful applications for managing big data and resultant data sets Maintains a firm focus on massive data and large networks Unveils innovative techniques to help readers handle big data Matthias Dehmer received his PhD in computer science from the Darmstadt University of Technology, Germany. Currently, he is Professor at UMIT – The Health and Life Sciences University, Austria, and the Universität der Bundeswehr München. His research interests are in graph theory, data science, complex networks, complexity, statistics and information theory. Frank Emmert-Streib received his PhD in theoretical physics from the University of Bremen, and is currently Associate professor at Tampere University of Technology, Finland. His research interests are in the field of computational biology, machine learning and network medicine. Stefan Pickl holds a PhD in mathematics from the Darmstadt University of Technology, and is currently a Professor at Bundeswehr Universität München. His research interests are in operations research, systems biology, graph theory and discrete optimization. Andreas Holzinger received his PhD in cognitive science from Graz University and his habilitation (second PhD) in computer science from Graz University of Technology. He is head of the Holzinger Group HCI-KDD at the Medical University Graz and Visiting Professor for Machine Learning in Health Informatics Vienna University of Technology.
Intellectual Property Statistics
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Author : Eskil Ullberg
language : en
Publisher: Springer Nature
Release Date : 2023-09-01
Intellectual Property Statistics written by Eskil Ullberg and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-09-01 with Business & Economics categories.
Patents and other intellectual property (IP) rights are increasingly part of cross-border trade in their own rights. Patent transfers and patent licensing between inventors, investors and innovators create new business strategies of cooperation in the creation of new technology – increasing the productivity in the stock of technology assets – and efficient “distribution” of these rights. The rights bundles are then used – also increasingly – in products and services being traded cross-border, furthering economic efficiency created by this cooperative strategy. Today’s international trade statistics, however, lack statistics explicitly on trade flows from ideas, based on IP rights. This book offers an idea based statistical framework to measure IP, (i.e., increasingly depends on trade in ideas) and explores ways to introduce the framework into international standards. Specifically, it offers a theory of value to measure the flows from IP and an asset view of IP to deal with allocation of resources and who owns these rights. This is then contrasted with the current way IP is treated and a “gap analysis” is used to identify what needs to change in the standards. This new framework can help develop theories, policies, practices and inform the decisions needed to better leverage the human capital formation of inventors everywhere. Praise for Intellectual Property Statistics... “In this book, Prof. Ullberg has undertaken a Herculean task – to lay out a paradigm for the collection of IP Statistics to ensure that the ... market of trade in ideas has the information and data necessary to function well. [the] volume should be viewed as a starting point, a work in progress, but an important one that could very well influence the development of this important set of data on trade in ideas. At a time when global issues ... require both new ideas and the spread of those ideas widely to help ensure both economic growth and continued global economic convergence data that helps us monitor and evaluate what is happening in trade in ideas will be extremely valuable.” – Robert Koopman, American University, Washington, DC, USA and Former Chief Economist, World Trade Organization, Geneva, Switzerland
Data Envelopment Analysis
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Author : Joe Zhu
language : en
Publisher: Springer
Release Date : 2016-03-22
Data Envelopment Analysis written by Joe Zhu and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-03-22 with Business & Economics categories.
This handbook compiles state-of-the-art empirical studies and applications using Data Envelopment Analysis (DEA). It includes a collection of 18 chapters written by DEA experts. Chapter 1 examines the performance of CEOs of U.S. banks and thrifts. Chapter 2 describes the network operational structure of transportation organizations and the relative network data envelopment analysis model. Chapter 3 demonstrates how to use different types of DEA models to compute total-factor energy efficiency scores with an application to energy efficiency. In chapter 4, the authors explore the impact of incorporating customers' willingness to pay for service quality in benchmarking models on cost efficiency of distribution networks, and chapter 5 provides a brief review of previous applications of DEA to the professional baseball industry, followed by two detailed applications to Major League Baseball. Chapter 6 examines efficiency and productivity of U.S. property-liability (P-L) insurers using DEA, while chapter 7 presents a two-stage network DEA model that decomposes the overall efficiency of a decision-making unit into two components. Chapter 8 presents a review of the literature of DEA models for the perfoemance assessment of mutual funds, and chapter 9 discusses the management strategies formulation of the international tourist hotel industry in Taiwan. Chapter 10 presents a novel use of the two-stage network DEA to evaluate sustainable product design performances. In chapter 11 authors highlight limitations of some DEA environmental efficiency models, and chapter 12 reviews applications of DEA in secondary and tertiary education. Chapter 13 measures the relative performance of New York State school districts in the 2011-2012 academic year. Chapter 14 provides an introductory prelude to chapters 15 and 16, which both provide detailed applications of DEA in marketing. Chapter 17 then shows how to decompose a new total factor productivity index that satisfies all economically-relevant axioms from index theory with an application to U.S. agriculture. Finally, chapter 18 presents a unique study that conducts a DEA research front analysis, applying a network clustering method to group the DEA literature over the period 2000 to 2014.
Big Data Science In Finance
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Author : Irene Aldridge
language : en
Publisher: John Wiley & Sons
Release Date : 2021-01-08
Big Data Science In Finance written by Irene Aldridge and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-01-08 with Computers categories.
Explains the mathematics, theory, and methods of Big Data as applied to finance and investing Data science has fundamentally changed Wall Street—applied mathematics and software code are increasingly driving finance and investment-decision tools. Big Data Science in Finance examines the mathematics, theory, and practical use of the revolutionary techniques that are transforming the industry. Designed for mathematically-advanced students and discerning financial practitioners alike, this energizing book presents new, cutting-edge content based on world-class research taught in the leading Financial Mathematics and Engineering programs in the world. Marco Avellaneda, a leader in quantitative finance, and quantitative methodology author Irene Aldridge help readers harness the power of Big Data. Comprehensive in scope, this book offers in-depth instruction on how to separate signal from noise, how to deal with missing data values, and how to utilize Big Data techniques in decision-making. Key topics include data clustering, data storage optimization, Big Data dynamics, Monte Carlo methods and their applications in Big Data analysis, and more. This valuable book: Provides a complete account of Big Data that includes proofs, step-by-step applications, and code samples Explains the difference between Principal Component Analysis (PCA) and Singular Value Decomposition (SVD) Covers vital topics in the field in a clear, straightforward manner Compares, contrasts, and discusses Big Data and Small Data Includes Cornell University-tested educational materials such as lesson plans, end-of-chapter questions, and downloadable lecture slides Big Data Science in Finance: Mathematics and Applications is an important, up-to-date resource for students in economics, econometrics, finance, applied mathematics, industrial engineering, and business courses, and for investment managers, quantitative traders, risk and portfolio managers, and other financial practitioners.