Data Science For Financial Econometrics
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Data Science For Financial Econometrics
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Author : Nguyen Ngoc Thach
language : en
Publisher: Springer Nature
Release Date : 2020-11-13
Data Science For Financial Econometrics written by Nguyen Ngoc Thach and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-11-13 with Computers categories.
This book offers an overview of state-of-the-art econometric techniques, with a special emphasis on financial econometrics. There is a major need for such techniques, since the traditional way of designing mathematical models – based on researchers’ insights – can no longer keep pace with the ever-increasing data flow. To catch up, many application areas have begun relying on data science, i.e., on techniques for extracting models from data, such as data mining, machine learning, and innovative statistics. In terms of capitalizing on data science, many application areas are way ahead of economics. To close this gap, the book provides examples of how data science techniques can be used in economics. Corresponding techniques range from almost traditional statistics to promising novel ideas such as quantum econometrics. Given its scope, the book will appeal to students and researchers interested in state-of-the-art developments, and to practitioners interested in using data science techniques.
Financial Data Analytics
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Author : Sinem Derindere Köseoğlu
language : en
Publisher: Springer Nature
Release Date : 2022-04-25
Financial Data Analytics written by Sinem Derindere Köseoğlu and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-04-25 with Business & Economics categories.
This book presents both theory of financial data analytics, as well as comprehensive insights into the application of financial data analytics techniques in real financial world situations. It offers solutions on how to logically analyze the enormous amount of structured and unstructured data generated every moment in the finance sector. This data can be used by companies, organizations, and investors to create strategies, as the finance sector rapidly moves towards data-driven optimization. This book provides an efficient resource, addressing all applications of data analytics in the finance sector. International experts from around the globe cover the most important subjects in finance, including data processing, knowledge management, machine learning models, data modeling, visualization, optimization for financial problems, financial econometrics, financial time series analysis, project management, and decision making. The authors provide empirical evidence as examples of specific topics. By combining both applications and theory, the book offers a holistic approach. Therefore, it is a must-read for researchers and scholars of financial economics and finance, as well as practitioners interested in a better understanding of financial data analytics.
Data Science For Economics And Finance
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Author : Sergio Consoli
language : en
Publisher: Springer Nature
Release Date : 2021
Data Science For Economics And Finance written by Sergio Consoli 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 with Application software categories.
This open access book covers the use of data science, including advanced machine learning, big data analytics, Semantic Web technologies, natural language processing, social media analysis, time series analysis, among others, for applications in economics and finance. In addition, it shows some successful applications of advanced data science solutions used to extract new knowledge from data in order to improve economic forecasting models. The book starts with an introduction on the use of data science technologies in economics and finance and is followed by thirteen chapters showing success stories of the application of specific data science methodologies, touching on particular topics related to novel big data sources and technologies for economic analysis (e.g. social media and news); big data models leveraging on supervised/unsupervised (deep) machine learning; natural language processing to build economic and financial indicators; and forecasting and nowcasting of economic variables through time series analysis. This book is relevant to all stakeholders involved in digital and data-intensive research in economics and finance, helping them to understand the main opportunities and challenges, become familiar with the latest methodological findings, and learn how to use and evaluate the performances of novel tools and frameworks. It primarily targets data scientists and business analysts exploiting data science technologies, and it will also be a useful resource to research students in disciplines and courses related to these topics. Overall, readers will learn modern and effective data science solutions to create tangible innovations for economic and financial applications.
Big Data Science In Finance
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Author : Irene Aldridge
language : en
Publisher: John Wiley & Sons
Release Date : 2021-01-27
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 2021-01-27 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.
Adventures In Financial Data Science The Empirical Properties Of Financial And Economic Data Second Edition
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Author : Graham L Giller
language : en
Publisher: World Scientific
Release Date : 2022-06-27
Adventures In Financial Data Science The Empirical Properties Of Financial And Economic Data Second Edition written by Graham L Giller and has been published by World Scientific this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-06-27 with Business & Economics categories.
This book provides insights into the true nature of financial and economic data, and is a practical guide on how to analyze a variety of data sources. The focus of the book is on finance and economics, but it also illustrates the use of quantitative analysis and data science in many different areas. Lastly, the book includes practical information on how to store and process data and provides a framework for data driven reasoning about the world.The book begins with entertaining tales from Graham Giller's career in finance, starting with speculating in UK government bonds at the Oxford Post Office, accidentally creating a global instant messaging system that went 'viral' before anybody knew what that meant, on being the person who forgot to hit 'enter' to run a hundred-million dollar statistical arbitrage system, what he decoded from his brief time spent with Jim Simons, and giving Michael Bloomberg a tutorial on Granger Causality.The majority of the content is a narrative of analytic work done on financial, economics, and alternative data, structured around both Dr Giller's professional career and some of the things that just interested him. The goal is to stimulate interest in predictive methods, to give accurate characterizations of the true properties of financial, economic and alternative data, and to share what Richard Feynman described as 'The Pleasure of Finding Things Out.'
Adventures In Financial Data Science
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Author : Graham L. Giller
language : en
Publisher:
Release Date : 2022
Adventures In Financial Data Science written by Graham L. Giller and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022 with Financial services industry categories.
"This book provides insights into the true nature of financial and economic data, and is a practical guide on how to analyze a variety of data sources. The focus of the book is on finance and economics, but it also illustrates the use of quantitative analysis and data science in many different areas. Lastly, the book includes practical information on how to store and process data and provides a framework for data driven reasoning about the world. The book begins with entertaining tales from Graham Giller's career in finance, starting with speculating in UK government bonds at the Oxford Post Office, accidentally creating a global instant messaging system that went "viral" before anybody knew what that meant, on being the person who forgot to hit "enter" to run a hundred-million dollar statistical arbitrage system, what he decoded from his brief time spent with Jim Simons, and giving Michael Bloomberg a tutorial on Granger Causality. The majority of the content is a narrative of analytic work done on financial, economics, and alternative data, structured around both Dr Giller's professional career and some of the things that just interested him. The goal is to stimulate interest in predictive methods, to give accurate characterizations of the true properties of financial, economic and alternative data, and to share what Richard Feynman described as 'The Pleasure of Finding Things Out.'"--
Handbook Of Financial Econometrics Mathematics Statistics And Machine Learning In 4 Volumes
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Author : Cheng Few Lee
language : en
Publisher: World Scientific
Release Date : 2020-07-30
Handbook Of Financial Econometrics Mathematics Statistics And Machine Learning In 4 Volumes written by Cheng Few Lee and has been published by World Scientific this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-07-30 with Business & Economics categories.
This four-volume handbook covers important concepts and tools used in the fields of financial econometrics, mathematics, statistics, and machine learning. Econometric methods have been applied in asset pricing, corporate finance, international finance, options and futures, risk management, and in stress testing for financial institutions. This handbook discusses a variety of econometric methods, including single equation multiple regression, simultaneous equation regression, and panel data analysis, among others. It also covers statistical distributions, such as the binomial and log normal distributions, in light of their applications to portfolio theory and asset management in addition to their use in research regarding options and futures contracts.In both theory and methodology, we need to rely upon mathematics, which includes linear algebra, geometry, differential equations, Stochastic differential equation (Ito calculus), optimization, constrained optimization, and others. These forms of mathematics have been used to derive capital market line, security market line (capital asset pricing model), option pricing model, portfolio analysis, and others.In recent times, an increased importance has been given to computer technology in financial research. Different computer languages and programming techniques are important tools for empirical research in finance. Hence, simulation, machine learning, big data, and financial payments are explored in this handbook.Led by Distinguished Professor Cheng Few Lee from Rutgers University, this multi-volume work integrates theoretical, methodological, and practical issues based on his years of academic and industry experience.
Advances In Econometrics Operational Research Data Science And Actuarial Studies
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Author : M. Kenan Terzioğlu
language : en
Publisher: Springer Nature
Release Date : 2022-01-17
Advances In Econometrics Operational Research Data Science And Actuarial Studies written by M. Kenan Terzioğlu and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-01-17 with Business & Economics categories.
This volume presents techniques and theories drawn from mathematics, statistics, computer science, and information science to analyze problems in business, economics, finance, insurance, and related fields. The authors present proposals for solutions to common problems in related fields. To this end, they are showing the use of mathematical, statistical, and actuarial modeling, and concepts from data science to construct and apply appropriate models with real-life data, and employ the design and implementation of computer algorithms to evaluate decision-making processes. This book is unique as it associates data science - data-scientists coming from different backgrounds - with some basic and advanced concepts and tools used in econometrics, operational research, and actuarial sciences. It, therefore, is a must-read for scholars, students, and practitioners interested in a better understanding of the techniques and theories of these fields.
The Elements Of Financial Econometrics
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Author : Jianqing Fan
language : en
Publisher: Cambridge University Press
Release Date : 2017-03-23
The Elements Of Financial Econometrics written by Jianqing Fan and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-03-23 with Business & Economics categories.
A compact, master's-level textbook on financial econometrics, focusing on methodology and including real financial data illustrations throughout. The mathematical level is purposely kept moderate, allowing the power of the quantitative methods to be understood without too much technical detail.
Financial Econometrics And Empirical Market Microstructure
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Author : Anil K. Bera
language : en
Publisher: Springer
Release Date : 2014-11-18
Financial Econometrics And Empirical Market Microstructure written by Anil K. Bera and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-11-18 with Business & Economics categories.
In the era of Big Data our society is given the unique opportunity to understand the inner dynamics and behavior of complex socio-economic systems. Advances in the availability of very large databases, in capabilities for massive data mining, as well as progress in complex systems theory, multi-agent simulation and computational social science open the possibility of modeling phenomena never before successfully achieved. This contributed volume from the Perm Winter School address the problems of the mechanisms and statistics of the socio-economics system evolution with a focus on financial markets powered by the high-frequency data analysis.