Big Data Science In Finance

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Big Data Science in Finance

Irene Aldridge,Marco Avellaneda
Big Data Science in Finance Book PDF

Author : Irene Aldridge,Marco Avellaneda
Publisher : John Wiley & Sons
Page : 336 pages
File Size : 41,6 Mb
Release : 2021-01-08
Category : Computers
ISBN : 9781119602972

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Big Data Science in Finance by Irene Aldridge,Marco Avellaneda Pdf

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.

Machine Learning and Data Science Blueprints for Finance

Hariom Tatsat,Sahil Puri,Brad Lookabaugh
Machine Learning and Data Science Blueprints for Finance Book PDF

Author : Hariom Tatsat,Sahil Puri,Brad Lookabaugh
Publisher : "O'Reilly Media, Inc."
Page : 432 pages
File Size : 48,7 Mb
Release : 2020-10-01
Category : Computers
ISBN : 9781492073000

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Machine Learning and Data Science Blueprints for Finance by Hariom Tatsat,Sahil Puri,Brad Lookabaugh Pdf

Over the next few decades, machine learning and data science will transform the finance industry. With this practical book, analysts, traders, researchers, and developers will learn how to build machine learning algorithms crucial to the industry. You’ll examine ML concepts and over 20 case studies in supervised, unsupervised, and reinforcement learning, along with natural language processing (NLP). Ideal for professionals working at hedge funds, investment and retail banks, and fintech firms, this book also delves deep into portfolio management, algorithmic trading, derivative pricing, fraud detection, asset price prediction, sentiment analysis, and chatbot development. You’ll explore real-life problems faced by practitioners and learn scientifically sound solutions supported by code and examples. This book covers: Supervised learning regression-based models for trading strategies, derivative pricing, and portfolio management Supervised learning classification-based models for credit default risk prediction, fraud detection, and trading strategies Dimensionality reduction techniques with case studies in portfolio management, trading strategy, and yield curve construction Algorithms and clustering techniques for finding similar objects, with case studies in trading strategies and portfolio management Reinforcement learning models and techniques used for building trading strategies, derivatives hedging, and portfolio management NLP techniques using Python libraries such as NLTK and scikit-learn for transforming text into meaningful representations

Data Science for Economics and Finance

Sergio Consoli,Diego Reforgiato Recupero,Michaela Saisana
Data Science for Economics and Finance Book PDF

Author : Sergio Consoli,Diego Reforgiato Recupero,Michaela Saisana
Publisher : Springer Nature
Page : 357 pages
File Size : 41,8 Mb
Release : 2021
Category : Application software
ISBN : 9783030668914

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Data Science for Economics and Finance by Sergio Consoli,Diego Reforgiato Recupero,Michaela Saisana Pdf

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.

Financial Data Analytics

Sinem Derindere Köseoğlu
Financial Data Analytics Book PDF

Author : Sinem Derindere Köseoğlu
Publisher : Springer Nature
Page : 393 pages
File Size : 40,5 Mb
Release : 2022-04-25
Category : Business & Economics
ISBN : 9783030837990

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Financial Data Analytics by Sinem Derindere Köseoğlu Pdf

​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.

Big Data Science in Finance

Irene Aldridge,Marco Avellaneda
Big Data Science in Finance Book PDF

Author : Irene Aldridge,Marco Avellaneda
Publisher : John Wiley & Sons
Page : 336 pages
File Size : 50,9 Mb
Release : 2021-01-27
Category : Computers
ISBN : 9781119602989

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Big Data Science in Finance by Irene Aldridge,Marco Avellaneda Pdf

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.

New Horizons for a Data-Driven Economy

José María Cavanillas,Edward Curry,Wolfgang Wahlster
New Horizons for a Data Driven Economy Book PDF

Author : José María Cavanillas,Edward Curry,Wolfgang Wahlster
Publisher : Springer
Page : 303 pages
File Size : 53,5 Mb
Release : 2016-04-04
Category : Computers
ISBN : 9783319215693

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New Horizons for a Data-Driven Economy by José María Cavanillas,Edward Curry,Wolfgang Wahlster Pdf

In this book readers will find technological discussions on the existing and emerging technologies across the different stages of the big data value chain. They will learn about legal aspects of big data, the social impact, and about education needs and requirements. And they will discover the business perspective and how big data technology can be exploited to deliver value within different sectors of the economy. The book is structured in four parts: Part I “The Big Data Opportunity” explores the value potential of big data with a particular focus on the European context. It also describes the legal, business and social dimensions that need to be addressed, and briefly introduces the European Commission’s BIG project. Part II “The Big Data Value Chain” details the complete big data lifecycle from a technical point of view, ranging from data acquisition, analysis, curation and storage, to data usage and exploitation. Next, Part III “Usage and Exploitation of Big Data” illustrates the value creation possibilities of big data applications in various sectors, including industry, healthcare, finance, energy, media and public services. Finally, Part IV “A Roadmap for Big Data Research” identifies and prioritizes the cross-sectorial requirements for big data research, and outlines the most urgent and challenging technological, economic, political and societal issues for big data in Europe. This compendium summarizes more than two years of work performed by a leading group of major European research centers and industries in the context of the BIG project. It brings together research findings, forecasts and estimates related to this challenging technological context that is becoming the major axis of the new digitally transformed business environment.

Summary of Irene Aldridge & Marco Avellaneda's Big Data Science in Finance

Everest Media
Summary of Irene Aldridge Marco Avellaneda s Big Data Science in Finance Book PDF

Author : Everest Media
Publisher : Everest Media LLC
Page : 11 pages
File Size : 45,9 Mb
Release : 2022-03-01T21:00:00Z
Category : Business & Economics
ISBN : 9781669347774

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Summary of Irene Aldridge & Marco Avellaneda's Big Data Science in Finance by Everest Media Pdf

Please note: This is a companion version & not the original book. Sample Book Insights: #1 Radio-Frequency Identification, or RFID, chips are embedded in almost every product you can buy, and are used to collect data about your shopping preferences, habits, and lifestyle. #2 Big Data is not just affecting financial organizations, but all corporations. The amount of data generated by financial institutions is at a record-setting number, and yet few portfolio managers have the skills to process it. #3 Big Data Finance is not just opening doors to a select group of data scientists, but also an entire industry that is developing new approaches to harness these data sets and incorporate them into mainstream investment management. #4 The demand for Big Data scientists is growing, as companies realize the importance of efficient Big Data operations. According to Business Insider, US bank J. P. Morgan has spent nearly $10 billion dollars just in 2016 on new initiatives that include Big Data science.

Adventures In Financial Data Science: The Empirical Properties Of Financial And Economic Data (Second Edition)

Graham L Giller
Adventures In Financial Data Science The Empirical Properties Of Financial And Economic Data Second Edition Book PDF

Author : Graham L Giller
Publisher : World Scientific
Page : 512 pages
File Size : 53,5 Mb
Release : 2022-06-27
Category : Business & Economics
ISBN : 9789811251825

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Adventures In Financial Data Science: The Empirical Properties Of Financial And Economic Data (Second Edition) by Graham L Giller Pdf

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.'

Big Data Analytics for Internet of Things

Tausifa Jan Saleem,Mohammad Ahsan Chishti
Big Data Analytics for Internet of Things Book PDF

Author : Tausifa Jan Saleem,Mohammad Ahsan Chishti
Publisher : John Wiley & Sons
Page : 402 pages
File Size : 46,8 Mb
Release : 2021-04-20
Category : Mathematics
ISBN : 9781119740759

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Big Data Analytics for Internet of Things by Tausifa Jan Saleem,Mohammad Ahsan Chishti Pdf

BIG DATA ANALYTICS FOR INTERNET OF THINGS Discover the latest developments in IoT Big Data with a new resource from established and emerging leaders in the field Big Data Analytics for Internet of Things delivers a comprehensive overview of all aspects of big data analytics in Internet of Things (IoT) systems. The book includes discussions of the enabling technologies of IoT data analytics, types of IoT data analytics, challenges in IoT data analytics, demand for IoT data analytics, computing platforms, analytical tools, privacy, and security. The distinguished editors have included resources that address key techniques in the analysis of IoT data. The book demonstrates how to select the appropriate techniques to unearth valuable insights from IoT data and offers novel designs for IoT systems. With an abiding focus on practical strategies with concrete applications for data analysts and IoT professionals, Big Data Analytics for Internet of Things also offers readers: A thorough introduction to the Internet of Things, including IoT architectures, enabling technologies, and applications An exploration of the intersection between the Internet of Things and Big Data, including IoT as a source of Big Data, the unique characteristics of IoT data, etc. A discussion of the IoT data analytics, including the data analytical requirements of IoT data and the types of IoT analytics, including predictive, descriptive, and prescriptive analytics A treatment of machine learning techniques for IoT data analytics Perfect for professionals, industry practitioners, and researchers engaged in big data analytics related to IoT systems, Big Data Analytics for Internet of Things will also earn a place in the libraries of IoT designers and manufacturers interested in facilitating the efficient implementation of data analytics strategies.

Financial Statistics and Data Analytics

Shuangzhe Li,Milind Sathye
Financial Statistics and Data Analytics Book PDF

Author : Shuangzhe Li,Milind Sathye
Publisher : MDPI
Page : 232 pages
File Size : 47,9 Mb
Release : 2021-03-02
Category : Business & Economics
ISBN : 9783039439751

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Financial Statistics and Data Analytics by Shuangzhe Li,Milind Sathye Pdf

Modern financial management is largely about risk management, which is increasingly data-driven. The problem is how to extract information from the data overload. It is here that advanced statistical and machine learning techniques can help. Accordingly, finance, statistics, and data analytics go hand in hand. The purpose of this book is to bring the state-of-art research in these three areas to the fore and especially research that juxtaposes these three.

Big Data and Visual Analytics

Sang C. Suh,Thomas Anthony
Big Data and Visual Analytics Book PDF

Author : Sang C. Suh,Thomas Anthony
Publisher : Springer
Page : 263 pages
File Size : 46,9 Mb
Release : 2018-01-15
Category : Computers
ISBN : 9783319639178

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Big Data and Visual Analytics by Sang C. Suh,Thomas Anthony Pdf

This book provides users with cutting edge methods and technologies in the area of big data and visual analytics, as well as an insight to the big data and data analytics research conducted by world-renowned researchers in this field. The authors present comprehensive educational resources on big data and visual analytics covering state-of-the art techniques on data analytics, data and information visualization, and visual analytics. Each chapter covers specific topics related to big data and data analytics as virtual data machine, security of big data, big data applications, high performance computing cluster, and big data implementation techniques. Every chapter includes a description of an unique contribution to the area of big data and visual analytics. This book is a valuable resource for researchers and professionals working in the area of big data, data analytics, and information visualization. Advanced-level students studying computer science will also find this book helpful as a secondary textbook or reference.

Fintech with Artificial Intelligence, Big Data, and Blockchain

Paul Moon Sub Choi,Seth H. Huang
Fintech with Artificial Intelligence Big Data and Blockchain Book PDF

Author : Paul Moon Sub Choi,Seth H. Huang
Publisher : Springer Nature
Page : 306 pages
File Size : 41,8 Mb
Release : 2021-03-08
Category : Technology & Engineering
ISBN : 9789813361379

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Fintech with Artificial Intelligence, Big Data, and Blockchain by Paul Moon Sub Choi,Seth H. Huang Pdf

This book introduces readers to recent advancements in financial technologies. The contents cover some of the state-of-the-art fields in financial technology, practice, and research associated with artificial intelligence, big data, and blockchain—all of which are transforming the nature of how products and services are designed and delivered, making less adaptable institutions fast become obsolete. The book provides the fundamental framework, research insights, and empirical evidence in the efficacy of these new technologies, employing practical and academic approaches to help professionals and academics reach innovative solutions and grow competitive strengths.

Big Data Concepts, Theories, and Applications

Shui Yu,Song Guo
Big Data Concepts Theories and Applications Book PDF

Author : Shui Yu,Song Guo
Publisher : Springer
Page : 437 pages
File Size : 50,5 Mb
Release : 2016-03-03
Category : Computers
ISBN : 9783319277639

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Big Data Concepts, Theories, and Applications by Shui Yu,Song Guo Pdf

This book covers three major parts of Big Data: concepts, theories and applications. Written by world-renowned leaders in Big Data, this book explores the problems, possible solutions and directions for Big Data in research and practice. It also focuses on high level concepts such as definitions of Big Data from different angles; surveys in research and applications; and existing tools, mechanisms, and systems in practice. Each chapter is independent from the other chapters, allowing users to read any chapter directly. After examining the practical side of Big Data, this book presents theoretical perspectives. The theoretical research ranges from Big Data representation, modeling and topology to distribution and dimension reducing. Chapters also investigate the many disciplines that involve Big Data, such as statistics, data mining, machine learning, networking, algorithms, security and differential geometry. The last section of this book introduces Big Data applications from different communities, such as business, engineering and science. Big Data Concepts, Theories and Applications is designed as a reference for researchers and advanced level students in computer science, electrical engineering and mathematics. Practitioners who focus on information systems, big data, data mining, business analysis and other related fields will also find this material valuable.

Data Science in Theory and Practice

Maria Cristina Mariani,Osei Kofi Tweneboah,Maria Pia Beccar-Varela
Data Science in Theory and Practice Book PDF

Author : Maria Cristina Mariani,Osei Kofi Tweneboah,Maria Pia Beccar-Varela
Publisher : John Wiley & Sons
Page : 404 pages
File Size : 42,9 Mb
Release : 2021-10-12
Category : Mathematics
ISBN : 9781119674689

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Data Science in Theory and Practice by Maria Cristina Mariani,Osei Kofi Tweneboah,Maria Pia Beccar-Varela Pdf

DATA SCIENCE IN THEORY AND PRACTICE EXPLORE THE FOUNDATIONS OF DATA SCIENCE WITH THIS INSIGHTFUL NEW RESOURCE Data Science in Theory and Practice delivers a comprehensive treatment of the mathematical and statistical models useful for analyzing data sets arising in various disciplines, like banking, finance, health care, bioinformatics, security, education, and social services. Written in five parts, the book examines some of the most commonly used and fundamental mathematical and statistical concepts that form the basis of data science. The authors go on to analyze various data transformation techniques useful for extracting information from raw data, long memory behavior, and predictive modeling. The book offers readers a multitude of topics all relevant to the analysis of complex data sets. Along with a robust exploration of the theory underpinning data science, it contains numerous applications to specific and practical problems. The book also provides examples of code algorithms in R and Python and provides pseudo-algorithms to port the code to any other language. Ideal for students and practitioners without a strong background in data science, readers will also learn from topics like: Analyses of foundational theoretical subjects, including the history of data science, matrix algebra and random vectors, and multivariate analysis A comprehensive examination of time series forecasting, including the different components of time series and transformations to achieve stationarity Introductions to both the R and Python programming languages, including basic data types and sample manipulations for both languages An exploration of algorithms, including how to write one and how to perform an asymptotic analysis A comprehensive discussion of several techniques for analyzing and predicting complex data sets Perfect for advanced undergraduate and graduate students in Data Science, Business Analytics, and Statistics programs, Data Science in Theory and Practice will also earn a place in the libraries of practicing data scientists, data and business analysts, and statisticians in the private sector, government, and academia.

Python for Finance

Yves Hilpisch
Python for Finance Book PDF

Author : Yves Hilpisch
Publisher : "O'Reilly Media, Inc."
Page : 720 pages
File Size : 44,6 Mb
Release : 2018-12-05
Category : Computers
ISBN : 9781492024293

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Python for Finance by Yves Hilpisch Pdf

The financial industry has recently adopted Python at a tremendous rate, with some of the largest investment banks and hedge funds using it to build core trading and risk management systems. Updated for Python 3, the second edition of this hands-on book helps you get started with the language, guiding developers and quantitative analysts through Python libraries and tools for building financial applications and interactive financial analytics. Using practical examples throughout the book, author Yves Hilpisch also shows you how to develop a full-fledged framework for Monte Carlo simulation-based derivatives and risk analytics, based on a large, realistic case study. Much of the book uses interactive IPython Notebooks.