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Big Data Science In Finance

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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.

Data Science For Economics And Finance

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Author : Sergio Consoli
language : en
Publisher: Springer Nature
Release Date : 2021-06-09

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 2021-06-09 with Computers 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.

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.

Big Data Science In Finance

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Author : Irene Aldridge
language : en
Publisher: John Wiley & Sons
Release Date : 2021-01-08

Data Science And Risk Analytics In Finance And Insurance

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Author : Tze Leung Lai
language : en
Publisher: CRC Press
Release Date : 2024-10-02

Data Science And Risk Analytics In Finance And Insurance written by Tze Leung Lai and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-10-02 with Business & Economics categories.

This book presents statistics and data science methods for risk analytics in quantitative finance and insurance. Part I covers the background, financial models, and data analytical methods for market risk, credit risk, and operational risk in financial instruments, as well as models of risk premium and insolvency in insurance contracts. Part II provides an overview of machine learning (including supervised, unsupervised, and reinforcement learning), Monte Carlo simulation, and sequential analysis techniques for risk analytics. In Part III, the book offers a non-technical introduction to four key areas in financial technology: artificial intelligence, blockchain, cloud computing, and big data analytics. Key Features: Provides a comprehensive and in-depth overview of data science methods for financial and insurance risks. Unravels bandits, Markov decision processes, reinforcement learning, and their interconnections. Promotes sequential surveillance and predictive analytics for abrupt changes in risk factors. Introduces the ABCDs of FinTech: Artificial intelligence, blockchain, cloud computing, and big data analytics. Includes supplements and exercises to facilitate deeper comprehension.

Fintech With Artificial Intelligence Big Data And Blockchain

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Author : Paul Moon Sub Choi
language : en
Publisher: Springer Nature
Release Date : 2021-03-08

Fintech With Artificial Intelligence Big Data And Blockchain written by Paul Moon Sub Choi and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-03-08 with Technology & Engineering categories.

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 And Machine Learning In Quantitative Investment

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Author : Tony Guida
language : en
Publisher: John Wiley & Sons
Release Date : 2019-03-25

Big Data And Machine Learning In Quantitative Investment written by Tony Guida 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 2019-03-25 with Business & Economics categories.

Get to know the ‘why’ and ‘how’ of machine learning and big data in quantitative investment Big Data and Machine Learning in Quantitative Investment is not just about demonstrating the maths or the coding. Instead, it’s a book by practitioners for practitioners, covering the questions of why and how of applying machine learning and big data to quantitative finance. The book is split into 13 chapters, each of which is written by a different author on a specific case. The chapters are ordered according to the level of complexity; beginning with the big picture and taxonomy, moving onto practical applications of machine learning and finally finishing with innovative approaches using deep learning. • Gain a solid reason to use machine learning • Frame your question using financial markets laws • Know your data • Understand how machine learning is becoming ever more sophisticated Machine learning and big data are not a magical solution, but appropriately applied, they are extremely effective tools for quantitative investment — and this book shows you how.

Data Science And Multiple Criteria Decision Making Approaches In Finance

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Author : Gökhan Silahtaroğlu
language : en
Publisher: Springer Nature
Release Date : 2021-05-29

Data Science And Multiple Criteria Decision Making Approaches In Finance written by Gökhan Silahtaroğlu and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-05-29 with Business & Economics categories.

This book considers and assesses essential financial issues by utilizing data science and fuzzy multiple criteria decision making (MCDM) methods. It introduces readers to a range of data science methods, and demonstrates their application in the fields of business, health, economics, finance and engineering. In addition, it provides suggestions based on the assessment results on each topic, which can help to enhance the efficiency of the financial system and the sustainability of economic development. Given its scope, the book will help readers broaden their perspective on the assessment and evaluation of financial issues using data science and MCDM approaches.

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.

Big Data Mba

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Author : Bill Schmarzo
language : en
Publisher: John Wiley & Sons
Release Date : 2015-12-21

Big Data Mba written by Bill Schmarzo 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 2015-12-21 with Computers categories.

Integrate big data into business to drive competitive advantage and sustainable success Big Data MBA brings insight and expertise to leveraging big data in business so you can harness the power of analytics and gain a true business advantage. Based on a practical framework with supporting methodology and hands-on exercises, this book helps identify where and how big data can help you transform your business. You'll learn how to exploit new sources of customer, product, and operational data, coupled with advanced analytics and data science, to optimize key processes, uncover monetization opportunities, and create new sources of competitive differentiation. The discussion includes guidelines for operationalizing analytics, optimal organizational structure, and using analytic insights throughout your organization's user experience to customers and front-end employees alike. You'll learn to “think like a data scientist” as you build upon the decisions your business is trying to make, the hypotheses you need to test, and the predictions you need to produce. Business stakeholders no longer need to relinquish control of data and analytics to IT. In fact, they must champion the organization's data collection and analysis efforts. This book is a primer on the business approach to analytics, providing the practical understanding you need to convert data into opportunity. Understand where and how to leverage big data Integrate analytics into everyday operations Structure your organization to drive analytic insights Optimize processes, uncover opportunities, and stand out from the rest Help business stakeholders to “think like a data scientist” Understand appropriate business application of different analytic techniques If you want data to transform your business, you need to know how to put it to use. Big Data MBA shows you how to implement big data and analytics to make better decisions.

Big Data Science In Finance

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