Praise for BIG DATA SCIENCE IN FINANCE

"Irene Aldridge and Marco Avellaneda are articulate enthusiasts for Big Data Finance. They have a deep knowledge of neural networks, artificial intelligence, machine learning, and many other tools--and they are excited to share their skills. Each chapter of this wonderful book entices the reader with a broad overview, and then shows how these new concepts can be applied in financial markets. The authors are Big Data visionaries whose book belongs on your desk, not on your bookshelf."
--Elroy Dimson, Professor of Finance, Cambridge Judge Business School

"A timely, engaging, satisfying read told in a clear and lively style that wins access to a host of complex ideas. Big Data Science in Finance reaches for a broader audience than the usual subject-matter experts--and succeeds."
--Bruce Ells, VP and Director, Infrastructure Investments, TD Greystone Asset Management

"Asset managers and hedge funds are acutely aware that delivering alpha is becoming simultaneously more important and difficult. Given this background, Big Data and machine learning have become essential sources of new differentiating alpha. This much needed timely text on Big Data in finance is a refreshingly hands-on introduction to this essential subject matter that should advance the understanding of these methods and their application in modern portfolio management."
--Bernd Wuebben, Global Head, Fixed Income Quantitative Research and Systematic Investing, AllianceBernstein

"WOW! My first glance reminds me of the tried and true approach--provide theoretical background, then show implementable examples. I am actually thinking of using the book for a 'Data in Finance' offering I am working on."
--John Paul Broussard, Professor of Finance, Rutgers University and Estonian Business School

"Two of the most important figures in AI Finance have come out with a must-read Tour de Force! Soon to be a stable textbook in all of our top MBA programs."
--Jim Kyung-Soo Liew, Professor, Johns Hopkins Carey Business School

Big Data Science in Finance delivers the mathematics, theories, and applications of Big Data techniques in finance. Distinguished authors and professionals Irene Aldridge and Marco Avellaneda offer readers brand-new, updated material on the latest world-class research taught in the top Financial Mathematics and Engineering programs in the world. The book's materials have been tested in prestigious classrooms within the Cornell University Financial Engineering program and have proven highly engaging and instructive.

In Big Data Science in Finance, Aldridge and Avellaneda walk readers through the foundational and advanced topics necessary to comprehensively understand the intersection of the worlds of Big Data and finance. Readers will learn about how Big Data differs from Small Data, in-depth techniques in supervised, semi-supervised, and unsupervised learning, the techniques for separating signal from noise, how to effectively deal with missing data values, data clustering, and much more, all in the context of profitable applications of Big Data to finance.

Big Data Science in Finance and its supplementary web resources include lesson plans, end-of-chapter questions, and teaching slides that will aid readers in remembering and retaining the complex material within. Readers will obtain a complete and fulsome understanding of the Big Data techniques currently revolutionizing the finance and investment industries. From fundamental concepts to advanced subjects like supervised and unsupervised machine learning, the book walks readers through every subject they'll need to navigate the intersection of the worlds of Big Data and finance.

Perfect for undergraduate and graduate students in economics and econometrics, finance, applied mathematics, industrial engineering, and business, the book also belongs on the shelves of investment managers, quantitative traders, risk managers, and portfolio managers who aim to improve their ability to find success in the financial markets.

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.