Details

Big Data Science in Finance


Big Data Science in Finance


1. Aufl.

von: Irene Aldridge, Marco Avellaneda

80,99 €

Verlag: Wiley
Format: EPUB
Veröffentl.: 08.01.2021
ISBN/EAN: 9781119602972
Sprache: englisch
Anzahl Seiten: 336

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Beschreibungen

<p><b>Explains the mathematics, theory, and methods of Big Data as applied to finance and investing</b></p> <p>Data science has fundamentally changed Wall Street—applied mathematics and software code are increasingly driving finance and investment-decision tools. <i>Big Data Science in Finance</i> 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.</p> <p>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:</p> <ul> <li>Provides a complete account of Big Data that includes proofs, step-by-step applications, and code samples</li> <li>Explains the difference between Principal Component Analysis (PCA) and Singular Value Decomposition (SVD)</li> <li>Covers vital topics in the field in a clear, straightforward manner</li> <li>Compares, contrasts, and discusses Big Data and Small Data</li> <li>Includes Cornell University-tested educational materials such as lesson plans, end-of-chapter questions, and downloadable lecture slides</li> </ul> <p><i>Big Data Science in Finance: Mathematics and Applications </i>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.</p>
<p>Foreword</p> <p>Why Big Data?</p> <p>Neural Networks in Finance</p> <p>Supervised Models</p> <p>Semi-supervised Learning</p> <p>Letting the Data Speak with Unsupervised Learning</p> <p>Big Data Factor Models</p> <p>Data as a Signal versus Noise</p> <p>Applications: Big Data in Options Pricing and Stochastic Modeling</p> <p>Data Clustering</p> <p>Conclusions</p>
<p><b>IRENE ALDRIDGE</b> is President and Managing Director, Research of AbleMarkets, a company that provides Big Data services to capital markets. She is also a visiting professor at Cornell University. <p>More information at <b>irenealdridge.com</b> <p><b>MARCO AVELLANEDA, P<small>H</small>D,</b> is associated with Finance Concepts, a consulting firm he founded in 2003 and is a faculty member at New York University-Courant. He is regularly published in scientific journals like <i>Quantitative Finance, Risk Magazine,</i> and the <i>International Journal of Theoretical and Applied Finance.</i> <p>More information at <b>marco-avellaneda.com</b>
<p><b>Praise for BIG DATA SCIENCE IN FINANCE</b> <p>"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."<br> <b> —Elroy Dimson</b>, Professor of Finance, Cambridge Judge Business School <p>"A timely, engaging, satisfying read told in a clear and lively style that wins access to a host of complex ideas. <i>Big Data Science</i> <i>in Finance</i> reaches for a broader audience than the usual subject-matter experts—and succeeds."<br> <b> —Bruce Ells</b>, VP and Director, Infrastructure Investments, TD Greystone Asset Management <p>"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."<br> <b> —Bernd</b> <b>Wuebben</b>, Global Head, Fixed Income Quantitative Research and Systematic Investing, AllianceBernstein <p>"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."<br> <b> —John Paul Broussard</b>, Professor of Finance, Rutgers University and Estonian Business School <p>"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."<br> <b> —Jim Kyung-Soo Liew</b>, Professor, Johns Hopkins Carey Business School

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