Irene Aldridge, Marco Avellaneda

Big Data Science in Finance (eBook, PDF)

• Format: PDF

Produktbeschreibung

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.

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Produktdetails

• Verlag: John Wiley & Sons
• Seitenzahl: 336
• Erscheinungstermin: 31. Dezember 2020
• Englisch
• ISBN-13: 9781119602996
• Artikelnr.: 60984272

Autorenporträt

IRENE ALDRIDGE 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. More information at irenealdridge.com MARCO AVELLANEDA, PHD, 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 Quantitative Finance, Risk Magazine, and the International Journal of Theoretical and Applied Finance. More information at marco-avellaneda.com

Inhaltsangabe

Foreword

Why Big Data?

Neural Networks in Finance

Supervised Models

Semi-supervised Learning

Letting the Data Speak with Unsupervised Learning

Big Data Factor Models

Data as a Signal versus Noise

Applications: Big Data in Options Pricing and Stochastic Modeling

Data Clustering

Conclusions