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Big Data Science in Finance

Big Data Science in Finance

  • ISBN: 9781119602989
  • Editorial: John Wiley & Sons, Inc.
  • Lugar de la edición: Hoboken (NJ). Estados Unidos de Norteamérica
  • Encuadernación: Cartoné
  • Medidas: 26 cm
  • Nº Pág.: 328
  • Idiomas: Inglés

Papel: Cartoné
133,12 €
Sin Stock. Disponible en 5/6 semanas.


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.


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



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