Big Data Science in Finance

Aldridge, Irene, Avellaneda, M.

  • 出版商: Wiley
  • 出版日期: 2021-01-27
  • 售價: $3,590
  • 貴賓價: 9.5$3,411
  • 語言: 英文
  • 頁數: 400
  • 裝訂: Hardcover - also called cloth, retail trade, or trade
  • ISBN: 111960298X
  • ISBN-13: 9781119602989
  • 相關分類: 大數據 Big-data資料科學

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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.