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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