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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.
List of contents
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
About the author
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
Summary
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.
