Buch, Englisch, 336 Seiten, Format (B × H): 178 mm x 254 mm, Gewicht: 816 g
Buch, Englisch, 336 Seiten, Format (B × H): 178 mm x 254 mm, Gewicht: 816 g
ISBN: 978-1-119-60298-9
Verlag: Wiley
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.
Aldridge / Avellaneda
Big Data Science in Finance jetzt bestellen!
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.
Autoren/Hrsg.
Fachgebiete
Weitere Infos & Material
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
Bitte ändern Sie das Passwort
