Big Data Science in Finance

Description

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. Contents Preface 1 Why Big Data? 2 Neural Networks in Finance 3 Supervised Learning 4 Modeling Human Behavior with Semi-Supervised Learning 5 Letting the Data Speak with Unsupervised Learning 6 Big Data Factor Models 7 Data as a Signal versus Noise 8 Applications: Unsupervised Learning in Option Pricing and Stochastic Modeling 9 Data Clustering Conclusion Index