000			03124nam a22002657a 4500
999			_c2933 _d2933
005			20220704123352.0
008			220704b ||||| |||| 00| 0 eng d
020			_a9781119602989
082			_a332.028557 _bALD
100			_aAldridge, Irene _96808
245			_aBig data science in finance
260			_bJohn Wiley & Sons, Inc. _aNew Jersey _c2021
300			_aviii, 328 p.
365			_aUSD _b124.95
504			_aTABLE 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
520			_aExplains 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.
650			_aBig data _9212
650			_aNeural networks (Computer science) _92344
650			_aFinance--Decision making--Data processing _97270
650			_aFinance--Mathematical models _9180
650			_aFinance--Data processing _97271
650			_aInvestments--Data processing _97272
700			_aAvellaneda, Marco _97273
942			_2ddc _cBK