000 | 02520nam a22002297a 4500 | ||
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020 | _a9781119863373 | ||
041 | _aENG | ||
082 |
_a332.0285 _bCHE |
||
100 | _aChen, Sam | ||
245 |
_aFinancial data analytics with machine learning, optimization and statistics/ _cSam Chen, Ka Chun Cheung and Phillip Yam |
||
260 |
_aHoboken : _bJohn Wiley & Sons, Inc., _c2025. |
||
300 |
_axxvi, 784 pages : _billustrations ; _c26 cm. |
||
440 | _aWiley finance series | ||
505 | _aIntroduction Part One Data Cleansing and Analytical Models Chapter 1 Mathematical and Statistical Preliminaries Chapter 2 Introduction to Python and R Chapter 3 Statistical Diagnostics of Financial Data Chapter 4 Financial Forensics Chapter 5 Numerical Finance Chapter 6 Approximation for Model Inference Chapter 7 Time-Varying Volatility Matrix and Kelly Fraction Chapter 8 Risk Measures, Extreme Values, and Copulae Part Two Linear Models Chapter 9 Principal Component Analysis and Recommender Systems Chapter 10 Regression Learning Chapter 11 Linear Classifiers Part Three Nonlinear Models Chapter 12 Bayesian Learning Chapter 13 Classification and Regression Trees, and Random Forests Chapter 14 Cluster Analysis Chapter 15 Applications of Deep Learning in Finance | ||
520 | _aIn Financial Data Analytics with Machine Learning, Optimization and Statistics, a team consisting of a distinguished applied mathematician and statistician, experienced actuarial professionals and working data analysts delivers an expertly balanced combination of traditional financial statistics, effective machine learning tools, and mathematics. The book focuses on contemporary techniques used for data analytics in the financial sector and the insurance industry with an emphasis on mathematical understanding and statistical principles and connects them with common and practical financial problems. Each chapter is equipped with derivations and proofs—especially of key results—and includes several realistic examples which stem from common financial contexts. The computer algorithms in the book are implemented using Python and R, two of the most widely used programming languages for applied science and in academia and industry, so that readers can implement the relevant models and use the programs themselves. | ||
650 | _aFinance Data processing | ||
650 | _aFinances Informatique | ||
650 |
_aMachine learning _xEconomic aspects |
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700 | _aCheung, Ka Chun | ||
700 | _aYam, Phillip | ||
942 |
_2ddc _cBK |
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999 |
_c23765 _d23765 |