Machine Learning in Finance- From Theory to Practice-Springer (2020)

• Authors: Matthew F. Dixon, Igor Halperin, Paul Bilokon
• Publisher: Springer
• Publication Year: 2020
• Format: PDF
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Introduction

This book introduces in detail the core concepts and practical methods related to Machine Learning in Finance-From Theory to Practice-Springer (2020). This book provides an in-depth exploration of machine learning methods in finance, and unifies machine learning with various statistical and computational disciplines in quantitative finance, such as financial econometrics and discrete-time stochastic control. The book emphasizes how theory and hypothesis testing can guide the selection of financial data modeling and decision-making algorithms.

The book is divided into three parts, each covering theory and applications. The first part introduces the application of supervised learning to cross-sectional data, including Bayesian and frequentist perspectives, with an emphasis on neural networks (including deep learning) and Gaussian processes, and provides examples of investment management and derivatives modeling. Part II explores the application of supervised learning to time series data, the most common data type in finance, covering examples such as trading, stochastic volatility, and fixed income modeling. The third part introduces reinforcement learning and its applications in trading, investment and wealth management. The book also provides Python code examples to help readers understand the methodology and applications, and contains more than 80 mathematical and programming exercises.

Main features

• Solid theoretical foundation
• Rich practical cases
• Clear operating instructions
• Suitable for practical applications

Suitable for the crowd

• Quantitative Analyst
• Algorithmic Trader
• Financial Engineer
• Data Scientist