Machine Learning in Finance: From Theory to Practice by Matthew F. Dixon, Igor Halperin, Paul Bilokon (Paperback, 2021)

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Product Information

This book introduces machine learning methods in finance. It presents a unified treatment of machine learning and various statistical and computational disciplines in quantitative finance, such as financial econometrics and discrete time stochastic control, with an emphasis on how theory and hypothesis tests inform the choice of algorithm for financial data modeling and decision making. With the trend towards increasing computational resources and larger datasets, machine learning has grown into an important skillset for the finance industry. This book is written for advanced graduate students and academics in financial econometrics, mathematical finance and applied statistics, in addition to quants and data scientists in the field of quantitative finance. Machine Learning in Finance: From Theory to Practice is divided into three parts, each part covering theory and applications. The first presents supervised learning for cross-sectional data from both a Bayesian and frequentist perspective. The more advanced material places a firm emphasis on neural networks, including deep learning, as well as Gaussian processes, with examples in investment management and derivative modeling. The second part presents supervised learning for time series data, arguably the most common data type used in finance with examples in trading, stochastic volatility and fixed income modeling. Finally, the third part presents reinforcement learning and its applications in trading, investment and wealth management. Python code examples are provided to support the readers' understanding of the methodologies and applications. The book also includes more than 80 mathematical and programming exercises, with worked solutions available to instructors. As a bridge to research in this emergent field, the final chapter presents the frontiers of machine learning in finance from a researcher's perspective, highlighting how many well-known concepts in statistical physics are likely to emerge as important methodologies for machine learning in finance.

Product Identifiers

PublisherSpringer Nature Switzerland A&G
ISBN-139783030410704
eBay Product ID (ePID)13049040397

Product Key Features

Number of Pages548 Pages
LanguageEnglish
Publication NameMachine Learning in Finance: from Theory to Practice
Publication Year2021
SubjectGovernment, Mathematics
TypeTextbook
AuthorMatthew F. Dixon, Igor Halperin, Paul Bilokon
FormatPaperback

Dimensions

Item Height235 mm
Item Weight872 g
Item Width155 mm

Additional Product Features

Country/Region of ManufactureSwitzerland
Title_AuthorIgor Halperin, Paul Bilokon, Matthew F. Dixon

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