Dixon, Matthew F. Halperin, Igor Bilokon, Paul Machine learning in finance 9783030410674

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Автор: Dixon, Matthew F. Halperin, Igor Bilokon, Paul
Название: Machine learning in finance
Перевод названия: Мэтью Ф. Диксон, Мгорь Гальперин, Павел Белоконь: Машинное обучение в сфере финансов
ISBN: 9783030410674
Издательство: Springer
Классификация:

Финансы

Вероятность и статистика

Прикладная математика

Машинное обучение

ISBN-10: 3030410676
Обложка/Формат: Hardcover
Страницы: 548
Вес: 1.00 кг.
Дата издания: 02.07.2020
Язык: English
Издание: 1st ed. 2020
Иллюстрации: 83 illustrations, color; 14 illustrations, black and white; xxv, 548 p. 97 illus., 83 illus. in color.; 83 illustrations, color; 14 illustrations, bla
Размер: 23.39 x 15.60 x 3.18 cm
Читательская аудитория: Professional & vocational
Подзаголовок: From theory to practice
Ссылка на Издательство: Link
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Поставляется из: Германии
Описание: 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 researchers perspective, highlighting how many well-known concepts in statistical physics are likely to emerge as important methodologies for machine learning in finance.

Machine learning in finance, Dixon, Matthew F. Halperin, Igor Bilokon, Paul