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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.
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