An Introduction to Machine Learning in Finance, With Mathematical
Background, Data Visualization, and R
Nonparametric function estimation is an important part of machine
learning, which is becoming increasingly important in quantitative
finance. Nonparametric Finance provides graduate students and finance
professionals with a foundation in nonparametric function
estimation and the underlying mathematics. Combining practical
applications, mathematically rigorous presentation, and statistical data
analysis into a single volume, this book presents detailed instruction
in discrete chapters that allow readers to dip in as needed without
reading from beginning to end.
Coverage includes statistical finance, risk management, portfolio
management, and securities pricing to provide a practical knowledge
base, and the introductory chapter introduces basic finance concepts for
readers with a strictly mathematical background. Economic significance
is emphasized over statistical significance throughout, and R code is
provided to help readers reproduce the research, computations, and
figures being discussed. Strong graphical content clarifies the methods
and demonstrates essential visualization techniques, while deep
mathematical and statistical insight backs up practical applications.
Written for the leading edge of finance, Nonparametric Finance:
- Introduces basic statistical finance concepts, including univariate
and multivariate data analysis, time series analysis, and prediction
- Provides risk management guidance through volatility prediction,
quantiles, and value-at-risk
- Examines portfolio theory, performance measurement, Markowitz
portfolios, dynamic portfolio selection, and more
- Discusses fundamental theorems of asset pricing, Black-Scholes
pricing and hedging, quadratic pricing and hedging, option portfolios,
interest rate derivatives, and other asset pricing principles
- Provides supplementary R code and numerous graphics to reinforce
complex content
Nonparametric function estimation has received little attention in the
context of risk management and option pricing, despite its useful
applications and benefits. This book provides the essential background
and practical knowledge needed to take full advantage of these
little-used methods, and turn them into real-world advantage.
Jussi Klemelä, PhD, is Adjunct Professor at the University of Oulu.
His research interests include nonparametric function estimation,
density estimation, and data visualization. He is the author of
Smoothing of Multivariate Data: Density Estimation and Visualization
and Multivariate Nonparametric Regression and Visualization: With R and
Applications to Finance.
