Why this book?
Everything is practical in this book, which isn’t what you would expect from a textbook! There is no math for math’s sake. I have used the techniques discussed in this book for real trading, and for creating features at my machine learning SaaS predictnow.ai. Examples: What’s the difference between net and log return? How are returns typically distributed? What do “factors” in factor investing really mean? What is mean reversion trading based on? How can we predict volatility?
Why should I read it?
1 author picked Statistics and Data Analysis for Financial Engineering as one of their favorite books, and they share why you should read it.
What is this book about?
The new edition of this influential textbook, geared towards graduate or advanced undergraduate students, teaches the statistics necessary for financial engineering. In doing so, it illustrates concepts using financial markets and economic data, R Labs with real-data exercises, and graphical and analytic methods for modeling and diagnosing modeling errors. These methods are critical because financial engineers now have access to enormous quantities of data. To make use of this data, the powerful methods in this book for working with quantitative information, particularly about volatility and risks, are essential. Strengths of this fully-revised edition include major additions to the R code…