A Quantitative Primer on Investments with R
Quantitative analysts and financial engineers often skip taking an investments course. This omission robs them of the fundamental knowledge needed to create better, more profitable models.
A Quantitative Primer on Investments with R fills that gap by taking a quantitative approach to investments and analyzing real data using R, the open source statistical computing language. This illuminates the commonalities among investment theories and builds intuition.
This material has been tested over a decade of teaching and has enabled many students to enter the world of quantitative finance and succeed.
Quantitative finance professionals; graduate students in finance and economics; students in statistics, math, operations research, engineering, physics, and computer science who are interested in finance.
TABLE OF CONTENTS
Asset Classes (slides)
Markets in General (slides)
Modern Markets (slides)
Efficiency and the Macroeconomy (slides)
Risk versus Returns (slides)
Risk Measures (slides)
Statistical Modeling (slides)
Fixed Income (slides)
Yield Curves (slides)
Equity Valuation (slides)
Capital Asset Pricing Model (slides)
Factor Models and the APT (slides)
Examining Microfoundations (slides)
Global Investing (slides)
Foreign Exchange (slides)
Forwards, Futures, and Swaps (slides)
Options Basics (slides)
Option Valuation (slides)
Structured Finance and Private Equity
Active Portfolio Management
R code is featured to implement ideas in the text. Short quizzes at the end of each chapter ensure the basics are retained. Longer exercises use theory, R code, and data to build intuition with real analyses. Quiz answers are provided in the back so you can teach yourself.
Pages: XVI, 764+2; 75 figures, 31 tables; full-color; with bibliographies and index.
PDF slides are available for instructors!
You may now download a gzipped tarball of all the R code in the text!
A small number of errors made their way into some versions of the text. Those are listed in the errata.
Q36 has made a number of conscious choices to keep this text affordable while reducing errors. We went against industry standards to eliminate retyping of content and expensive human intervention when automated tools could do the job better. We believe you will appreciate the result and we are proud to help make the publishing market more efficient.