This book is a comprehensive introduction to financial modeling that teaches advanced undergraduate and graduate students in finance and economics how to use R to analyze financial data and implement financial models. This text will show students how to obtain publicly available data, manipulate such data, implement the models, and generate typical output expected for a particular analysis.

This text aims to overcome several common obstacles in teaching financial modeling. First, most texts do not provide students with enough information to allow them to implement models from start to finish. In this book, we walk through each step in relatively more detail and show intermediate R output to help students make sure they are implementing the analyses correctly. Second, most books deal with sanitized or clean data that have been organized to suit a particular analysis. Consequently, many students do not know how to deal with real-world data or know how to apply simple data manipulation techniques to get the real-world data into a usable form. This book will expose students to the notion of data checking and make them aware of problems that exist when using real-world data. Third, most classes or texts use expensive commercial software or toolboxes. In this text, we use R to analyze financial data and implement models. R and the accompanying packages used in the text are freely available; therefore, any code or models we implement do not require any additional expenditure on the part of the student.

Demonstrating rigorous techniques applied to real-world data, this text covers a wide spectrum of timely and practical issues in financial modeling, including return and risk measurement, portfolio management, options pricing, and fixed income analysis.

Teaches students how to use R to analyze financial data and implement financial models from start (e.g., obtaining data) to finish (e.g., generating output expected for a particular analysis) using real-world data Guides students step-by-step through the modeling process and reports intermediate output Exposes students to the notion of data checking and the issues that arise when using real-world data? Includes supplementary material: sn.pub/extras

Autorentext

Clifford Ang is an Executive Vice President in the Oakland, CA and Chicago, IL offices of Compass Lexecon, where he specializes in valuing businesses & hard-to-value assets and analyzing complex financial statement issues. He has worked on hundreds of engagements involving firms across a broad-spectrum of industries concerning a wide-range of financial and economic issues, such as appraisals, complex asset pricing, solvency, lost profits, market efficiency, loss causation, and damages. Ang also teaches equity and bond valuation courses in DataCamp, an interactive learning platform for data science.

Inhalt
Chapter 1 Prices.- Chapter 2 Individual Security Returns.- Chapter 3 Portfolio Returns.- Chapter 4 Risk.- Chapter 5 Factor Models.- Chapter 6 Risk-Adjusted Portfolio Performance Measures.- Chapter 7 Markowitz Mean-Variance Optimization.- Chapter 8 Fixed Income.- Chapter 9 Options.- Appendix A Getting Started with R. Appendix B Constructing a Hypothetical Portfolio.