cliffordang
Author of Analyzing Financial Data and Implementing Financial Models Using R

Compass Lexecon · 1111 Broadway Suite 1500 · Oakland, CA 94607 · 510.285.1285 · csa@cliffordang.com

Welcome!

Cliff Ang My name is Cliff Ang. I am a Vice President at Compass Lexecon's Oakland, CA and Chicago, IL offices. Compass Lexecon is an economic consulting firm specializing in the application of economics to a variety of legal and regulatory issues. At Compass Lexecon, I specialize in the areas of valuation and securities. Over the last 10 years, I have worked on hundreds of engagements involving firms across a broad-spectrum of industries concerning issues such as appraisals, solvency, market efficiency, materiality, loss causation, and damages.

CL logoMy expertise is in asset pricing and corporate finance. I am the author of the financial modeling textbook Analyzing Financial Data and Implementing Financial Models Using R, which is described in more detail elsewhere on this page. My paper titled "Understatement of the Valuation Impact of Future Stock-Based Compensation Grants: Implications from the Ancestry.com Opinion" appears in the September/October 2015 issue of the Value Examiner. For several years now, I have written summaries of academic articles for the CFA Digest. I have held teaching appointments at DePaul University, the University of the Philippines, and Ateneo de Manila University, where I have taught courses in investments, investment management, corporate finance, and international finance.

I am a CFA Charterholder and hold an MS in Finance from the University of the Philippines. I also hold a BSBA in finance and accounting from Washington University in St. Louis, where I subsequently completed doctoral coursework in finance, economics, and econometrics.

I am an active volunteer with the CFA Institute in support of the CFA program. I also serve on the Olin Business School Alumni Board at Washington University in St. Louis.

More information about me can be found in my CV or LinkedIn page.


My Models (models.cliffordang.com)

I have extensive experience building sophisticated financial models in Excel/VBA and R. Aside from the models in my financial modeling book, I have developed a number of models for teaching purposes, which can be found at models.cliffordang.com. I also presented at the R in Finance Conference in 2012 a practical method of estimating the market value of illiquid debt. The presentation in that conference can be found here.


R and R for Finance Links

  • Comprehensive R Archive Network (CRAN) Download R here and get the latest information on it
  • R Studio Free software with a better user interface for R that works with Windows, Mac, and Linux
  • R Studio Quick List of Useful R Packages
  • R in Finance Conference Contains past and current presentations
  • CRAN - Empirical Finance Packages Extensive list of finance-related packages for R
  • Quantmod Website for Quantitative Financial Modeling & Trading Framework for R
  • Performance Analytics Package Library of econometric functions and performance and risk analysis
  • Springer Use R! Series Published and forthcoming R books under the Use R! series

    Publicly Available Finance Data Sources

  • Yahoo! Finance Data on publicly traded stocks and ETFs
  • Google Finance Another source of data on publicly traded stocks and ETFs
  • Yahoo! Finance Bonds Center Data on bonds
  • Professor Kenneth French's Data Library Updated data on the Fama-French factors
  • Professor Damodaran's Useful Data Sets Data on a broad-range of financial metrics primarily for valuation
  • St. Louis Federal Reserve Electronic Database (FRED) Data on various economic and financial metrics
  • EDHEC-Risk Institute Scientific Beta Indexes representative of Smart Beta 2.0 approach promoted by EDHEC-Risk Institute
  • QuandL Access to various US and international financial data, such as financials, stocks, bonds, futures, options, foreign exchange
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    My Financial Modeling Textbook . . .

    For those interested in learning financing modeling using R, please check out my book Analyzing Financial Data and Implementing Financial Models Using R. The models in this book are geared towards investments and asset pricing. It is published by Springer and available at, among other places, Amazon.com and Springer's website. You can also view the product flyer and find more information about the book on my publisher's website.

    As a quick summary, the key features of the book are as follows:

  • Teaches students how to use R to analyze financial data and implment 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

    I would like to thank those that have purchased my book. The options data used in the book can be found below:

    Call Options Data
    Put Options Data

    Should you have any questions, comments, or feedback, please feel free to send me an e-mail.


    Why R?

    The following are just a few reasons why I like R: R is free to you and to your collaborators. No need to shell out a lot of money for excellent software. R has a wide community that helps contribute to the quality of R and the vast collection of packages available on R. The R community also has numerous R blogs and forums that help beginners and advanced users with programming issues aross a broad-range of topics.

    Consequently, R has been increasing in popularity. For example, as the following Google Trends chart shows, since 2004, the interest in R has been increasing and the interest in other software such as SAS and MATLAB had been declining. In addition, as reported in a Forbes magazine article on October 21, 2015, R is the top skill (aside from the generic term "data analysis") reported by data scientists on LinkedIn. The graph below produced by RJMetrics, which appears in the Forbes article, summarizes the top 20 skills for data scientists:


  • © 2016 Clifford S. Ang. All rights reserved.