Practical analysis for investment professionals
04 January 2012

The DCF Model: Question Your Assumptions

Posted In: Equity Investments

Daily deal website operator Groupon (GRPN) recently sold shares to the public via a highly anticipated IPO and saw its stock surge more than 30% on its first day of trading. Earlier this year, social networking company LinkedIn (LNKD) saw its shares more than double on its first day of trading. Though the share prices of both firms have since retreated to trade well below their post-IPO peaks, these public debuts of companies showing great revenue growth but little to no earnings were reminiscent of the dot-com bubble of the late 1990s, and there is a natural temptation to get caught up in the hype. For this reason, it is as important as ever for analysts to rely on the fundamental principles of investing and valuation. And the discounted cash flow (DCF) model is a great place to start.

DCF analysis is one of the most reliable of analytical tools, and when applied to equity valuation, it derives the fair market value of common stock as the present value of its expected future cash flows. While the DCF model arguably provides the best estimate of a stock’s intrinsic value, it also relies on a number of forward-looking assumptions that analysts need to consider carefully. As the saying goes, “Garbage in, garbage out.” CFA charterholder Gregrory A. Gilbert’s timeless overview of the DCF model underscores this message and reminds us of some of the potential pitfalls associated with this valuation approach.

Professor Aswath Damodaran from the Stern School of Business at New York University, a prolific author and noted authority on valuation matters, addresses the sensitivities of the DCF model in much of his work, including this brief interview. And his presentation titled “Valuation Inferno: Dante Meets DCF — Avoiding Common Mistakes in Valuation Analysis” is a step-by-step dissection of traditional DCF analysis wherein he guides the audience through the process of calculating a more accurate estimate of fair market value. With hyperbole, Damodaran invokes the 14th-century poet Dante Alighieri to describe the “hellish” challenge analysts often face in DCF valuation. He focuses on several critical component variables of the DCF model in which analysts are most prone to misstep and offers an alternative route. Some of Damodaran’s suggestions include:

  • Don’t overestimate growth. Analysts are generally too optimistic when it comes to estimating firm growth rates. Success begets competition, which invariably leads to slower growth. Professor Andrew Metrick, from the Yale School of Management, introduced a model for forecasting a young company’s growth in his book Venture Capital and the Finance of Innovation and found that start-ups usually revert to an industry average growth rate within five years.
  • Avoid regression betas. Regression betas, commonly used in calculating the cost of equity, generally have large standard errors. Betas should reflect the business the firm operates in, its operating leverage, and its debt level. Damodaran calls for the use of sector betas as a way to eliminate the noise that comes with regression betas calculated on individual firms.
  • Don’t calculate terminal values using relative multiples. The terminal value, or the value of the firm at the end of a cash flow projection period, has a great impact on the present value calculation. Unfortunately, analysts often simply apply a multiple relative to a peer group, turning what should be an intrinsic valuation into a relative valuation. Instead, use a stable growth dividend discount model like the Gordon growth model to estimate the terminal value.
  • Use long-term risk-free rates. Rather than simply plugging in a short-term risk-free rate, as many analysts do, the term of the risk-free rate should approximate the term of the expected cash flows. In most cases, this means using the long-term government bond as the risk-free rate.

Depending on their underlying inputs, fair value estimates based on DCF modeling can vary significantly, making it clear that analysts using the DCF model would be well served to consider the validity of the assumptions they are making.

For those interested, Damodaran has posted his DCF analysis of GRPN and LNKD on his blog.

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All posts are the opinion of the author. As such, they should not be construed as investment advice, nor do the opinions expressed necessarily reflect the views of CFA Institute or the author’s employer.

About the Author(s)
David Larrabee, CFA

David Larrabee, CFA, is director of Member and Corporate Products at CFA Institute and serves as the subject matter expert in portfolio management and equity investments. Previously, he spent two decades in the asset management industry as a portfolio manager and analyst. He holds a BA in economics from Colgate University and an MBA in finance from Fordham University. Topical Expertise: Equity Investments · Portfolio Management

6 thoughts on “The DCF Model: Question Your Assumptions”

  1. Jimmy Dotiwala says:

    Hello David, this is a good article. I think the important factor that contributes to the noise in a DCF model is the need to forecast in perpetuity. Though this practice agrees with the accounting principle of going concern, it simply is at variance with the physical law that resources are limited. Despite of all the care in estimating inputs described, it is difficult to reconcile the model with intuitiveness.

  2. finance says:

    Pretty nice post. I just stumbled upon your weblog
    and wished to say that I have truly enjoyed surfing around your blog
    posts. In any case I’ll be subscribing to your rss feed and I hope you write again soon!

    1. Dave Larrabee says:

      finance,

      Thanks very much for visiting our blog. Glad to have you on board!

      Dave

  3. Henry Akeni says:

    Very interesting article. Captured the weaknesses of the DCF model and also tries to proffer an alternative to these loopholes. The biggest drawback to the DCF method is that the future cannot be determined with certainty; and almost al the variables used are based on assumptions that are futuristic.

  4. Himanshu Arora says:

    Nice

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