New York University professor Aswath Damodaran has been teaching corporate finance and valuation for decades. His message is simple: When valuing a publicly traded stock, the ubiquitous discounted cash flow model is a trustworthy framework. However, he admonishes financial analysts to challenge conventional wisdom and biases (i.e., no shortcuts) that create internal inconsistencies and questionable results.
At the 2013 CFA Institute Annual Conference in Singapore, the legendary Stern School of Business professor delivered his message in a talk titled “Valuation in the Face of Uncertainty.” Damodaran confessed that he’s been doing this talk for so long that he has to find new ways to repackage his message. In fact, he delivered a similar talk in late 2012 at the CFA Institute Equity Research and Valuation Conference that my colleague Dave Larrabee, CFA, nicely summarized on our Enterprising Investor blog.
The core of Damodaran’s argument is for analysts to apply a common-sense, mathematical approach to challenge general “rules of thumb” of valuation while still using accepted methodologies, such as the discounted cash flow model. During his conference double session, Damodaran walked the audience through the valuation of four companies at different points in time to illustrate several of his techniques.
3M is a diversified, mature company and a good illustration of how to tackle the equity risk premium and assumptions about terminal value. Damodaran joked that 12 September 2008 was his “last day of innocence” because it was the Friday before the U.S. Treasury department decided the fate of Lehman Brothers. The Monday after the investment bank collapsed, market premiums were drastically different than the week before. To capture this change, Damodaran calculated a market-implied equity risk premium derived from modifying a yield-to-maturity formula to account for the value of the S&P 500 using projected future cash flows. He argues that the conventional practice of using Ibboston data for the equity risk premium not only doesn’t capture the dynamic premium investors require to invest in stocks over risk-free assets, but it also has such a high standard error that it makes the data meaningless.
As for 3M’s terminal value, he warned that growth in perpetuity can never exceed GDP growth or else the company will become the economy. One simple proxy for the nominal growth rate of the economy is the risk-free rate, which equals expected inflation plus expected real interest rates.
Damodaran doesn’t use standard regression betas. Instead, he uses an average of all the betas in a given industry. In this case, the law of large numbers is on the analyst’s side. The standard error in a regression beta is typically too large to make any individual beta meaningful. In the case of Tata Motors, by using an average of 111 publicly traded companies in the auto industry, it reduces the standard error of the beta by 90%, he said.
Since Tata Motors is based in India, its valuation either needs to be all in Indian rupees or all in another currency (presumably U.S. dollars). If you use rupees for cash flows, then every other variable has to be internally consistent with rupees, including the risk premium (country specific) and discount rate, which also reflects the difference in inflation across multiple currencies.
In the year 2000, Amazon was a young, high-growth company that was losing a lot of money. The challenge for any analyst attempting to value AMZN is to predict when the company is going to reach profitability. Damodaran’s trick is to imagine what AMZN would look like 10 years in the future as a reasonably mature operation. To do so, he assembled a portfolio of retail companies to determine revenue size and operating margins. There is no magic in determining the path to profitability. Damodaran simply used some calculations to smooth the progress from losing money through a high-growth phase to relative maturity.
The public offering of Facebook is a good example of “valuation” versus “pricing.” Damodaran wrote about the Facebook IPO on his blog and concluded that the IPO price of $38 was just that: a price. He awarded Facebook a Google-like growth rate and Apple-like margins and still only arrived at a share price in the $25–$28 range. The takeaway is to build in checks for reasonableness. A company cannot have more than 100% of the market and profitability cannot be orders of magnitude larger than the competition.
These examples merely scratch the surface of Professor Damodaran’s body of work. He cautions analysts not to “fight mathematics. Mathematics will always win.” After all, models need to reflect reality and maintain internal consistency.
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