If you have been a reader of security analysis and business valuations, you must have heard about or read Aswath Damodaran.
Damodaran is a Professor of Finance at the Stern School of Business at New York University, where he teaches corporate finance and equity valuation. He is widely quoted on the subject of valuation, with “a great reputation as a teacher and authority”.
In other words, Damodaran is to business valuations what Peter Drucker was to business strategy.
I recently picked up his Little Book of Valuation. The first chapter reiterates an important fact about “value” – that it’s more than a number, and that understanding it well is a way to stay ahead of the pack.
I’ve met a lot of investors over the years who’ve been good students of businesses, but when it came to valuations, they argued that value lies in the eyes of the beholder…and that any price can be justified if there are other investors who perceive an investment to be worth that amount.
This is obviously absurd. But then this is the way valuations are treated – with the aim of looking for a bigger fool who will pay a higher value in the future!
Surprisingly, not just unqualified investors, this is also the way the smart and qualified analysts set targets on the stocks they recommend – that they will be able to find people who will get fooled by the glamorous returns the recommended stock promises by way of its target price.
Anyways, the idea of this post is not to discuss the authenticity of stock market analysts and their valuation skills. Instead, the core idea of this post is to share with you what Damodaran writes in his book on some truths about valuations.
The reason I call these as bitter truths is because these are something that we as investors rarely realize in our busyness of calculating a stock’s intrinsic value. More importantly, knowing these truths can keep us grounded and not get obsessed about the numbers that our valuation models throw up.
So what are these truths?
1. All Valuations are Biased
However hard we try to remove biases – those leaks in our brains – we are prone to fall in love with our ideas. A leading theory of romantic love is that it functions to make one feel committed to one’s beloved, as well as to signal this commitment to the beloved.
Stock analysis is no different. We first like an idea (a business), and only then we study it deeper. Then, once we start to like what we study of the business, the likeness turns to our love for the stock. And before we could know it, we are committed to it. This commitment creates a bias when we are working on estimating the stock’s intrinsic value.
“How could the intrinsic value be so low as compared to the current price?” I would tend to ask myself on finding that a “company I’ve started loving” is selling expensive in the stock market.
The next reaction is – “Let me raise the free cash flow growth rate a bit and then see. Ah! Instead of 8%, if I assume a growth of 12%, then the intrinsic value is almost around the current stock price.”
And then – “If this company has grown its FCF at 15% over the past 10 years, a 12% growth over the next 10 years isn’t impossible. So let me keep it at 12%.”
Notice the love getting deeper…and deeper.
As Damodaran writes…
The inputs that you use in the valuation will reflect your optimistic or pessimistic bent; thus, you are more likely to use higher growth rates and see less risk in companies that you are predisposed to like.
What he writes after this is comical…
There is also “post-valuation garnishing”, where you increase your estimated value by adding premiums for the good stuff (synergy, control, and management quality)…
There you are! In taking care of all the other biases in the world – confirmation, anchoring, herding, vividness etc. – you forget to remove the “I love this company” bias, and this shows in your valuations for the stock.
So how can you remove this bias, if at all? Damodaran suggests putting all your thoughts on the company on paper even before you start calculating its intrinsic value.
In addition, he suggests you to confine your background research on the company to “information sources” i.e., company’s financial statements, rather than “opinion sources” i.e., equity research reports about the company.
As a general rule, the more bias there is in the process, the less weight you should attach to the valuation judgment.
Just consider the moat stories surrounding a lot of stocks these days. It’s easy for an investor to fall in love with such stories. Now, while the only reason to fall in love with such stocks for most investors is their rising stock prices, it is garbed under the veil of stuff like – great management, good business, wonderful growth prospects, etc. etc. (which may also be true, but not truer than the beautiful stock price chart). 🙂
2. Most Valuations are Wrong
Now this can be shocking to you if you spend a lot of time arriving at that magical number (intrinsic value) that helps you ascertain whether you must buy a stock or not.
Damodaran talks about three kinds of errors that cause most valuations – even the ones meticulously calculated – to go wrong:
- Estimation error…that occurs while converting raw information into forecasts.
- Firm-specific uncertainty…as the firm may do much better or worse than you expected it to perform, resulting in earnings and cash flows to be quite different from your estimates.
- Macro uncertainty…which can be a result of drastic shifts in the macro-economic conditions that can also impact your company.
The year 2008 is one classic example when most valuations – even the good ones – went horribly wrong owing to the last two factors – firm-specific and macro uncertainties.
As I remember now, my careful 2007 valuations, for instance, of stocks like GE Shipping, NTPC, and Crompton Greaves, look hopelessly optimistic, in hindsight, simply because I underestimated the damage brought about by the 2008 crisis.
As Damodaran writes…
While precision is a good measure of process in mathematics or physics, it is a poor measure of quality in valuation.
So, To Value or Not Value?
Knowing that your valuation could be wrong (and in most cases, it would be) despite any kind of precision you employ in your calculations, it should not lead you to a refusal to value a business at all.
This makes no sense, since everyone else looking at the business faces the same uncertainty.
Instead what you must do to increase the probability of getting your valuations reasonably (not perfectly) right is…
- Stay within your circle of competence and study businesses you understand. Simply exclude everything that you can’t understand in 30 minutes.
- Write down your initial view on the business – what you like and not like about it – even before you start your analysis. This should help you in dealing with the “I love this company” bias.
- Run your analysis through your investment checklist. A checklist saves life…during surgery and in investing.
- Avoid analysis paralysis. If you are looking for a lot of reasons to support your argument for the company, you are anyways suffering from the bias mentioned above.
- Estimate intrinsic values using simple models, and avoid using too many input variables. In fact, use the simplest model that you can while valuing a stock. If you can value a stock with three inputs, don’t use five. Remember, less is more.
- Use the most important concept in value investing – ‘margin of safety’. Without this, any valuation calculation you perform will be useless.
At the end of it, Damodaran writes…
Will you be wrong sometimes? Of course, but so will everyone else. Success in investing comes not from being right but from being wrong less often than everyone else.
So don’t justify the purchase of a company just because it fits your valuation. Don’t fool yourself into believing that every cheap stock will yield good returns. A bad company is a bad investment no matter what price it is.
I love how Charlie Munger explains that – “a piece of turd in a bowl of raisins is still a piece of turd”…and…“there is no greater fool than yourself, and you are the easiest person to fool.”
So, get going on valuing stocks…but when you find that the business is bad, exercise your options. Not a call or a put option, but a “No” option. 🙂