Investor Series

In this video, I interview Vinod Nair. He is a Managing Partner at Altavista Investment Management. I hope this video will prove to be the first of a useful series. I intend to focus more on content than production values.

Investor Howard Marks on Luck, Risks and the Job that Got Away -- Knowledge@Wharton

In this post, I have picked out a few quotations I found interesting from a K@W post. I hope you will find these nuggets of wisdom as insightful as I do! If you take one thing away, let it be that “Success in investing is not a function of what you buy. It’s a function of what you pay.”

Price is king

Investing is ultimately about buying a company for less than you think it is worth, or at least less than what you think it will be worth in the future. That being said, it is probably best to buy great businesses at great prices, because you can then hold your position 'forever'.

The official dictum was if you were buying the stock of a good enough company, it didn’t matter how high a price you paid.” But it did matter, Marks noted, and people were paying about five times what the stocks were worth. “By 1973, the people who held those stocks had lost 90% of their money.”

Citibank had invested in “the best companies in America and lost a lot of money.” Then it invested in “the worst companies in America and made a lot of money,” Marks noted, adding that “it shouldn’t take you too long to figure out that success in investing is not a function of what you buy. It’s a function of what you pay.”

"Not-Loser's Tennis" & "No-called-strike Baseball"
In investing, you don't need to try to hit a winning shot all the time. You just need to stay in the game long enough, that when a winning opportunity you like presents itself, you can take advantage of it. In fact, you never need to hit that winning shot at all; you could even just stay in till your opponent (the market) makes a mistake. This is why investing is such an advantageous game. 

It’s not a crapshoot like — if you’ll pardon the expression — venture capital, where you invest in 10 companies but if one of them turns out to be Google, you’re a success.”

He plays “not-loser’s” tennis. “If you think you can see the future and the world is going to go according to your decisions, go for winner’s tennis. But if you think the world is full of randomness and uncertainty, spend your time trying to avoid losers.” 

If we can make a large portfolio of investments where none of them [strike out], then we’ll have … no bad ones to pull down the average.”

Self-fulfilling Prophecies
Low business confidence - or fears of a recession - can trigger a recession, and high business confidence can cause a boom. Self-fulfilling prophecies occur regularly on the stock-market. 

Sometimes I think confidence is the only thing that determines economic outcomes. Confidence is largely self-fulfilling.

The Stupidity of Spending the Future Today
Borrowing is simply a means for consuming tomorrow's money today. The thing is, (i) you have to pay for this right, and (ii) there is a chance that tomorrow's money will never materialise!

Credit is one of the reasons the world is in trouble now. Countries like Greece, France, Italy, Portugal and Spain have to practice austerity now because that’s what happens when you spend money you don’t have.

The Bermuda Triangle of Valuation (Learning from Damodaran)

As I have mentioned in previous posts, I did a course about Valuation over the summer. This post follows a similar post about the dangers of valuation models. In my previous post - also inspired by an Ashwath Damodaran talk - I explored the importance of 'stories', which necessarily drive every valuation. I would like for this post to serve as a cautionary note for myself - and any readers - about the common errors we might make when valuing a company. I have attempted to capture the main points Damodaran makes in his talk.

The Bermuda Triangle of Valuation is:

• Bias and Preconceptions
• Uncertainty
• Complexity

Bias and Preconceptions

• It is important to distinguish between Pricing and valuation. Most people think of a number first and build a valuation to fit the number. Value cannot precede valuation, because models can only tell you what you want to here. If you want to buy, you will keep fiddling with the inputs till the model says it is a buy. Thus, it is probably useful to question how each input number was derived.

• You start out with an opinion, and then your valuation simply confirms what you learned. The more you know about a company the more biases there are in your valuation. That being said you need to know the business to develop a story.

• If you know the managers of the company, you know too much to value the company effectively. If you get too close to the company, you are less effective.

• When everyone is saying something in the market, it is hard to step back and objectively disagree. The less confident you are the closer your valuation gets to the market. 

• Suggestion bias. If I say I think its worth 15, but value it anyways, you will probably arrive at a similar price.

• When you see decimals in a valuation, the valuer is using decimals to intimidate you. Decimals mean nothing, because you cannot be that precise about the value of a company.

• Small changes have significant effects. Thus, it is important to question each input.

• Don’t let comparable multiples tell you what the price is. If you pick the comparable, you are making several assumptions!

• Several people deny bias. Denial is no good (Stop lying to yourself). Instead, you must think of ways to structure processes that help reduce bias. 

• Ask people to be transparent about where they come from. What was there hypothesis? What were their findings (attempt to disprove the hypothesis?) The hypothesis is full of bias, so you can then take the findings with a pinch of salt. That being said, its much easier to point out the (often small) issues with a hypothesis than it is to create a hypothesis yourself.

• Where M&A is concerned, you don’t ask the deal maker if the deal makes sense; they have bias!

Uncertainty - We often ignore this as we don’t know how to deal with it.

• Every input involves assumption. We make estimates. The difficulty of making estimates is variable. We want companies where it is possible to make estimates. The harder it is to make an estimate, the greater the uncertainty is. Uncertainty is an opportunity, and so the pay-off to doing valuation is greatest when there is greater uncertainty. However, we must be aware that our numbers are only one possibility.

• Estimation uncertainty (Micro uncertainty) can be refined through cashflows and KPI estimates.

• Economic uncertainty (Macro uncertainty) can be accounted for through the discount rate.

• Spending more time doing valuation doesn’t make uncertainty go away.

• Continuous uncertainty is much easier to deal with than discrete uncertainty (bankruptcy, nationalisation). We must deal with discrete uncertainty. This is because these improbable - but not impossible - occurrences, can result in insurmountable difficulties for companies.

• Things we can do to deal with uncertainty:
• Less is more: Aggregate things instead of breaking them down, when you make forecasts and assumptions. That being said, looking at the lines for historical data allows you to spot significant changes, and can help guide your research.
• Make sure your valuation is not at war with itself; be clear what your assumptions are.
• Make sure your assumptions affect all the relevant inputs, (eg. inflation affects interest rates, discount rates, and growth rates)
• Be realistic.
• Even if you disagree with the market, try to understand what the market is telling you.
• Law of large numbers: Use averages to compare. But, blind averages may conceal significant anomalies.
• Don’t use the discount rate to account for fear.
• Use distributions instead of fixed figures. Considers the different possible outcomes. Draw a bell-curve?
• Don’t look for precision. You will be wrong 100% of the time! You are only looking for a ball-park figure to confirm what your story will look like in numbers.

Complexity - As valuations and models are complex, we may lose sight of what exactly it is we set out to achieve.

• Companies are 'tentacled octopuses'. This complexity may make it difficult to understand a company's business.

• Data accessibility means we have more data. However, this means we must filter the useless noise, and hunt for the jewels.

• Legal/accounting complexity also raises issues.

• Build simple models.
• In complex models with lots of variables, input fatigue sets in. (You may start putting random numbers in.)
• So, the model becomes a blackbox. You don't know what went in and don't know why a given output came out.

Daniel Kahneman on Confidence.

• The Loss Illusion is noteworthy. We fear the downside more than we desire the upside. This may lead us to be unnecessarily risk-averse. As a result, we may be unable to take advantage of opportunities. As humans, we have optimistic and risk-averse tendencies. These two tendencies conflict, and one must monitor them.

• 'Expert'-opinion must be treated with skepticism, because people are generally over-confident.

Identifying Long-term winners.

• Your circle of competence is where you can 'understand' the future economics of the business. You must define your circle of competence and stay inside of it. You don't need to be smart; if you are strong is some spots and you stick around these spots, you will do well.

• It is hard to identify the winners even in a winning industry. Industries and companies are different things. (cars, airlines: both industries have consistently lost money) 

• It is easier to identify the losers than the winners, when change is coming. 

• The aristocrats of business change over time. Today's Giants will eventually be giant no-more.