An extract from our 2012 Annual Letter
"I can calculate the motion of heavenly bodies, but not the madness of men."
- Isaac Newton
[after he lost his fortune speculating in the South Sea Bubble, more than $3m in today’s dollars]
Successful investing requires taking action only when a great business becomes temporarily undervalued. The market is “mostly efficient” rather than “always efficient”: the distinction makes all the difference in the world.
Thus, the task of an investor is to constantly search for mis-priced assets and pounce when Mr Market becomes irrational.
If this sounds simple, why isn’t it easy? Why do most individual investors and active managers fail to outperform market indices?
One reason is that, by definition, successful investing requires standing apart from the crowd. Well away from the crowd. This is extremely uncomfortable, and not just in a metaphysical way: recall John Olsen, the analyst fired by Merrill Lynch in 1998 for not being bullish on Enron like all his peers.
Another reason is that learning the rules of the investment game is hard, despite the brain having incredible pattern recognition technology.
Consider other complex tasks like playing golf, elite chess … or flying a plane: if you make a mistake, you know it almost immediately. Feedback is unequivocal. The rules are clear.
Contrast this with stock picking, as Wall Street Journal columnist Jason Zweig did incisively:
Now think about feedback in the financial markets. You buy ChristineCorp and you pay $10 a share. So by the end of the day it’s at $10.05, and you pat yourself on the back and say, “I’m a good stock-picker.” And the next day it goes down to $9.50, and suddenly you think you’re a bad stock-picker. So you sell it and the next thing you know it goes up to $12.
And then what do you decide? Well, you can assume you’re a good stock-picker, because it went up 20% above your original purchase price, or you can conclude you don’t know what you’re doing because you sold at exactly the wrong time. So there’s lousy feedback in the financial markets. It’s noisy, it’s delayed, it’s ambiguous, and also you can cherry-pick it to lie to yourself, or to present yourself in a better light to other people.
The stock market is, in the jargon, a wicked learning environment. The rules of the game (the signals) are hidden in randomness, noise and further obscured by behavioural biases and emotional responses.
Moreover, great investments are ephemeral, requiring a combination of super-human patience married with aggressive opportunism when a ‘fat pitch’ presents itself.
No wonder successful stock picking is a rare skill!
Indeed, the average stock picks by professional managers perform worse than the market (research by Inalytics revealed that the average hit ratio is lightly less than 50%, meaning that fund managers, on average, are wrong more than half the time).
While there are many ways to investment heaven – and many investors have demonstrative skill – G2’s approach is to use machines to learn the rules of the game.
That is, we apply machine learning to the task of finding reliable patterns in company data. This goes beyond typical “Quant” approaches based on blunt factor exposures and regression, and instead looks to identify granular patterns in fundamentals at the company level.
A key advantage the machine has in competing against humans is that it can assess the entire stock universe deeply and rapidly. It then becomes a “rejection machine”, saying no more than 96% of stocks rather than analyzing a few and getting to yes.
Thus, in a sense the system lives by the old maxim: The difference between successful people and really successful people is that really successful people say no to almost everything.