Charlie Munger: What Would he Make of Stock Recommendations and Incentives?

Charlie Munger has wisely drawn attention to the power of incentives to drive human behavior. For example, he cites the case of FedEx’s challenge of having the night shift finish processing packages on schedule. FedEx tried all kinds of things to accomplish this objective without success. Finally, they stopped paying the night shift workers by the hour and started paying them a fixed amount for the entire shift. Workers were free to go home when all the packages were processed. Problem solved.

If you are using any type of service that produces stock recommendations on a regular basis – monthly, weekly, or even daily – it makes sense to examine the recommendations in light of Munger’s thoughts on incentives. For this post, I’m not talking about the obvious incentive bias involved when someone is touting a stock. Here I’m talking about legitimate services that produce a steady stream of recommendations.

Is it possible to find such a stream of stock picks that are available at a compelling bargain price according to some fixed cycle or publishing schedule? Would subscribers keep coming back to a stock recommendation service that announced week after week that no compelling bargains were available?

In spite of Munger’s staunch disdain for efficient market theory as traditionally taught in academia, Munger teaches that the market is actually quite efficient most of the time. He likens the market to a pari-mutual race track where the odds generally reflect the capabilities of the various horses.

Here’s Munger in a talk at the USC Business School in 1994.

The model I like – to sort of simplify the notion of what goes on in a market for common stocks – is the pari-mutuel system at the racetrack.  If you stop to think about it, a pari-mutuel system is a market. Everybody goes there and bets and the odds change based on what’s bet.  That’s what happens in the stock market.

Any damn fool can see that a horse carrying a light weight with a wonderful win rate and a good post position etc., etc. is way more likely to win than a horse with a terrible record and extra weight and so on and so on.  But if you look at the odds, the bad horse pays 100 to 1, whereas the good horse pays 3 to 2.  Then it’s not clear which is statistically the best bet using the mathematics of Fermat and Pascal. The prices have changed in such a way that it’s very hard to beat the system.

Munger goes on to say that in spite of this situation, there are a select few people who are able to make good money betting on horses, even after the track taking 17% off the top. They do it by totally focusing on nothing but the performance of the horses and waiting – as long as it takes – until they see an anomaly where the odds are clearly in their favor. Then they bet heavily.

It’s something to think about the next time you turn to a source that cranks out recommendations like clockwork for scores of stocks. Think about whether it would be possible to make a living at the track trying to bet every race because you thought you could accurately handicap them all – or you used a publication that purported to do so – in a way that profitably exploited the available odds net of all transaction costs.

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