I don’t mean you as an individual, but you as a human being, when it comes to investing in shares.
I’ve just finished reading a whole bunch of research from various sources which clearly show that human beings make terrible decisions when it comes to investing in shares.
I always remember a story that Tobias Carlisle, founder of this website, shared about Joel Greeblatt and one such experiment. Joel Greenblatt is famous for his bestselling books “You Can Be a Stock Market Genius” and “The Little Book that Beats the Market”.
Joel’s firm developed a simple algorithm that ranked companies high on an average of their cheapness and their quality. He then offered clients the opportunity to invest in two different separately managed accounts which utilise the algorithm. The options were:
Professionally managed accounts – which simply followed the model’s output and systematically bought and sold top ranked shares, with trades scheduled at predetermined intervals
Self-managed accounts – which allowed clients to use the model’s output and then make a number of their own choices about which top ranked shares to buy or sell and when to make these trades
From 2009 to 2011 he gathered results on the two accounts to see which had performed better.
The professionally managed accounts earned a total return of 84.1%, beating the S&P 500 Index’s 62.7% mark by over twenty percentage points. The self-managed accounts, which allowed the clients to pick and choose from the model’s output at their discretion, earned a respectable 59.4%. However, the 59.4% figure was worse than the passive benchmark, and much worse than the account performance for the automatic accounts.
The lesson therefore is that mechanical, systematic or statistical prediction models produce better investment results that human intervention models. Or put another way, human intervention only assists in reducing performance over systematic investment models but, we just can’t help ourselves!
The reason that we (humans) continue to under-perform are overconfidence, cognitive biases, and something known as ‘The Broken Leg Problem” (Bishop and Trout, 2002.). Tobias, covers the topic of “The Broken Leg Problem” in his book Deep Value: Why Activist Investors and Other Contrarians Battle for Control of Losing Corporations.
Suppose an actuarial formula accurately predicts an individual’s weekly movie attendance. If we know that the subject has a broken leg, it would be wise to discard the actuarial formula.
Statistical prediction rules get broken-leg problems incorrect because the particular case is so different from the base rate. If that is the case, goes the argument, then surely these anomalous cases could benefit from an expert overriding the rule?
The studies find that they do not!
In fact, experts predict less reliably than they would have if they had just used the statistical prediction rule. The statistical prediction rule tends to be a ceiling from which the expert detracts, rather than a floor to which the expert adds.
So, make sure you have a clear and systematic investment strategy using your investing criteria and then strictly adhere to it. For me, I use the mechanical investing strategy provided here at The Acquirer’s Multiple to help me sleep better at night.
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