Dear Financial Voice Reader,

Wednesday 08th January 2020 05:50 EST

Why do so few people become rich in trading and investing. When I was writing my weekly Financial Times column of the ‘Diary of an Internet Trader’ telling the world what is working and making money and what is not these truths I realized:

First, Charlie Munger is right.

Charlie Munger, half of the most successful investment team in world history:

‘There isn’t one novel thought in all of how Berkshire [Hathaway] is run. It’s all about … exploiting unrecognized simplicities… It’s a community of like-minded people, and that makes most decisions into no-brainers. Warren [Buffett] and I aren’t prodigies. We can’t play chess blindfolded or be concert pianists. But the results are prodigious, because we have a temperamental advantage that more than compensates for a lack of IQ points.’

When I lectured at Oxford on behavioral financial I used to talk about the research of a man who went on to win the Nobel Prize – He said, “To know whether you can trust a particular intuitive judgment, there are two questions you should ask: Is the environment in which the judgment is made sufficiently regular to enable predictions from the available evidence? The answer is yes for diagnosticians, no for stock pickers. Do the professionals have an adequate opportunity to learn the cues and the regularities? The answer here depends on the professionals’ experience and on the quality and speed with which they discover their mistakes. Anesthesiologists have a better chance to develop intuitions than radiologists do. Many of the professionals we encounter easily pass both tests, and their off-the-cuff judgments deserve to be taken seriously. In general, however, you should not take assertive and confident people at their own evaluation unless you have independent reason to believe that they know what they are talking about.” Daniel Kahneman - Nobel Laureate.

Kahneman summarises the evidence:

‘People have competed against algorithms in several hundred contests of accuracy over the past 60 years, in tasks ranging from predicting the life expectancy of cancer patients to predicting the success of graduate students. Algorithms were more accurate than human professionals in about half the studies, and approximately tied with the humans in the others. The ties should also count as victories for the algorithms, which are more cost-effective…

‘The bottom line here is that if you plan to use an algorithm to reduce noise, you need not wait for outcome data. You can reap most of the benefits by using common sense to select variables and the simplest possible rule to combine them…’

And that’s what I did, year in year out winning awards from Bloomberg and in the Financial Times with my algorithm. You know what happened. The money went to people I beat – people like Neil Woodford. Even though they lost in the same competition they entered.

Institutional bias? Yes. That’s the name for it. If you’re not an institution money will not flow to you. Doesn’t matter if you’re rubbish. Sadly for me, I’m not rubbish, else I’d be at an institution.

Alpesh Patel

The author is founder of , and

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