{"id":1939971,"date":"2014-03-14T10:16:44","date_gmt":"2014-03-14T04:46:44","guid":{"rendered":"http:\/\/stockviz.biz\/index.php\/?p=1939971"},"modified":"2014-03-14T10:27:10","modified_gmt":"2014-03-14T04:57:10","slug":"models-beat-experts","status":"publish","type":"post","link":"https:\/\/stockviz.biz\/index.php\/2014\/03\/14\/models-beat-experts\/","title":{"rendered":"Why Do Models Beat Experts?"},"content":{"rendered":"
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\n\"algorithms\"\n<\/div>\n<\/div>\n

Academic research continue to show that models beat experts. Here’s what we had said back in September last year:<\/p>\n

Models beat experts. Models represent a ceiling, not a floor. Humans with a model improve performance, but underperform the model. Humans without a model are ineffective. Following a model, but then trying to add value via intuition, actually destroys the model\u2019s benefit and causes investors to underperform the market. Experts need to design the models, but COMPUTERS NEED TO IMPLEMENT THE MODEL.<\/div>\n

We also discussed the “Seersucker Theory”<\/a> where people generally ignore available evidence and continue paying for forecasts.<\/p>\n

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We often fool ourselves into believing that the more we pay for advice, the better it is.<\/p>\n

One explanation is that the client is not interested in accuracy, but only in avoiding responsibility. A client who calls in the best wizard available avoids blame if the forecasts are inaccurate. The evasion of responsibility is one possible explanation for why stock market investors continue to purchase expert advice in spite of overwhelming evidence that such advice is worthless.<\/p>\n<\/div>\n

It is not that we are dumb. It is just that we cannot help ourselves. There’s a term for it: “identity protective cognition thesis”, which is a self-sabotage of cognitive ability where it conflicts with a deeply-held belief. Basically, human beings have a tendency to want to hear information presented in the form of a story. This presents the risk of us getting psychologically attached to a single narrative prediction, which could then cloud our interpretation of new and potentially \u2018inconvenient\u2019 facts.<\/p>\n

Say, for example, you have identified yourself as a raging bull on some US tech stock, the fact such a bias could lead you to make mistakes when analysing fresh data on that business does not bode well for the success of your portfolio.<\/div>\n

John Maynard Keynes said: “When my information changes, I alter my conclusions. What do you do, sir?”<\/em><\/p>\n

Turns out most of us will ignore the information and keep trucking. Is it any wonder then, that models beat experts?<\/p>\n

Source:<\/p>\n