To make money on the stock market, DON’T listen to the experts (according to research)

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Most of us know nothing about the stock market so we think our best bet would be to take the advice of analysts to decide what stocks to invest in.

That is logical and rational reasoning, but not good for making money on the stock market.

It turns out: the advice of analysts will cost you.

Here’s the point:

Research has found that the surest way to make money, is to invest in shares least favored by analysts, reports the Daily Mail.

In fact, over the last 35 years, investing in the 10% of U.S. stocks analysts were most pessimistic about would have yielded a staggering 15% a year.

On the other hand, investing in stocks analysts were most optimistic about would have yielded a miserly 3%.

Research by Nicola Gennaioli and his team at Bocconi University, Italy, found that over the last thirty-five years, investing in the 10% of U.S. stocks analysts were most optimistic about would have yielded an average of only 3% a year.

In the video below Porf. Gennaioli explains this puzzling phenomenon and others like booms and busts in the volume of credit and interest rate spreads as well as the unwillingness of women to compete in mathematics.

Prof. Gennaioli and his colleagues use cognitive sciences and the Kahneman and Tversky’s concept of representativeness to explain why decision makers often make such big mistakes.

According to this view, decision makers give too much weight or value to the representative features of a group or a phenomenon. Representative features are those that occur more frequently in the group than in a baseline reference group.

Here is an easy example Prof. Gennaioli gives to explain the idea.

“In a classical example, we tend to think of Irishmen as redheads because red hair is much more frequent among Irishmen than among the rest of the world,” Prof. Gennaioli says. “Nevertheless, only 10 percent of Irishmen are redheads.

How does this translate to analysts who make mistakes when they predict which shares will go up?

Analysts monitor the earnings growth of firms and when they notice that a firm shows sharp earnings growth, they draw the conclusion that the firm will be the next Google.

Now, it’s true that Google-like firms are more frequent among firms experiencing strong growth, which makes them representative.

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So amongst many firms experiencing growth, a “Google” will be one of many, therefore representative of the group.

Here’s the catch:

In absolute terms, “Googles” are very rare.

The result is that expectations become too optimistic, and future performance disappoints.

The mistake analysts make is to give too much weight to the representative features (earnings growth) of a group of companies, but these features occur more frequently in that group, not the baseline reference group – that is, all companies on the stock exchange.

Let’s delve a bit deeper into this representativeness principle as explained at Breaking Down Finance.

It turns out representativeness Is a decision-making shortcut, or heuristic, where we use past experience to help us make decisions.

These shortcuts are useful because they make it simple for us to make a decision quickly, but this can result in inaccurate decisions.

The point is, when people rely on representativeness to make judgments, they are likely to judge wrongly because the fact that something is more representative does not actually make it more likely.

Representativeness heuristics in finance

Representativeness heuristics often lead investors to make wrong predictions. For example, investors might be tempted to forecast future earnings using the short histories of high earnings growth observed in the past.

These estimates are then used to price the company’s stock and could thus lead to overpricing.

The thing is, the high earnings growth is unlikely to repeat itself and might actually lead to disappointment. In addition, when future earnings are lower than forecasted, the stock price could drop considerably.

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Written by Coert Engels

I'm a South African based writer and am passionate about exploring the latest ideas in artificial intelligence, robotics and nanotechnology. I also focus on the human condition, with a particular interest human intuition and creativity. To share some feedback about my articles, email me at [email protected]

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