Nobel Prize Winning Ideas Can Help You Become a Better Investor

Finance and economics professors have spent years trying to explain how stock markets work. One of the most popular theories, the Efficient Market Hypothesis (EMH), starts with the assumption that investors are rational and they rationally set prices using all of the information that is available. This theory fails to explain market crashes like the one that occurred in October 1987.

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  • There was no news to justify a one-day, 22% selloff in the Dow Jones Industrial Average. If investors are rational, there was no need for selling. In fact, they should have been buying under the EMH since prices were quickly at bargain levels after the crash. Actually, under the theory, there shouldn’t ever be crashes. The fact that there are pushed the academic community to search for new explanations for market action. Among the most promising is behavioral finance, a field that acknowledges markets aren’t rational because investors aren’t always rational. Behavioral finance uses psychology to explain how the stock market works.

    Nobel Prize winning economist Daniel Kahneman, a renowned expert in the field of behavioral finance, summarized why the field is worth studying, “Economists think of what people ought to do. Psychologists watch what they actually do.” Other researchers suggested investors are neither irrational nor rational but rather “normal” which means they display elements of both characteristics.

    The cornerstone of behavioral finance is the concept of heuristics. A heuristic is simply a rule of thumb or a way of looking at data. Major heuristics defining investor behavior include:

    • Availability bias which means investors use information that is readily available. For example, the price-to-earnings (P/E) ratio is not the best valuation metric available. Tests generally show it is not even one of the better tools to use. But, the P/E ratio is widely used, most likely simply because it is readily available.
    • Anchoring which means we expect the future to look largely like the past. Even when presented with new information, investors often stick closely to their original beliefs. They anchor to the past and either ignore new information or change their beliefs slightly because they underestimate the importance of the new information. This explains why earnings estimates change slowly even after a company delivers better than expected results. Analysts expect the company to continue experiencing problems even after management demonstrated a turnaround is underway.
    • Herding is the tendency to want to be part of the crowd. This can be seen in everyday life and explains why groups of teens tend to dress in clothes bought at the same stores. In the markets, herding explains why some stocks go up for months at a time and why others fall for months at a time. Herding explains why most institutional investment managers tend to hold positions in the most popular stocks. It’s safe to underperform, from a career perspective, if you hold Apple and other stocks most managers own. If you underperform while those stocks rise, you are at risk of being fired.
    • Loss aversion means losses hurt and investors will often strive to avoid losses. Consider a stock that has fallen 80%, from $100 down to $20. Investors rationalize holding it with the belief that it can’t fall much further. It can, of course, fall 100% further. But selling means admitting a loss. Holding allows the investor to hope the stock will bounce back. This is not rational behavior. If it has fallen 80%, the investor needs a 400% gain to break even. Few stocks deliver gains of that size. A rational investor might sell and pursue gains in other stocks but loss aversion prevents many investors from selling losers.
    • Representativeness is the tendency to stereotype or assume a small sample is representative of a larger group. This heuristic explains why the success of one social media company can lead to gains in the stocks of other companies in the sector.
    • Overconfidence is found in many aspects of life. Most of us would say we are a better than average driver but just half of us can really be better than average. Many of us are overconfident in our ability to drive and in our ability to select stocks.

    These heuristics explain much of the behavior we see in the stock market. For example, anchoring and herding, explain why trends develop in the markets.

    Chart patterns can also be explained by heuristics. The head and shoulders pattern is one example.

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  • The left shoulder (LS on the chart) and the head (H) are parts of a normal uptrend where prices move higher and then pullback before moving to new highs. As we saw, trends result from herding and anchoring. The right shoulder in the head and shoulders pattern (RS in the chart) requires traders to reevaluate their beliefs. Buyers at the neckline (NL) had expected new highs. When they don’t see the uptrend continue and prices fall back to the neckline, loss aversion develops and some will sell rather than risk a loss. These tend to be the better traders.

    Heuristics also explain a rectangle pattern which consists of prices bouncing between support and resistance. Traders buy when they believe prices are too low (the support level at the lower edge of the rectangle) and sell when they think prices are too high (seen as resistance at the upper end of the rectangle).

    Here we see availability bias and representativeness. Traders want to buy low and sell high and as a large number of traders take action based on their beliefs about whether prices are high or low, we see chart patterns form. Chartists, traders relying on charts, are acting on availability bias and remembering the limited sample of patterns that worked, ignoring the sample of patterns that failed which is most likely larger. They believe the small number of patterns they have seen are representative of the market and buy when prices hit the lower bound of the rectangle, planning to sell at the upper bound.

    As these chart patterns demonstrate, behavioral finance is more than a theory. There are practical implications for traders. It’s possible to study the detailed literature in the field to identify the most common biases identified by researchers. Traders can then develop strategies to overcome these biases in their personal trading.

    Among the most important findings of behavioral finance is the problem of loss aversion, the finding that losses have a greater psychological impact on traders than wins. This explains the practice of averaging down where traders add money to losing positions, hoping to avoid a loss by reducing their cost basis on the position. This can also be thought of as “throwing good money after bad.” Loss aversion can explain why it is so difficult to let winners run even though that is one of the most important strategies successful traders highlight. Traders worry that the gain will become a loss and to avoid the pain of a loss they may sell with a small gain rather than allowing their profits to grow.

    Other traders study behavioral finance hoping to develop insights into how the majority of traders are likely to react to different market events. They believe they can gain an edge in the markets if they know how other traders will act. This is possible, but this could also be an example of overconfidence and could lead to failure in a different way. This demonstrates one of the problems inherent to trading – traders need new ideas but they need to understand the limits of what they can do. Sometimes the crowd is right and that’s why trends occur. Being a contrarian because you recognize herding could mean missing the trend. Success most likely lies somewhere between herding and overconfidence.

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