Investors are Not Vulcan Nerds
by David Rasmussen on Nov 8, 2019
Economists and financial academics have created elegant theories about how markets and assets prices should behave. However, any theory, and there are many, that assumes market participants are always logical and never biased, is invalid. The reality is that investors are not strictly logical, in fact we are quite flawed, because we are human. We are not machines. For example, it is very common for a professional investor to hold onto losing positions while, conversely, selling a winning position too hastily. Consequently, investors and especially traders, can frequently end up with small winners, while retaining large losses. This phenomenon is known as loss aversion and it stems from our human egos. We don’t want to admit when we are wrong so we tend to hold onto losing positions hoping it will turn around.
Behavioral Finance is a relatively new field of economic study relating to the human divergence from logic caused by emotional biases and cognitive errors. The truth is that we make these errors in all parts of our life, not just while making economic decisions. Let me layout a few common biases and errors for edification and then explain how we can, perhaps, rise above them.
- Conservatism Bias: We tend to hold onto our initial beliefs about something. First impressions are hard to shake. Based on the time and effort required to come to a conclusion about an investment, investors tend to be too slow to change their mind even if the initial conditions have changed dramatically. This explains why Wall Street Analysts are slow to update their price targets after new earnings reports.
- Illusion of Control Bias: Describes the belief that we should be able to anticipate or control outcomes when we cannot. The economy and future assets prices are inherently unpredictable over the short run. Those inflicted with this bias tend to be overconfident in their belief that a certain outcome will occur. This is common in investing and in life. Hindsight Bias is related; we tend to remember when we correctly predicted an outcome and forgot when we failed to do so. We also unfairly judge an investment outcome by believing it should have been foreseeable.
- Availability Bias: A simple example of this bias is when an investor selects an investment simply because they recently heard about it in the media. In this case, they may ignore superior investment options just because they didn’t do further research. This is perhaps a catch all for just being lazy.
- Loss-Aversion Bias: This is the big one. As described in my intro, professional investors and laymen alike tend to strongly prefer avoiding losses as opposed to achieving gains. This has been studied heavily and is a pervasive emotional hinderance when investing.
- Endowment Bias: It is common for an investor to lack diversification because they ascribe special emotional attachment to a particular investment. Perhaps, for example, a stock was bequeathed to you from your folks after their death. In this case you may be less likely to sell it and diversify your holdings optimally. Another example, would be an unwillingness to lower the price of your house, as required by the market, as you believe your house to be special despite the oak trim and plaid wallpaper.
- Overconfidence Bias: Is a bias where people place unwarranted faith in their gut instincts and their own cognitive abilities. For example, most of us believe we are above-average drivers. The same applies to professional investors, many believe that the market has it wrong about a particular stock or the next quarters GDP. Relatively, humans are notoriously bad at estimating probabilities and tend to give a higher probability than warranted to their own ability to beat the odds. This is why casinos are a profitable endeavor.
Why do we seek to better our understanding of cognitive flaws? We believe that the first step towards making better decisions is to understand the causes of bad decisions. The next step is to build models, or algorithms, designed to remove or reduce our reliance upon intuition or “hunches.” These models can be a simple mental process, or a sophisticated, data oriented computer model, it all depends on what you are trying to accomplish.
When it comes to our investments at Calibrate Wealth, we use a systematic process where portfolios are adjusted along predefined metrics. For example, in order to “hack” the loss-aversion bias, we have developed a simple algorithm to balance risk each month due to changing market conditions. The metrics are mathematically defined with data. We follow this process automatically in order to circumvent our gut instincts. There are times when our guts tell us to do otherwise but we have to adhere to a process. In this way we are perhaps half Vulcan and half human like Spock. It’s cool I swear.
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