Back in the early 1960s, then University of Chicago PHD candidate, Eugene Fama published a thesis which was later developed into a theory called the Efficient Market Hypothesis (EMH). This theory gained widespread acceptance in the finance industry (at least among academics) for several decades afterwards and is still commonly referenced to this day. Essentially what Professor Fama was postulating is that stock markets, or most markets for that matter, were efficiently priced at any point in time and that it was inherently impossible to outperform the market on a risk adjusted basis outside of the aid of pure luck, thus making individual stock selection a futile pursuit.
Around the 1990s, Fama’s theory started to lose its appeal among the mainstream finance community. Empirical evidence and research did not support the EMH’s conclusion that capital markets were perfectly efficient and select investment strategies, such as buying stocks with low price-to-earnings and cash flow multiples, did demonstrate an ability to outperform the market on a risk-adjusted basis over time. Providing an explanation of the short-comings of the Efficient Market Hypothesis was a relatively new field known as Behavioral Finance. This new field sought to study the emotional traits of investors and the impact they had on investment decisions and market activity. EMH was largely based on the assumption that humans were perfectly rationale beings and that decisions were made instantaneously with full knowledge of all potential outcomes in an unbiased and emotionless process. However, studies in both psychology and finance have demonstrated that this perfectly rationale investor was largely a myth. Human beings in fact rely on emotional to a large extent when making important decisions and are subject to a wide variety of potential biases. An objective of Behavioural Finance is to integrate these real world human biases into modern day financial theory to create a more realistic explanation of how the markets work.
Behavioral finance and psychology have defined numerous biases that can lead to poor investment decisions. We have provided a few examples below.
Bias: Bandwagon Effect
Definition: This occurs when a certain idea, investment type, or investment style starts to become more popular. As popularity increases, more and more people adopt the groupthink mentality and adopt the mentality themselves which further accelerates popularity, and in the case of investing, asset overvaluation.
Example: Alex has been looking at the market for potential investment opportunities. He has noticed that many small-cap tech companies have been doing well. A few of his friends have started to invest heavily in the sector, with good results, but Alex is worried about the high risk nature of the securities. As time passes, more of Alex’s friends have gravitated towards the sector and he is starting to see more portfolio managers and experts talk about it on the financial news. More time passes and the popularity increases. Finally, Alex has grown tired of missing out on the returns and decides to make a significant purchase of these stocks. Unfortunately, the growing popularity of the asset class has pushed valuation far beyond reasonable levels and the sector is now in serious risk of a crash.
Bias: Recallability Trap
Definition: This occurs when an individual’s opinions and decisions are overly influenced by large scale (and often dramatic) events that have taken place in the past.
Example: John receives a call from his financial advisor informing him that he has compiled a report of several successful technology companies that offer strong investment value. All of the companies in the report are profitable, growing, maintain healthy financial positions, and are trading at attractive value. John tells his advisor that he has no interest in receiving the report as he was heavily invested in tech stocks shortly before the market crashed 2001. His opinion is that the sector is far too volatile and he has decided to stay out of it completely. Although John’s decision is understandable, he is now limiting the flexibility of his portfolio based on an irrational bias. The tech market crashed in 2001 because it was substantially overvalued but that does not mean that current opportunities do not exist in that space.
Bias: Confirmation Bias
Definition: When people have an existing belief, such as an opinion on an individual stock or the movement of the economy, we tend to overweight evidence that supports our original view and underweight, or even disregard, evidence that that conflicts with this view.
Example: Jane recently made a purchase of Company B which is advancing a new technology. She spent a great deal of time reading the company reports and speaking to management. About a week after the initial purchase, she hears an analyst on the news reiterate what the company said about the technology. Pleased to see more people taking notice of the company, Jane increases her position that day. About a week later, she hears another analyst with a respected background in science discuss the technology and conclude that it is not as commercially viable and the company suggests. Although somewhat concerned with the statements, Jane takes no action.
Bias: Anchoring and Adjustment
Definition: Very similar to confirmation bias, anchoring and adjustment is a tendency to not fully reflect and adjust for new information when reviewing an existing opinion or forecast. This is a very common bias in the professional analyst community, but can also been seen regularly with retail investors.
Example: Jim owns shares in Company C and believes that the stock price will appreciate from $5.00 to $9.00 in 12 months as a result of increased sales from a new product offered by the company. Company C releases a statement later on indicating that sales of the new product are falling significantly short of initial expectations. Disappointed, Jim decides that the stock is probably only worth $7.00 over the next 12 months and cuts his price expectation by $2.00. Considering the lack of visibility going-forward, a large reduction is the price expectation is justified but Jim is still being influenced (he is anchored) to this original target.
Bias: Overconfidence
Definition: This is when investors tend to place too much confidence in their ability to pick stocks or make investment decisions. It is typically the result of a past success or successes which may or may not have been the result of luck. Overconfidence can be dangerous because it can cause investors to underestimate risk, under-diversify their portfolio, and even disregard relevant information.
Example: Garth has been a buyer and seller of speculative junior mining stocks for the last several years with mixed success. Recently, he bought shares in Company E which made a notable discovery and appreciated in price substantially. Garth also noticed that many of his other junior mining stocks had been doing well over the past year but that his diversified portfolio had underperformed. Garth concluded that his experience in identifying opportunities in the sector had started to pay off. He was also ignoring the fact that many of his stocks were doing well as a result of a generally strong market over that period. The problem was that Garth didn’t buy enough of Company E to really boost his portfolio value. Confident in his abilities, Garth decides that he is going to search hard for another stock like Company E, only this time he plans to concentrate a large portion of this portfolio in the stock so that he can make a huge return.
Bias: Mental Accounting
Definition: This refers to the way that the people have a tendency to mentally compartmentalize their finances and separate capital to psychological accounts.
Example: Taylor is reviewing his finances and deciding how much money he can contribute to his investment account which is currently worth $20,000 and has been generating a 6% annual return. Taylor also noticed that his credit card balance was a hefty $10,000 on which he is paying 18% interest. Taylor understands the importance of paying down debt as well as saving for retirement so he splits his $5,000 annual contribution 50%/50% to debt repayment and investment. While this may seem appropriate, it is actually highly irrational. For this to be a rational decision, Taylor would need to generate a minimum return of 18% in his investment account which is highly unrealistic. Taylor’s investment portfolio would be better long term if he were to use both his annual contribution and his investment portfolio to completely pay down the high interest debt.
Now that we are aware of a few of the common investor biases we can start to evaluate whether or not our own decisions are impacted by irrational tendencies and counterproductive habits. The first step is simply awareness. While it may be asking too much of ourselves to be perfectly rational at all times, simply being aware of the common biases and reviewing our behaviour in that context can be highly beneficial with respect to making better investment decisions in the future.
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10/30/2012
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