Whether we realize it or not, behavioural finance plays a major role in our investment decisions. But what is behavioural finance?
Behavioural finance aims to expand on the cookie-cutter approach of traditional finance, which assumes rational investors and efficient markets, and explain the “human” aspect in investing. It explores biases that influence our decisions and the consequences we may face as a result.
In its simplest form, behaviourial biases are categorized as either cognitive errors or emotional biases.
What are Cognitive Errors?
Cognitive errors are “blind spots” in the human mind driven by mistakes made in how we process statistics, information and our own memories. It can be separated into two categories, belief perseverance and process errors.
Belief perseverance is linked to the discomfort investors feel when new information conflicts with previously held beliefs. As a result, investors may exhibit:
- Conservatism bias where individuals maintain prior views by not acting on new information. Consequently, investors risk holding onto a security longer than a “rational” investor. Example: Paul has most of his portfolio invested in direct real estate since he believes house prices will rise despite recent reports supporting a downturn.
- Confirmation bias where individuals pay more attention to information that supports their views. Investors may inadvertently hold large, concentrated positions thus increasing their risk exposure. Example: Paul talks to a real estate agent who agrees that home prices will rise. He then decides to increase his portfolio allocation to real estate to 90% but soon after, the market goes down.
- Representative bias where individuals use past experiences to interpret new information. Consequently, investors may make decisions based on a small sample that results in more frequent trading and thus reduced returns. Example: Paul is invested in Manager A whose short term performance (1, 2 and 3-year returns) have underperformed its benchmark. He decides to replace this fund with Manager B who has outperformed over the same period. At the end of the year, Manager A starts to outperform Manager B.
- Hindsight bias where individuals believe they could have anticipated the outcome of an event after it happened. It’s the “knew-it-all-along” notion that can lead investors to a false sense of overconfidence and cause them to take additional risk. Example: In the early 2000s, Paul wanted to invest in mortgage-backed securities in the US but had too many expenses at the time. After the crisis happened, he attributes his decision to not invest to his “belief” that the crash was inevitable.
The second category of cognitive error involves processing errors that focuses on how individuals interpret information. This includes:
- Mental accounting bias where individuals value money differently based on its source. Example: Mary receives her tax refund that she perceives as “free money”. She spends the refund on a lavish vacation instead of placing it in her savings account, which is where she usually deposits her income.
- Framing bias where a person answers a question differently based on how it’s asked. Example: Mary has a low risk tolerance and meets with an advisor who offers two funds; one that has a 10% chance of loss and the other that has a 90% chance of not losing. She chooses the latter even though both funds have the same probability of loss.
What are Emotional Biases?
Unlike cognitive errors, emotional biases arise from intuition and impulse where decisions are highly influenced by feelings. Consequently, investors may experience:
- Loss-aversion bias where the thought of losing outweighs potential gains. Investors may reduce their upside potential by selling winners and holding losers. Example: Ann is invested in stock A which has a large unrealized loss. As the price continues to drop, she decides to hold the stock in hopes of breaking even. The stock subsequently never reaches her purchase price.
- Overconfidence bias where individuals overestimate their knowledge. Investors may choose stocks with little supporting evidence and consequently underperform the markets. Example: Ann actively trades tech stocks as she believes she can select undervalued securities. Though some of her stocks yield high returns, her net returns are lower than the market because of trading costs associated to frequent trading.
- Self-control bias where individuals put their short-term needs ahead of long-term goals. Investors may not have enough saved for future goals such as retirement and may resort to riskier assets to generate more income.
- Status quo bias is the do-nothing approach. Investors may unknowingly hold securities that have increased in risk above their tolerance level.
- Endowment bias where individuals place higher value to assets with a sentimental attachment. Like status quo, investors may hold securities above their preferred risk tolerance and financial goals. Example: Ann inherits a large stock position but chooses not to sell or hedge the position as it was a gift from her grandmother. She therefore inherits more risk than her recommended tolerance level.
- Regret-aversion bias where individuals act out of fear of making the wrong decision. Investors may limit their upside potential by choosing to invest in conservative assets. Example: Ann previously invested in a high-yield bond that eventually defaulted. As a result, she chooses to invest a majority of her portfolio into government bonds out of fear of losing money in lower quality bonds.
Overcoming behavioural biases
Overall, cognitive errors are easier to correct than trying to alter an emotional response. But in both cases, we can seek advice from a trusted wealth manager and conduct our own research to educate ourselves on the investment decision-making process. We can also ask ourselves appropriate questions to identify our own biases. If we are proactive with these approaches, we can minimize the pitfalls associated with behaviour biases and thus, make more effective investment decisions.
Source: Pompian, Michael M. 2011. “The Behavioural Biases of Individuals.” In Behavioural Finance, Individual Investors, and Institutional Investors. Charlottesville, VA: