This article is part of Seleya's blog series "Investment Research: Time For Innovation".
Investors strive to make decisions based on logical and rational judgment. However, as humans, we all have blind spots and the investment research process is inherently vulnerable to cognitive biases. To achieve better investment outcomes, we believe it is important to understand how human psychology works, actively identify cognitive biases, and minimize their impact.
Below we take a detailed look at five major cognitive biases that investors can be susceptible to during the investment process.
Confirmation bias
When conducting research, investors have a tendency to seek and favor data that justify their existing beliefs or “confirm” what they already know. Confirmation bias presents challenges because a well-researched thesis entails weighing both sides of the coin. It can be hard to avoid this bias when new data appears to support similar past decisions. It is often helpful to challenge one’s own views by asking how a thesis could be wrong.
Anchoring bias
An anchoring bias is a disproportionate tendency towards a certain reference point or a benchmark. An obvious example of an anchor in investing is the stock price. Anchoring bias takes effect when an investor disproportionately overweights a buy or sell decision price based on the price he or she bought the stock before, while underweighting other factors. Similarly, investors could be unconsciously anchoring to the level of market indices in their decisions.
Herd mentality
Market rallies and sell-offs are often driven by herd mentality, especially during times of uncertainty. Herd-like behaviors are in part fueled by FOMO (fear of missing out), because human beings by nature desire to be part of a community and are influenced heavily by others. Irrational group think can cause massive flows into certain asset classes, leading to speculative bubbles such as the dot-com bubble in 2000 or the subprime mortgage crisis in 2008. Independent research and taking a contrarian view can be helpful to mitigate this bias.
Self-attribution bias
Self-attribution bias can occur when an investor attributes investment success to his or her personal capabilities while downplaying the role of external factors, including market conditions, one-off events, and, simply, luck. Self-attribution bias can make investors overconfident and lead to false conclusions when evaluating investment outcomes.
Hindsight bias
Related to self-attribution bias is hindsight bias, also commonly known as the “I-knew-it-all-along” phenomenon. This occurs when an investor views favorable past events as predictable and bad ones as unpredictable. It reveals a sense of regret at not having bought or sold stocks before major market events took place. Again, this can cloud investors’ perspectives and be a distraction.
While virtually impossible to eradicate biases, investors can mitigate the impact by adopting a robust, process-driven decision-making approach. Essentially, this means having processes in place that enhance investors’ awareness of potential biases.
Taken one step further, this also means systematizing core elements of investment research, quantitative or qualitative, into best-in-class frameworks and equipping analysts with tools to trace the evolution of their investment ideas in a rigorous and disciplined manner. This is where Seleya's proprietary technology and expertise in operationalizing AI comes into play.
In our next blogpost, we will evaluate the data science initiatives undertaken by asset managers in response to the pain points discussed. Stay tuned and follow us on LinkedIn / our website at www.seleyatech.com.
Seleya Technologies is an industry expert in AI and quantitative analytical tools for financial institutions. We leverage AI to augment human perspectives, enabling financial institutions to make decisions faster, more accurately, and with less bias.
Our two solutions include ExpertAI for institutional investors and ExpertAI ESG™ that scales up a financial institution’s in-house, proprietary ESG assessments.
Our team has been authoring the intersection of AI and investing since 2013. The company is founded by experienced investors and computer scientists with over 20 years of experience developing solutions for financial institutions.