The Psychology Behind Stock Picking: An Analysis and Mitigation Technique | by Kalan Karuppana | Jul, 2024

Whether you’re an institutional or individual investor, hand picking stocks you think will outperform the market is never an easy task. Some investors like to do hours of research, build financial models to value a company, and perform technical analysis before purchasing a stock; others may make investment decisions based on personal convictions and news headlines. However, no matter which philosophy you fall into, there will always be a contributing factor that’s impossible to get rid of: the human brain. Every investment decision is influenced by human psychology; hence, it’s impossible to make a purely data-driven decision. So, let’s explore human psychology’s effect on investment decisions and how we can strive to make more data-driven investments in the future.

Specific Psychological Factors

To understand the effect that the human brain has on investments, we first have to understand three key phenomena which play a role in the process:

Overconfidence Bias is a cognitive bias where an individual overestimates their ability to predict an outcome, leading to overconfidence in someone’s personal beliefs about future situations. When people think they know more than they actually do, they are likely to disregard doing proper research and pick stocks based on conviction instead of evidence. Overconfidence bias enables investors to make excessive trades, underestimate risks, and make poor decisions, as human ego outshines data in investments influenced by overconfidence bias.

Herding Behavior refers to situations where investors follow and replicate the actions of a larger group, disregarding individual analysis in the decision-making process. Popularized by social media “gurus” and internet sensations, herding behavior has become especially prevalent within the past 10 to 15 years. Consequently, if every investor moves as one big “herd”, market phenomena like asset bubbles, market crashes, and increased volatility can become widespread, distributing the group’s damage over every individual who was once part of the “herd”.

Lastly, loss aversion, a more behavioral economic concept, explains how people tend to prefer avoiding losses than acquiring gains of similar size. Coined by Daniel Kahneman and Amos Tversky, loss aversion can lead to a reluctance in selling losing investments, missing out on great opportunities, and succumbing to emotional decision-making, hence negatively influencing investment decisions.

All three of these cognitive factors eventually end up undermining a data-driven investment process, as psychological factors play an increasingly prevalent role in decision making. While it’s recommended to be confident, listen to others’ advice, and be cautious, adhering to these cognitive biases do more harm than good. As investors, it’s great to have both psychological and evidence-based factors influencing our investment management. However, when we fall into these cognitive biases, the psychological influence heavily outweighs the data-driven influence, leading to suboptimal decisions and outcomes for our portfolios.

Bayes’ Theorem: the Mathematical Approach to Mitigation

Bayes’ theorem (in its statistical definition) is a mathematical formula used to update the probability of a hypothesis based on a set of new evidence. However, we can use this formula and concept as an attempt to mitigate overconfidence bias, herding behavior, and loss aversion by replacing it by mathematical probability and factual evidence. Although the theorem still incorporates aspects of human psychology and multiple assumptions (in its simplest form), using Bayes’ theorem to predict a stock’s future price movement is likely more accurate than basing decisions purely off conviction or personal intuition.

To apply this formula to investing, we first have to define the different letters and variables within the equation as well as making the formula applicable to an investment scenario. Let’s complete this process in a couple steps:

Step 1: Determine your hypothesis and evidence

• Hypothesis (A): A company’s stock price will increase over the next year
• Evidence (B): The company’s quarterly report saw a larger-than-expected increase in key financial metrics, and the company is finalizing an acquisition of a high-potential smaller company

Step 2: Define the Prior Probability

• Prior Probability (P(A)): After examining past price action, you find that the probability of a stock increasing after any given period of time is approximately 40% (0.40)

Step 3: Examine the Likelihood

• Likelihood (P(B|A)): After examining past price action, you find the probability of positive earnings report being released after a stock’s price has increased is around 50% (0.50)

Step 4: Find the Marginal Likelihood

• Marginal Likelihood (P(B)): The likelihood of a positive earnings report regardless of stock price in the time period before the report’s release is about 30% (0.30)

Step 5: Calculate

P(A∣B)= (0.9 x 0.55) / (0.30)

P(A∣B) = 66.67%

Hence, given a positive earnings report, we’d now assume that the probability of the stock price increasing to be 66.67%.

Obviously, Bayes’ theorem is not a perfect measuring tool for a stock’s future success, but it does give us a good start on probabilities of future success. It forces us to think objectively and critically about the future of said stock, enabling us to mitigate the psychological barriers that prevent us from making biased stock-picking decisions.

Other Mitigation Techniques

Since mathematically modeling the success of the stock may seem intimidating or foolish to some investors, there are other ways in which we can help prevent human psychology from heavily influencing our investment decisions.

The first is seeking feedback from a mentor or professional financial analyst, as getting a second opinion from someone more experienced can never hurt. If that person brings up an intriguing perspective on a decision, hopefully that can force us to consider different scenarios and courses of action we can take instead.

The second is creating a clear rationale document where before you invest in something, you must write out a clear reasoning detailing exactly why you think investing in a specific stock would be a good idea. Going through this process may illuminate errors in a specific thought process, forcing an investor to reconsider investing in that specific stock and approach the decision more rationally.

The last is scenario analysis — where an investor considers worst-case and best-case scenarios for the future of the stock they pick — to dissuade investors from being overly optimistic. By taking into account different scenarios, investors can begin to see the whole picture better than if they adhere to psychological barriers like overconfidence bias.

Conclusion

While an investor’s worst enemy may not be himself, it surely can be his brain. Psychological phenomena like overconfidence bias, herding behavior, and loss aversion can negatively affect the stock-picking process, leading to unnecessary losses. To fight back against these factors and make more evidence-based stock picks, it’s imperative that each investor creates a specific plan to mitigate the risk of falling into these psychological traps. By using Bayes’ theorem, seeking feedback, creating a rationale document, and conducting scenario analysis, we can all get one step closer to making purely data-driven investments 100% of the time.