14 Mind Traps Afflicting Equity Research Analysts (Part 3 of 3)

This post covers this element(s) of GAMMA PI: #3. Make Accurate Stock Recommendations, GAMMA PI™ Area

When you’re reading reviews of a product that has your interest on Amazon.com, do you first go to the positive or negative reviews? When I poll students in my classes they almost always say “positive,” which is also the conclusion reached in similar psychological studies.  But if equity research analysts are striving to be rigorous in their work, they should spend time disproving their view.  Would you rather discover you’re wrong before or after you’ve made the stock call? The next time you’re buying something on Amazon, start by going to the negative reviews and see if it gives you a new perspective on validating your thesis. I’m not suggesting you let a single negative review sway you from your purchase, but see if it helps you to be a more critical thinker.

If you are striving to be rigorous in your equity research work, you should be spending time disproving your view

This is Part 3 of a 3-part series on avoiding the most common mind traps that afflict equity research analysts (see Part 1 and Part 2 which cover the first 9 of the 14). In this bulletin, we provide strategies for avoiding the psychological biases that fall under categories we term “Pollyannaish or Hopeful Thinking.” Understanding these biases, as well as best practices to eliminate them, helps increase the odds equity research analysts generate alpha.

Pollyanna Whittier is an orphan girl in the classic 1913 children’s book, Pollyanna, who is always trying to find something good in every situation, even when there isn’t any to be found. The book became so popular, the term “Pollyannaish” made its way into the dictionary.  Dictionary.com defines Pollyannaish as “an excessively or blindly optimistic person,” which may help us to understand where these particular biases stem from.

I’ve fallen prey to all five of the biases listed below at some point in my career, but the most prevalent I’ve experienced, as well as witnessed in others, is “confirmation bias,” which is seeking information to prove one’s own view (such as going first to the positive reviews on Amazon.com).

Pollyannaish or Hopeful Thinking

BiasManifestationExampleBest Practices for Increasing Self-Awareness
Confirmation BiasSeeking out information that supports your view and rejecting, distorting, or ignoring information that conflicts with itWhile conducting research on a stock you’re recommending, you come across a new competitive threat but choose to downplay it as insignificant• In making recommendations, wait until you’ve done all of the research before considering the rating. Deciding your rating early in the process will bias you toward finding insights that support your view and rejecting those that conflict with it.
• Approach all new information with an open mind, regardless of your current view toward the stock
• Build your upside and downside scenarios while conducting the research, documenting as you go along, which should be reviewed before changing a recommendation
• If you’re serious about making a recommendation, ensure that you know the opposing view
Over-confidenceAssuming you’re smarter than everyone else, which prevents you from exploring the real risks or reasons that a stock is not currently at your price targetAfter a few good stock calls, you begin to let down your guard in terms of assessing risks for future recommendations• Be humble by realizing that no professional investor is right 100% of the time
• Fully understand the “other side of the trade” before making a recommendation
• Ask a trusted colleague or investment committee to put your thesis under scrutiny
• Any time you think “I can’t lose”, find the downside or risks of the investment because they exist
Self-Attribution Bias Taking full credit for wins and placing blame on others for lossesAfter a recommended stock goes the wrong way, blaming a colleague (or the sell-side) for conducting shoddy research. (Remember, part of your job is to validate your information sources)• When you have a big win, go back to the documents you wrote when you recommend the stock, and see if your thesis really played out. (Or was it some other factor?)
• Before placing blame on others, or saying, “The surprise couldn’t have been foreseen,” ask yourself these questions:
o Did anyone else see this coming (sell-side or buy-side)?
o What could have been done to know about this surprise earlier?
• Examine constructive or negative feedback provided by others. (Don’t just internalize the positive praise.)
Optimism BiasBeing too optimistic about your stock’s valuation and future earnings potentialModeling a company’s EPS growth at 12% CAGR over the next 3 years, even though it has grown EPS at an 8% CAGR for the past 10 years• Research history for your companies and industries, specifically the growth rates and valuation multiples. If you settle on a price target based on factors running well outside historical trends, make sure you have a sound reason for doing so.
• Spend as much time identifying risks as catalysts
• Ask a trusted colleague or investment committee to put your thesis under scrutiny
Falling in Love With a StockBecoming so emotionally attached to a stock that it can’t be analyzed objectivelyAn analyst is hesitant to downgrade a stock that’s hit its price target because it’s a name that the analyst is associated with• Periodically review your universe of stocks to ask if you’re less likely to change the recommendation other than for risk and return considerations (such as concern over upsetting company management or disappointing those who rely on your research)
• When reviewing your comp table, hide the company names and tickers and look only at only the numbers to see if you have the same view when the names are revealed

It can be very difficult to fight the biases above because we seek information that proves our position, have egos that tell us we’re smarter than the market and occasionally become emotionally attached to a stock. Periodically review these best practices for avoiding psychological biases until they’re part of your routine, and you’ll be following the best practices of the best equity research analysts. (AnalystSolutions provides equity research training with a workshop where we discuss how to spot and avoid most of these pitfalls.)

This Best Practices Bulletin™ targets activity #3, “Make Accurate Stock Recommendations” within our GAMMA PI™ framework.  Please send us examples of how you have been negatively impacted in the past by any of the biases above. We’ll anonymously include some reader examples in a future Best Practices Bulletin™.

©AnalystSolutions LLP All rights reserved. James J. Valentine, CFA is author of Best Practices for Equity Research Analysts, founder of AnalystSolutions and was a top-ranked equity research analyst for ten consecutive years

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