Regularly Review Recommendations to Prevent Stock Calls from Rotting on the Vine

This post covers this element(s) of GAMMA PI: #3. Make Accurate Stock Recommendations
regularly review recommendations

If you planted a garden in the spring, you wouldn’t wait until the fall to see if you had any ripe vegetables. And yet the equivalent occurs all too often when analysts don’t make the effort to regularly review their recommendations. When I was an analyst, one of my favorite buy-side clients was known for shouting at sell-side analysts “I don’t need help knowing when to buy a stock, but when to sell.” He was right in that the sell-side (and buy-side) are overly focused on when to get into a stock with relatively minimal emphasis on when to get out.

It’s not that analysts are intentionally neglecting this important step, but they get distracted in searching for the next “buy” rather than tending to what’s ripening in their garden. The scenario goes something like this…the analyst upgrades a stock and due to the personal pride attached to his thesis, over time he loses sight of the recommendation’s risk/return trade-off relative to other opportunities within his universe. It’s such a common problem, the term “round trip” is used among some analyst circles when describing the great stock call that goes up, only to then return to its original price, all the while being rated a “buy.”

Too often analysts get lost looking for the next killer stock call only to neglect their current recommendations

Given the complexity of stock-picking, I built the TIER™ framework, all in an effort bring more rigor to the process. The “R” of TIER™ is for “Review performance and thesis” which is the focus of the table below. By focusing on these steps, analysts can ensure they’re always recommending the stocks with the best risk/return trade-off within their universe.

StepDetails
Dynamically rank and review your stocks regularlyReview your comparison ("comp") table regularly (i.e. daily or a few times each week) to ensure you’re always recommending the stocks with the best risk/return proposition. To provide the best analysis, the table should:
  • Update stock prices automatically
  • Update your EPS (or CFPS) and consensus' forecasts automatically
  • If you are evaluated for your stock picks relative to the market, include for each stock:
    • Valuation relative to an index
    • Valuation relative to stocks in other sectors with similar growth and return characteristics
    • Historical relative valuation parameters (on a forward-looking basis)
    • The upside/downside to price target, adjusted for risk
  • Allow for quick sorting based on the metrics above
Be open to selling poor performers and buying stocks you haven’t liked in the pastThis is an important part of the investment process to ensure you’re avoiding the mind traps I've identified as "Fear of failure" which include:
  • Sunk cost fallacy: afraid to back away from a recommendation due to the amount of time or capital invested
  • Loss-aversion: afraid to sell at a loss (remember the loss could get worse)
  • Anxiety: Changing a rating in a panic, based on emotions rather than a change in fundamentals
  • Snakebite effect: Not considering stocks that have been poor performers for you in the past
Review original documentation
  • When a stock call is going well, avoid a potential “round trip” (going back to its original stock price) by ensuring you’re not falling prey to the over-confidence and self-attribution biases. This is best done by reviewing original documentation to see if the thesis is truly playing out or if it’s more luck (periodically review your original model, report or presentation)
  • When a stock call goes poorly, be disciplined about using the stop-loss thresholds created at the time of the original recommendation. Due to the "loss aversion" psychological pitfall, it's much more difficult to create objective stop-loss points when a stock has not played out as expected.
Re-think recommendation if thesis wanesIf new, reliable information comes to light that derails the basis of the stock call, do an about-face on the rating as quickly as possible. It will be painful, but not as bad as living with a stock thesis that's never going to play out.
Re-think recommendation if catalyst is ineffectiveIf the key catalyst for the stock recommendation occurs and the stock doesn't move to the price target, strongly re-think the recommendation and avoid the temptation to find another catalyst to justify the recommendation.
Review unbiased comparisonsReduce sunk-cost, loss-aversion and other biases by periodically (once a month or quarter):
  • Masking the company names/tickers within the comp table, looking only at the numbers to see if the ratings appear correct relative to potential risk-adjusted returns
  • Ask, “If I switched firms tomorrow, would my stock recommendations at the new firm match my current ones?” If not, investigate why
Avoid placing blame or denying responsibilityWhen a stock call goes poorly, avoid placing blame on others for a bad stock call, or saying, “The surprise couldn’t have been foreseen.” Instead, ask yourself these questions:
  • What could have been done to know about this surprise earlier?
  • Did anyone else see this coming (sell-side or buy-side)?

In addition to the steps above, here are some philosophical considerations that help in the area of regularly reviewing recommendations:

  1. Don’t mistake good stock picking with a bull market: Always evaluate performance relative to a similar basket of stocks. (This also holds true when evaluating company management’s comments about its stock performance.)
  2. Mistakes can be valuable lessons: Stock calls that go bad can have some salvage value, as long as the shortcoming is analyzed and internalized to avoid a similar bad call in the future
  3. Automation will lead to more frequent reviews: Automate your comp table by having it draw key data directly from market data providers (and possibly from your financial models). This will reduce the laborious manual entry process which increases the frequency that this valuable table is reviewed.
  4. Accept that past losses shouldn’t impact future decisions

Using my garden analogy above, make sure to check your stock recommendations regularly so you can spot if they’re ripe or, potentially rotting on the vine.  It takes some extra work, but it’s critical to ensure you’re getting out of your recommendations at the optimal time.

This Best Practices Bulletin™ targets #3. Make Accurate Stock Recommendations of GAMMA PI™, within our Pathway to Success Framework. Let me know if this Best Practice Bulletin™ helps and how I can improve upon it. If you’re interested in exploring this topic further, AnalystSolutions provides equity research training with a specialized workshop to help Master the Stock Call Techniques of Highly Experienced Analysts

Improve you or your team’s stock picking and communication skills with our equity research analyst training tools, which includes workshops such as the one above, as well as our GAMMA PI™ assessment and one-on-one coaching.  Also, consider ordering the book that inspired the founding of AnalystSolutions and the Best Practices Bulletin: Best Practices for Equity Research Analysts.

©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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