22 Best Practices for Avoiding Common Forecasting Blunders (Part 2 of 2)

This post covers this element(s) of GAMMA PI: #2. Accurately Forecast, #3. Make Accurate Stock Recommendations
Avoid Simple Forecasting Errors

As a reminder, this post is Part 2 of my discussion highlighting the sources that cause us to derive inaccurate forecasts. In my first post, I highlighted all the ways we can be misled by company management, which is usually the fault of the analyst because, after all, our investors pay us to do more than simply take the company’s view at face value. In this post, I focus on the three other areas where analysts allow themselves to be misled (as I did many times during my career). By following these best practices, you’re less likely to make mistakes with your forecasts, which in turn will make them more accurate.

Don’t Be Misled By Yourself

  1. Reduce confirmation biases, by building upside and downside financial forecasts while conducting research (not afterwards), documenting as you go along, and then review before making a recommendation
  2. Reduce the sunk-cost psychological bias, by resisting the temptation to reverse-engineer a new price target (or justify an existing one) by simply changing the financial forecast (or valuation multiple)
  3. Similarly, if a price target is based on factors running well outside historical trends, reduce optimism bias by rigorously identifying a sound reason for doing so. Recall when oil prices shot above $120/bbl, many analysts went on to forecast a new paradigm of $150/bbl until it dropped back below $75/bbl which led to a new paradigm of lower oil prices. Here’s a reality check: take forecast revenue growth of all the stocks in a sector (your estimates or consensus) and compare it to the industry’s historical revenue growth rate. The consolidated forecast will almost always be too high because analysts are too optimistic at the individual stock level. They are looking from a bottom-up approach, which often is too optimistic compared to a more realistic top-down approach. How can the six stocks that make up 90% of a sector all grow revenue faster than the sector’s historical average? They can’t but it happens all too often in forecasting.
  4. Reduce Pollyannaish or hopeful thinking biases by:
    1. Fully understanding the “other side of the trade” before making a recommendation
    2. Asking a trusted colleague or investment committee to put your out-of-consensus thesis under scrutiny
  5. Avoid self-attribution biases by self-examining constructive or negative feedback provided by others (don’t internalize only the positive praise)

 

Proprietary insights are hard to find; don’t let their value be neutralized by simple forecasting errors

Don’t Be Misled by Consensus

  1. When your financial forecasts differs materially from consensus, assume you’re wrong until substantiated otherwise (the collective wisdom of consensus is more often correct than not). Question market participants, such as portfolio managers, buy-side analysts, sell-side analysts, and company management to understand the difference from consensus.
  2. Identify the most valid estimate among “consensus thinking”:
    1. See if there is a material difference between the most accurate sell-side analysts (“informed” consensus) and the overall consensus number
    2. Ensure the published consensus estimate includes many estimates, and is not isolated to just a few who happen to have forecasts for the time period being reviewed (such as 2 or 3 years out)
  3. Ensure the individual estimates that make up consensus are not stale, and there is no disagreement in terms of special items that may be in the number
  4. Buy-side analysts, who routinely rely on specific sell-side analyst forecasts, should check with third-party services to ensure those analysts have a strong track record of high forecast accuracy.

 

Don’t Be Misled by the Economy or Economists

I’m a fan of Peter Lynch who said, “I’ve always said if you spend 13 minutes a year on economics, you’ve wasted 10 minutes.”

  1. Current economic data is immediately stale, at least by a month, and often by 3-4 months, and therefore should be used sparingly as a forecasting tool. In the midst of a recession, market prognosticators are routinely stunned when the financial markets rally well before news flow turns positive– the market is anticipating real-time while the economic data is looking backwards.
  2. Minimal research time should be dedicated to forecasting factors that cannot be forecast with accuracy (e.g. commodity prices, the next recession, political unrest). Understand the current place in the business cycle (or the sector’s cycle), interest rates and investor’s appetite for risk, but leave the macro-economic forecasts for others. Do not expect to routinely generate alpha by being smarter on the macroeconomic forecast than the market.

So there you have my list of the 22 most common forecasting errors, and best practices to avoid them. These best practices alone are not going to create great forecasts, because I didn’t discuss how to generate the informed insights required for great forecasting (that’s the subject of many other posts classified under “Generate Informed Insights”). But it would be a shame if you did all the work to come up with a killer stock call insight, only to have it negated by a forecasting error that could have been avoided. Let me know if I missed anything by sending me an email to jvalentine@analystsolutions.com.

Within our GAMMA PI™ framework, the first “A” is for “Accurately Forecast” and the first “M” is for “Make Accurate Stock Recommendations,” which are the focal points of this Best Practices Bulletin™ post.

©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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