22 Best Practices for Avoiding Common Forecasting Blunders (Part 1 of 2)

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

Back in business school, it was easy to forecast a company’s prospects. Simply plug the numbers from the case into Excel and voilà, out pops a forecast accurate enough to please the professor. Wow, I’m smart!

It was only after arriving on Wall Street and getting my teeth kicked in a few times did I realize there is a lot…and I mean a lot…that can go wrong when it comes to forecasting. Thanks to tips from mentors, colleagues and distraught clients, I’ve put together this list of 22 best practices to help improve forecasting.

From my perspective, there are four sources that can trip up our forecasting accuracy:

  1. The companies (we need to take the blame when we’re gullible enough to accept the company’s view at face value)
  2. Ourselves
  3. Consensus
  4. The economy

I’ll address the best practices related to the first in this post, followed by the remaining three in our next post.

One of the top 3 reasons we have so many under-performing active managers is due to analysts’ overreliance on guidance from company management

Don’t Be Misled by the Companies

  1. No companies are recession-proof even though almost all will say they are. I covered trucking companies and railroads that would explain how they were recession-proof (or “resistant”); yes, heavily industrialized, cyclical companies. Guess what? They were wrong.
  2. When your financial forecast (or one you rely on) assumes one company will be a big winner (rapid growth or margin expansion), identify the loser(s) because it’s usually a zero-sum game. (e.g. Apple’s stock soared at a time when Nokia, Motorola and RIM collapsed, while Google and Facebook have been taking marketing dollars from traditional advertising channels.)
  3. Market share shifts are usually most pronounced when times are tough, not in the boom era. Look for the strongest players to come out of slowdowns with more share than the marginal players
  4. Cash is more important than earnings, but don’t ignore non-cash items
  5. Be highly skeptical of forecasts built on:
    1. Hot products or services, because all good things come to an end (except the iPhone)
    2. Turnaround stories, because they usually disappoint
    3. Roll-ups, because they rarely work (when they do, it’s because the company has made the tough decision to eliminate all but one of the former brands and fire all but one management team, which is more of an acquisition than roll-up)
    4. Companies with substantial related-party transactions, because it implies lax controls
  6. Avoid the common rookie mistake of forecasting higher than consensus, simply based on greater faith in an unproven or weak management team (I made this mistake multiple times early in my career)
  7. Superior technology or a patent doesn’t guarantee success. Companies need highly qualified management to execute a plan to generate shareholder value. Be suspicious of companies relying on a new IT system to fix all of their problems.
  8. Acquisitions are complicated. Unless the management team has a successful acquisition track record, be leery about forecasting synergy. Ask management to support its synergy forecasts with details – the few great ones have them, while most others do not. Early in my sell-side days I was brought “over the wall” to review the details of one of the largest mergers in my industry, which included synergy benefits I later discovered were built primarily by the investment bankers (rather than the company).
  9. Being a first mover isn’t always a competitive advantage, because it may have the highest investment cost relative to followers, who can learn from mistakes of the first mover (don’t forget Apple and Tesla are very late to their respective industries).
  10. A company expanding beyond its core competency is usually a problem (“di-worse-ification”)
  11. For the most part, unionized labor causes a company to be competitively disadvantaged. Employees extract a disproportionate amount of value, which leads to less for the shareholders (e.g., pre-bankruptcy General Motors) or customers (e.g., pre-bankruptcy United Airlines).

I don’t have enough room to cover the other 11 best practices and so I’ll discuss in my next post. Hopefully the above help illustrates that even when analysts find their own unique sources of insight (which is the hallmark of a great analyst), they can run into problems converting those insights into great forecasts.

As a reminder this Best Practices Bulletin™ post covered “Accurately Forecast” which is the first “A” in our GAMMA PI™ framework covering the 7 critical areas to master for success as an equity research analyst, as well as the first “M” which is for “Make Accurate Stock Recommendations.”

©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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