When looking at how a company performs, oftentimes the first steps include looking at how company performance versus analyst expectations. More often not, share price performance following the release of the results depends on how a company has performed relative to estimates.

While analyst estimates can come from very detailed and thorough analysis, these are still estimates. Estimates change as new developments come in, fundamentals change, or the macroeconomic environment shifts. Analyst expectations can be a good reflection of what is expected of the company and what is being priced at the current valuation. Given the significance, there are still limitations and challenges in forecasting that investors must consider: 

Limitations of data (economic and company)

The period of rising interest rates and high inflation has been a regime shift this year. The implication of a shift in economic policy can mean changes in performance, correlations, and forecast variables. For example, high inflation impacts the definition of growth (how much of the growth is coming from inflation passing to customers vs actual growth), and prospects and rising interest rates affect how investors view leveraged companies. The nonstationary nature of data can often make predictions misleading or inaccurate. 

Psychological biases

This is when analysts covering the same company try to match forecasts and target prices within a range of one another, so as to not appear as an outlier with an “extreme” view. This is so that if a company reports unfavorable results, an analyst is not singled out as one with poor judgment and appears more in line with average expectations.

Model uncertainty

Analyst targets and forecasts depend on combinations of various models which can include regression, variable analysis, and discounted cash flows. The structure of the model can overlook variables with stronger prediction power and/or be manipulated to give a favorable output. No model could have accurately predicted the stock market behavior so far this year, and that is an application limitation. 

Misinterpretation of correlations

Correlations are very easy to misinterpret and can be confused with causation. Comparing correlations across time horizons and with mean returns often means skipping over changes in fundamentals, growth prospects, and industry cycle. There is no shortage of data and charts to suit any thesis (even contradictory), and it comes down to which correlation convinces us more. It is a commonly used statistic in finance and can lead to false confidence around its meaning resulting in misleading conclusions.  

Despite the challenges, good forecasts are unbiased and well-researched. They need to be consistent across industry players, cross-sectionally, and across time horizons. Forecasting includes quantitative methods and judgment, both of which can be fairly subjective. So, while there is benefit in studying analyst reports and estimates, these should only be a part of your due diligence and not the driving factor.

All the best!