Bankers are bombarded with views, opinions and predictions. Now that the FOMC raised interest rates by 25 basis points and has embarked on a tightening cycle, most economists, pundits and correspondent salespeople are trying to convince community bankers of their specific rate view. But bankers should be circumspect of others’ rate views and should not buy into one rate path but instead consider various possible interest rate paths. Bankers should be mindful of the dispersion around that mean.
There are a few forecasting biases that bankers need to be aware of. One obvious one is sales misrepresentation or puffery. Every person that earns a living making projections has some personal stake in trying to persuade the audience of their view. If a prognosticator did not want to persuade you of their view, you would never hear from them. Economists, analysts and sales people are all compensated to some degree by trying to influence the consensus estimate. Sales people especially are prone to puffery for obvious reasons. Bankers should be alert to projections, stories and analysis that are biased to the advantage of the specific product being sold.
A more insidious forecasting error is forecast bias, and this is especially true as the forecasting horizon extends beyond a few months. We can measure differences between forecasted values and realized values, however, as forecasting horizons move beyond a calendar quarter we find that the dispersion around a set of forecasts increases substantially. This is typical and reflects the wide disagreement between forecasters as future variables become less certain. While the futures market is broadly used to demonstrate consensus or the mean view, the futures market can and should also be used to measure dispersion or standard deviation around the mean forecast.
Dispersion Around Rate Forecasts
The ability of any individual to accurately forecast interest rates beyond a few months is very limited. The Federal Open Market Committee’s 17 members demonstrate an interesting and elegant way of forecasting mean and dispersion. Periodically the Committee submits the median, central tendency and range for five variables (real GDP, unemployment rate, PCE inflation, core PCE inflation, and Federal funds rate). The PCE inflation graph is shown below with the mean, central tendency and range all represented in the graph.
The median is represented by the red line, and is the average of all the member forecasts. The central tendency is shown in dark blue and excludes the three highest and lowest predictions for each year. Finally, the range is shown for all members in the light blue. What is interesting is that the central tendency and range become more widespread as the forecast horizon extends. This phenomenon is especially evident for the committee’s forecast of PCE inflation – which is arguably the most important predictor for the committee’s outlook for future fed funds rate.
Simply put, the committee is relatively aligned on the fed funds rate in 2016, but the committee’s view becomes more divergent over a longer time horizon. This makes sense, since forecasting events further out in the future takes on many more unknowns, so variability around the mean widens. For bankers, this is an important concept. For planning and analysis purposes, we must incorporate not a single number but an array of numbers for interest rates in the future and assign probabilities for each of those rates. As we plan and analyze our business beyond one quarter, the dispersion around the average becomes quite broad and our risk is greatly magnified. The central tendency shows that the Fed funds rate will be somewhere between 1.25% and 1.50% by the end of 2016, and the central tendency becomes substantially wider in 2017 and 2018.
Bankers need to be aware of forecasting errors and how this may impact their businesses. While sales puffery is easily identified and filtered, the greater risk to bankers is holding only one particular assumption for interest rates in their planning and analysis. Instead, bankers must stress their business model under a wide distribution of rates to make sure that all possible scenarios result in outcomes that are acceptable within their policies and guidelines.
Submitted by Chris Nichols on January 07, 2016