“Forecasts may tell you a great deal about the forecaster; they tell you nothing about the future.” – Warren Buffet
Amid the chaos of the largest move in government bonds since 2008, businesses, investors and governments have spent an inordinate amount of time on economic forecasts in hopes of having an edge. Using economic indicators, forecasts attempt to predict the future state of the economy and by extension market prices set by expected growth and inflation. As a result, changing expectations are the root of changing market prices.
The issue with this process is that economic forecasts are inaccurate. In his book on forecasting, Nate Silver explains that “economic forecasts are blunt instruments at best, rarely being able to anticipate economic turning points more than a few months in advance.”[1] Expansive technology has made possible a rapid increase in the amount of statistical data available, but the increase in complexity has generally led to a decrease in the accuracy of forecasting models to date.
The Inaccuracies of Economic Forecasting
Methodologically, quantitative models deal with past observations, but as economic systems are constantly changing, this data does not always lend itself to predicting the future.
Figure 1: Federal Reserve's Economic Projections
Source: Board of Governors of the Federal Reserve
The US Federal Reserve’s Board of Governors is made up of the most renowned economists in the country, and even they are consistently inaccurate in their projections. As depicted above, they have regularly been overly exuberant in their predictions of GDP as even “the best of the best” prove to have a less than perfect batting average.
When it comes to forecasting markets, inaccuracies exist because of the challenges in predicting underlying variables. For variables such as inflation and interest rates, it is difficult enough to predict the future trends, nonetheless the magnitude of the change. Aside from these tangible factors, there lies the inherent variable of human behavior. People’s reactions to new information, like the Fed increasing rates, can’t be predicted as everyone processes information differently. Human behavior and rationality, especially in times of uncertainty, are immeasurable, making market forecasts a crapshoot.
As Warren Buffet represented in his view, the resources deployed by economists to predict economic data have an awful return on capital. Economic outcomes are produced by a huge number of variables themselves. Forecasting these is like trying to put together a puzzle with an endless number of pieces. The forecaster’s beliefs and bias are reflected in which “pieces” they find most important in guessing how to complete the puzzle.
The Surprise is More Important than the Forecast
If the prices of bond markets are a result of changing expectations of growth and inflation, then market pricing is based off the consensus of economic forecasts. Taking these forecasts and how they compare to the realized data, one can create an index that tracks the degree of “surprise.”
An economic surprise index provides a framework for analyzing these dynamics by measuring “surprises” relative to consensus market expectations for a set of key macroeconomic indicators. A positive reading means that the data released is stronger than expected, a negative reading means that the data released is weaker than expected.
For example, at the beginning of 2015 US economic output was lower than expected. This does not mean that the economy was performing poorly on an outright basis, instead that forecasts were overly optimistic. Since forecasts reflect market expectations, it also means that the markets were pricing in better news than what materialized. This resulted in government bonds moving to new low yields.
What Does This All Mean?
In times of investor optimism, demand is high and prices move above fundamental values. Conversely, extreme pessimism leads to a decline in market prices as investors move money out of markets, often as a short-term reaction. By observing optimism or pessimism as depicted by a surprise index, investors can better understand the environment we are in and use this as a framework for portfolio positioning moving forward.
[1] Silver, Nate. The Signal and The Noise: Why so many predictions fail-but some don’t. New York: The Penguin Press, 2012. 177. Print.
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By Mark Landis, Founding Partner & Matt Brophy, Research Analyst
