Forecasters Agree to Disagree
In this age of big data, it’s not uncommon to see an analysis like this made-up example: After a positive year for the S&P 500, if January the following year is also up, that year is also positive 74% of the time with an average return of 9.3%. Assuming that scenario is the current case, it can sound very compelling as most people would assume they have a 74% chance of a 9.3% return. Nothing could be further from the truth.
As data becomes increasingly important, it becomes increasingly important that we have some rudimentary understanding of statistics and probabilities. Charlie Munger famously said “If you don’t get this elementary, but mildly unnatural, mathematics of elementary probability into your repertoire, then you go through a long life like a one-legged man in an ass-kicking contest.”
In this case, the first lesson is that looking at data since 1950, the S&P500 has been up 74% of the time with an average gain of 9.3%. So, it would make no difference at all what happened last year or in January of the next year. I understand this is an extreme, made-up, example, but the point is that just because something is observed, doesn’t necessarily imply that the circumstances are somehow related to the results. With the growth of big data, the amount of useless statistics has also grown enormously.
The second lesson is that the average has very little meaning unless you also know the standard deviation. If, in our example above, that 9.3% average return was calculated from returns that ranged from -5% to +15%, then the circumstances do indeed appear compelling. I have higher odds of making money with virtually no risk of losing a lot of money.
However, again using data from 1950, actual returns that created that 9.3% average return ranged from a high of 45% to a low of -38%. Now the risk-reward has materially changed because the standard deviation of the returns is much larger. I might make a lot of money, but I might lose a lot of money too. That higher standard deviation alters my investment analysis.
The reason we bring this up is due to a research note we received this week from Torsten Slok, the chief economist at Apollo Global Management. This week he examined Wall Street interest rate projections for the 10-year Treasury Note. We will allow Mr. Slok to explain from here.
“Some forecasters are currently predicting that 10-year rates will end the year above 5%, others are predicting a level below 3%, and the chart below shows the standard deviation of the 12-month ahead forecast for 10-year Treasury yields for 26 private sector forecasters since 2019.”
“The rising trend in the standard deviation of forecasts shows a very high level of disagreement among forecasters about what will happen to long-term interest rates in 2024.”
“This is not surprising because some would argue that a soft landing with Fed cuts and lower inflation would result in lower long-term interest rates.”
“Others would argue that a soft landing with no recession and the risk of reacceleration will push rates higher.”
“On a different note, others would argue that the key driver of rates in 2024 will be a higher term premium, driven by the coming massive increase in the supply of Treasuries.”
“What is most remarkable about the high level of disagreement among forecasters is that the same elevated level of uncertainty is entirely absent in the MOVE Index (bond volatility) and the VIX Index (stock volatility).”
“The bottom line is that we have a busy year ahead of us in markets with extreme disagreement about the forces driving longer-term interest rates.”
The economy dodged a bullet in 2023, but despite the overall optimism about the direction of inflation and interest rates that is so prevalent in the financial press, there remains a strong divergence of professional opinion. This story is not over yet.
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Economy, Inflation, Interest Rates, Jerome Powell, Volatility