If the Fed can't get it right, why do investors always fall for the folly of forecasting?

if the fed can't get it right, why do investors always fall for the folly of forecasting?

Three Federal Reserve chairs made incorrect forecasts on three significant events that ended up roiling global markets and economies: Alan Greenspan on the dotcom bubble; Ben Bernanke on the U.S. housing bubble and Jerome Powell on inflation.

By Noah Solomon

“Losing an illusion makes one wiser than finding a truth,” according to satirist Karl Ludwig Borne.

I have become completely disavowed of the illusion that people are able to predict the future with any degree of accuracy or consistency, and that investors can improve their results by forecasting, or by following the forecasts of others.

Not even the almighty United States Federal Reserve, with its vast resources, near limitless access to data and armies of economists, has been particularly successful in its forecasting endeavours.

For example, near the height of the dotcom bubble in 1999, Fed chair Alan Greenspan argued that the internet was bringing a new paradigm of permanently higher productivity, thereby justifying lofty stock price valuations and encouraging investors to push prices up even further to unsustainable levels.

In 2006, Fed chair Ben Bernanke brushed off the most pronounced housing bubble in U.S. history, stating that “U.S. house prices merely reflect a strong U.S. economy.”

And in late 2021, the Fed determined the spike in inflation was “transitory.” It neglected to combat it, leaving itself in a position where it had no choice but to subsequently ratchet up rates at the fastest pace in 40 years and risk throwing the U.S. (and perhaps the global) economy into recession.

Inflation was ‘transitory’

The near infinite number of factors that influence economies and markets make it nearly impossible to convert them into useful forecasts. Further complicating matters is that economies and markets are non-stationary. They are constantly evolving. The things that influence markets change over time, and so do their relative importance. To produce accurate forecasts, economists need to hit an incredibly small target, and one that is constantly moving.

This problem is well summarized by former General Electric Co. executive Ian Wilson, who stated, “No amount of sophistication is going to change the fact that all your knowledge is about the past and all your decisions are about the future.”

It’s hard enough to be right on any single prediction when it comes to economies and markets. A forecaster who gets it right 70 per cent of the time would be a rare (and perhaps even a freakish) specimen. But investment theses are rarely predicated on a single prediction.

A forecaster predicting that inflation will remain stubbornly high, rates will rise further and that these two developments will cause stocks to fall is technically making three separate predictions. Even with a 70-per-cent chance of being right on each of these forecasts, their overall prediction about the market has only a 70-per-cent chance of a 70-per-cent chance of a 70-per-cent chance of being right, which is only 34.3 per cent.

And let’s be real here, I am being generous with handicapping the accuracy of any forecaster at 70 per cent. At 60 per cent, the chances of being right on their market call falls to a paltry 21.6 per cent. Fortunately, beleaguered prognosticators get saved by the forces of randomness, which default to 50/50.

Psychology, emotion drive markets

Economies and markets are ultimately driven by the decisions of people. These decisions are heavily influenced by psychology and emotions, which by definition are difficult to model or predict. Nobel Prize winning physicist Richard Feynman epitomized the difficulties of predicting human behaviour when he said, “Imagine how much harder physics would be if electrons had feelings.”

If you wanted to predict future bear markets, then it would be logical to study previous bear markets and analyze their contributing factors. In theory, this type of analysis would enable you to identify the conditions that preceded and/or accompanied historical bear markets, thereby enabling you to flag similar conditions when they appear and avoid large losses.

Unfortunately, modern economies and markets haven’t been around long enough for anyone to draw any assertions that shouldn’t be taken with a generous helping of salt. In the postwar era, the U.S. has only experienced 13 recessions. There are simply not enough observation points. To use statistical parlance, this makes for an exceedingly large “error term.”

Further compounding the problem is that no two bear markets have been or are likely to be the same in terms of their respective causes and characteristics.

Most people would assess the manager’s record when contemplating investing with them. Paradoxically, these very same people are perfectly willing to listen to (and perhaps act upon) the advice of economists and strategists without any indication of how successful their historical forecasts have been. I am not aware of any study that has ever quantified the investment results that could have been achieved by following the recommendations of any single forecaster or group of forecasters.

I’m not saying the emperor has no clothes. Rather, I am merely stating that we have no way of knowing whether they do or don’t. And yet there is no shortage of professional forecasters. There is also no dearth of investors willing to pay for their purported wisdom and prescience.

I am not implying that forecasts can never be right. I am merely saying they can’t be right often enough to be valuable. This begs an obvious question: Why do investors keep listening to or acting on predictions?

Perhaps the best explanation is that certainty is so valuable that investors will never give up the quest for it. If you could predict markets with a high degree of accuracy, your results would put even Warren Buffett to shame. In the words of ancient Greek philosopher Demosthenes, “Nothing is easier than self-deceit. For what every man wishes that he also believes to be true.”

Rather than trying to divine a future that is simply not knowable, the objective should be to invest as wisely and as prudently as possible in the absence of that knowledge. Or, as American journalist and scholar Henry Lewis Mencken said:  “We are here and it is now. Further than that, all human knowledge is moonshine.”

Of course, systematic processes based on data analysis and machine learning have a clear advantage over their “human” counterparts when making investment decisions based on existing (rather than forecasted) conditions.

The jury has ruled: a multitude of academic research spanning more than half a century overwhelmingly demonstrates that rules-based models outperform discretionary decision-making by human experts.

Noah Solomon is chief investment officer at Outcome Metric Asset Management LP.


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