Losing an Illusion makes you Wiser than finding a Truth

Image courtesy Outcome/picpedia.org

By Noah Solomon

Special to Financial Independence Hub

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

I have become completely disavowed of the illusion that:

1.) People are able to predict the future with any degree of accuracy or consistency.

2.) Investors can improve their results by forecasting (or by following the forecasts of others).

Not even the almighty Federal Reserve, with its vast resources, near limitless access to data, and armies of economists and researchers has been particularly successful in its forecasting endeavors. For example:

  • Near the height of the dotcom bubble in 1999, Fed Chairman 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, Chairman 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.”
  • In late 2021, the Fed determined that 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 global) economy into recession.

The following commentary describes the underlying challenges relating to economic and market predictions. I will also provide some of the reasons why, despite strong evidence to the contrary, investors continue to incorporate them into their processes.

The Three Enemies of Forecasting: Complexity, Non-Stationarity and People

There is a near infinite number of factors that influence economies and markets. The sheer magnitude of these variables makes it near, if not completely impossible, to convert them into a useful forecast. Further complicating the matter is the fact that economies and markets are non-stationary. Not only do the things that influence markets change over time, but so do their relative importance. To produce accurate forecasts economists and strategists not only need to hit an incredibly small target, but also one that is constantly moving!

For most of the postwar era, economists and central banks relied heavily on the Phillips curve to inform their forecasts and policies. An unemployment rate of approximately 5.5% indicated that the U.S. economy was at “full employment.” Until the global financial crisis, any declines below this level had spurred inflation. Confoundingly, when unemployment fell below 5.5% in early 2015 and hit a low of 3.5% in late 2019, an increase in inflation failed to materialize.

This problem is well summarized by former GE executive Ian H. 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.”

Saved by 50/50

When it comes to economies and markets, it’s hard enough to be right on any single prediction. A forecaster who gets it right 70% of the time would be a rare (and perhaps even a freakish) specimen.

However, investment theses are rarely predicated on a single prediction. When a forecaster predicts that inflation will (a) remain stubbornly high, (b) rates will rise further, and (c) that these two developments will cause stocks to fall, they are technically making three separate predictions. Even with a 70% chance of being right on each of these forecasts, their overall prediction about the market has only a 70% chance of a 70% chance of a 70% chance of being right, which is only 34.3%!

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

Electrons don’t have Feelings

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 understanding and predicting human behavior in his assertion “Imagine how much harder physics would be if electrons had feelings.”

Alternately stated, if the very actions that drive markets are hard to foresee, then it would be illogical to assume that markets themselves are predictable.

More Data Needed

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 unearth the conditions which 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 humble pie. In the postwar era, the U.S. has experienced only 13 recessions. There are simply not enough observations. To use statistical parlance, this makes for an exceedingly large “error term” (i.e., a high chance of being wrong). Further compounding the problem is the fact the historical observations are not uniform: no two bear markets have been or are likely to be the same in terms of their respective causes and characteristics.

No Feet, No Fire

When contemplating investing with a manager, most people would assess the manager’s record. 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. There is no study of which I am aware 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 that 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 who are willing to pay for their purported wisdom and prescience!

Temptation and Delusion

To be clear, I am not implying that forecasts can never be right. I am merely saying that they can’t be right often enough to be valuable. This begs the obvious question why investors keep listening to or acting on predictions.

Perhaps the best explanation lies in the fact 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 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.”

Achieving the Possible

Rather than trying to divine a future which is simply not knowable, the objective should be to invest as wisely and prudently as possible in the absence of that knowledge.

In God we trust. All others bring data. None of the Outcome strategies are predicated on market or macroeconomic predictions, nor are any of our investment decisions dependent on intuition or human judgment. Our systematic investment processes are 100% algorithmically driven and are based on quantitative models which are derived from data analysis and machine learning.

With respect to our Global Tactical Asset Allocation (GTAA) mandate, these models have proven successful in protecting our clients from large losses during challenging markets, including late 2018, the Covid crash of early 2020, and more recently during the bear market of 2022.

As mentioned in our special bulletin earlier in the month, our models recently signaled a liquidation of all risk assets, leaving the portfolio 100% invested in cash.

Our machine-learning-based algorithms have produced best-in-class results for our long equity strategies. Since its inception in October 2018, the Outcome Canadian Equity Income Fund has returned 56.4%, as compared to a gain of 43.9% for the TSX Composite Index. Our U.S. Equity Income mandate has also outperformed, declining 2.1% since its inception in April 2022, in comparison with a loss of 11% for the S&P 500 Index.

Noah Solomon is Chief Investment Officer for Outcome Metric Asset Management. As CIO of Outcome, Noah has 20 years of experience in institutional investing. From 2008 to 2016, Noah was CEO and CIO of GenFund Management Inc. (formerly Genuity Fund Management), where he designed and managed data-driven, statistically-based equity funds. Between 2002 and 2008, Noah was a proprietary trader in the equities division of Goldman Sachs, where he deployed the firm’s capital in several quantitatively-driven investment strategies. 

Prior to joining Goldman, Noah worked at Citibank and Lehman Brothers. Noah holds an MBA from the Wharton School of Business at the University of Pennsylvania, where he graduated as a Palmer Scholar (top 5% of graduating class). He also holds a BA from McGill University (magna cum laude). This article originally appeared in the February  edition of the Outcome newsletter and is republished here with permission

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