The Biggest Myths In Investing, Part 10 – Forecasts Are Useless

<< Read More: The Biggest Myths In Investing, Part 1 – The “Investing” Myth 

<< Read More: The Biggest Myths in Investing, Part 2 – The Stock Market Is Where You Get Rich

<< Read More: The Biggest Myths In Investing, Part 3 – You Need To Beat The Market

<< Read More: The Biggest Myths in Investing, Part 4 – Indexing is Average

<< Read More: The Biggest Myths in Investing, Part 5 – Bonds Lose Value if Rates Rise

<< Read More: The Biggest Myths In Investing, Part 6 – Gold Is A Good Portfolio Hedge 

<< Read More: The Biggest Myths In Investing, Part 7 – Fees Are A Small Price To Pay For Expert Advice

<< Read More: The Biggest Myths In Investing, Part 8 – More Information Will Give Me An Immediate Advantage 

<<Read More: The Biggest Myths In Investing, Part 9 – Risk Is Something We Can Quantify

Smart asset allocation is really about establishing an intelligent set of probability distributions. When we build a diversified and operationally grounded portfolio we don’t have to be exactly right. Instead, what we largely avoid is being precisely wrong. This improves our odds of financial success by creating a high probability of an asymmetric outcome. As I’ve described before, great investors think in terms of probabilities. And while it’s become fashionable in recent years to shun forecasts and expert opinions, I believe this is a dangerous misunderstanding about how we should approach the process of portfolio construction.

As we learned in Myth 4, we’re all active investors allocating assets by making implicit forecasts about the future. For instance, a Vanguard Balanced Index investor is explicitly saying that a combo of stocks and bonds in a 60/40 allocation is likely to meet their financial goals. But the difference between this investor succeeding and failing comes down not only to understanding that this portfolio has performed well in the past, but also understanding why it might perform well in the future. This involves not only a reasonable forecast of its likely future returns, but also having the confidence that its underlying instruments are structured to generate those returns.

The rise of technology and data driven information has allowed us to better test portfolio outcomes. But testing these outcomes is only half the battle. Using past evidence to forecast the future (called extrapolative expectations) is a useful means of portfolio testing, but does not necessarily mean we are utilizing an operationally sound methodology. In order for these implicit forecasts to have a high probability of success they should also be grounded in a sound operational understanding.

When we think about the asset allocation process and creating high probability outcomes it’s useful to think in terms of operational realities. Building a portfolio isn’t exactly like engineering, but it has similarities. For instance, when we build a plane we engineer it so that it is designed to take advantage of certain operational realities. If we build the plane in a certain way to create a certain amount of thrust it will create lift which will create flight. Once an engineer has a sound operational theory for creating flight they can construct the plane and test it thereby giving it a historical track record of evidence supporting the underlying operational engineering. This is how we establish high probability forecasts about their performance.

The financial markets are not that dissimilar. By thinking of our process in terms of operational asset allocation we can create higher probability outcomes by undergoing the same process that the engineer does when building their plane. This is achieved via a rigorous two step process:

  1. Understanding if this asset allocation is consistent with an operational understanding of how these instruments are structured and designed to perform.
  2. Testing the structure to see if there is evidence supporting the underlying operational structure showing that these instruments actually perform the way we expect.

In order to better understand this process we can look at a simple example of stocks and bonds. In building our operational asset allocation of stocks and bonds we must first understand what these instruments are and how they are structured to perform:

  • Stocks are a contractual obligation structured in such a way that they give the owner access to the residual profit earned by the underlying entity.
  • Bonds are a contractual obligation structured in such a way that they give the owner access to what is usually a fixed income over a defined period of time.

Stocks earn what is called an equity risk premium because it does not make sense to own stocks if one can earn an equivalent return in a lower risk instrument like a bond. Stocks, therefore, are operationally structured to outperform bonds. When we study the evidence testing if this operational structure is supported by historical testing it confirms our underlying thesis that stocks are operationally designed to generate higher returns than bonds. This is an exceedingly simple example, but one could expand on this to better understand how any asset class fits into this methodology.

Of course, nothing in life is guaranteed. Planes don’t always fly and stocks don’t always outperform bonds. But by taking this sort of operational approach to asset allocation we can improve our odds of financial success because we are making high probability forecasts about the future that are grounded in operational realities.

Disclosure: None.

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