Daily Academic Alpha: Warren Buffett Market Predictions

/Daily Academic Alpha: Warren Buffett Market Predictions

Daily Academic Alpha: Warren Buffett Market Predictions

By | 2017-08-18T17:07:03+00:00 August 3rd, 2015|Tactical Asset Allocation Research|2 Comments
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(Last Updated On: August 18, 2017)

Last week we had a fairly long post on a valuation based asset allocation strategy that might actually work. This post followed a couple of other research projects on the issue, which showed limited evidence for simple valuation-based timing strategies.

Now there is a new paper on Warren Buffett’s favorite timing mechanism, Market Cap/ GNP (or GDP). We’ve discussed this metric in 2013, which suggested one get out of the market, and the out-of-sample results were horrific–the market has been on a tear!

Below is a link to the new paper and abstract (h.t. CXO advisory).

Select quotes from the paper:

Our analysis shows that Warren Buffett’s market value of all publicly traded securities as a percentage of GNP (MV/GNP), and its parent the lorgarithm of the market value of all publicly traded securities as a percentage of GNP (lnMV/GNP), can be a statistically significant predictors of future market downturns. However, for these measures to work, we need to use time-varying confidence-based thresholds rather than fixed thresholds.

The authors continue:

This conclusion dispels a common myth about the MV/GNP ratio: that absolute level matters. This myth has led market commentators and investment practitioners to suggest that the level of the MV/GNP is the harbinger of an impeding market meltdown.

The authors examine a bevy of measures:

  • MV/GNP with a fixed threshold at a 120% level;
  • MV/GNP with a threshold computed using a standard 95% one-tail confidence interval based on a Normal distribution;
  • MV/GNP with a threshold computed using Cantelli’s inequality;
  • logMV/GNP with a fixed threshold at a 120% level;
  • logMV/GNP with a threshold computed using a standard 95% one-tail confidence interval based on a Normal distribution;
  • logMV/GNP with a threshold computed using Cantelli’s inequality;

And here are the core results, highlighting that absolute measures stink, but time-varying metrics may be promising:

market cap to gnp predictions

The results are hypothetical results and are NOT an indicator of future results and do NOT represent returns that any investor actually attained. Indexes are unmanaged, do not reflect management or trading fees, and one cannot invest directly in an index. Additional information regarding the construction of these results is available upon request.

Conclusion: The results are mixed (absolute doesn’t work while time-varying works) when it comes to tactically allocating assets based on market valuations. This result is in line with much of our own research.

We provide more information on our take on tactical asset allocation and our summary of various concepts.

  • The views and opinions expressed herein are those of the author and do not necessarily reflect the views of Alpha Architect, its affiliates or its employees. Our full disclosures are available here. Definitions of common statistics used in our analysis are available here (towards the bottom).
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About the Author:

After serving as a Captain in the United States Marine Corps, Dr. Gray earned a PhD, and worked as a finance professor at Drexel University. Dr. Gray’s interest in bridging the research gap between academia and industry led him to found Alpha Architect, an asset management that delivers affordable active exposures for tax-sensitive investors. Dr. Gray has published four books and a number of academic articles. Wes is a regular contributor to multiple industry outlets, to include the following: Wall Street Journal, Forbes, ETF.com, and the CFA Institute. Dr. Gray earned an MBA and a PhD in finance from the University of Chicago and graduated magna cum laude with a BS from The Wharton School of the University of Pennsylvania.
  • Ben

    Interesting – Hussman uses a variation of MV/GDP to forecast Long-term returns on the market.

    He shows shows that while the metric itself doesn’t tell you when exactly the market is going to tank, you can still reliably use it to:

    1- Forecast long term market outcomes (7-10 years) – see the second graph in the link below

    2- Have an idea of range of expected drawdowns over a 5-year period. As expected when valuations are rather ‘normal’ the range is very large… but when the market starts getting very highly valued (1.6x+) then historical outcomes have all been bad!


    120% as used in this paper might be too low of an absolute threshold: at that level of valuation, historical outcomes have been way too diverse to tell us anyting (from little drawdown all the way to -45% in the following 5 years). Today we are at 200%, which seems consistent with -45% drawdowns historically!

  • Hi Ben,

    1) Valuation metrics–of all stripes–have been great forecasters of long-term market outcomes, historically. So that makes sense to me.

    2) Can’t argue with the data. However, the challenge is sticking to valuation-based tactical asset allocation strategies that require long periods of sitting on cash (5-10yr+). We also need to compare these solutions to other strategies, such as LT trend-following, which achieve similar goals as valuation-based allocation timers, but are ‘easier’ to follow. Bottomline: market timing is insanely difficult.

    Thanks for sharing!