Can Warren Buffett Also Predict Equity Market Downturns?
H/T Alpha Architect
NEOMA Business School
University of British Columbia (UBC) – Sauder School of Business
July 26, 2015
In a 2001 interview, Warren Buffett suggested that the ratio of the market value of all publicly traded stocks to the Gross National Product could identify potential overvaluations and undervaluations in the US equity market. In this paper, we investigate whether this ratio is a statistically significant predictor of equity market downturns.
Can Warren Buffett Also Predict Equity Market Downturns? - Introduction
Pointing to the spectacular rise in the level of the S&P500, which has more than tripled since the March 9th, 2009 trough and rose nearly 40% in 2013 and 2014 alone, investors and pundits worry that the US equity market might be dangerously overvalued. In fact, legendary investment manager George Soros already entered a $2.2 billion put position on the U.S. stock market as early as August 20141. Is the U.S. equity market about to crash?
In an attempt to answer this question, investors and pundits have turned to a variety of measures, ranging from Nobel laureate Robert Shiller's Cyclically-Adjusted Price-to-Earnings (CAPE) (Campbell and Shiller, 1988, 1998; Shiller, 2006, 2015) to Warren Buffett's ratio of the market value of all publicly traded stocks to the current level of the GNP (MV/GNP) (Buffett and Loomis, 2001). In this paper, we investigate whether the MV/GNP ratio is an accurate predictor of equity market downturns, defined as a drop of more than 10% in the value of the S&P 500 within a year. To that end, we use the likelihood ratio test proposed by Lleo and Ziemba (2015) to assess the accuracy of the CAPE and of the Bond-Stock Earnings Yield Differential.
The paper is organized as follows. Section I presents the MV/GNP ratio and addresses a common misspecification: replacing GNP by GDP. In section II, we give a definition of equity market downturns and list the 19 downturns that occurred between the last quarter of 1970 and the first quarter of 2015. In Section III, we transform the MV/GNP ratio into six testable downturn prediction models. Section IV presents the main statistical test step by step, from the construction of the predictive sequence to the maximum likelihood estimate of the model's accuracy, and to the likelihood ratio test. Finally, we perform a Monte Carlo study to address the small sample bias in Section V.
I. Buffett's Market Value-to-GNP Ratio
In an article co-authored with Carol Loomis (Buffett and Loomis, 2001), Warren Buffett discussed the \market value of all publicly traded securities as a percentage of the country's business - that is, as a percentage of GNP:"
The ratio has certain limitations in telling you what you need to know. Still, it is probably the best single measure of where valuations stand at any given moment. And as you can see, nearly two years ago the ratio rose to an unprecedented level. That should have been a very strong warning signal.
In this article, Buffett and Loomis (2001) follow up on an earlier interview (Buffett and Loomis, 1999), discussing the Dot.Com bubble and stock (over)valuation.
The idea behind the MV/GNP ratio is to gauge the total market value of companies against the value of the goods and services that these companies produce. The market value of all publicly traded US securities reflects the capacity of US firms to generate revenue, and translate these revenues into stable earnings. The US GNP represents the market value of all the products and services produced by US citizens and companies regardless of where they are produced. By contrast, the US GDP is the market value of all the products and services produced in the US, regardless of who produced it. To illustrate, the production of Apple in China would be part of the US GNP but not GDP, while the cars produced in the United States by Toyota would count in the US GDP but not GNP. This argument justifies the use of the GNP in the ratio.
How different would the ratio be if one used GDP instead of GNP? Figure 1 shows two comparison of the US GDP and GNP between the last quarter of 1970 and the first quarter of 2015. Panel (a) shows the evolution of the GDP and GNP on a quarterly and seasonally-adjusted basis. The difference between the two measures is small, with GNP rising slightly above the GDP in recent years. Panel (b) shows that the GNP-to-GDP ratio has remained in a narrow 1.00 to 1.02 range over the whole period. This suggests that using the US GDP rather than the GNP does not have a material impact on the results.
We confirm this intuition with an ordinary least square regression of the quarterly GNP against the quarterly GDP. The regression's R2 coefficient is 0.9999 and the F-statistic is 3,086,864 with 176 degrees of freedom, indicating a very close fit. The slope of the regression line is 1.011, with a standard error of 0.00058. Although it is significantly different from 1 at a 99% confidence level, it is not significantly different from 1.011.
See full PDF below.