A key point of hedge fund analysis of equity valuations centers on forecasting the returns of various stocks. For over 100 years, academics and practitioners have developed various quantitative methods to forecast equity market returns. These methods have often generated hot debate, as some were proven the valuation strategies do not find value and thus don’t outperform the market.

Add to this string of equity valuation models a rather interesting development from Stephen E. Jones, president of String Advisors.

In his research. Jones outlines the Demographically Adjusted and Market Adjusted (DAMA) method to provide what a statistical analysis shows is a better method of stock price forecasting. one that considers societal demographics, government debt and gross domestic product in the equation, something most relevant to today’s market environment but not done in other economic forecasting models. Through back testing, Jones shows in his research the DAMA method of equity valuation forecasting is more accurate. Perhaps most interesting is the models forecasting for the next decade.

Statistical analysis comparing DAMA economic forecasting with other popular methods

The controversial history of economic forecasting

Perhaps the most popular early example of attempts at economic forecasting is the somewhat simplistic “Dow Theory Forecasting” method championed by Dow Jones publishing scion Charles Dow, who through a series of editorials in The Wall Street Journal outlined his belief that the stock market itself and various stock performance could forecast future economic activity. This led to the creation of the Dow Jones Industrial Average and more significantly the transportation index was a true indicator of economic activity. The accuracy of his indexes as forecasting tools was later proven to under-perform the market by 3.5 percent a year in a 1932 academic study by Alfred Cowles titled “Can Stock Market Forecasters Forecast .”

This would set up a series of academic theories that would later be challenged and debated at the highest levels, debating perhaps one of the most important topics in economic forecasting.

Much later noted efficient market theorists such as Eugene Fama of University of Chicago fame and most notably the 1973 book by Burton Malkiel “A Random Walk Down Wall Street ” peddled the view embraced in the 1980s that the markets were efficient and thus could not be predicted.  This work was countered by Robert Shiller, author of the book “Irrational Exuberance ,” who claimed that markets are in fact irrational and proposed what is known as P/E 10, a measure for stock valuation that considers the stock price relative to earnings over a period of ten years.  This measure was later popularized by John Campbell and Shiller in the book “Valuation Ratios and the Long-Run Stock Market Outlook” (1998).  Shiller’s model proved slightly more accurate, particularly in 1998 and 2001 when their ten year forecasts were proven accurate.

However, Jones points out all these models were missing a key component most relevant in todays economic environment.

Earnings is not as reliable a forecast method as is gross domestic product

Jones work conflicts with that of Shiller in identifying that the reason for his past under-performance in forecasting is due to the inclusion of less reliable future indicators, such as earnings, which can be a backward, not forward looking, indicator. Jones says societal demographic measures such as a populations age and gross domestic product provide more accurate analysis, which he demonstrates through back testing. This becomes more relevant since increases in both personal and governmental debt have entered into the equation to a never before witnessed level.  Jones uses a formula that divides stock market value by a country’s gross domestic product and considers the age of a population.

His work builds on the Market Value / Gross Domestic Product (MV/GDP) measure is proven to have a better forecasting track record but was never widely adopted. Jones explains the lack of popularity in the original MV/GDP valuation metric as its lack of popularity among Wall Street brokerage firms because it tends to reflect a more negative view of the markets going forward, particularly at this moment in time.  “With the use of out-of-sample testing, we then show that MV/GDP has, from a statistical perspective, also been most accurate at forecasting future real 10-year market returns,” Jones notes, pointing to past statistical analysis in his study.

Jones explains the resistance to what he proves is statistically more accurate method of equity valuation :

As bullish forecasts both provide customers what they want to hear as well as end up boosting the brokerages’ bottom lines. As Bill Gross (2015) notes, “…it never serves their business interests to forecast a decline in the product they sell.” Another logical reason for the measure’s absence from research, and for its unpopularity in the investment world, is a perception that the variable lacks theoretical justification as a forecaster of equity returns. Such a lack of theoretical justification would raise concerns of a spurious relationship between market value and GDP, and thus discourage its use as a forecasting variable.

Jones’ work is not only applicable to the U.S. as GDP figures, an economic indicator sensitive to debt levels, play a role around the world.  “Statistics showing the recent record imbalances of global debt levels indicate that our conclusions are also applicable to the other global developed equity markets.”

The impact of debt on economic activity

Jones notes the MV/GDP approach delivers “a better measure for forecasting equity returns,” and in doing so helps clarify the relationship between macro earnings and debt, both public and private, and various investments.

A point to understand this valuation model rests in the impact that extreme debt has on an economy. Jones writes:

Increases of debt relative to GDP cannot continue over the long term, and because it will incur future costs, the ability of higher debt relative to GDP to continually increase earnings is limited. Likewise, increased savings or reductions in debt would initially create a negative impact on earnings; however, the resulting increased savings or lower debt levels places the economy in a better position to spend savings or increase debt, and thus increase earnings, in the future. Therefore, all else being equal, if earnings-based valuation models use the reported earnings of the overall market, these models should, but fail to, place lower/higher valuation multiples on earnings which are higher/lower due to increased/decreased government debt, relative to GDP.

While increases in debt can, at the initial stages, boost economic activity, the issue is sustainability.

All else being equal, an increase/decrease in personal savings would bring about a comparable decrease/increase in corporate earnings during that period, and valuations should reflect the non-persistence of those changes. Also, when viewing the situation from a forward looking perspective, large historical increases/decreases, relative to GDP, in government debt leads to a greater chance of a reversion of that change, suggesting larger than average decreases/increases in future earnings.

To provide partial support his thesis, Jones notes “a very strong negative relationship between the changes in government and personal saving and the changes in corporate profits six quarters later.” Citing various studies, Jones notes that GDP has, in fact, generally been on a steady rise since the Great Depression and is sitting at all-time highs. “Without discriminating between sustainable and unsustainable earnings, it would appear that positive fundamental drivers have been steadily pushing earnings, relative to GDP, increasingly higher,” he writes. However, it becomes evident that the primary drivers behind the earnings growth, relative to GDP, have been increased government debt and reduced personal savings, characteristics which are usually considered economic weaknesses rather than strengths. The trend in high profits as a percentage of GDP is not “progress,” he says.

What is progress is adding a societial demographic overlay to the MV/GDP formula, a significant contribution Jones puts forth.

The addition of demographic trends in economic forecasting

Jones crunches the numbers and shows that demographic trends, in particular the age of a population, strongly influence the future performance of the stock market.

The impact of a societys age on economic activity has been virtually undisputed as it has benefited the U.S. for the past several decades. Those trends have been generally acknowledged when driving the market higher but the question is will analysts give the same credence as demographic trends turn negative. Jones writes:

This baby boom generation has had, and will continue to have, a large impact on the U.S. economy. The boomer’s earnings and investing powers began to escalate in the early 1980’s, and probably peaked in the early years of the 21st century. If that is the case, historical evidence suggests that their retirement years would likely bring about a selloff of their assets, and thus depress equity values.

His study data leads him to conclude the forecast for real equity returns over the coming decade “indicates that the current economic environment is a unique, if not dangerous, situation.” The model not only shows the greatest deviation in history relative to commonly used forecasting methods, but it forecasts stock returns over the coming decade to be worse than any period of time in the model’s 60-year history.

He observes:

The large cycles in the past may not repeat, but, historically, reversions from extreme high or low levels have tended to revert to levels significantly beyond the mean. Typically, the peak-to-trough cycles have taken up to 15 years. It is too soon to tell if the stimulative monetary policies over the past decade will succeed in breaking this current cycle or just delay it, but DAMA suggests that there is significant downside to come. The peak valuation levels reached in 2000 substantially exceeded the prior peaks in the later 1960’s and even 1929, as suggested by comparisons to older, simpler, market valuation yardsticks, as evident in Figure 15, below. It would be unusual for the bottom of the 2000 peak to be the quick and short a reversion to historical norms the market experienced in early 2009. If one assumes that the impact of inflation on real returns over the next 10 years is offset by the impact of dividends, DAMA’s projections would position the S&P near 765 in 2025. This forecast initially appears quite dire; however, as indicated in Figure 15, above, this level would still be well above the 2025 level of historical bear-market bottoms (of about 570).

Jones work may not sit well with market perma bulls, who gladly accept the concept that a large demographic bubble the baby boom generation created the greatest era of economic prosperity in world history. It will be interesting to see if the very same demographic trends and cold mathematical analysis will be given the same level of attention when the numbers point to a result that powerful Wall Street brokerage firms likely wont embrace.

To read the full report, click here .

Like this article? Sign up for our free newsletter to get articles delivered to your inbox