Forecasting Equity Returns- An Analysis of Macro vs. Micro Earnings and an Introduction of a Composite Valuation Model

8/17/2019 Forecasting Equity Returns- An Analysis of Macro vs. Micro Earnings and an Introduction of a Composite Valuation …

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Forecasting Equity Returns: An Analysis of Macro vs. Micro Earnings & an Introduction of a Composite Model

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Forecasting Equity Returns:

An Analysis of Macro vs. Micro Earnings and an

Introduction of a Composite Valuation Model

Stephen E. Jones, CFA*

President, String Advisors, Inc.

Analyses of P/E10 and Market Value/GDP (MV/GDP) market valuation ratios reveal P/E10’s

reliance on misconceptions of the differences between micro and macro earnings. Kalecki’s profit function

is used to identify and avoid these problems, contest P/E10’s theoretical support, reveal MV/GDP as the

metric providing better theoretical and statistical support, introduce the concept of “macro-earnings

negativity”, and provide other important implications for economic theory. Based on the MV/GDP metric,

we develop a multi-variable forecasting model utilizing both new and prior-researched variables, the most

effective of which is a demographic measure. The resulting composite model is much more accurate than

popular benchmark metrics, and, relative to popular benchmarks, forecasts considerably lower returns for

the coming decade.

*Stephen Jones, CFA, is President of String Advisors, Inc., 245 E. 58 th St.,, #29A, New York, NY 10022, USA, Tel.: 212-599-

3571, E-mail: [email protected]. Special thanks go to Professors Terence Agbeyegbe, Anthony Laramie, Caleb

Stroub, and other reviewers. The use of “we” is largely in recognition of their contributions; however, this is not to imply that

they agree with the views of this paper or hold responsibility for any errors.


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Forecasting Equity Returns: An Analysis of Macro vs. Micro Earnings

and an Introduction of a Composite Valuation Model

1. Literature Review

For over a century, researchers have developed strategies to forecast equity market returns, only to

see others conclude that such strategies do not outperform the market. Thorough surveys of the history of

these studies can be found in Huang and Zhou (2013); Scholz, Nielsen, and Sperlich (2013); Rapach and

Zhou (2012); and Campbell and Thompson (2008). An early notable strategy is the approximately 255 Wall

Street Journal editorials written by Charles H. Dow (1851-1902). Though Dow never used the expression

“Dow Theory,” that term typically refers to these works. Later, Cowles (1933), in “Can Stock Market

Forecasters Forecast?” tracked Dow Theory forecasts and found that they underperformed the market by

about 3.5% a year. Cowles also found that recommendations by 24 other publications underperformed by

4% a year. From Cowles (1933) through the mid-1980s, the efficient market hypothesis dominated, and

market returns were generally considered to be unpredictable. Major research supporting this view includes

those of Godfrey, Granger and Morgenstern (1964); Fama (1965); Malkiel and Fama (1970); and Malkiel’s

(1973) book, A Random Walk Down Wall Street.

The 1980’s, however, saw a surge of research backing up the claim that market returns could be

forecasted. The research supported a variety of variables:

Book to Market: Kothari and Shanken (1997), Pontiff and Schall (1998), Welch and Goyal (2008),

Campbell and Thompson (2008); Consumption Wealth Ratio: Lettau and Ludvigson (2000), Welch and Goyal (2008), Campbell

and Thompson (2008);

Corporate Activities: Lamont (1988), Baker and Wurgler (2000), Boudoukh, Michaely,Richardson, and Roberts (2007), Welch and Goyal (2008), Campbell and Thompson (2008);

Dividend Yields: Hodrick (1982), Rozeff (1984), Fama and French (1988), Campbell and Shiller

(1988a, 1988b), Nelson and Kim (1993), Kothari and Shanken (1997), Lamont (1998), Lettau andVan Nieuwerburgh (2008), Cochrane (2008), Welch and Goyal (2008), Campbell and Thompson

(2008);


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Economic Combined with Technical: Huang and Zhou (2013);

Earnings: Fama and French (1988), Campbell and Shiller (1988a, 1988b), Lamont (1998), Welchand Goyal (2008), Campbell and Thompson (2008);

Inflation Rate: Nelson (1976), and Fama and Schwert (1977), Campbell and Vuolteenaho (2004),Welch and Goyal (2008), Campbell and Thompson (2008);

Interest Rates & Bond Yields: Fama and Schwert (1977), Keim and Stampaugh (1986), Campbell

(1987), Breen, Glosten, and Jaganathan (1989), Fama and French (1989), Campbell (1991), Ang

and Bekaert, (2007), Welch and Goyal (2008), Campbell and Thompson (2008);

Relative Valuations of High and Low Beta Stocks: Polk, Thompson, and Vuolteenaho (2006);

Stock Volatility: French, Schwert, and Stambaugh (1987), Guo (2000), Goyal and Santa-Clara

(2003), Welch and Goyal (2008), Campbell and Thompson (2008).

However, after claims that several variables were able to forecast market returns, arguments disputing

those claims returned, the most prominent of which comes from Goyal and Welch (2007). Their study

reexamined “the performance of variables that have been suggested by the academic literature to be good

predictors of the equity premium,” and, based on extensive out-of-sample testing, they found that these

models “would not have helped an investor with access only to available information to profitably time the

market.” Goyal and Welch also brought out-of-sample testing to widespread, if not universal, acceptance

as a benchmark for testing investment strategies. Goyal and Welch’s findings brought a response from

Campbell and Thompson (2008), which accepted the use of out-of-sample results, but “show that many

predictive regressions beat the historical average return once weak restrictions are imposed on the signs of

coefficients and return forecasts.” Campbell and Thompson’s response appeared to accelerate research into

alternative methods of identifying and testing forecasting variables. Rapach and Zhou (2012) covered this

topic thoroughly, and, in brief, show that “recent studies provide forecasting strategies that deliver

statistically and economically significant out-of-sample gains, including strategies based on:

economically motivated model restrictions (e.g., Campbell and Thompson, 2008; Ferreira andSanta-Clara, 2011);

forecast combination (e.g., Rapach et al., 2010);

diffusion indices (e.g., Ludvigson and Ng, 2007; Kelly and Pruitt, 2012; Neely, Rapach, Tu, and

Zhou, 2012);

regime shifts (e.g., Guidolin and Timmermann, 2007; Henkel, Martin, and Nadari, 2011; Dangl andHalling, 2012).”


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Both efficient market theorists and their critics continue to have strong proponents on each side.

Evidence that both sides of the field are highly respected is the concurrent awarding, in 2013, of the Nobel

Prize in economics to both Eugene Fama, a proponent of efficient markets, and Robert Shiller, who claims

markets are irrational.

Our research does not utilize the alternative strategies offered by Rapach and Zhou (2012), above,

although utilization of such strategies may improve the already statistical and economically significant gains

we find available. Our focus returns to the use of fundamental and macro factors to forecast long-term (10-

year) equity returns. Currently, the most popular of this type of measure are probably P/E10 (sometimes

called CAPE), and Tobin’s q. Each of these methods gained popularity in 2000 by the publication of two

books. The more popular of these two books is Robert J. Shiller’s Irrational Exuberance, which proposed

the P/E10 measure. Also well received was Andrew Smithers’ and Stephen Wright’s Valuing Wall Street ,

which supported “Tobin’s q”, a measure of the market’s price to its book value, introduced in 1969 by

Nobel laureate James Tobin. Each of the above books’ 2000 forecast correctly foretold poor equity returns

over the coming decade, and propelled their proposed ratios into prominence. Of the two metrics, the most

common is P/E10, a measure of the price of the broad market relative to its earnings over the prior 10 years.

Despite evidence that Tobin’s q is simpler and more effective (see, Harney, Tower, 2003), there is still a

strong preference for P/E10’s earnings based measure. This preference appears to be due to the belief that

earnings are the most important factor behind holding a specific equity, and that the sum of historical

combined individual (micro) company earnings is the best indicator of future macro earnings.

John Campbell and Robert Shiller first popularized P/E10 in Valuation Ratios and the Long-Run

Stock Market Outlook (1998). Although their earnings-based equity valuation model possessed good

predictive ability, and their 1998 and 2001 forecasts for poor market returns over the coming ten years were

largely correct, our research into earnings factors on a macro level reveals a conflict with using historical

collective individual corporate earnings as an indicator of future macro earnings. Moreover, significant


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increases in government and personal debt since the 1998 popularization of P/E10 have resulted in this

conflict being more obvious and more important than ever.

2. Identification of a Forecasting Variable

Despite efforts to identify methods to forecast equity returns, conspicuously uncommon is a variable

with the strongest predictive abilities: Market Value1/Gross Domestic Product2 (MV/GDP). Proving a

scarcity of coverage is difficult, but MV/GDP is not even mentioned in any of the following research:

“Valuation Ratios and the Long-Run Stock Market Outlook,” by Campbell and Shiller (1999 and2001).

“Forecasting Stock Returns,” an extensive review of forecasting strategies, by Rapach and Zhou(2012).

“A Comprehensive Look at the Empirical Performance of Equity Premium Prediction,” by Welchand Goyal (2008). This award winning article, which “comprehensively reexamines the

performance of variables that have been suggested by the academic literature to be good predictors

of the equity premium,” does not include MV/GDP.

“Predicting Excess Stock Returns Out of Sample: Can Anything Beat the Historical Average?” Thisstudy of at least 12 “standard predictor variables” does not include MV/GDP.

In summary, there is no academic study, to our knowledge, that researches MV/GDP as a variable to

forecast equity returns. In the investment community, MV/GDP has been used, but rarely so, despite Warren

Buffet’s claim that “it is probably the best single measure of where valuations stand at any given moment.”3

No study of the popularity of market valuation variables appears to be available, but several analyses have

pointed out the overwhelming popularity of P/E ratios4,5,6,7. We found only one study of market valuation

measures based on their degree of popularity, and it did not list MV/GDP among its six metrics5. Additional

evidence of MV/GDP’s lack of popularity is that the variable is rarely even mentioned in the more popular,

non-academic coverage of market valuation measures. For example, it was not a metric covered in

Vanguard’s 2012 study, “Forecasting Stock Returns: What Signals Matter, and What Do They Say Now?”,

which tried “to assess the predictive powers of more than a dozen metrics .” And, in their August 2013


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Strategy Snippet (Subramanian, 2013), Bank of America Merrill Lynch reported on “the 15 valuation

metrics we analyze;” none of which were MV/GDP. The omission of MV/GDP, and, moreover, the lack

(to our knowledge) of criticism for the omissions, is evidence that MV/GDP is not considered to be as

popular or widely accepted as other valuation measures.

Given MV/GDP’s strong forecasting ability, it is difficult to determine why it isn’t used more often. Of

course, one could justifiably argue that brokerages want to avoid the measure’s bearish forecasts, 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. Another potential

argument against the measure is that large fluctuations in the proportions of private vs. public company

ownership could distort the accuracy of this measure. In markets with low or fluctuating proportions of

private vs. public company ownership, this latter argument may be a valid criticism; however, in the U.S.

market, with a fairly consistently high percentage of pubic versus private companies, this is not an important

factor. Therefore, the primary theoretical reason behind not using the MV/GDP measure appears to be a

concern that the factor lacks proper theoretical justification.

2.1. Verifying a Variable’s Theoretical Fundamentals

Our response to concerns that MV/GDP lacks theoretical justification begins with a comparison

between the theoretical justifications of PE10 and MV/GDP. Our findings will both reject the theoretical

support of P/E10 and, perhaps ironically, conclude that MV/GDP is a better indicator of true future earnings

and has, therefore, stronger theoretical justification. Section 3 first explains how P/E10, proposed as a

measure to value the entire market, was founded on the principles of evaluating individual equities. We


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next reveal a conflict in valuing the overall market on the same principles of valuing individual equities by

revealing how the earnings processes of individual companies differ significantly from those of the overall

market. Evidence is then presented which suggests that PE/10 largely obtains its predictive power (relative

to the one-year P/E) from the strengths of the MV/GDP ratio, and then reveals how and why MV/GDP is a

better measure, both theoretically and statistically, of recurring earnings. Kalecki’s profit equation is

introduced in this argument, with the purpose of, first, identifying the sources of macro earnings and

revealing additional differences between macro and micro earnings. Second, we reveal how these sources

of macro earnings experience non-fundamental and non-sustainable fluctuations, and then explain the

importance of adjusting for these fluctuations in order to derive a more fundamental or permanent measure

of earnings. An adjustment process is then introduced which normalizes the factors in Kalecki’s identity on

the basis of historical averages. These “normalized” earnings, calculated as a basis of GDP, are shown to

equate to MV/GDP, therefore indicating that MV/GDP is a better theoretical “P/E” measure. 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. The section concludes by addressing the causality

issue in Kalecki’s equation.

Using historical data, we then explain, clarify and confirm the theoretical support presented earlier.

Section 4 concludes with a comparison of MV/GDP to the price/sales metric as well as introduces further

important implications which, though unnecessary for the composite model, are informative. Likewise,

statistics showing the recent record imbalances of global debt levels indicate that our conclusions are also

applicable to the other global developed equity markets.

2.2. Development of a Composite Model

Section 5 introduces the development of a composite model to forecast future real 10-year equity

returns. Though not original, the use of a composite model is uncommon, despite an abundance of individual


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forecasting variables. The model is based on the MV/GDP metric, and is improved significantly with a

unique implementation of a demographic metric. Further improvements come from the addition of both

newly developed and prior researched variables. Historical evidence suggests that the resulting model is

able to forecast future real equity returns significantly better than any model we are aware of.

This research not only provides a better measure for forecasting equity returns, but, as it does so,

clarifies the nature of macro earnings and their relationship with public and private debt, corporate

investments, dividends, and other economic variables. This understanding of the relationship of macro

earnings to economic variables, combined with the composite model’s forecast for real equity returns over

the coming decade, indicates that the current economic environment is in a unique, if not dangerous,

situation. Although this uniqueness makes forecasting more difficult, from a timeliness perspective it is

worth noting that the model’s current forecast is not only at its greatest deviation in history relative to the

commonly used measures, but is also forecasting returns over the coming decade to be worse than at any

time in the model’s 60-year history.

3. Evidence of Differences Between Micro and Macro Markets

John Campbell and Robert Shiller most prominently proposed the P/E10 measure in Valuation

Ratios and the Long-Run Stock Market Outlook, in 1998, as well as in an update in 2001. Although their

P/E10 measure, which they named CAPE, did well at forecasting a sub-par market performance over the

following decade, our research into earnings factors on a market-wide (macro) level reveals a conflict with

using measures of historical individual corporate (micro) earnings as appropriate indicators of future macro

earnings, and explains how the theoretical justification behind P/E10’s macro (overall equity market) based

earnings is mistakenly founded on micro (individual equity) theory.

In valuing the market, it has been common, historically, to apply the same methods used in valuing

individual securities. Campbell and Shiller’s development of P/E10 is an example of this. In “Valuation

Ratios and the Long-Run Stock Market Outlook: An Update” (2001) Campbell and Shiller wrote:


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“A clearer picture of stock market variation emerges if one averages earnings over several years.

Benjamin Graham and David Dodd, in their now famous 1934 textbook Security Analysis, said that

for purposes of examining valuation ratios, one should use an average of earnings of “not less than five years, preferably seven or ten years” (p. 452). Following their advice we smooth earnings by

taking an average of real earnings over the past ten years” (p. 6 -7).

This quote was not simply interesting supplemental information, but also appears to function as the

theoretical justification of the P/E10 measure. Years earlier, Campbell and Shiller (1988b) had noted that

the thirty-year moving average earnings-price ratio performed much better than the 10-year measure at

forecasting future equity market returns. The 30-year measure explained 56.6% of the variance of ten-year

real forward returns; however, the ten-year moving average ratio only explained 40.1% of the variance. The

obvious inclination is to use the ratio with the higher predictive ability; however, without theoretical

justification, models lack validity, and are unlikely to be any better predictors of future events than spurious

indicators, such as which league wins the Super Bowl8 (this topic of spurious relationships is covered again

in the discussion of the MV/GDP ratio). There is no theoretical justification for a measure having 30 years

of earnings; however, Campbell and Shiller thought they found theoretical justification for the P/E10

measure in Graham and Dodd’s methodology for valuing individual securities. Therefore, without

questioning the differences between the earnings of an individual company and the earnings of the overall

market, Campbell and Shiller ’s model— along with most of the investment community — values the overall

market with methods used to value individual securities. However, the following perspectives reveal that

there are very different, even conflicting, fundamental differences between micro and macro earnings.

3.1. Earnings Impacts from a Transactions Perspective

One may think that the impact of a single transaction on an individual company would be

comparable to the impact of the same transaction upon all the companies in the market. However, such is

not the case, and evidence suggests that the earnings process of corporations from a macro perspective is


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very different, and in many way oppositional to, the earnings process of an individual company. For

example: If an individual company were to reduce redundant staff by 10%, that company’s costs would

generally fall by the amount of staff cuts, and earnings would likely increase by the amount of staff cuts.

However, if the whole market were to cut staff by 10%, such a cut would also result in a comparable cut to

personal incomes and, as a result, to a comparable reduction to overall (macro) spending for the economy

and, therefore, to revenues for corporations. Therefore, if the market in general were to cut staff by 10%,

such cuts would unlikely benefit earnings of the market as a whole, or at least the overall earnings gains per

company would be significantly smaller. Similarly, if an individual company were to make an investment

in a long-term asset, such an investment would have little to no near-term impact on earnings, and have a

comparable negative impact on cash flow. However, if all companies were to make a similarly sized

investment in a long-term asset, such investments would generally lead to similar increases in near-term

earnings of all companies and have little significant impact on cash flow. These examples show that the

same activities applied to both micro and macro situations can produce dramatically different, and even

opposite, results.

3.2. Earnings Impacts from an Accounting Perspective

The process of deriving the earnings of an individual company is well known, and is described in

the following simplified income statement:

ABC CompanyIncome Statement for the Year Ended December 31, 2001

+ Revenues 10,000- Cost of Sale 4,500

= Gross Profit 5,500

- General & Admin. Expenses 3,000

= Net Profit 2,500


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However, the derivation of earnings on the macr o level is very different. Kalecki’s profit equation,

described in more detail later, recognizes the following identity:

+ Net Investment

+ Government Net Borrowing – Foreign Savings (Current Account Balance)

+ Dividends

– Personal saving

– Net Capital Transfers – Statistical Discrepancy

Corporate Profits (after taxes)

Therefore, not only do identical corporate transactions have different impacts on the micro

and macro markets, but the accounting derivations of micro and macro earnings are different as well. Thus,

it is not reasonable to conclude, as is implied by the P/E10 model, that valuation processes applied to

individual companies (the micro level) are equally applicable to the results of all companies combined (the

macro level). For a deeper analysis of the tendency within economics to falsely reduce macroeconomics to

microeconomic processes, see Debunking Economics, (Keen, 2011).

3.3. Impact on the P/E Ratio by Extending the Earnings Period: P/E83?

Yet another perspective of the differences between macro and micro earnings comes from

examining the number of years chosen in the P/E10 metric. The rationale for using 10 years in the P/E10

measure is based on valuation procedures for individual companies, as shown in the earlier quote, on page

9xxx, of Campbell and Shiller. However, considering the use of measures with different numbers of years

produces informative results. Given that 1871 is the oldest date — and the date Shiller starts with — for

available earnings data, and given that 1954 is the starting point of our study, the P/E ratio with the highest

possible number of years is P/E83. When looking at P/E83, it becomes conspicuously apparent that the

ability of P/E83 to forecast returns, as indicated by adjusted R 2 of 0.50, is 34% better than the predictive


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ability of P/E10, which has an adjusted R 2 of only 0.38. Initially P/E83 appears to be a positive find;

however, despite being significantly more accurate, P/E83, like Camp bell and Shiller’s P/E30 measure,

loses the necessary theoretical association to earnings which P/E10 claims to have, above. Furthermore, it

would be difficult to imagine that the predictive strength that comes from such a long period of macro

earnings could originate from the valuation process of individual corporate earnings. Our detailed

examination of MV/GDP shows why the derivation of P/E10’s predictive ability is more attributable to the

MV/GDP ratio, which, perhaps ironically, is shown below to be a better indicator of recurring earnings than

actual earnings measures.

3.4. Comparing the P/Es’ Extended Earnings Period to MV/GDP

As the earnings periods in P/E ratios are extended, the correlation between the P/E ratio and the

MV/GDP ratio approaches one.

Figure 1:

Thus, by steadily increasing the earnings period used in the P/E ratio, two important characteristics

are discovered. First, the accuracy of the P/E ratio’s ability to forecast returns increases from 0.28 for one

year, to 0.38 for 10 years, to 0.50 for 83 years, when it approaches that of the MV/GDP ratio, of 0.52.

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0.60

0.65

0.70

0.75

0.80

0.85

0.90

0.95

1.00

1 2 3 5 10 20 30 40 50 60 70 83

Correlation P/E Ratio, by Number of Years, to MV/GDP


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Second, the correlation of the P/E ratio to the MV/GDP ratio merges towards one. The fact that increasing

the number of years in the P/E denominator causes the P/E ratio’s forecasting ability to merge towards the

forecasting ability of the MV/GDP ratio, and causes their correlation to approach one, suggests a strong

association between earnings and GDP.

Figure 2: Performance and Relationships of Metrics: Correlation to

Regressed to ten-year future real total returns: Adj. R 2 MV/GDP t Stat.Real Price9/Real One-Year Earnings9: .28 .58 -15.5

Real Price/Real 10-Year Earnings (P/E10)9: .38 .92 -19.3

Real Price/Real 20-Year Earnings (P/E20)9: .44 .93 -22.0Tobin’s q12: .49 .94 -24.2

Real Price/Real 83-Year Earnings (P/E83)

9

: .50 .97 -24.8Market Value1/GDP2: .52 1.00 -25.5

Thus, increasing the number of years in the P/E ratio increases its effectiveness towards that of the

MV/GDP metric, while also increasing the correlation of the two ratios towards one. Therefore, the

effectiveness of P/E10 appears to be largely attributable to the numerator (price), and the increased

correlation of the P/E ratio to MV/GDP as the number of years in the denominator increases. Further

evidence of this is presented in Figure 2, above. Given that the numerators of the P/E and MV/GDP variables

are both market-price driven, then comparisons indicate that earnings, the denominator in the weaker

measure, actually reduces the effectiveness of the variable. This becomes clearer when comparing the

adjusted 2 of P/E10 to the adjusted 2 of MV/GDP (see directly above), a variable that is both steadier

and easier to calculate than P/E10. The reason why the earnings denominator reduces the effectiveness of

the variable becomes clearer later, most notably in Section 4.2, when it is shown why increases in earnings

relative to GDP are typically associated with deteriorating economic fundamentals and, likewise, why

decreases in earnings relative to GDP are typically associated with improving economic fundamentals.

Likewise, one will also likely find that the ratio of the price of the overall market to any variable that closely

tracks GDP also tends to forecast future real returns approximately as well as P/E10. Therefore, it appears


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to be the tendency of longer periods of historical earnings to track GDP which provides PE10 with its

forecasting ability. We will later provide more evidence of why this is the case.

Another indication that P/E10’s forecasting ability is already included in MV/GDP is that the

addition of P/E10 to MV/GDP to form a composite indicator does not support the necessary premise that

higher earnings, at a given price, should lead to improved returns over the model’s 10-year forecasting

period. Compared to the MV/GDP standalone results, the adjusted 2 does climb from 0.52 to 0.64, and

the P/E10 indicator initially appears to be very significant, with a t-stat of 14.8; however, the sign of the

coefficient switches, indicating that, adjusted for MV/GDP, higher/lower earnings for a given earnings

multiple lead to lower/higher real equity returns 10 years later. This is, of course, contrary to the

assumptions behind using the 10-Year PE to forecast future equity returns, presents another conflict when

trying to justify the theoretical assumptions of P/E ratios to value the macro market, and further supports

the concept that higher macro earnings relative to GDP are often associated with deteriorating economic

fundamentals, and that lower macro earnings relative to GDP are often associated with improving economic

fundamentals. This is statistical support of our concept of “negativity of macro earnings”, which relates that

that macro earnings growth in excess of GDP is negatively correlated with future growth in macro earnings,

relative to GDP. This concept will be explained in more detail later.

3.5. Using Multiple Years of Earnings to Value Individual Companies

Given the differences between micro and macro earnings, the following is not needed for our

argument; however it is worth noting that, despite Graham and Dodd’s indisputably deserved positive

reputation, empirical evidence (Gray and Vogel (2012), Gray and Carlisle (2012), and Loughran and

Wellman (2012)) indicate that longer-term metrics are not better at predicting returns than one-year metrics.

Therefore, other than Graham and Dodd’s hypothesis, there is no support behind P/E10’s assumption that

a measure with multiple years of earnings results in a better valuation measure than does one year of


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earnings. Therefore, not only is Graham and Dodd’s hypothesis about the valuation of individual companies

unjustly applied when it is used to support an indicator which values the overall market, the notion that

more years of earnings help value an individual company is simply not correct. As indicated above and

below, the strength P/E ratios derive from more years of earnings is the result of the increasing association

with the MV/GDP variable.

In summary, P/E10 lacks theoretical justification its predictive ability does not come from the sum

of the earnings of individual companies, as the measure is defined, but from the predictive abilities of

MV/GDP. This argument is further strengthened by our following analysis, which provides theoretical

justification for MV/GDP by revealing its relationship to earnings.

4. What Are Earnings?

To clarify the relationship between GDP and earnings, we utilize Kalecki’s profit identity to take a

closer look at macro and micro earnings. With a clearer understanding of the differences between macro

and micro earnings, and of the relationship of macro earnings to GDP, it becomes apparent that MV/GDP

does not have the problems inherent in traditional macro earnings based measures, such as P/E10, and why

MV/GDP is, theoretically, a better indicator of real future equity returns. As a result, MV/GDP should be

recognized as both a theoretically and statistically better metric to forecast equity returns. Also important

is that the divergences between these measures have recently reached their largest levels ever. Moreover,

the relationships between macro and micro earnings and GDP introduce other important implications which,

although they unnecessary for the legitimacy of our model, also merit attention.

4.1. Where Do Earnings Come From?

The accounting behind determining profits for an individual company is widely recognized. From a

macroeconomic perspective, however, where do profits come from, and what determines how much they

are? Such was the question that Michal Kalecki sought to answer when he developed his profit equation.


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Although practically unheard of by the general public, Kalecki’s profit equation is a long utilized

and well regarded accounting identity which equates macro earnings with macroeconomic factors.

Kalecki’s profit equation may have been first discovered by Jerome Levy about a decade before Kalecki,

and later Keynes, utilized it extensively in the 1930s; however, Kalecki is generally credited with doing the

most work in the area. Despite the model’s longevity and respect within economics, the identity is not well

known, and it is rarely utilized as a measure to forecast equity market returns. Here, however, Kalecki ’s

profit equation is used to identify, quantify, and theoretically justify the extent to which the sum of historical

corporate earnings is not the best indicator of future macro profits. The process of identifying and

quantifying the problems behind summing up actual historical earnings also provides solid theoretical

justification for using MV/GDP as a better variable to forecast future earnings and equity returns.

Kalecki’s profits equation— an accounting identity, not a theory — shows how corporate profits are

derived on a macro scale. An understanding of this formula will help determine the sources of

macroeconomic corporate profits and to understand why reported corporate (macro) profits ought to revert

to a ratio of GDP. Kalecki’s profits equation yields the following formula:

4.a . Kalecki’s Profit’s Equation:

Corporate Profits + Net Investment(after taxes) + Government Net Borrowing

– Foreign Savings (Current Account Balance)

+ Dividends

– Personal saving

– Net Capital Transfers

– Statistical Discrepancy

An excellent source (and the basis for our derivation, in Appendix 1) which identifies and quantifies

the variables in Kalecki’s profits equation is Laramie and Mair’s (2008), “ Accounting for Changes in

Corporate Profits: Implications for Tax Policy.” Thorough coverage of the topic is found in the book

“Profits and the Future of American Society”, (Levy & Levy, 1983). Typically, Kalecki’s equation is used


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to forecast how recent or proposed events affect near-term earnings or economic trends. The clearest

example of this is the Jerome Levy Forecasting Center. Seldom is the formula used as a contrary indicator

of longer-term corporate profits. An exception to this is Montier’s “What Comes Up Must Come Down”,

which utilizes Kaleck i’s equation to explain a negative forward outlook for profit margins and corporate

profits.

Our use of Kalecki’s profits equation reveals why higher earnings relative to GDP, even under

conditions of a stable P/E, could be a negative indicator of future equity returns if the earnings had been

driven by non-sustainable and/or non-fundamental factors. One example would be increases in macro-level

earnings caused by increased government and/or personal debt levels. However, this increased debt, nor the

boost that it provides to macro earnings, is sustainable. Similarly, if the government and/or consumers were

to reduce their debt, this increased savings nor the reduction it provides to macro earnings is sustainable.

Again, neither the reduction of savings, nor the boost that it provides to macro earnings, is sustainable. The

P/E10 measure, and most of the financial community, does not identify the extent to which earnings are

impacted by these unsustainable changes in debt. Furthermore, even if the investment community were to

appropriately discount unsustainable earnings with a lower market value, the P/E10 measure would still

forecast above-average future returns, given the lower P/E10 ratio. If the investment community valued

equities with an average P/E10 multiple, the average multiple would imply average future returns; however,

this forecast would not take into account the higher probability of an eventual return to normal debt levels

and the negative impact such a move would have on future earnings. Regardless of the equity valuation —

the numerator — established by the market, the ability of P/E10 to forecast future equity returns is

compromised because the denominator in the P/E10 ratio is not able to distinguish between sustainable and

unsustainable earnings. Without adjusting for the unsustainable changes, the levels of macroeconomic

earnings are not suitable for identifying sustainable earnings. Utilizing Kalecki’s profit equation to identify

and quantify these non-sustainable factors leads to the development of “normalized” earnings and reveals


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a relationship between normalized earnings and GDP. The development of “normalized” earnings will then

form the basis for new theoretical justification for MV/GDP as a better valuation variable.

4.2. Normalized Earnings, and the Negativity of Increases in Macro Earnings

We have just seen how and why fluctuations in the components of Kalecki’s profit equation— most

specifically government and consumer debt — produce unsustainable fluctuations in macro earnings. Here,

Kalecki’s profit’s equation is used to explain the tendency for earnings to revert to a ratio of GDP, to show

why such a ratio represents “normalized” earnings, and then to develop “normalized” earnings into a

variable. We first consider an increase in government net borrowing. According to Kalecki’s profits

equation, net increases in government and or personal borrowing boosts corporate profits. However,

because such 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. The same argument applies to the other variables in Kalecki’s equation,

such as personal savings. For example, 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.


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The above are further examples of how increases of debt boost earnings, and how this earnings boost

is not sustainable. The same argument applies generally to factors in Kalecki’s equation, suggesting a

negative aspect behind increased macro-level earnings. This does not just apply to increased debt, which is

generally accepted to be a negative factor of economic fundamentals. For example, increases in capital

spending relative to GDP is typically considered an economic positive; however, from a macro earnings

perspective increases in capital spending have already gone to earnings, from a macro perspective, and the

assumption that future capital spending will return to historical norms is a negative for future macro

earnings. The evidence presented in both Section 3.4 and in Section 4.4 provide statistical support for this

concept of macro earnings negativity.

When looking at the variables in Kalecki’s profit equation, it is important that they are not measured

from an absolute perspective, but relative to GDP, which adjusts over time for the impact of inflation and

the size of the economy. When measured relative to GDP, Kalecki’s profit equation can then be used to

explain the tendency for earnings, relative to GDP, to revert to historical norms. As the factors in Kalecki’s

equation naturally tend to revert to historical norms relative to GDP, we will show how Kalecki’s profits

equation reveals that earnings, the sum of these factors, will, by definition, likewise tend to revert to a ratio

of GDP. We will clarify the theory behind this argument, and identify the level to which earnings revert as

“normalized” earnings. Below, we will see that although all factors in Kalecki’s profits equation influence

reported earnings, it is government debt, personal savings, and net investment which are the largest

contributors to the equation.

4.3. Not Just an Identity

As an accounting identity, Kalecki’s equation suggests neither the direction nor existence of a

cause/effect relationship. While the conclusions in our research do not require causality in Kalecki’s profits

equation, recognizing the causality in the relationship significantly improves the understanding of


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underlying economic forces. Although it is impossible to prove a cause/effect relationship between profits

and the variables in Kalecki’s equation, several perspectives provide convincing evidence that it is the

variables in Kalecki’s equation which influence earnings, and not the earnings which influence the

variables.

4.3.1. Intuitive Support

An intuitive argument that the factors in Kalecki’s equation are causal is made extensively by Levy

& Levy (1983) in their book “ Profits and the Future of American Society.” Their illustration reveals how

increases in government and personal debt mean — all else being equal — increased expenditures on goods

and services and, thus, increased corporate revenues. Depending on the nature of fixed costs, corporate

profits in such an environment will likely increase even more than the increase in revenues. As a result, it

is understandable how increased debt leads to increased earnings. Otherwise, the most likely cause/effect

relationship which could explain Kalecki’s equation would be for increased earnings to somehow cause

increased government and personal debt — a relationship that is difficult to envision. In What Goes Up Must

Come Down, James Montier (2012) also argued for causality for f actors in Kalecki’s profits equation when

he said:

“This is, of course, an identity— a truism by construction. However, it can be interpreted with some

causality imposed. After all, profits are a residual; they are the remainder after the factors of

production have been paid. Thus, it can be comfortably argued that the left-hand side of the equation

(profits) is determined by the right hand side.” (p.4)

4.3.2. Support of Actual Results

Support for a causal relationship between debt and profits is also evident in actual results. Figure 3,

below, shows a very strong negative relationship between the changes in government and personal saving

and the changes in corporate profits six quarters later.


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Figure 3:

Changes in Government & Personal Savings vs. Growth in Corporate Profits:

Source: John Hussman, Weekly Market Comment, 6/17/2013

An in-depth statistical perspective is found in “What Drives Profits? An Income-Spending Model,”

in which Giovannoni and Parguez (2007) “inquire into the role and determinants of aggregate profits.”

Their several cause/effect studies support the notion that it is the factors of profits that cause changes in

profits, and not vice versa. Furthermore, they also point out that:

There is a puzzle in consumption fostering profits and compensation dragging them. The

reconciliation between the two findings could be that a growing share of American consumption is

being funded by credit, a well-known phenomenon. This amounts to stating that the major source of profits, consumption, actually hides an increased indebtness trend (p. 114).

Moreover, the degree to which debt can be directly and indirectly controlled further supports the

notion that profits are caused, or at the very least it diminishes the relevance of arguing the extent to which

causality is a factor in Kalecki’s profit equation.


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4.4. Historical Evidence

In Figures 4 and 5, it is apparent that earnings, in relation to GDP, have generally been on a steady

rise since the Great Depression, and have recently hit 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. However, by breaking down Kalecki’s profits

equation 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.

Figure 4:

Again, this trend is not “progress,” but indicates that profits as a percent of GDP have trended higher

as a result of higher proportions, relative to GDP, of the factors in Kalecki’s profits equation.

4.0%

6.0%

8.0%

10.0%

12.0%

14.0%

16.0%

18.0%

1 9 3 0

1 9 3 3

1 9 3 6

1 9 3 9

1 9 4 2

1 9 4 5

1 9 4 8

1 9 5 1

1 9 5 4

1 9 5 7

1 9 6 0

1 9 6 3

1 9 6 6

1 9 6 9

1 9 7 2

1 9 7 5

1 9 7 8

1 9 8 1

1 9 8 4

1 9 8 7

1 9 9 0

1 9 9 3

1 9 9 6

1 9 9 9

2 0 0 2

2 0 0 5

2 0 0 8

2 0 1 1

2 0 1 4

Historical Profits, As a % of GDP

Profits, as % of GDP

Average

10-Year Average


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Figure 5: Corporate Profits as Percent of GDP:(Impact of Each Factor on Corporate Profits)

STD: 4.3% 4.8% 1.9% 1.0% 3.1% 0.2% 0.7% 2.5%

Avg. 15.8% 4.7% -0.7% 3.1% 10.2% -0.2% 0.5% 12.4%

Date

Net

Invest.

Govt.

Borrowing

Foreign

Savings

Net

Dividends

Personal

Savings

Capital

Transfers

Stat.

Disc.

Corp.

Profits

1930 8.8% 1.0% 0.8% 6.0% 8.0% -0.2% -0.4% 9.2%

1931 5.3% 4.7% 0.3% 5.3% 8.5% -0.3% 0.9% 6.3%

1932 1.3% 3.9% 0.3% 4.2% 5.4% -0.3% 0.5% 4.4%

1933 7.7% 3.1% 0.3% 3.5% 4.5% -0.3% 0.9% 9.4%

1934 7.3% 4.3% 0.6% 3.9% 6.0% -0.3% 0.6% 9.7%

1935 10.2% 3.6% -0.1% 3.8% 8.2% -0.5% -0.3% 10.0%

1936 11.9% 4.6% -0.1% 5.3% 9.5% -0.6% 1.4% 11.4%

1937 14.0% 0.4% 0.2% 5.1% 9.1% -0.5% -0.1% 11.1%

1938 7.9% 2.7% 1.4% 3.7% 6.6% -0.6% 0.8% 8.9%

1939 11.7% 3.7% 1.1% 4.1% 8.2% -0.5% 1.4% 11.4%

1940 14.4% 1.7% 1.5% 3.9% 8.9% -0.5% 1.1% 11.9%

1941 16.9% 4.6% 1.0% 3.4% 13.7% -0.4% 0.2% 12.5%1942 7.8% 21.1% -0.1% 2.6% 22.2% -0.4% -0.5% 10.2%

1943 4.0% 24.0% -1.0% 2.2% 21.5% -0.3% -0.9% 9.0%

1944 4.2% 25.2% -0.9% 2.0% 21.2% -0.3% 1.1% 8.5%

1945 5.7% 19.6% -0.6% 2.0% 17.8% -0.4% 1.7% 7.6%

1946 16.9% -0.1% 2.2% 2.5% 11.3% -0.4% 0.5% 9.9%

1947 17.2% -3.7% 3.7% 2.5% 7.6% -0.4% 1.2% 11.4%

1948 19.1% -1.5% 0.9% 2.5% 9.6% -0.4% -0.1% 11.9%

1949 13.6% 3.2% 0.3% 2.6% 8.7% -0.3% 0.6% 10.7%

1950 20.5% -0.2% -0.6% 2.9% 10.4% -0.3% 0.4% 12.1%

1951 18.4% 0.8% 0.3% 2.5% 11.1% -0.3% 1.0% 10.1%

1952 15.3% 3.5% 0.2% 2.3% 11.2% -0.3% 0.7% 9.6%

1953 15.8% 4.0% -0.3% 2.3% 11.1% -0.3% 1.0% 9.8%

1954 14.9% 4.4% 0.1% 2.4% 10.9% -0.3% 0.7% 10.4%

1955 17.7% 1.8% 0.1% 2.5% 10.3% -0.3% 0.5% 11.6%

1956 17.9% 1.3% 0.6% 2.5% 11.4% -0.4% -0.5% 11.7%

1957 16.4% 2.5% 1.0% 2.5% 11.5% -0.4% -0.1% 11.3%

1958 14.8% 5.0% 0.2% 2.4% 11.9% -0.4% 0.1% 10.7%

1959 16.5% 3.3% -0.2% 2.4% 10.8% -0.3% 0.0% 11.4%

1960 16.0% 2.4% 0.6% 2.5% 10.6% -0.1% -0.3% 11.2%

1961 15.3% 3.7% 0.7% 2.5% 11.5% -0.2% -0.2% 11.1%

1962 16.0% 3.6% 0.6% 2.5% 11.1% -0.1% 0.0% 11.7%

1963 16.2% 2.8% 0.8% 2.5% 10.7% -0.2% -0.2% 12.0%

1964 16.4% 3.3% 1.1% 2.7% 11.3% -0.2% 0.0% 12.3%

1965 17.6% 2.8% 0.8% 2.7% 11.1% -0.2% 0.1% 13.0%

1966 18.0% 3.1% 0.5% 2.5% 10.8% -0.2% 0.6% 12.9%1967 16.7% 4.6% 0.4% 2.5% 11.7% -0.3% 0.4% 12.5%

1968 17.0% 3.4% 0.2% 2.5% 10.9% -0.4% 0.3% 12.3%

1969 17.6% 2.1% 0.2% 2.4% 10.7% -0.3% 0.2% 11.8%

1970 16.4% 4.6% 0.3% 2.3% 12.3% -0.4% 0.5% 11.1%

1971 17.2% 5.4% 0.0% 2.1% 12.9% -0.4% 0.8% 11.5%

1972 18.3% 4.1% -0.3% 2.1% 11.9% -0.4% 0.6% 12.1%

1973 20.1% 2.7% 0.6% 2.1% 12.7% -0.6% 0.4% 13.0%

1974 20.2% 3.3% 0.4% 2.1% 12.8% -0.6% 0.5% 13.4%

1975 15.9% 7.3% 1.2% 2.0% 13.3% -0.3% 0.8% 12.5%


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DateNet

Invest.Govt.

BorrowingForeignSavings

NetDividends

PersonalSavings

CapitalTransfers

Stat.Disc.

Corp.Profits

1976 18.0% 5.1% 0.4% 2.1% 11.7% -0.4% 1.1% 13.2%

1977 19.8% 3.9% -0.5% 2.1% 11.0% -0.4% 0.9% 13.8%

1978 21.3% 3.1% -0.5% 2.2% 11.0% -0.2% 1.0% 14.3%

1979 22.0% 2.6% 0.0% 2.2% 10.8% -0.3% 1.7% 14.5%

1980 20.0% 4.0% 0.3% 2.2% 11.7% -0.4% 1.5% 13.6%

1981 20.4% 3.5% 0.1% 2.3% 12.1% -0.4% 1.1% 13.5%

1982 17.6% 6.0% -0.1% 2.3% 12.7% -0.3% 0.2% 13.3%

1983 17.7% 6.7% -1.0% 2.3% 11.0% -0.3% 1.5% 13.5%

1984 20.4% 5.5% -2.2% 2.2% 11.7% -0.2% 1.0% 13.5%

1985 19.1% 5.7% -2.6% 2.2% 10.0% -0.2% 1.2% 13.4%

1986 18.3% 5.9% -3.1% 2.3% 9.8% -0.2% 1.7% 12.1%

1987 18.7% 4.9% -3.2% 2.3% 9.2% -0.1% 0.8% 12.8%

1988 18.3% 4.1% -2.2% 2.5% 9.6% -0.1% 0.0% 13.2%

1989 18.0% 4.0% -1.6% 2.8% 9.5% -0.1% 1.1% 12.6%

1990 16.8% 5.0% -1.3% 2.8% 9.5% -0.1% 1.5% 12.4%

1991 15.2% 5.7% 0.1% 2.9% 9.8% -0.1% 1.4% 12.8%

1992 15.5% 6.7% -0.7% 2.9% 10.3% -0.1% 1.7% 12.6%

1993 16.1% 5.9% -1.1% 3.0% 9.1% -0.2% 2.2% 12.7%1994 17.4% 4.5% -1.6% 3.2% 8.3% -0.2% 1.9% 13.6%

1995 17.4% 4.2% -1.4% 3.4% 8.4% -0.3% 1.2% 14.3%

1996 17.6% 3.0% -1.4% 3.7% 8.0% -0.3% 0.7% 14.6%

1997 18.4% 1.6% -1.5% 3.9% 7.8% -0.3% 0.1% 14.9%

1998 18.9% 0.4% -2.2% 3.9% 8.2% -0.3% -0.7% 13.9%

1999 19.5% 0.0% -3.0% 3.6% 6.8% -0.3% -0.3% 14.0%

2000 19.9% -0.8% -4.0% 3.7% 6.7% -0.3% -0.9% 13.4%

2001 18.1% 1.4% -3.7% 3.5% 7.1% -0.3% -1.0% 13.5%

2002 17.5% 4.8% -4.1% 3.6% 7.8% -0.2% -0.6% 14.8%

2003 17.7% 6.0% -4.5% 3.8% 7.7% 0.0% -0.1% 15.3%

2004 18.9% 5.5% -5.1% 4.6% 7.7% 0.0% -0.1% 16.3%

2005 19.5% 4.3% -5.6% 4.4% 6.3% 0.1% -0.3% 16.4%

2006 19.6% 3.1% -5.7% 5.2% 7.0% -0.1% -1.6% 16.9%

2007 18.5% 3.7% -4.9% 5.7% 6.8% 0.0% 0.1% 16.0%

2008 16.7% 7.2% -4.6% 5.5% 8.3% 0.3% 0.7% 15.4%

2009 13.0% 12.8% -2.6% 3.9% 9.3% 0.8% 0.5% 16.5%

2010 14.3% 12.2% -3.0% 3.8% 8.7% 0.4% 0.3% 17.9%

2011 14.7% 10.7% -2.9% 4.5% 8.6% 0.3% -0.3% 18.4%

2012 15.3% 9.3% -2.7% 4.7% 8.4% 0.1% -0.1% 18.2%

2013 15.9% 6.4% -2.3% 5.4% 7.6% 0.0% -0.8% 18.5%

2014 16.0% 6.1% -2.5% 5.1% 7.7% 0.0% -0.6% 17.7%

From the above charts, we can see that from 1930 to 1960, corporate profits as a percent of GDP

peaked at 12.5%. During the 1960’s; the measure peaked at 13.0%; during the ‘70’s and ‘80’s the measure

peaked at 14.5%; during the ‘90’s the measure peaked at 14.9%; during the first decade in 2000, the measure

peaked at 16.9%; and since then, the annual measure recently reached another annual peak in 2012 at

18.1%. It is also evident that the greatest historical contributors to the increases in corporate earnings


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relative to GDP have been higher government debt and lower personal savings. These changes — higher

government debt and lower personal savings — are typically considered negatives, not positives, for longer-

term fundamentals, and suggest that such earnings trends relative to GDP are not sustainable over the long

term. At least part of this long term shift can be attributable to changing global dynamics. Given that savings

investment, our relatively closed economy during the roughly initial two thirds of the twentieth century

had historically promoted more of a balance in these factors. An example of this is during World War II,

when the greatly higher levels of government debt were largely balanced by the higher levels of savings.

However, greater openness in the global economy in the past few decades has facilitated the expansion of

government and personal debt, even while reducing savings and investments, and thus increased earnings

relative to GDP. These changes over the past few years heighten the importance of using Kalecki’s profit

equation, and MV/GDP, to highlight the extent to which earnings have increased well beyond their norms

by unsustainable factors. Furthermore, in terms of our model, it appears that, historically, investors were

not aware of, or did not appropriately consider, the extent to which earnings were elevated by unsustainable

factors, and have tended to overpay/underpay for markets when earnings are relatively higher/lower to

GDP. This is supported by the earlier example of the sign change, discussed in section 3.4., of the 10-Year

PE coefficient when adjusted by MV/GDP, and further supported by the following variable:

Corp. Profits10/GDP (5 Year Avg.) * Market Value/GDP

With an 2 of 0.40, not only is the product of the above variables more effective on a standalone

basis than the 10-Year PE method, but it is able to measure the extent to which investors tend to improperly

value earnings relative to GDP. The negative coefficient of this variable indicates that, even with a fixed

market value relative to GDP, higher earnings lead to lower market returns. This process also provides

statistical support of the concept of “macro earnings negativity”, discussed in Section 4.2. While this


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argument further supports the use of MV/GDP as a valuation measure, the following clarifies a theoretical

identity between the MV/GDP ratio and a P/E (price/“normalized” earnings) ratio.

Figure 6, below, shows, on a relative scale, the simple ratio of market value divided by earnings, as

derived from Kalecki’s profits equation. The resulting measure tracks relatively closely with the other,

traditional, valuation indicators.11

Figure 6:

However, the above earnings have not been adjusted for the degree to which they have been driven

by unsustainable components in Kalecki’s profit equation. Basing the components to historical norms

makes adjustments to the components straightforward, making it evident that, market valuation levels being

equal, earnings which are higher/lower relative to GDP suggest lower/higher future market returns.

Therefore, it becomes evident that historical “normal” levels of earnings relative to GDP indicate “normal”

or average future market returns. As such, adjusting earnings by the extent to which they are higher/lower

relative to historical GDP averages would yield a more effective price/earnings (P/E) indicator.

Furthermore, the resulting steps yield a logical and interesting conclusion. When taking the market value

and dividing it by the historical norm of earnings relative to GDP — such as Market Value/12.4% of GDP —

as the appropriate measure of the components of Kalecki’s profits equation, then adjusting that formula to

historic norms results in “normalized” earnings being a consistent ratio of GDP. Depending on the

0.30

0.80

1.30

1.80

2.30

1 9 5 4

1 9 5 5

1 9 5 7

1 9 5 8

1 9 6 0

1 9 6 1

1 9 6 3

1 9 6 4

1 9 6 6

1 9 6 7

1 9 6 9

1 9 7 0

1 9 7 2

1 9 7 3

1 9 7 5

1 9 7 6

1 9 7 8

1 9 7 9

1 9 8 1

1 9 8 2

1 9 8 4

1 9 8 5

1 9 8 7

1 9 8 8

1 9 9 0

1 9 9 1

1 9 9 3

1 9 9 4

1 9 9 6

1 9 9 7

1 9 9 9

2 0 0 0

2 0 0 2

2 0 0 3

2 0 0 5

2 0 0 6

2 0 0 8

2 0 0 9

2 0 1 1

2 0 1 2

2 0 1 4

2 0 1 5

Comparison of MV/Earnings vs. Popular Measures

Relative Market Value/Earnings

Relative Tobin's q

Relative P/E10


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timeframe being utilized, this ratio will likely vary, just as historical norms of P/E10 or Tobin’s q vary.

However, given that we have determined that normalized earnings would be a percentage of GDP, then

whatever that percentage of GDP is, the ratio of MV/GDP is a consistent multiple of that ratio, and, thus,

MV/GDP represents a simpler equivalent. As such, when plotted on a relative scale, the chart of

“normalized” earnings is equivalent to that of Market Value/GDP, a ratio which is simply a consistent

multiple of “normalized” earnings. Therefore, the MV/GDP ratio has, ironically, better theoretical

justification as a price/sustained-earnings indicator than do traditional earnings-based measures. This

valuation measure, seen in black in Figure 7, below, has also been, historically, a much more effective

forecaster of future real equity returns.

Figure 7:

What becomes increasingly obvious in Figure 7, above, is the growing disparity over the past 15

years between MV/GDP and the other measures. This is due to the fact that the corporate profits/GDP ratio

has averaged 17.3% over the past decade, vs. 13.6% in the 1990’s, 13.2% in the 1980’s, 12.9% in the 1970’s,

12.1% in the 1960’s, 10.9% in the 1950’s, and 10.4% in the 1940’s. Therefore, the evidence that MV/GDP

is a better indicator — both theoretically and statistically — of future real equity returns, and the fact that the

ratio is near its greatest disparity ever relative to traditional ratios, should raise investors ’ attention.

0.30

0.80

1.30

1.80

2.30

1 9 5 4

1 9 5 5

1 9 5 7

1 9 5 8

1 9 6 0

1 9 6 1

1 9 6 3

1 9 6 4

1 9 6 6

1 9 6 7

1 9 6 9

1 9 7 0

1 9 7 2

1 9 7 3

1 9 7 5

1 9 7 6

1 9 7 8

1 9 7 9

1 9 8 1

1 9 8 2

1 9 8 4

1 9 8 5

1 9 8 7

1 9 8 8

1 9 9 0

1 9 9 1

1 9 9 3

1 9 9 4

1 9 9 6

1 9 9 7

1 9 9 9

2 0 0 0

2 0 0 2

2 0 0 3

2 0 0 5

2 0 0 6

2 0 0 8

2 0 0 9

2 0 1 1

2 0 1 2

2 0 1 4

2 0 1 5

Comparison of MV/GDP vs. Popular Measures

Relative Market Value/Earnings

Relative Tobin's q

Relative P/E10

Relative Market Value/GDP


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Furthermore, excluding the bubble periods since 1995, this ratio suggests that markets are currently about

50% more overvalued than during its earlier peak in the late 1960’s, a time which preceded flat real equity

returns over the following 15 years.

With an R 2 of 0.52 , the historical ability of MV/GDP to forecast future real equity returns has also

easily exceeded that of the other traditional valuation metrics. Furthermore, with a steady denominator and

a numerator that can be easily adjusted with the current market value, it is even simpler to calculate.

4.5. Market Value/GDP and Price/Sales:

Generally, calculating GDP includes the changes in inventory. For example, if companies

manufactured more than consumers purchased, the excess manufactured would still contribute to

inventories, the latter reduction of which would reduce future GDP. Likewise, the reduction of inventories

means that consumers purchased more than was produced, and this portion of consumer purchasing was

not reflected in GDP. Therefore, our calculation of GDP adjusts for the changes in private inventories to

derive a more appropriate measure of GDP. This adjustment to GDP is a good introduction to the price/sales

ratio, because adjusting GDP adjusted for changes in private inventories brings the measure closer to Real

Final Sales. As such, MV/GDP is sometimes compared to the price/sales measure. There is some

justification for the comparison; however, it is reasonable to think that, looking at Kalecki’s profit identity,

that the profit factors are also likely to influence profit margins, and not just sales. Also, a major difficulty

in valuing the S&P 500 by a price/sales measure is the insufficient length and accuracy of the data; therefore,

statistically supporting the price/sales metric is also more difficult.

4.6. Additional Implications

The issues discussed above bring up other important implications and considerations, although they

are not necessary for the primary issues in this research. For example, it is worth noting that the components

of GDP — inflation, population growth, and productivity — are not directly affected by earnings; additional


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evidence that earnings may not be the most effective denominator to prices as an indicator of valuation or

future returns. Furthermore, one may assume that 1), real long-term equity returns are not affected by

inflation, and 2), population growth would likely produce a proportional increase in the number of

companies (for example: the uniting of two identical countries would result in doubling the population and

GDP of the newly formed country, but the market value/GDP would unlikely change). Given these

assumptions, it is interesting to note that the primary determinants of long term total real equity returns are,

therefore, dividends and productivity. Also note that productivity, though important to GDP, does not have

to result in higher earnings, a fact which provides further support of our argument that reported earnings

are not as good as GDP as an indicator of stock-market valuations. Another important and interesting

implication of our use of Kalecki’s profits equation is that, on a macroeconomic perspective, earnings are

not so much produced by corporations collectively as they are allocated to corporations as a whole as the

result of corporate, government, and personal spending decisions. Although the collective activities of

corporations can influence GDP, and thus have an influence on earnings at the macro level, individual

corporations largely compete for as large a share as possible of a relatively predetermined level of macro

earnings. In brief, macro-level earnings are a pie, the size of which is largely determined by the factors in

Kalecki’s profit equation, and each individual corporation is competing for as large of a slice of this pie as

they can get. This understanding of earnings provides further evidence that macro earnings have not have

been as greatly boosted by widespread cost-cutting and lower rates, as is often argued, but largely by the

higher levels, relative to GDP, of personal and government debt.

Furthermore, while we often note how high debt levels are affecting corporate earnings, the scope

of this research is insufficient to make a judgment on the appropriateness of these levels. Likewise, the

following discussion on the global debt imbalances does not influence the validity of our arguments, but

does reveal the importance of the debt issues we are highlighting, and that these imbalances are also at or

near historic levels globally. As such, the following discussion emphasizes the importance of our issue.


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The Relationship of Government Debt to GDP Growth

If the practical limits of personal and government debt are well above current levels, then there is plenty

of time for further increases in debt. However, the issue is widely debated. It is import to understand the effects

and potential limits of government debt, as the impacts of debt on Kalecki’s profits equation are substantial. The

subject of the appropriate level of government debt has been well examined. While some, such as Paul Krugman

(2012), minimize the importance of debt relative to other issues, Checherita and Rother, 2010, investigated the

average effect of government debt on per-capita GDP growth in 12 Euro-area countries over a four-decade period

beginning in 1970. Their research

“finds a non-linear impact of debt on growth with a turning point — beyond which the government debt-to-GDP

ratio has a deleterious impact on long-term growth — at about 90-100% of GDP. Confidence intervals for the debt

turning point suggest that the negative growth effect of high debt may start already from levels of around 70-80%of GDP, which calls for even more prudent indebtedness policies. At the same time, there is evidence that the

annual change of the public debt ratio and the budget deficit-to-GDP ratio are negatively and linearly associatedwith per-capita GDP growth. The channels through which government debt (level or change) is found to have an

impact on the economic growth rate are: (i) private saving; (ii) public investment; (iii) total factor productivity

(TFP) and (iv) sovereign long-term nominal and real inter est rates.”12

The first two of their “channels through which government debt (level or change) is found to have an impact on

the economic growth rate” play an integral role in Kalecki’s profits equation, the third features productivity, a

major factor in GDP, and the fourth, interest rates, has been found in prior research to strongly influence future

equity returns. (Though interest rates are an effective (negative) indicator of future equity returns, their correlation

with demographic measures, discussed later, largely eliminated their effectiveness in our composite model.)

In their updated (corrected for earlier errors) study — which also reviews other research on the topic —

Reinhart, Reinhart, and Rogoff (2012) researched the periods since the early 1800s in which advanced economies

endured public debt/GDP levels exceeding 90% for at least five years. They found:

“the cumulative effects can be quite dramatic. Over the twenty-six public debt overhang episodes we

consider, encompassing the preponderance of such episodes in advance economies since 1800, growth averages1.2% less than in other periods. That is, debt levels above 90% are associated with an average growth rate of

2.3% (median 2.1%) versus 3.5% in lower debt periods. Notably, the average duration of debt overhang episodeswas 23 years, implying a massive cumulative output loss. Indeed, by the end of the median episode, the level of

output is nearly a quarter below that predicted by the trend in lower-debt periods. This long duration also

suggests the association of debt and growth is not just a cyclical phenomenon.”


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Reinhart and Rogoff (2010), also point out:

“For example, war debts are arguably less problematic for future growth and inflation than are large debts

accumulated in peacetime. Postwar growth tends to be high as wartime allocation of manpower and resources

funnels to the civilian economy. Moreover, high wartime government spending, typically the cause of the debtbuildup, comes to a natural close as peace returns. In contrast, a peacetime debt explosion often reflects unstable

political economy dynamics that can persist for very long periods.”Reinhart and Rogoff (2010)

In the post war period, they found that average GDP growth for those countries with public debt less than

30% was 4.2%; 30% - 60%, 3.0%; 60% - 90%, 2.5%; >90%, 1.0%. Government debt levels for 2013, as estimated

the IMF13, are: Austria, 74%; Belgium, 100%; Canada, 86%; France, 90%; Germany, 82%; Greece, 159%

Ireland, 117%; Italy, 127%; Japan, 238%; Singapore, 111%; Spain, 84%; UK, 90%; US, 107%.

While commenting on the current global situation, they also note that:

“The scope and magnitude of the debt overhang public, private, domestic and external facing the advanced

economies as a group is in many dimensions without precedent. As such, it seems likely that our historicalestimates of the association between high public debt and slow growth might, if anything, be understated when

applied to projections going forward.”

Moreover, the rise in global private debt appears to have been too recent to research long-term impacts

however, its increase has been dramatic.

Figure 8:Total (Public & Private) External Debt, % of GDP:

(22 Advanced and 25 Emerging Market Economies, 1970-2011)

1970 1975 1980 1985 1990 1995 2000 2005 2010

Contrary to popular views, the world has not started to delever. Furthermore, although our research focuses

on the United States, much of it applies globally.


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5. Expanding MV/GDP into A Composite Model

The process of determining the merits of the MV/GDP ratio to forecast equity market returns

introduced us to additional forecasting metrics. The scarcity of composite models was surprising,

especially given the wide variety and number of individual variables used to value and forecast the

market. Therefore, after establishing the merits of MV/GDP as a predictor of equity returns, we

considered other variables — both original and from prior research — to combine with MV/GDP to form a

composite model. The realization of the negativity of macro earnings, as explained above, suggested

that there are other macro forces important in forecasting normalized earnings and future equity returns.

Although our research into earnings and Kalecki’s profit equation reveals that individual corporations

play a smaller role in macro profits than originally thought, corporations as a whole do play important

roles in wages and salaries, and, thus, personal spending. Importantly, personal income is surprisingly

negatively correlated to corporate earnings (Laramie, 2007) and, as such, the two personal income

variables we identified are not only effective forecasters of future equity returns, but are uncorrelated

with the Market Value/GDP variable described abo

Documents

Forecasting Equity Returns- An Analysis of Macro vs. Micro Earnings and an Introduction of a Composite Valuation Model