Download Macroeconomic Expectations and the Stock Market

Macroeconomic Expectations and
the Stock Market: The Importance
of a Longer-Term Perspective
Vanguard Investment Counseling & Research
Executive summary. This paper aims to help long-term investors better
understand how to evaluate economic information in forming expectations
about the stock market. Given the tight long-run relationship between the
stock market and the economy, investment commentary routinely suggests
that the market’s short-run prospects have changed in response to observed
changes in macroeconomic expectations. Of course, such logic presumes
a predictive relationship: that today’s expectations for tomorrow’s economy
are correlated with tomorrow’s stock returns. Is this valid?
We begin by examining the link between macroeconomic expectations and the
subsequent near-term performance of the U.S. stock market. Specifically, we
run quarterly predictability regressions for excess stock returns on expectations
for real growth and inflation derived from (1) economist surveys and (2) financial
market indicators. We repeat these regressions for two stock-return spreads:
(1) value minus growth and (2) small-cap minus large-cap.
We show that the consensus view of future macroeconomic conditions—whether
derived from professional forecasters or financial indicators—has explained virtually
none of the short-term volatility in stock returns over the past 40 years. While this
may be surprising to some given the considerable attention paid to such expectations,
our results are consistent with the view of a stock market that is reasonably efficient
(it tends to “price in” the mainstream macro outlook), that is forward-looking (as a
leading indicator, it tends to anticipate economic shifts rather than lag them), and
that can be quite volatile in the short run.
1 I would like to thank Glenn Sheay for excellent research assistance.
Connect with Vanguard > www.vanguard.com
Author
Joseph H. Davis, Ph.D.1
In place of reliance on near-term forecasts, we urge macro-minded investors to
adopt a more strategic perspective regarding the future outlook for the economy
and the stock market. We provide a “game plan” for those who want to better
integrate macroeconomics into their portfolio decision-making by focusing on
the fundamental building blocks of the stock market’s long-term performance.
The link between the economy
and the stock market
The value of a broad stock index reflects current
and expected earnings and dividend growth and
other systematic return and valuation factors. A
simplified version of the well-known Gordon model,
for instance, expresses the expected excess return
of the stock market (over cash) as the dividend yield
plus expected real earnings growth. All else being
equal, the higher the earnings yield at purchase and
the higher the expected long-term real earnings
growth, the higher the expected stock return
(Asness, 2003).
Figure 1. The U.S. stock market, its fundamentals,
and the U.S. economy over the long run
Annual data for 1872–2007, in logarithmic scale
100,000
10,000
1,000
100
10
1
The building blocks of the stock market’s long-run
performance—earnings and dividends per share—
are ultimately co-integrated with the economy’s
performance.2 Figure 1 illustrates that the trend of
the broad U.S. stock market has historically tracked
the productivity level of the U.S. economy, measured
here as gross domestic product per capita. In the
past, the trend growth rate in real corporate earnings
has paralleled the economy’s real productivity growth,
while dividend yields have been highly correlated
with the level of inflation expectations.
Of course, stock market risk premiums vary
significantly in the short run. Given the tight longrun relationship between the stock market and the
economy in Figure 1, it is perhaps not surprising
that news stories and investment analysts focus
keenly on expectations for an economy’s near-term
performance to gauge the future prospects of its
stock market.
0.1
1880
1900
1920
1960
1980
2000
GDP per capita ($000)
S&P 500 Index earnings per share
S&P 500 Index dividends per share
S&P 500 Index
Sources: Standard & Poor’s, U.S. Census Bureau, and
Robert Shiller’s website, http://www.econ.yale.edu/~shiller.
Recently in the United States, expectations for
future economic growth have declined appreciably,
in part as a result of the subprime-mortgage and
financial crisis. At the same time, certain measures
of inflation expectations have risen because of
record-high commodity prices and the weak U.S.
dollar. This unwelcome combination of lower growth
and higher inflation expectations has led some
analysts to warn of disappointingly low U.S. stock
market returns in coming quarters.
2 For an excellent discussion of alternative ways to decompose long-run stock returns, see Ibbotson and Chen (2003).
2 > Vanguard Investment Counseling & Research
1940
Yet is the link between expected macro variables
and future stock returns as strong as commonly
suggested? In the following section, we examine
this link in detail with the goal of helping long-term
investors better understand the implications of
macroeconomic expectations for the stock market.
Do macroeconomic expectations
predict near-term stock returns?
Academic researchers (e.g., Chen et al., 1986;
Fama and French, 1989; Schwert, 1990) have
long argued that business conditions should be
positively correlated with future stock returns,
because an economy’s business cycle should
affect corporate cash flows and discount rates.3
Of course, such logic assumes a causal relationship:
that today’s expectations for tomorrow’s economy
are strongly correlated with tomorrow’s stock returns.
To test this logic, we run regressions to examine
the correlation between macroeconomic expectations
(for real growth and inflation) and the subsequent
performance of the U.S. stock market over four
decades. We consider two distinct sources for
macro expectations: (1) professional surveys and
(2) financial market indicators. We first focus on
survey-based macro expectations.
Consensus forecasts for real GDP growth and
inflation come from the Federal Reserve Bank of
Philadelphia’s Survey of Professional Forecasters
(SPF). Conducted since 1968, the SPF is the oldest
quarterly survey of macroeconomic forecasters in the
United States. Each quarter, the survey asks a group
of these professionals to provide quarter-by-quarter
forecasts on a variety of macroeconomic variables.4
The median SPF forecast—hereafter the consensus
forecast—has historically been highly correlated
with the actual reported values for various macro
variables.5 For example, the correlation coefficients
between the quarter-ahead consensus forecast and
actual value for both real GDP growth and the inflation
rate have been close to 60% since the inception of
the SPF program in 1968. Indeed, the professional
forecasters, as a group, appear to incorporate relevant
forward-looking information in their macroeconomic
projections in that the consensus SPF forecast is
more accurate than a random-walk forecast.
To examine the correlation between macroeconomic
expectations (for real growth and inflation) and nearterm U.S. stock returns, we estimate predictability
regressions of the general form:
R ts+1 – R tcash
+1 = f (E t [SPFt +i ]),
where next quarter’s excess return of the U.S. stock
s
cash
market, R t +1 – R t +1 , is defined as the total return of
the aggregate, market-cap-weighted stock market
less the return of the 3-month U.S. Treasury bill.
That is, the dependent variable in the regression
is the equity risk premium, or the expected excess
return of stocks over the return of a risk-free investment (i.e., cash). Table 1, on page 4, demonstrates
that virtually all of the annual volatility in stock returns
observed since the 1870s has come from changes
in the equity risk premium.
Notes about risk and performance data
Investments are subject to risk. Investments in bonds are subject to interest rate, credit, and inflation
risk. Prices of mid- and small-cap stocks often fluctuate more than those of large-company stocks.
Foreign investing involves additional risks including currency fluctuations and political uncertainty.
Past performance is no guarantee of future returns. The performance of an index is not an exact
representation of any particular investment, as you cannot invest directly in an index.
3 More recently, however, certain academic studies have argued that expected excess stock returns are negatively correlated with the business cycle, because
either systematic risk or risk-aversion increases during recessions. For an overview, see Andersen et al. (2005) and citations therein.
4 See Croushore (1993) for more details about the Survey of Professional Forecasters.
5 We construct the median growth-rate forecast by aggregating the individual survey responses; our empirical results are nearly identical using the mean forecast.
Vanguard Investment Counseling & Research > 3
Table 1. Summary statistics of annual U.S. stock market returns: 1872–2007
Average
return
Standard
deviation
Correlation to
total return
S&P 500 Index total return
9.0%
17.9
100.0%
Cash return (“risk-free” asset)
4.2
2.7
–9.7
Equity risk premium (excess return of stocks over cash)
4.6
18.3
98.9
Note: The sum of the average equity risk premium and the average risk-free (cash) return does not equal the total return of the S&P 500 Index because of rounding and
the omission of a small geometric interaction term.
Source: Author’s calculations from data sources cited in Figure 1.
The variables on the right hand side of the regression,
(Et [SPFt +i ]) , include the median forecast surveyed in
quarter t for real GDP growth and inflation for either
the next quarter (i =1) or the next four quarters (i = 4).
A statistically significant coefficient on any right-handside variable in this regression would provide evidence
of in-sample stock market predictability, meaning that
today’s expectations for tomorrow’s economy are
correlated with tomorrow’s stock returns.6
Table 2 presents the future stock return regressions
on the SPF macro expectations data. The sample
period begins in the first quarter of 1969 (1969Q1),
since the first available SPF observation is in 1968Q4.
The regression coefficients in Table 2 reveal that none
of the SPF consensus series significantly predict nextquarter stock returns. The correlations between today’s
expectations for future macroeconomic conditions and
tomorrow’s excess stock returns are not statistically
significant for either real GDP growth or inflation. The
regressions in Table 2 are so weak, in fact, that the
average adjusted R 2 is negative. This is true whether
one focuses on the median expectation for the next
quarter or for the next year.7 Forecast dispersion, a
proxy for disagreement or uncertainty among
professional forecasters, is uncorrelated with future
stock returns, too.8
Robustness checks
One explanation for the weak empirical results could
be that survey expectations—which tend to be quite
smooth and persistent—may be distorted by a trend
specific to the sample period.9 Table 3 thus repeats
the stock-return regressions using changes in SPF
macroeconomic expectations. A positive and statistically
significant regression coefficient on, say, real GDP
growth would indicate that an upward revision today
to future growth expectations would be associated
with higher stock returns next quarter.
6 Inoue and Kilian (2004), Campbell and Thompson (2005), and others argue that this type of stock-return regression is more meaningful than out-of-sample
methods in investigating whether a right-hand-side variable (i.e., SPF) predicts future stock returns.
7 Although the regression coefficients are insignificant, they tend to be negative and countercyclical, as suggested by Andersen et al. (2005) and others: Stock
returns are generally higher when either growth or inflation expectations are lower.
8 The cross-sectional dispersion among individual forecasts is a common proxy for forecaster uncertainty about future outcomes, although variation in predictions
need not imply disagreement among forecasters. For details, see Mankiw et al. (2003).
9 Stambaugh (1999), for instance, argues that the regression coefficients in stock predictability regressions will be biased when the right-hand-side variables are
highly persistent and correlated with future stock returns. Campbell and Yogo (2003) document the presence of “Stambaugh bias” for slow-moving series such
as the dividend yield, and the year-ahead SPF variables appear to possess similar characteristics.
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Table 2. Survey-based macro expectations and future stock returns: 1969Q1–2008Q1
Variable
Model 1
Model 2
Model 3
Model 4
1.84
0.95
1.81
0.92
3.32
1.51
3.36
1.52
Expected real GDP growth for next quarter
–0.13
–0.13
–0.13
–0.38
Expected inflation rate for next quarter
–0.13
–0.10
–0.10
–0.19
–0.65
–1.48
–0.68
–1.47
0.01
0.04
–0.09
–0.17
Constant term
Expected real GDP growth for next year
Expected inflation rate for next year
0.84
0.15
Forecast dispersion
(A proxy for forecast uncertainty)
Adjusted R2 (in %)
Standard deviation of dependent variable
Standard error of model equation
Model’s F-statistic p-value
–1.2%
8.66
8.71
0.95
1.35
0.23
–1.9%
8.66
8.74
0.99
–0.4%
8.66
8.68
0.49
–1.0%
8.66
8.70
0.69
Notes: SPF data reflect the median survey response. The regression’s dependent variable is the quarter-ahead excess return of U.S. stocks over the 3-month
T-bill return. Listed beneath the regression coefficients are the Newey-West-corrected t-statistics (in italics). No coefficients in the table are statistically different
from zero at 10% level.
Source: Author’s calculations.
Table 3. Changes in survey-based macro expectations and future stock returns: 1969Q1–2008Q1
Variable
Constant term
Change in expected real GDP growth for next quarter
Change in expected inflation rate for next quarter
Model 1
Model 2
Model 3
Model 4
1.39*
1.93
1.39*
1.92
1.38*
1.94
1.38*
1.93
–0.52
–0.70
–0.52
–0.68
1.37
0.89
1.37
0.87
Change in expected real GDP growth for next year
Change in expected inflation rate for next year
–1.09
–0.69
0.32
0.14
0.26
0.11
0.30
0.05
Change in forecast dispersion
(A proxy for forecast uncertainty)
Adjusted R2 (in %)
Standard deviation of dependent variable
Standard error of model equation
Model’s F-statistic p-value
–1.11
–0.69
0.3%
8.68
8.67
0.29
–0.3%
8.68
8.70
0.48
1.86
0.33
–0.5%
8.68
8.70
0.54
–1.1%
8.68
8.73
0.71
Notes: SPF data reflect changes in median survey response versus previous survey. The regression’s dependent variable is the quarter-ahead excess return of
U.S. stocks over the 3-month T-bill return. Listed beneath the regression coefficients are the Newey-West-corrected t-statistics (in italics). Bolded coefficients
with an asterisk are statistically different from zero at 10% level.
Source: Author’s calculations.
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The pattern in Table 3 parallels that for Table 2:
None of the SPF consensus series significantly
predict next-quarter stock returns. Quarter-to-quarter
revisions to the median SPF forecasts for current
growth and inflation expectations are not significantly
correlated with next-quarter excess stock returns. This
is true whether the revised forecasts are for the next
quarter or the next year. As in Table 2, the average
regression-adjusted R 2 is slightly negative: Changes
in consensus macroeconomic expectations explain
none of the volatility in future stock returns.
We checked the results in Tables 2 and 3 against
other expectation variables derived from the
Survey of Professional Forecasters, including
(but not limited to): (1) corporate profits, (2) industrial
production, (3) nominal GDP growth, and (4) housing
starts. The regressions were similarly weak for all
four of these variables at various SPF forecast
horizons. The “expectations spreads” between the
next-quarter and next-year median SPF forecasts
were insignificant, too.
In addition, we verified the robustness of our
results using a monthly sample of macroeconomic
expectations provided by the Blue Chip Survey.
Although the Blue Chip sample does not begin until
1977, the monthly predictability regressions parallel
our results displayed in Tables 2 and 3.10 Finally, we
ran regressions of annual stock market returns on
the previous year’s median SPF expectation. The
results were similarly weak, and the adjusted R 2
was again slightly negative.
Macro expectations: Economist surveys
versus financial market proxies
Another potential explanation for the zero correlation
documented in Tables 2 and 3 could be that economist
surveys are either “stale” or biased in that they do
not incorporate all information available in the financial
market.11 If that were true, then we would expect
the stock market to lead (1) changes in the median
SPF forecast (particularly for real GDP growth) and
(2) “surprises” to the median SPF forecast, meaning
outcomes that the forecast did not predict. These two
patterns should be related, since announcements of
surprising economic data often lead to revisions in
economist forecasts.
Figure 2 reveals the correlation between
“surprises” to the median SPF forecasts for
two values—real GDP growth and inflation—
and past, present, and future excess stock returns.
Here, we define “surprises” as the difference
between what was actually reported for those
values and the consensus expectation for them
in the prior quarter’s survey.
Perhaps not surprisingly, the cross-correlation patterns
demonstrate that the U.S. stock market anticipates
“shocks” to real GDP growth at least two quarters
ahead of the SPF survey. Figure 2 shows that excess
stock returns at a given point in time are positively
correlated (at a statistically significant 30%) with real
GDP growth surprises announced three quarters later.
The stock market does a somewhat poorer job of
anticipating inflation surprises, although stock returns
are likelier to lead SPF inflation surprises than to lag
them. Indeed, Granger causality tests reveal that
excess stock returns predict changes in survey-based
macroeconomic expectations, not vice versa.12
10 We should note that our SPF survey results contradict Campbell and Diebold (2005), who find that consensus real GDP growth expectations from the biannual
Livingston Survey significantly predict future stock returns (controlling for other variables). For a number of reasons, it is likely that the Campbell and Diebold
results suffer from Stambaugh bias and thus are overstated. For one, the time series for real GDP growth expectations derived from the biannual Livingston
Survey is highly persistent; tests fail to reject the presence of a unit root. Second, we can show that the Campbell and Diebold (2005) results do not hold
when their real GDP expectation term is expressed in changes, as we do in Table 3. Third, Campbell and Diebold use the original Lettau and Ludvigson (2001)
consumption-to-wealth ratio, rather than a version that is calculated in real time and appropriately lagged to avoid look-ahead bias. Finally, the positive
Campbell and Diebold results are not observed using either the quarterly SPF or the monthly Blue Chip expectations databases.
11 While Croushore (2006) finds the median SPF inflation forecasts to be unbiased, Rudebusch and Williams (2007) show that the average SPF forecaster
places too little weight on financial-market information—especially the Treasury yield curve—when projecting real GDP growth.
12 We cannot reject the null hypothesis that changes in the median SPF forecast do not Granger-cause excess stock returns, but we can reject at the 1%
significance level the null hypothesis that excess stock returns do not Granger-cause changes in the median SPF forecast.
6 > Vanguard Investment Counseling & Research
An alternative measure of macro
expectations: Financial variables
For these and other reasons, many researchers use
financial indicators as a proxy for expected macroeconomic conditions. One could certainly argue that
the financial markets’ expectations for future U.S.
economic performance are more “representative”
than any one survey, as the number of participants
in the global financial markets dwarfs the 50 or so
forecasters canvassed by the SPF in any quarter.
Since they are available in real time, financial indicatorbased expectations should be at least as up-to-date
as the quarterly SPF surveys.
Figure 2. The stock market tends to anticipate
“surprises” to survey-based macro expectations
Correlation of stock returns with outcomes not predicted
by the median SPF forecast
30%
20
Correlation
10
0
–10
–20
Stock returns lead surprises
Stock returns lag surprises
–30
–6 –5 –4 –3 –2 –1
0
1
2
3
4
5
6
Quarters before (–) / after (+) economic announcement
Correlation of stock returns with real GDP growth surprises
Correlation of stock returns with inflation surprises
Notes: Quarterly sample for 1969Q1–2008Q1. “Surprises” are
defined as the difference between the actual, as-reported values
for real GDP growth and inflation and the consensus expectation
for those two values from the SPF survey conducted in the prior
quarter. A positive (negative) surprise would mean that real
GDP growth or inflation data were higher (lower) than expected.
Correlations are asymptotically consistent approximations.
Source: Author’s calculations.
Academic studies contend that a select number of
financial market indicators significantly predict future
stock returns precisely because they represent realtime expectations for (and uncertainty about) future
macroeconomic conditions.13 According to Campbell
and Diebold (2005) and Goyal and Welch (2006), the
four most common and significant indicators include:
• The shape of the Treasury yield curve (TERMt ),
defined as the difference between the 10-year
Treasury yield and the 3-month Treasury bill yield.
• The default spread (DEFt ), defined as the
difference between yields of high-grade AAA-rated
and riskier BBB-rated corporate bonds.
• The dividend yield of the S&P 500 Index (DIVt ).
• The consumption-to-wealth ratio (CAYt -1 ), calculated
in real time and appropriately lagged.14,15
13 See, among many others, Campbell and Shiller (1988), Fama and French (1989), and Goyal and Welch (2006).
14 Lettau and Ludvigson (2001) show that the consumption-wealth ratio (CAYt )—the error term from the cointegration relation among consumption, net worth,
and labor income—forecasts stock market returns out of sample because it summarizes expected returns on aggregate wealth. However, Avramov (2002) shows
that when the consumption-wealth ratio is calculated in real time to avoid look-ahead bias, the resultant variable (CAYt-1) does not significantly predict future
stock returns.
15 When we run a contemporaneous regression of the SPF data (both one and four quarters ahead) on the values for the four financial indicators above, we find
that the survey-based and financial indicator-based measures of U.S. macroeconomic expectations are indeed positively correlated. Specifically, the regression
reveals that the SPF inflation expectations series are highly and positively correlated with the dividend yield and negatively correlated with the term spread,
producing an adjusted R 2 exceeding 80%. This nearly one-to-one conditional relationship between dividend yields and inflation expectations would seem to
accord with the dividend discount model, which predicts that the dividend yield should rise when necessary to offset higher expected inflation (Asness, 2003).
The financial indicators are also positively correlated with SPF expectations for real GDP growth, although the relationship is weaker, with an adjusted R 2 of
approximately 15%. Consistent with the above discussion, the weaker correlation can be explained by the fact that the financial indicators—especially the
shape of the Treasury yield curve—are updated before the SPF real GDP growth forecast. Indeed, the R 2 on the real GDP regression more than doubles to over
30% when the financial indicators are regressed on the median SPF response for the following quarter, rather than on the contemporaneous forecast.
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Table 4. Financial indicator-based macro expectations and future stock returns: 1969Q1–2008Q1
Financial proxies for macro expectations/risk
Dependent variable and time period
Constant
DIVt
DEFt
TERMt
CAYt-1
Equation
Adjusted R2
Excess stock returns
–4.03
–1.35
0.88
1.12
0.91
0.36
1.15*
2.40
–0.60
–0.79
1.6%
1st subsample: 1969Q1–1988Q4
–13.67*
–2.72
2.21*
2.27
2.61
0.90
1.63*
2.77
–0.07
–0.05
9.6%
2nd subsample: 1989Q1–2008Q1
–3.76
–0.63
3.53
1.67
–3.99
–0.83
0.39
0.52
–1.44
–1.28
0.7%
Full sample: 1969Q1–2008Q1
Notes: The regression’s dependent variable is the quarter-ahead excess return of U.S. stocks over the 3-month T-bill return. Bolded coefficients with asterisks are
statistically significant at the 10% level. Listed beneath the regression coefficients are the Newey-West-corrected t-statistics (in italics).
Source: Author’s calculations.
Table 5. Financial indicator-based macro expectations and future U.S. equity style returns: 1969Q1–2008Q1
Financial proxies for macro expectations/risk
Dependent variable and time period
Constant
DIVt
DEFt
TERMt
CAYt-1
Equation
Adjusted R2
Value stocks minus growth stocks
Full sample: 1969Q1–2008Q1
1.85
0.80
–0.62
–1.14
1.60
1.18
–0.39
–0.77
0.76
0.94
–1.0%
1st subsample: 1969Q1–1988Q4
5.06
1.48
–1.01
–1.47
1.08
0.67
–0.56
–1.21
0.21
0.28
–2.6%
2nd subsample: 1989Q1–2008Q1
7.94
1.44
–3.32*
–1.93
0.86
0.40
–0.10
–0.11
2.00
1.47
0.9%
–3.75*
–2.36
0.48
0.74
1.72
1.10
0.60*
2.01
–0.51
–1.08
2.3%
1st subsample: 1969Q1–1988Q4
–10.74*
–3.20
2.40*
2.63
0.56
0.29
0.43
0.83
–0.35
–0.42
10.1%
2nd subsample: 1989Q1–2008Q1
–1.79
–0.46
4.65
1.57
0.96*
2.97
0.23
0.36
5.5%
Small-cap stocks minus large-cap stocks
Full sample: 1969Q1–2008Q1
–1.37
–1.31
Notes: Bolded coefficients with asterisks are statistically significant at the 10% level. Listed beneath the regression coefficients are the Newey-West-corrected
t-statistics (in italics). Value minus growth is measured as the difference between the MSCI US Investable Market Value Index and the MSCI US Investable Market
Growth Index. Small-cap minus large-cap is measured as the difference between the MSCI US Small Cap 1750 Index and the MSCI US Large Cap 300 Index.
Source: Author’s calculations.
8 > Vanguard Investment Counseling & Research
We reran the predictability regressions for future
excess stock returns, this time with macroeconomic
expectations represented by the four financial proxies.
The results are shown in Table 4. As was the case
in Table 2, the financial markets’ current expectations
for future U.S. macroeconomic conditions explain less
than 2% of the future volatility in the next quarter’s
stock returns over the full sample period. Consistent
with various academic studies, Table 4 shows that
dividend yields are poor indicators of future stock
returns over short horizons. Only the term spread has
been significantly related to future stock returns: The
steeper the Treasury yield curve is today, the higher
stock returns should be next quarter, all else equal.
Table 4 also reveals that the full-sample relationships
change through time. For instance, Fama and French
(1989) document that the dividend yield, the default
spread, and the term spread predict future excess
stock returns. In our 1989–2008 subsample that
spans the 20-year period since the publication of
Fama and French’s research, we fail to uncover any
significant relationship between these proxies for
expected business conditions and next quarter’s
stock returns.16 We observe that the statistical
significance of the coefficients, including the
term spread, is period-dependent.
Robustness check: Style and size returns
A critic of the disappointing results in Table 4
could argue that consensus macro expectations
have meaningful—albeit offsetting—correlations
with the four primary style and size categories
of the U.S. equity market: value and growth styles,
and large- and small-cap size indexes. That is, even
if consensus expectations do not meaningfully
predict next quarter’s absolute stock returns,
they might predict future relative returns.
As an example, large-capitalization stocks are
believed to outperform small-cap stocks when
economic growth is expected to be weak and/or
when inflation is expected to be high. Kao and
Shumaker (1999) claim that that it can be extremely
profitable to exploit changes in expected business
conditions by tactically shifting the allocation
of growth stocks and value stocks in a portfolio
based on the shape of the yield curve (TERMt ).
Table 5 presents the results of regressing the
future return spreads for (1) value minus growth
and (2) large-cap minus small-cap on the four
financial indicators. Overall, the regressions are
as weak for style and size premiums as they
are for future stock market returns. Consensus
expectations explain nothing of the time-series
volatility of the value premium over our sample
period. For the size premium, we do find some
evidence of predictability. However, the size,
magnitude, and statistical significance of the
financial proxies for expected business conditions
change between the two subsamples.
16 See also Tokat and Stockton (2006) for a discussion of the sensitivity of tactical signals in projecting future stock returns.
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Figure 3. The consensus macro view is uncorrelated
with future near-term stock market returns
Excess stock returns: Actual versus predicted
30%
20
10
0
–10
–20
–30
1969 1971 1973 1975 1977 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007
Actual returns
Predicted returns: Survey-based expectations
Predicted returns: Financial indicator-based expectations
These results may be surprising to
some, given the considerable attention
paid to such expectations by the
investment community. However, our
results are consistent with the view
of a stock market that is reasonably
efficient (it tends to “price in” the
mainstream macro outlook), that is
forward-looking (as a leading indicator,
it tends to anticipate economic shifts
rather than lag them), and that can
be quite volatile in the short run for a
number of reasons, including changes
in investor sentiment (Fisher and
Statman, 2000). In addition, consensus
expectations for the economy can
prove inaccurate.17
An economic game plan for
investors: Four key macro
considerations, plus one caveat
Source: Author’s calculations from the regression models presented in Tables 2 and 4.
How, then, should long-term investors
think about the economy when constructing portfolios? For sophisticated
The importance of focusing strategically
investors, a strategic asset allocation will incorporate
on macroeconomic trends
expectations regarding long-term returns, volatility,
and correlations among asset classes. Forming
Figure 3 combines our findings and underscores
such expectations requires an understanding of the
that the consensus view of future macroeconomic
components that make up long-term returns. For
conditions—whether derived from financial indicators
stocks, the primary building blocks include growth
or professional forecasters—has provided little insight
in corporate earnings per share and dividends per
regarding the near-term direction of stock returns over
share, as well as changes in market valuations,
the past 40 years. We have also shown this to be true
such as the price/earnings (P/E) ratio. Recalling
for the return spreads of (1) value versus growth style
Figure 1, the trends in these building blocks are
indexes and (2) small-cap versus large-cap indexes.
highly correlated with fundamental trends for
The results in this paper supplement the research of
productivity and inflation.
Goyal and Welch (2006), who find little robust evidence
of near-term stock return predictability using a wide
set of financial variables. In short, foreseeing the
stock market’s short-term fluctuations is an extremely
difficult endeavor.
17 See, for instance, Brooks and Gray (2004).
10 > Vanguard Investment Counseling & Research
As part of this assessment, investors should focus on
a big-picture view of the global economic landscape.
To do so requires asking several important and related
questions. First, what will be the dominant themes in
the economy and financial markets in the years ahead?
Second, are these macro trends and concerns already
priced in? This step is critical because, as we have
shown, the consensus view has had little correlation
with near-term stock returns. If one’s own assessment
differs sufficiently from the mainstream view, then one
may have identified the potential macro “surprises”
that may later move the stock market. Naturally, the
future success of such a macro-driven portfolio
strategy will be heavily dependent on one’s accuracy
in forecasting future economic trends.
Figure 4. Growth in U.S. stock market fundamentals
and economic growth, by decade
Dividends and earnings growth per share
12
1940s
2000s
8
4
1920s
1970s
1960s
1900s
1980s
1990s
1950s
1910s
1890s
0
1880s
1870s
–4
–8
1930s
–2
0
2
4
6
8
10
Nominal GDP growth per capita
Below we list four key macroeconomic concepts that
can help investors build reasonable expectations for
long-term stock returns.
Macroeconomic trend 1: Productivity growth
Since the 1870s, the growth in U.S. corporate
earnings and dividends per share has averaged
slightly over 4% per year. Figure 4 demonstrates that
this growth has correlated with the average growth
rate of economic productivity. In the past, productivity
booms—such as those witnessed in the 1920s and
1990s—have coincided with robust growth in
corporate earnings and/or dividends. Productivity
booms are often driven by the rapid diffusion of
general-purpose technologies that raise (at least for
a time) the economy’s natural “speed limit” for
noninflationary growth.
Note: Data for the 1870s begin with 1872; data for the 2000s
end with 2007.
Source: Author’s calculations from the NBER Macro History
Database and the data sources cited in Figure 1.
What is the outlook for the U.S. economy’s
future productivity growth? Presently, consensus
expectations match the historical average: Nominal
productivity is expected to grow at approximately
4.5% annually over the next ten years. That figure
combines a real productivity growth rate of 2%
and an average expected inflation rate of 2.5%.
A key consideration for macro-minded investors
is how innovation and technological progress will
influence actual corporate productivity growth in
the years ahead.
Some key terms
The F-statistic reported in the regression output is from a test of the hypothesis that all of the slope
coefficients (excluding the constant, or intercept) in a regression are zero. The p-value of the F-statistic
is the marginal significance level of the F-test.
Predictability regression is a fitted relationship of the future values of the dependent variable regressed
on (or fitted to) past values of the independent variable(s).
R-squared represents the fraction of the variance of the dependent variable explained by the independent
variable(s).
Vanguard Investment Counseling & Research > 11
average inflation rate over the previous decade.
The productivity booms of the 1920s, 1950s, 1980s,
and 1990s were all associated with above-average
excess stock returns in part because the productivity
booms helped to engender disinflation.
Figure 5. The equity risk premium and changes
in realized inflation from previous decade
8
1910s
1940s
Change in average inflation rate
versus prior decade
6
4
1970s
1880s
1900s
2
2000s
1960s
0
1890s
–2
1930s
1950s
1980s
1990s
–4
1920s
–6
–8
–4
0
4
8
12
16
20
Realized excess return of U.S. stocks
Note: Data for the 1870s begin with 1872; data for the 2000s
end with 2007.
Source: Author’s calculations from the NBER Macro History
Database and the data sources cited in Figure 1.
Macroeconomic trend 2: Inflation
Although Figure 4 shows a tight correlation between
productivity growth and stock market fundamentals,
this relationship has not always translated into high
equity returns. A primary reason has been unexpected
accelerations in the rate of trend inflation. In the
1970s, for instance, nominal output and corporate
earnings appeared high by historical standards, but
they were inflated by the oil-price shocks of the
period. The war-related boosts in productivity during
the 1910s and 1940s were also associated with a
run-up in inflation.
In the short and medium run, unexpected changes
in the average inflation rate can have a negative
effect on stock returns until monetary policy is able
to return inflation expectations to a more stable and
desired level.18 Figure 5 shows a negative correlation
between the average realized excess return of U.S.
stocks (over cash) in a decade and the change in the
What is the secular outlook for U.S. inflation?
Presently, the consensus long-run forecast is for
an average inflation rate of 2%–3% per year, a
rate consistent with the Federal Reserve Board’s
objective of price stability. However, some experts
challenge this baseline forecast, noting the growing
global pressures on commodity prices and other
inflationary forces.19 Certainly the rate of global
inflation in the decades ahead will be a critical
risk factor for equity investors.
Macroeconomic trend 3: Productivity booms
and stock market valuations
Although periods of above-average economic
productivity have often helped to push down
inflation, they have also, on occasion, been
accompanied by a rise in stock market valuation.
Most notably, the information technology-related
productivity boom in the late 1990s witnessed
a sharp rise in P/E ratios, especially for technologyrelated companies, as investors grappled with
the possible long-term benefits of rapidly
diffusing technologies.
Over horizons of five or ten years, P/E ratios
can be powerful indicators of future stock returns
(i.e., Campbell and Shiller, 1988), although Ibbotson
and Chen (2003) demonstrate that P/E expansion
accounted for only a small portion of the total return
of U.S. stocks since 1926. Based on current valuation
metrics, what is the outlook for U.S. stocks? Figure 6
reveals that forward-looking P/E ratios (as calculated
by Vanguard’s Quantitative Equity Group using analyst
corporate earnings forecasts) are near their historical
average after having come down from the levels
observed in the late 1990s.
18 See Campbell and Vuolteenaho (2004) and citations therein on the concept of “inflation illusion” and the record of stocks as a long-term inflation hedge.
19 For a discussion of evolving U.S. inflation dynamics, see Davis (2007).
12 > Vanguard Investment Counseling & Research
Figure 6. Forward P/E ratio and subsequent 5-year
average U.S. stock return, 1955–2007
30
25
20
15
10
5
0
–5
1955
1960
1965
1970
1975
1980
1985
1990
1995
2000
2005
Average five-year geometric return of S&P 500 Index beginning in each year
Forward P/E ratio of index
Sources: Standard & Poor’s and Vanguard.
Figure 7. U.S. corporations are increasingly tied to
global economic conditions
Percentage of U.S. corporate profits earned overseas: 1929–2008Q1
25%
Macroeconomic trend 4: The global
economy is increasingly relevant
In the past, growth in real corporate
earnings per share has been closely
correlated with an economy’s own
inflation-adjusted productivity growth
rate. In the United States, this real rate
of growth has been roughly 2% per
year on average. This observation,
combined with a current dividend yield
of 2%, has led some practitioners to
forecast a much narrower return
premium for stocks over bonds and
cash in the decade ahead (Arnott and
Bernstein, 2002).
That prognostication may prove too
pessimistic, however, given that U.S.
corporations are deriving a greater
share of their earnings from overseas,
as illustrated in Figure 7. Starting now,
investors should be sure to examine
trends in global growth, which has
been as least twice as high as U.S.
economic growth over the past
several years.
20
15
10
5
0
1929 1935 1941 1947 1953 1959 1965 1971 1977 1983 1989 1995 2001 2007
Source: U.S. Bureau of Economic Analysis. Data for 2008 reflect first quarter only.
Vanguard Investment Counseling & Research > 13
One caveat: Structural breaks, “black swans,”
and long-run predictions
It is important to remember that stock returns
over the next decade are not necessarily more
predictable than the returns for the next quarter.
Kritzman (1994) points out that, although the
annualized dispersion of stock returns moderates
toward the expected mean, the dispersion of
an investor’s potential terminal wealth increases
as the investment horizon increases. To put this
another way: Although the probability of losing
money in stocks is lower over longer investment
horizons than over shorter ones, the size of the
potential loss increases. In surveying the debate
over “time diversification,” Bennyhoff (2008) finds
little empirical evidence to support the claim that
time moderates the risks inherent in risk assets.
Over the past 200 years, the American economy
and its financial system have undergone fantastic
changes and challenges. Amazingly, real stock
returns have been remarkably similar across long
periods, such as between the 19th and 20th
centuries. Of course, going forward, an unforeseen
structural break or “black swan” could fundamentally
alter the U.S. economy and its role in the global
financial system. Examples could include major
armed conflicts, drastic changes in the tax code
or the regulatory environment, technological change
that fundamentally alters the nation’s comparative
advantages, monetary policy mistakes, or even
simply bad luck.
For these reasons, global portfolio diversification
remains critical, and it is important for strategic
investors to periodically revisit their macro-based
assumptions regarding expected long-term stock
returns in the years ahead.
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Contributing authors
John Ameriks, Ph.D./Principal
Joseph H. Davis, Ph.D./Principal
Francis M. Kinniry Jr., CFA/Principal
Roger Aliaga-Diaz, Ph.D.
Donald G. Bennyhoff, CFA
Maria A. Bruno, CFP ®
Scott J. Donaldson, CFA, CFP ®
Michael Hess
Julian Jackson
Colleen M. Jaconetti, CPA, CFP ®
Karin Peterson LaBarge, Ph.D., CFP ®
Christopher B. Philips, CFA
Liqian Ren, Ph.D.
Kimberly A. Stockton
David J. Walker, CFA
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