| The
Dimensions of Stock Returns:
2002 Update
By Truman A. Clark
Vice President
Dimensional Fund Advisors Inc.
April 2002 |
|
An ongoing objective of financial research is to explain the
behavior of stock returns. Factors are sought that explain both
differences among the returns of individual stocks in any given
time period and the variation of stock returns through time. If
a factor does both, it is said to explain the common variation
of returns. In addition, if a factor is related to non-diversifiable
risk and possesses explanatory power independent of other factors,
the factor is considered a "dimension" of stock returns.
| Book-to-market ratio (BtM) is the
ratio of a firm's book value of equity to its market value
of equity. Book value of equity is determined by the firm's
accountants using historic cost information. Market value
of equity is determined by buyers and sellers of the stock
using current information. |
Fama and French (1992) found that two factors related to company
size and book-to-market ratio (BtM)
together explain much of the common variation of stock returns
and that these factors are related to risk. Small cap stocks have
higher average returns than large cap stocks, and high BtM (or
"value") stocks have higher average returns than low BtM (or "growth")
stocks. Based on Fama and French's findings, size and BtM are
dimensions of stock returns.1
Fama and French also investigated a market factor. A market factor
is needed to distinguish stocks from fixed income securities,
and it is important in explaining the variation of stock returns
through time. But, among stocks in a given time period, differences
in their sensitivities to the market factor are unrelated to differences
in their average returns, so the market factor is not a dimension
of stock returns.
The Fama/French results have important implications for domestic
equity portfolio design. Large capitalization growth stocks constitute
large portions of traditional "market-like portfolios" based on
indexes such as the S&P 500, the Russell 3000 and the Wilshire
5000. Domestic equity portfolios with greater commitments to small
cap stocks and value stocks offer higher average returns than
conventional market-like portfolios.
Size and BtM also are dimensions of international and emerging
markets stock returns. This confirms Fama and French's interpretation
of size and BtM effects as rewards for bearing risk that cannot
be eliminated by diversification.
The implications for global equity allocation are significant.
The MSCI EAFE index is the international equivalent of the S&P
500-a composite dominated by large cap growth stocks. For American
investors with large core holdings of S&P 500 stocks, EAFE
may provide less diversification benefits than other international
equity portfolios. Dimensional recommends that most investors
use international and emerging markets small cap and value stocks
for global diversification.
Risk and Return
| Standard deviation (σ) is
the statistical measure of the degree to which an individual
value in a probability distribution tends to vary from the
mean of the distribution. |
Figure 1 shows arithmetic averages and standard
deviations of the 1927-2001 annual returns of four asset
class portfolios. Stocks are grouped by size (large and small)
and BtM (low and high) to form these asset class portfolios. For
reference, statistics also are shown for two market indexes: the
S&P 500 (a composite of large cap stocks) and the CRSP 6-10
(a composite of small cap stocks).
| Figure 1 |
US Equities
Arithmetic Average Rates of Return |
| Annual Data: 1927-2001 |
| |
|
| |
Value and growth data
courtesy of Fama/French.
S&P data courtesy of © Stocks, Bonds, Bills and
Inflation YearbookT, Ibbotson Associates, Chicago (annually
updated works by Roger C. Ibbotson and Rex A. Sinquefield).
CRSP data courtesy of the Center for Research in Security
Prices, University of Chicago. |
Controlling for differences in BtM by comparing large cap value
to small cap value and comparing large cap growth to small cap
growth, small cap stocks had higher average returns than large
cap stocks.2 Controlling for differences in size
by comparing large cap growth to large cap value and comparing
small cap growth to small cap value, value stocks had higher
average returns than growth stocks. The higher average returns
of small cap and value stocks represent rewards for bearing risk.
If standard deviation were a complete measure of risk, average
returns would increase as standard deviations increase. Controlling
for differences in BtM, a direct relation between average returns
and standard deviations is found when large cap stocks are compared
to small cap stocks. But, controlling for differences in size,
a discrepancy appears. Small growth stocks had a lower average
return and a higher standard deviation than small value stocks.
Since greater standard deviations are not consistently associated
with higher average returns, standard deviation is not a reliable
measure of risk.
Size, Book-to-Market and Earnings
| Earnings-to-book ratio (EtB) is
the ratio of a firm's current (or predicted) earnings per
share to the book value per share of its common stock. |
Seeking a risk-based explanation for the relations of size and
BtM to average returns, Fama and French (1995) investigated the
behavior of the earnings of stocks grouped by size and BtM. Measuring
profitability by the ratio of annual earnings
to book value of equity, Figure 2 illustrates the evolution
of profitability over long periods before and after stocks are
classified by size and BtM. BtM is associated with persistent
differences in profitability. On average, low BTM stocks are more
profitable than high BtM stocks of similar size for at least five
years before and after portfolio formation. Low BtM indicates
sustained high earnings that are characteristic of firms that
are growing and financially robust. High BtM indicates protracted
low earnings that are typical of firms experiencing financial
distress.
Figure 2 also shows that profitability is related to firm size.
Controlling for differences in BtM, the earnings of small cap
stocks are lower than those of large cap stocks for at least five
years before and after portfolio formation.
The patterns observed in Figure 2 indicate that small stocks
and value stocks are subject to ongoing earnings pressure. Size
and BtM appear to be indicators of exposure to fundamental risk
factors related to financial distress.3
| Figure 2 |
Earnings on Book Equity
E(t+i+1) / B(t+1) |
| Portfolios Formed
at End of Year t: 1963-2000 |
| |
|
| |
| For each portfolio
formation year t = 1963-2000, the ratios are calculated
for t+i, i=-5,...,5. The ratio for t+i is then averaged
across portfolio formation years t. E(t+i+1) is earnings
before extraordinary items but after interest, depreciation,
taxes and preferred dividends for the fiscal year ending
in calendar year t+i+1. B(t+i) is book common equity
plus balance sheet deferred taxes for the fiscal year
ending in calendar year t+i. |
| Data courtesy of Fama/French. |
| Expected return ("E(R)") is the
mean value of the probability distribution of possible returns. |
| |
| The risk premium is the additional
return an investor requires to compensate for the risk borne. |
Expected Returns and the Cost of Capital
Financial markets channel funds from suppliers of capital to
users of capital. Expected returns
are the rewards investors anticipate for supplying capital. Investors
require a higher rate of return (or risk
premium) for bearing greater risk. Risk is something that
investors collectively shun and that cannot be eliminated by diversification.
The cost of capital is the price users of capital must pay to
obtain financing. Competition forces users of capital to bid higher
prices to obtain funding for more risky ventures.
In equilibrium, the expected rate of return and the cost of capital
are determined jointly as the price at which the demand for and
supply of capital are equal. In financial markets that function
efficiently, investors only receive risk premiums for bearing
risk. As risk increases, the expected rate of return and the cost
of capital increase.
| Market capitalization is the value
of a company as determined by the market price of its issues
and outstanding common stock. It is calculated as the product
of market price and shares outstanding. |
High BtM and small size often indicate companies that are experiencing
some degree of financial distress. On average, they have higher
costs of capital because they tend to be riskier than companies
with low BtM and large market capitalization. The higher average returns
of small stocks and value stocks reflect compensation for exposure
to non-diversifiable risk factors.
The Three-Factor Model
The findings of Fama and French suggest that much of the variation
in stock returns is explained by three systematic risk factors.
- The market factor measured by the returns of stocks
minus the returns of Treasury bills (or XRMKT).
- The size factor measured by the returns of small stocks
minus the returns of big stocks (or SMB).
- The value factor measured by the returns of high-BtM
stocks minus the returns of low-BtM stocks (or HML).
The three-factor model hypothesizes a linear relation between
the excess returns of a portfolio (or stock) and the premiums
of the three factors:
RP(t) - RF(t) = a + b · [RM(t) - RF(t)] + s
· SMB(t) + h · HML(t) + e(t)
RP(t) is the total rate of return of a portfolio
in month t. RF(t) is the return of a one-month
Treasury bill. RM(t) is the total rate of return
of the stock market. SMB(t) is the size factor premium,
and HML(t) is the value factor premium. Monthly
departures from the model's predictions (or errors) are represented
by e(t), and they are assumed to vary randomly about
an expected value of zero.
| Regression is a statistical technique
used to establish the relationship of a dependent variable
(i.e. excess return) and one or more independent variables
(i.e. exposure to market, size, and value risks). |
Using monthly data for the period January 1992 through December
2001, parameters of the model for two indexes and four portfolios
were estimated by regression
methods (Table 1). The slope coefficients (b, s
and h) measure a portfolio's sensitivity to each factor.
- Sensitivity to the market factor (b): The estimates
of b are close to 1.00 for the S&P 500, the Russell
3000 and the DFA Large Cap Value Portfolio. The DFA Small Cap
Portfolio, Micro Cap Portfolio and Small Value Portfolio are
less sensitive to overall market movements. Relative to the
market, these small-cap stock portfolios behave like a portfolio
composed roughly of 85 percent stocks and 15 percent bonds.4
- Sensitivity to the size factor (s): The S&P 500
and Russell 3000 are predominantly large-cap stock indexes,
and their excess returns are negatively related to the size
factor. The DFA Large Cap Value Portfolio's sensitivity to the
size factor is effectively zero. The three DFA small-cap portfolios
have strong, positive sensitivities to the size factor.
- Sensitivity to the value factor (h): The DFA Micro
Cap Portfolio's sensitivity to the value factor is effectively
zero. The estimated value sensitivities of the other five portfolios
are positive, and the value sensitivities of the DFA Large Cap
Value and Small Cap Value Portfolios are much greater than the
others.
| Table 1 |
| Three-Factor Model Estimates |
| Monthly Data: 1992-2001 |
| |
|
|
|
|
|
|
| S&P 500 Index |
0.03 |
1.00 |
-0.17 |
0.05 |
0.989 |
| |
0.75 |
0.10 |
-15.20 |
5.22 |
|
| Russell 3000 Index |
-0.01 |
1.00 |
-0.05 |
0.04 |
0.997 |
| |
-0.32 |
-0.18 |
-8.09 |
7.75 |
|
| DFA Small Cap Portfolio |
0.05 |
0.88 |
0.90 |
0.10 |
0.953 |
| |
0.42 |
-4.17 |
28.92 |
3.36 |
|
| DFA Micro Cap Portfolio |
0.28 |
0.81 |
1.09 |
0.04 |
0.918 |
| |
1.74 |
-4.50 |
23.74 |
0.86 |
|
| DFA Large Value
Portfolio |
0.00 |
1.08 |
0.04 |
0.61 |
0.836 |
| |
0.01 |
1.83 |
0.73 |
13.56 |
|
| DFA Small Value
Portfolio |
0.24 |
0.84 |
0.81 |
0.39 |
0.911 |
| |
1.88 |
-4.80 |
22.21 |
11.65 |
|
|
| |
| t-statistics
are in italics. For the market sensitivity coefficient,
the null hypothesis is b=1. For the other coefficients,
the null hypothesis is that each equals zero. Underlined
type indicates statistical significance at the .01 level
(2-tailed). |
S&P data courtesy
of © Stocks, Bonds, Bills and Inflation YearbookT, Ibbotson
Associates, Chicago (annually updated works by Roger
C. Ibbotson and Rex A. Sinquefield).
Russell data courtesy of Russell Analytic Services.
|
The intercept of each regression (a) measures the average
excess return that is not explained by the three factors. In each
case, the intercept is indistinguishable from zero indicating
that the model explains all of the average excess return.
| Variance (σ2) measures
the dispersion of a return distribution. It is the sum of
the squares of a return's deviation from the mean, divided
by n. The value will always be >=0, with larger
values corresponding to data that is more spread out. |
As indicated by the adjusted R-squared statistics, the model
explains at least 90 percent of the variance
of the excess returns of five of the six portfolios. For the DFA
Large Cap Value Portfolio, the model explains more than 80 percent
of the variance.
Portfolio Engineering
Many investors commit high proportions of their domestic equity
holdings to portfolios resembling the S&P 500, Russell 3000
or other market-like proxies. Large cap growth stocks are the
dominant holdings of the S&P 500 (Figure 3) and the Russell
3000 (Figure 4). As a result, market-like proxies are poor portfolio
structures for investors seeking exposure to the size and/or value
factors. Investors can get such exposure by increasing their relative
holdings of small cap and/or value stocks.
| Figure 3 |
| Size and BtM Composition
of the S&P 500 Index |
| December 31, 2001 |
| |
|
| |
| S&P data courtesy
of © Stocks, Bonds, Bills and Inflation YearbookT, Ibbotson
Associates, Chicago (annually updated works by Roger
C. Ibbotson and Rex A. Sinquefield). |
| Figure 4 |
| Size and BtM Composition
of the Russell 3000 Index |
| December 31, 2001 |
| |
|
| |
| Russell data courtesy
of Russell Analytic Services. |
Figure 5 is a scatter plot of the size (s) and value (h)
coefficients of five of the portfolios reported in Table 1. Each
DFA asset class portfolio is carefully designed to provide concentrated
exposures to the size and/or value factors. For diversification
purposes, each asset class portfolio includes many stocks that
have specific size and BtM characteristics. The asset class portfolios
can be used as building blocks for structured portfolios providing
targeted exposures to the size and/or value factors. To illustrate,
let the S&P 500 serve as an initial core portfolio.
- Increased size exposure can be obtained with the Small Cap,
Micro Cap and Small Cap Value funds.
- Increased value exposure can be obtained with the Large Cap
Value and Small Cap Value funds.
| Figure 5 |
| Estimated Portfolio Sensitivities:
Size and Value |
| |
|
| |
S&P data courtesy
of © Stocks, Bonds, Bills and Inflation YearbookT, Ibbotson
Associates, Chicago (annually updated works by Roger
C. Ibbotson and Rex A. Sinquefield).
Russell data courtesy of Russell Analytic Services.
|
The compositions of four structured portfolios are shown in Table
2. Portfolio P0 is the initial core portfolio invested
entirely in the S&P 500. Portfolio P1 provides a size
tilt through commitments to the Small Cap and Micro Cap funds.
As shown in Table 2 and Figure 6, portfolio P1 has positive
sensitivity to the size factor and only modest sensitivity to
the value factor.5 Portfolio P2 provides a value tilt
by combining the S&P 500 and the Large Cap Value fund. P2
has positive value sensitivity and, being composed almost entirely
of large cap stocks, negative size sensitivity. Portfolios P3
and P4 provide both size and value tilts. Portfolio P4
makes the biggest departures from the initial S&P 500 core
and has strong positive sensitivities to both the size and value
factors.
| Table 2 |
Portfolio Engineering
Substituting Small Cap and Value Stocks for the S&P
500 |
| |
|
|
|
|
|
|
| S&P 500 |
100 |
70 |
70 |
40 |
30 |
| DFA Small Cap Portfolio |
0 |
20 |
0 |
20 |
20 |
| DFA Micro Cap Portfolio |
0 |
10 |
0 |
10 |
10 |
| DFA Large Cap Value
Portfolio |
0 |
0 |
30 |
30 |
30 |
| DFA Small Cap Value
Portfolio |
0 |
0 |
0 |
0 |
10 |
| Estimated Porfolio
Sensitivities |
|
|
|
|
|
| Market
(b) |
1.00 |
0.96 |
1.03 |
0.98 |
0.97 |
| Size
(s) |
-0.17 |
0.17 |
-0.11 |
0.23 |
0.33 |
| Value
(h) |
0.05 |
0.06 |
0.22 |
0.23 |
0.26 |
| Expected Risk Premium
(% per year) |
-0.34 |
0.44 |
0.49 |
1.28 |
1.57 |
|
| |
| S&P data courtesy
of © Stocks, Bonds, Bills and Inflation YearbookT, Ibbotson
Associates, Chicago (annually updated works by Roger
C. Ibbotson and Rex A. Sinquefield). |
Expected risk premium does not represent actual investment
performance differences but rather expected performance
based on the Fama/French three-factor model.
|
The potential increases in expected returns due to these tilts
can be estimated with the three-factor model. For purposes of
illustration, it is assumed that the expected risk premiums are
six percent per year for the market factor and three percent per
year for both the size and value factors. Using these assumed
premiums and the factor sensitivities of portfolios P0
- P4, expected risk premiums are estimated relative to
a pure market portfolio with unit market sensitivity (b = 1) and
zero sensitivity to size (s = 0) and value (h = 0).
E(premium) = (b - 1) · 6 + s · 3
+ h · 3
The expected premiums are shown in Table 2 and Figure 6. The
expected premium of P0, the S&P 500, is -34 basis points
per year because of its negative sensitivity to the size factor.
P1, with a size tilt, has an expected premium of 44 basis
points per year (or 78 basis points more than P0). P2,
with a value tilt, has an expected annual premium of 49 basis
points. P3, with both size and value tilts, has an expected
annual premium of 128 basis points. P4, with stronger size
and value tilts, has an expected premium of 157 basis points per
year.
| Figure 6 |
| Structured Portfolios:
Size and Value Sensitivities and Expected Risk Premiums |
| |
|
| |
| Expected annual
risk premium over the market shown in brackets.
|
Words of Caution
Structured portfolios offer the prospect of higher long-term
returns than market-like portfolios, but the expected risk premiums
shown in Figure 6 are not sure things. Factor premiums vary widely
and randomly. For the 1927-2001 period, the standard deviations
of the annual premiums were approximately 21% per year for the
market factor, 14% for the size factor and 14% for the value factor.
Owing to their high variability, it may take decades before
rewards for bearing increased size and value risk are realized.
| Figure 7 |
| Cumulative Factor Premiums |
| Monthly Data: July
1927-December 2001 |
| |
|
| |
| Data courtesy of Fama/French. |
Cumulative premiums for each factor are computed by adding successive
monthly premiums for the period January 1927 through December
2001 (Figure 7). Although the cumulative premiums tend to rise
over long periods of time, each moves erratically with lengthy
episodes of downward drift. The market premium declined from December
1967 to July 1982-a period of more than 14 years. The size premium
declined from December 1983 to December 2001-a period of 18 years
(and still counting). The value premium declined from December
1987 to December 2000-a period of 13 years.
Structured portfolios are not appropriate for all investors.
Structured portfolios have higher expected returns because
they are riskier than market-like portfolios. Over periods
of less than 20 years, structured portfolios often will have lower
returns than market-like portfolios. It is only over periods of
20 years or more that it becomes more probable that structured
portfolios will outperform market-like portfolios. Investors with
short horizons or aversion to risk should stick with market-like
portfolios. Structured portfolios only make sense for investors
with long time horizons and sufficient tolerance for increased
risk.6
International and Emerging Markets Equities
Size and value effects are not confined to US equity markets.
The MSCI EAFE Index represents a portfolio of international stocks
from developed countries similar to the S&P 500. EAFE is composed
predominantly of large cap growth stocks. During 1975-2001, international
small cap stocks had a higher average return than EAFE indicating
a size effect, and international large cap value stocks had a
higher average return than EAFE indicating a value effect (Figure
8).7
| Figure 8 |
International Equities
Arithmetic Average Rates of Return |
| Annual Data: 1975-2001 |
| |
|
| |
International Large Value
courtesy of Fama/French prior to Dimensional's 7/93
portfolio inception.
MSCI data courtesy of Morgan Stanley Capital International.
International Small simulated by Dimensional prior to
4/86 portfolio inception. |
Performance information for International Large Value
and International Small are based in part on a model/backtested
simulation; the performance was achieved with the retroactive
application of a model designed with the benefit of
hindsight; it does not represent actual investment performance.
The model's investment objective is to achieve long-term
capital growth. The model's performance reflects the
reinvestment of dividends and other earnings, and is
net of fees. There are limitations inherent in model
performance. Past performance is no guarantee of future
results, and there is always the risk that an investor
may lose money. View SEC standardized performance
data and disclosures. |
| Figure 9 |
Emerging Markets Equities
Arithmetic Average Rates of Return |
| Annual Data: 1989-2001 |
| |
|
| |
Emerging Markets Value
data courtesy of Fama/French prior to Dimensional's
3/93 inception.
IFC data courtesy of International Finance Corporation.
Emerging Markets Small Cap data simulated by Dimensional
prior to 3/98 portfolio inception. |
Performance information for Emerging Markets Value and
Emerging Markets Small are based in part on a model/backtested
simulation; the performance was achieved with the retroactive
application of a model designed with the benefit of
hindsight; it does not represent actual investment performance.
The model's investment objective is to achieve long-term
capital growth. The model's performance reflects the
reinvestment of dividends and other earnings, and is
net of fees. There are limitations inherent in model
performance. Past performance is no guarantee of future
results, and there is always the risk that an investor
may lose money. View SEC standardized performance
data and disclosures. |
Based on the limited amount of data available, size and value
effects also appear in emerging markets. The IFC Investables Total
Return Index represents a portfolio of tradable stocks in emerging
markets countries that non-resident investors are permitted to
own. During 1989-2001, emerging markets small cap stocks and value
stocks had higher average returns than the IFC index (Figure 9).8
The international findings are consistent with Fama and French's
interpretation of the size and value effects as rewards for bearing
non-diversifiable risk. If size and value effects were related
to risk factors unique to the US, forming globally diversified
portfolios could eliminate them. Instead, the existence of similar
size and value effects in both domestic and international stock
returns demonstrates that these effects are global phenomena reflecting
exposures to ubiquitous sources of risk.
Implications for Global Equity Allocation9
EAFE is the international equivalent of the S&P 500. EAFE
returns, expressed in US dollars, are determined jointly by stock
returns computed in local currencies and foreign-exchange gains
or losses against the dollar. Because the two indexes contain
stocks with similar size and value characteristics, it is reasonable
to assume that the costs of capital of EAFE and the S&P 500
are approximately equal. If it is also assumed that currencies
have zero expected returns, EAFE should have about the same expected
gross rate of return as the S&P 500.
While their expected gross returns are similar, the expected
net return of an S&P 500 portfolio will be greater
than that of an EAFE portfolio due to differences in trading costs
and taxes. International stocks are more costly to trade, and
the dividends of international stocks are subject to foreign taxation-even
when the recipients are tax-exempt in the US For example, American
pension funds pay no taxes on dividends received from US firms,
but they are taxed at rates of 15 to 20 percent on dividends received
from many foreign companies.10
Many American investors rely on EAFE-like portfolios for international
diversification. But given the likelihood that the S&P 500
offers a higher expected net rate of return and similar
risk exposures, EAFE is nothing more than an expensive substitute
for the S&P 500. The diversification benefits afforded by
EAFE are minimal and not worth their cost.
Instead of EAFE, Dimensional recommends that American investors
use international and emerging markets small cap and value stocks
for global diversification. These asset classes have higher expected
gross returns than the S&P 500 that can compensate for the
higher costs and taxes of international investing.
Concluding Comments
The identification of size and value factors by Fama and French
has important implications for equity portfolio design. Relative
to traditional market-like portfolios, portfolios with greater
exposures to the size and value factors offer higher expected
long-term rates of return.
Structured portfolios can be designed that provide targeted sensitivities
to the size and value factors. Dimensional's asset class portfolios
can serve as building blocks for these structured portfolios.
International and emerging markets equity returns also exhibit
size and value effects. For global diversification, Dimensional
recommends the use of its international and emerging markets small
cap and value stock funds.
Structured portfolios only make sense for investors with long
time horizons and sufficient tolerance for increased risk. For
the right investors, structured portfolios are promising alternatives
to old-fashioned market-like portfolios.
My thanks to Jim Davis and Weston Wellington
for their helpful comments.
Davis, James L., Eugene F. Fama and Kenneth R.
French. "Characteristics, Covariances and Average Returns: 1929-1997."
Journal of Finance 55 (2000), 359-406.
Fama, Eugene F. and Kenneth R. French. "The Cross-Section
of Expected Stock Returns." Journal of Finance 47 (1992),
427-65.
Fama, Eugene F. and Kenneth R. French. "Size
and Book-to-Market Factors in Earnings and Returns." Journal
of Finance 50 (1995), 131-55.
Fama, Eugene F. and Kenneth R. French. "Value
versus Growth: The International Evidence." Journal of Finance
53 (1998), 1975-99.
Sinquefield, Rex A. "Where are the Gains from
International Diversification?" Financial Analysts Journal
52 (1996), 8-14.
This article contains the opinions
of the author and those interviewed by the author but not necessarily
Dimensional Fund Advisors Inc. or DFA Securities Inc., and does
not represent a recommendation of any particular security, strategy
or investment product. The author's opinions are subject to change
without notice. Information contained herein has been obtained
from sources believed to be reliable, but is not guaranteed. This
article is distributed for educational purposes and should not
be considered investment advice or an offer of any security for
sale. Past performance is not indicative of future results and
no representation is made that the stated results will be replicated.
April 2002