Download Market Pricing of Economic Risks and Stock Returns

Market Pricing of Economic Risks and Stock Returns
Liuren Wu
(joint work with Yi Tang)
Zicklin School of Business, Baruch College
Midwest Finance Association Meetings, March 1st, 2008
Liuren Wu
Market Pricing of Economic Risks
MFA, March 1, 2008
1 / 17
Objective
Relate stock returns to macroeconomic fundamentals:
Do macroeconomic risks matter in equity valuation? How?
Go back to the roots of asset pricing theories: mt+1 = β
Ct+1
Ct
−γ
Pt
Pt+1
The real side of the aggregate economy governs consumption growth, a
key determinant of the real pricing kernel.
[Merton (1973), Ross (1976), Lucas (1978), Breeden (1979)]
The nominal side (inflation) directly enters the nominal pricing kernel.
It can also affect the real pricing kernel through dynamic interactions
with the real side of the economy.
[Fama (1981), Piazzesi and Schneider (2006)]
It is an appealing exercise: A long list of papers look at similar questions...
but the findings are weak (Chan, Karceski, and Lakonishok (JFQA 1998),Flannery and
)
Protopapadakis, RFS (2002)...
Liuren Wu
Market Pricing of Economic Risks
MFA, March 1, 2008
2 / 17
The linkages are there, but identifying the linkages is
difficult
Macroeconomic indicators are released in monthly/quarterly frequency;
stock prices are updated faster than the blink of the eye (10 miliseconds)
The indicators are well anticipated before release. The un-anticipated
component contains lots of noise and little real surprise.
Many different measures of similar economic dimensions: Which of the
following measures inflation?
(core) CPI, (core) PPI, (core) PCE deflator, GDP deflator ...
Realized returns are very noisy indicators of the expected returns — Risk
premiums are hard to estimate.
We tackle these issues in a three-step procedure.
Liuren Wu
Market Pricing of Economic Risks
MFA, March 1, 2008
3 / 17
I. Define and measure systematic economic risks
Definition: We decompose the aggregate economy into the nominal side
>
(inflation rate, π) and real side (real output growth rate, g ). Xt ≡ [πt , gt ]
Estimation:
A large array of economic indicators are available.
Each reveals some information, together with much noise, about the
systematic states of the economy.
We use the Kalman filter to extract the information and suppress the
noise in 11 economic indicators.
We use maximum likelihood method to estimate the dynamics and
interactions between the two sides of the economy.
Liuren Wu
Market Pricing of Economic Risks
MFA, March 1, 2008
4 / 17
II. Estimate the economic risk exposure
For each company, we estimate the comovements between the company’s cash
flow and the economic risks via time-series regressions.
Regress stock returns on the two economic shocks.
Stock return is a combination of future cash flow and the pricing kernel.
Given the same pricing kernel, the cross-sectional variation of the slope
coefficients across different stocks should reflect their differences in
cash flow exposures.
Regress earning surprises on the two economic shocks (in progress)
Definition of earning surprises:
Residuals from statistical forecasting regressions
Deviations between realizations and analysts forecasts
All risk exposure estimates contain noise. Combine them to reduce noise...
Liuren Wu
Market Pricing of Economic Risks
MFA, March 1, 2008
5 / 17
III. Estimate the premium per unit economic risk exposure
Compare the cross-sectional difference in expected excess returns between
stocks with different economic risk exposures.
Regress excess returns cross-sectionally on risk exposures.
Liuren Wu
Market Pricing of Economic Risks
MFA, March 1, 2008
6 / 17
Literature
Time-series: ERtm = β(∆Economic Indicators)t + εt .
Bodie (1976), Fama (1981), Geske and Roll (1983), Pearce and Roley (1983, 1985), Chen (1991), Chan, Karceski, and
Lakonishok (1998), Flannery and Protopapadakis (2002) ...
The single time-series regression on market portfolio excess returns
(ERtm ) lacks statistical power.
Economic indicators are noisy. Issues:
Errors-in-variables, multicolinearity, interpretability.
Cross-section: Chen, Roll, and Ross (1986)
Estimate risk exposures for testing portfolios: ERti = βi Xt + εit ,
X = [YP, MP, DEI , UI , UPR, UTS].
Test cross-sectional relation between risk exposures and expected
returns.
Comments: This is better than the single time series regression on the
market portfolio.
Shanken & Weinstein (1990): Conclusion depends on how the testing
portfolios are formed.
Issues remain on the use of multiple indicators: EIV, multicolinearity,
interpretability
Liuren Wu
Market Pricing of Economic Risks
MFA, March 1, 2008
7 / 17
I. Linking economic indicators to economic factors
Economic Indicatorst
Economic Indicators
CPI
Core CPI
PPI
Core PPI
PCE
Core PCE
GDP Deflator
Nonfarm Payroll
Industrial Production
Real PCE
Real GDP
= hπ πt + hg gt + et ,
hπ (Inflation)
0.2737
0.2367
0.3248
0.2822
0.2360
0.1983
0.2152
0.0771
—
—
—
(
(
(
(
(
(
(
(
27.83
17.95
25.51
26.71
33.11
21.18
27.26
14.43
(—
(—
(—
R2
hg (Real growth)
)
)
)
)
)
)
)
)
)
)
)
—
—
—
—
—
—
—
0.3936
0.8205
0.2489
0.3849
(
(
(
(
(—
(—
(—
(—
(—
(—
(—
42.82
31.22
19.18
19.47
)
)
)
)
)
)
)
)
)
)
)
0.950
0.885
0.818
0.900
0.991
0.911
0.939
0.965
0.656
0.402
0.682
All indicators contain information, only to different degrees.
Liuren Wu
Market Pricing of Economic Risks
MFA, March 1, 2008
8 / 17
Inflation and real output growth dynamics
πt
gt
=
Φ
πt−1
gt−1
+
Φ
Inflation (π)
Real Growth (g )
1.0070
(203.5)
-0.0183
(-3.59)
√
Qεt ,
Q
0.0568
(5.62)
0.9700
(112.5)
1
(—)
-0.2541
(-4.67)
-0.2541
(-4.67)
1
(—)
High inflation predicts future high inflation and declined real growth.
High growth predicts future high growth and also increased inflation.
Real growth and inflation show negative instantaneous correlation.
The real part of the two eigenvalues of Φ is about 0.9885, indicating that
the two factors are stationary.
The two sides of the economy show intricate, dynamic interactions.
Liuren Wu
Market Pricing of Economic Risks
MFA, March 1, 2008
9 / 17
II. Estimate the economic risk exposure
Regress the stock excess return on innovations in the two economic risk
bt − X t .
factors: ERti = β0 + βπi ESπ,t + βgi ESg ,t + eti , ESt = X
Stock return is a combination of future cash flow and the pricing kernel.
Given the same pricing kernel, the cross-sectional variation of the slope
coefficients across different stocks should reflect their differences in
cash flow exposures.
Use a ten-year rolling window to allow time variation in risk exposure for
each company.
Liuren Wu
Market Pricing of Economic Risks
MFA, March 1, 2008
10 / 17
Economic risk exposure estimates
Cs stats on
ts averages
Ts stats on
cs averages
Market
portfolio
Panel A: Exposure to inflation risk
Mean
-0.6014
-0.7242
Median
-0.7703
-0.8893
Minimum
-12.9316
-1.9682
Maximum
15.7398
1.3289
Std. Dev.
2.8589
0.8589
-0.5149
-0.5490
-1.5520
0.7216
0.5541
Panel B: Exposure to output risk
Mean
-1.4045
-0.5255
-0.2686
Median
-0.4085
-0.0496
0.0426
Minimum
-34.8307
-3.5940
-2.3046
Maximum
16.8267
1.3192
0.6776
Std. Dev.
4.9265
1.2549
0.7973
Cross-sectional variation is much larger than time-series variation.
Liuren Wu
Market Pricing of Economic Risks
MFA, March 1, 2008
11 / 17
Average economic risk exposures by industry
Industry
Inflation
Real growth
Consumer NonDurables
-0.7768 *** -0.2715
Consumer Durables
-0.7388 *** 0.4198 *
Manufacturing
-0.6100 *** 0.1184
Energy Oil, Gas, Coal
-0.1808
0.1052
HiTec Business Equipment
-0.5091 *** 0.0781
Telephone, TV Transmission
-0.6408 *** 0.0624
Wholesale, Retail, & some services
-0.7535 *** -0.2526 ***
Healthcare, Medical Equipment, & Drugs -0.9402 *** -0.3728 ***
Utilities
-0.5738 *** -0.0476
Other
-0.7055 *** 0.0052
Most cyclical: Durables
Most counter-cyclical: Health care
Most inflation exposure: Health care
Least inflation exposure: Energy
Liuren Wu
Market Pricing of Economic Risks
MFA, March 1, 2008
12 / 17
III. Economic risk premiums: Portfolio return spreads
Economic risk premium can be captured by the average return spreads
between stocks with high and low risk exposures.
We form (10 × 10) portfolios based on the exposure to the two economic
risk sources:
The average premium on output risk exposure: (βg10 − βg1 ) is 0.19% per
month (t = 1.63).
Pro-cyclical companies earn higher stock returns on average than
counter-cyclical companies, due to their difference in real output
growth exposure.
The average premium on inflation risk exposure: (βπ10 − βπ1 ) is -0.3% per
month (t = 1.96).
Most industries have negative exposures to inflation.
Companies with higher negative exposures (in absolute magnitude)
earn higher expected excess returns.
Liuren Wu
Market Pricing of Economic Risks
MFA, March 1, 2008
13 / 17
Economic risk premiums: Cross-sectional regressions
Regress excess returns cross-sectionally on economic risk exposures:
i
ERt+1
Sample period
i
= α0,t + απ,t βπ,t
+ αg ,t βgi ,t + it ,
απ
01/1963 - 12/2005
-0.0546 (1.89)
01/1963 - 12/2005
—
—
01/1963 - 12/2005
-0.0673 (2.22)
Similar conclusions from the regressions:
αg
—
0.0397
0.0555
—
(1.74)
(2.43)
Companies with higher inflation exposure earn lower returns.
Increase in inflation exposure by one standard deviation (2.8589) drives
the expected return down by 0.19% per month.
Companies with higher real growth exposures earn higher returns.
Increase in output growth exposure by one standard deviation (4.9269)
moves the expected return up by 0.27% per month.
Liuren Wu
Market Pricing of Economic Risks
MFA, March 1, 2008
14 / 17
Economic interpretations
The findings have important implications for asset pricing theories.
Investors dislike positive exposure to real output growth risk, and ask for
positive premium to bear such risk.
Our real output growth factor can be regarded as a proxy for the real
aggregate consumption growth.
Investors dislike assets whose cash flows are positively correlated with
real consumption growth.
Empirical support for classic asset pricing theory.
Investors favor positive (higher) exposure to inflation risk, and are willing to
accept lower returns.
Inflation and real output growth are intricately related: ⇒high inflation
slows down future real output growth. [Fama (1981), Piazzesi and
Schneider (2006)]
Assets whose cash flows are positively correlated with inflation risk can
be used to hedge against real consumption risk.
Liuren Wu
Market Pricing of Economic Risks
MFA, March 1, 2008
15 / 17
Good beta, bad beta
Campbell & Vuolteenaho (2004): The beta of a stock against the market
portfolio can be decomposed into “good beta” (discount rates) and “bad beta”
(cashflows).
Positive exposure to real output growth generates “bad beta:”
Mainly a positive cash flow effect: High real output growth → high real
consumption.
Minor discount rate effect: Taylor rule: it = 1.5πt + 0.5(gt − g ).
Positive exposure to inflation generates “good beta:”
Negative cash flow effect: High inflation predicts lower real growth in
the future.
Positive discount rate effect: it = 1.5πt + 0.5(gt − g ).
Liuren Wu
Market Pricing of Economic Risks
MFA, March 1, 2008
16 / 17
Bottom line
Economic fundamentals do matter for stock pricing.
Different companies show different exposures to inflation and real output
growth risks.
Cross-sectional variations are much larger than time-series variations.
Regressing market portfolio returns on the economic indicators are
likely to generate insignificant results.
We can effectively identify the market pricing of the economic risks from the
cross-section of stock returns.
Higher real output growth exposure generates higher expected excess
returns ⇒ Pro-cyclical companies earn higher expected excess returns
than counter-cyclical companies.
For most industries, cash flows are negatively correlated with inflation
shocks.
The higher this negative exposure is (in absolute magnitude), the
higher the expected excess return on a stock.
Liuren Wu
Market Pricing of Economic Risks
MFA, March 1, 2008
17 / 17