Transparency and the Pricing of
Market Timing
Xin Chang
Nanyang Technological University
Zhihong Chen
City University of Hong Kong
Gilles Hilary
INSEAD
Research Questions
• Can managers lower the cost of equity by actively
timing the market when they issue external capital?
• What is the role played by corporate transparency
in this process?
Can Firm Time The Market?
• The “market timing theory” relies on the idea that
managers know more about the fundamental value of
their firm than outside investors,
– Managers detect temporary mispricings.
• Managers can try to take advantage of the
mispricing by issuing or buying back capital.
Can Firm Time The Market?
• If new investors take the issuance of capital as a
signal that a firm is overvalued, the price should
adjust. (e.g., Myers and Majluf, 1984).
– If true, managers and current shareholders cannot
take advantage of mispricings.
• If quasi-rational investors buy the new capital at
the inflated price, managers can transfer wealth
from these new investors to current shareholders.
Can Firms Time The Market?
• There is a positive correlation between good market
conditions and equity issuances (e.g., Loughran et al.
1994, Graham and Harvey 2001).
• It is not clear if this reflecting managers’ private
information or something else such as time-varying
investment opportunities (Schultz 2003, Baker, et al.
2007).
Pricing of Market Timing
• If current investors believe this is true, these issuance
gains should be reflected in the firm valuation.
• The price of successful market timers should be
higher for a given level of expected earnings.
– Equivalently, holding profitability and risk
constant, the discount rate implied by a price and a
given stream of expected earnings is lower.
Market Timing Pricing
• H1: Firms that are expected to time the market
when they issued or repurchased capital should
have a lower expected cost of equity capital.
Are SEOs overvalued on average?
• Firm level (Ritter 2003) and aggregate level (Baker
and Wurgler 2000) evidence on abnormal
performance after SEOs.
• But, all studies use ex-post returns and complex
procedures to address associated econometric
problems.
• Ritter (2003) indicates that “the conclusions
regarding abnormal performance are hotly debated
and sensitive to the methodology employed and the
sample used”.
Broader View
• We rely on the aggregate amount of capital issued
by firms, rather than focusing on special and rare
events such as IPOs or SEOs.
• Takeuchi (2008) reports that
– firms making SEOs represent only 6% of firms
with net increases in equity
– 18% of firms with net increases in equity of
more than 10% of assets in a given year.
Transparency
• Transparency affects financial policy.
– Poor accounting quality is associated with higher
SEO issuance costs (Lee and Masulis (2009)).
– Transparent firms have more flexibility to issue
equity (rather than debt), have a greater control
over the issuance size and are less influenced by
market conditions (Chang et al. (2006, 2009)).
Transparency
• Transparent firms obtain a fair price when they issue
equity in periods of low sentiment and capture excess
value in periods of high sentiment.
• Opaque firms “break-even” in periods of high
sentiment and abstain from issuing equity in periods
of low sentiment.
• H2: The effect of past market timing on the
expected cost of capital should be stronger for
transparent firms than for opaque firms.
Main Specification
Ri*,t  R f ,t    1MTCovi,[0,t ]  C  ei,t
• Measure of market timing:
MTCov = Cov(EF,MB) / Assets
where
EF: sum of net debt and equity issues for a given
year.
MB: market to book ratio for the year.
Estimate Implied Cost of Equity
• Four implied cost of equity models
• All based on dividends discount model but make
different assumptions on future earnings growth.
• Use the average of the four estimates to mitigate
model-specific measurement errors.
Why not ex post returns?
• Market timing ability relies on the existence of quasirational investors and information asymmetry
between different classes of investors.
– Properties of market equilibrium models in this complex setting are not
well-known.
• Debatable whether the ex post return is an appropriate
proxy for a firm’s cost of capital.
– May reflect the shocks to a firm’s growth opportunities, expected
growth rates or investors’ risk aversion.
– Fama and French (1997) conclude that expected returns estimated by ex
post returns are imprecise because of the uncertainty of factor
premiums and factor loading estimates.
• Firms may have a very active financing policy.
Control Variables
•
•
•
•
•
•
•
•
•
Beta
Size
Book-to-Market
Leverage
Price Momentum
Forecast Errors
Forecast of Long-term Growth
Lagged Industry Risk Premium
Year fixed effects.
Main Specification
Ri*,t  Rf ,t     MTCovi,[1,t ]   AQxMTCovi,[1,t ]   AQi,t  Z  ei,t
• AQ: a measure of accounting quality similar to
Francis et al. (2005).
Data and Sample Selection
• Start from Compustat/CRSP merged file.
• Eliminate utilities and financial firms.
• Require firms to be listed for at least 3 years.
• Require observations to have all four cost of equity
estimates and control variables.
• Final sample contains 26,286 firm-year observations
from 1981 to 2007.
Descriptive Statistics
Variables
R*-Rf
N
26,286
Mean
5.351
Stdev
Q1
3.134
MTCov(1, t)
26,286
0.007
AQ
26,286
Beta
LogMV
LogBM
Leverage
Price Momentum (MMT)
Forecast error (Ferr)
Long-term earnings growth forecast (Fltg)
Industry risk premium (IndRp)
26,286
26,286
26,286
26,286
26,286
26,286
26,286
26,286
3.362
Median
4.787
Q3
6.698
0.114
-0.009
0.003
0.022
-0.049
0.034
-0.063
-0.040
-0.026
1.161
6.632
-0.766
0.130
0.102
-0.017
0.163
5.182
0.628
1.659
0.704
0.131
0.404
0.053
0.072
1.448
0.754
5.446
-1.178
0.016
-0.113
-0.019
0.117
4.303
1.084
6.531
-0.724
0.096
0.105
-0.003
0.150
5.112
1.467
7.674
-0.299
0.201
0.323
0.003
0.200
6.030
Is Market Timing a Firm
Characteristic?
• For each year from 1970 to 2002 (or 1997), we
estimate the following cross-sectional regression
MTCovi,[t ,t  N ]    1MTCovi,[0,t 1]  ei,[t ,t  N ]
• We try N = 5 and 10.
• β1 is positive and significant at 5% level or below
– in 27 (32) of the 33 years at 5% (10%) when N=5
– in 26 (28) of the 28 years at 1% ( 5%) when N=10.
Table 3
Dependent variable: R* - Rf
Predicted
Signs
OLS Regression
MTCov(1,t)
?
Beta
+
0.150***
(2.34)
LogMV
-
-0.315***
(-7.13)
LogBM
+
0.657***
(6.59)
Leverage
+
3.581***
(11.79)
Price Momentum (MMT)
-
-1.873***
(-11.98)
Forecast error (Ferr)
-
-11.493***
(-10.60)
Long-term earnings growth forecast (Fltg)
?
8.009***
(12.46)
Industry risk premium (IndRp)
+
0.421***
(8.46)
Year Fixed Effects
Adjusted R2
N
-0.293***
(-3.25)
Yes
0.412
26,286
Table 4
Predicted
Sign
MTCov
-
AQ×MTCov
-
AQ
-
Beta
+
LogMV
-
LogBM
+
Leverage
+
Price Momentum (MMT)
-
Forecast error (Ferr)
-
Long-term earnings growth forecast (Fltg)
+
Industry risk premium (IndRp)
+
Year Fixed Effects
Adjusted R2
N
Model 1
-0.617***
(-4.97)
-6.040***
(-3.33)
-5.071***
(-4.23)
0.098***
(2.71)
-0.267***
(-13.44)
0.716***
(14.12)
3.784***
(15.82)
-1.878***
(-35.43)
-10.980***
(-17.73)
7.610***
(15.15)
0.411***
(18.94)
Yes
0.419
26,286
Table 5
(I)
(II)
(III)
Predicted Sign
MTCov_Sent(1,t)
-
MTCov_Resid(1,t)
-
MTCov_Pred(1,t)
0
Beta
+
0.175***
(2.88)
0.145**
(2.22)
LogMV
-
-0.308***
(-7.13)
-0.302***
(-6.75)
-0.303***
(-15.23)
LogBM
+
0.652***
(6.72)
0.671***
(6.60)
0.680***
(13.10)
Leverage
+
3.768***
(11.91)
3.617***
(11.91)
3.667***
(14.95)
Price Momentum (MMT)
-
-1.893***
(-12.04)
-1.886***
(-12.00)
-1.882***
(-35.04)
Forecast error (Ferr)
-
-11.477***
(-10.59)
-11.425***
(-10.66)
-11.439***
(-18.21)
Long-term earnings growth forecast (Fltg)
?
8.117***
(12.27)
7.942***
(11.53)
7.859***
(15.58)
Industry risk premium (IndRp)
+
0.414***
(8.50)
0.422***
(8.59)
0.419***
(19.22)
Year Fixed Effects
Adjusted R2
N
-0.442***
(-3.63)
-0.223***
(-3.22)
-0.099
(-1.35)
0.146***
(3.86)
Yes
Yes
Yes
0.413
26,286
0.412
25,935
0.411
25,935
Robustness Tests
• Estimation of cost of equity
• Estimation of transparency
• Estimation of market timing activity
Table 6 – Panel C
Alternative measure of transparency
Coefficient estimates of TRAN*MTCOV
(t-statistic)
TRAN = Innate component of AQ
-7.219***
(-2.21)
TRAN = Discretionary component
of AQ
-4.844***
(-2.07)
TRAN = negative absolute value of
discretionary revenue
-3.985**
(-1.93)
TRAN = natural logarithm of number
of analyst following.
-0.308***
(-3.26)
Table 7
Coefficient Estimate
(t-statistic)
*
Dependent variable: R - Rf
Predicted sign
MTCov
-
PIN
?
MTCov×PIN
+
DedOwn
?
MTCov*DedOwn
-
-1.098***
(-4.77)
-2.557***
(-3.14)
4.722***
(3.94)
+
LogMV
LogBM
+
Leverage
+
Price Momentum (MMT)
Forecast error (Ferr)
Long-term earnings growth forecast (Fltg)
?
Industry risk premium (IndRp)
+
Adjusted R2
N
-0.166
(-1.54)
-0.290
(-0.55)
-1.752**
(-2.19)
Beta
Year Fixed Effects
Coefficient
Estimate
(t-statistic)
0.171**
(2.54)
-0.357***
(-7.41)
0.789***
(9.14)
2.496***
(9.79)
-1.866***
(-10.59)
-12.353***
(-10.15)
7.787***
(11.02)
0.387***
(7.25)
0.151**
(2.33)
-0.314***
-(7.26)
0.880***
(8.25)
2.433***
(10.68)
-1.869***
-(12.00)
-11.747***
-(10.89)
7.915***
(12.27)
0.424***
(8.52)
Yes
Yes
0.42
21,092
0.41
26,286
Conclusions
• Our results suggest that managers can reduce
the cost of equity by timing the market.
– Effects are both statistically and economically
significant.
– Robust to multiple specifications
• The effects are stronger for transparent
firms than for opaque firms.
Thank You