Transparency and the Pricing of Market Timing Xin Chang Nanyang Technological University Zhihong...

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

• 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.

tititfti eCMTCovRR ,],0[,1,*,

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 quasi-

rational 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

• AQ: a measure of accounting quality similar to Francis et al. (2005).

tititititfti eZAQAQxMTCovMTCovRR ,,],1[,],1[,,*,

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 StatisticsVariables N Mean Stdev Q1 Median Q3

R*-Rf 26,286 5.351 3.134 3.362 4.787 6.698

MTCov(1, t) 26,286 0.007 0.114 -0.009 0.003 0.022

AQ 26,286 -0.049 0.034 -0.063 -0.040 -0.026

Beta 26,286 1.161 0.628 0.754 1.084 1.467

LogMV 26,286 6.632 1.659 5.446 6.531 7.674

LogBM 26,286 -0.766 0.704 -1.178 -0.724 -0.299

Leverage 26,286 0.130 0.131 0.016 0.096 0.201

Price Momentum (MMT) 26,286 0.102 0.404 -0.113 0.105 0.323

Forecast error (Ferr) 26,286 -0.017 0.053 -0.019 -0.003 0.003

Long-term earnings growth forecast (Fltg) 26,286 0.163 0.072 0.117 0.150 0.200

Industry risk premium (IndRp) 26,286 5.182 1.448 4.303 5.112 6.030

Is Market Timing a Firm Characteristic?

• For each year from 1970 to 2002 (or 1997), we estimate the following cross-sectional regression

• 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.

],[,]1,0[,1],[, NttitiNtti eMTCovMTCov

Table 3Dependent variable: R* - Rf

Predicted Signs

OLS Regression

MTCov(1,t) ? -0.293***

(-3.25) 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 Yes

Adjusted R2 0.412

N 26,286

Table 4

Predicted Sign

Model 1

MTCov - -0.617*** (-4.97)

AQ×MTCov - -6.040*** (-3.33)

AQ - -5.071*** (-4.23) Beta + 0.098***

(2.71)

LogMV - -0.267***

(-13.44)

LogBM + 0.716***

(14.12)

Leverage + 3.784***

(15.82)

Price Momentum (MMT) - -1.878***

(-35.43)

Forecast error (Ferr) - -10.980***

(-17.73)

Long-term earnings growth forecast (Fltg) + 7.610***

(15.15)

Industry risk premium (IndRp) + 0.411***

(18.94) Year Fixed Effects Yes Adjusted R2 0.419 N 26,286

Table 5

Predicted Sign (I) (II) (III)

MTCov_Sent(1,t) - -0.442***

(-3.63) MTCov_Resid(1,t) - -0.223***

(-3.22) MTCov_Pred(1,t) 0 -0.099

(-1.35) Beta + 0.175*** 0.145** 0.146***

(2.88) (2.22) (3.86)

LogMV - -0.308*** -0.302*** -0.303***

(-7.13) (-6.75) (-15.23)

LogBM + 0.652*** 0.671*** 0.680***

(6.72) (6.60) (13.10)

Leverage + 3.768*** 3.617*** 3.667***

(11.91) (11.91) (14.95)

Price Momentum (MMT) - -1.893*** -1.886*** -1.882***

(-12.04) (-12.00) (-35.04)

Forecast error (Ferr) - -11.477*** -11.425*** -11.439***

(-10.59) (-10.66) (-18.21)

Long-term earnings growth forecast (Fltg) ? 8.117*** 7.942*** 7.859***

(12.27) (11.53) (15.58)

Industry risk premium (IndRp) + 0.414*** 0.422*** 0.419***

(8.50) (8.59) (19.22)

Year Fixed Effects Yes Yes Yes

Adjusted R2 0.413 0.412 0.411 N 26,286 25,935 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 Dependent variable: R* - Rf

Predicted sign

Coefficient Estimate (t-statistic)

Coefficient Estimate

(t-statistic)

MTCov - -1.098*** -0.166 (-4.77) (-1.54) PIN ? -2.557*** (-3.14) MTCov×PIN + 4.722*** (3.94) DedOwn ? -0.290 (-0.55) MTCov*DedOwn - -1.752** (-2.19) Beta 0.171** 0.151** + (2.54) (2.33) LogMV -0.357*** -0.314*** - (-7.41) -(7.26) LogBM 0.789*** 0.880*** + (9.14) (8.25) Leverage 2.496*** 2.433*** + (9.79) (10.68) Price Momentum (MMT) -1.866*** -1.869*** - (-10.59) -(12.00) Forecast error (Ferr) -12.353*** -11.747*** - (-10.15) -(10.89) Long-term earnings growth forecast (Fltg) 7.787*** 7.915*** ? (11.02) (12.27) Industry risk premium (IndRp) 0.387*** 0.424*** + (7.25) (8.52)

Year Fixed Effects Yes Yes

Adjusted R2 0.42 0.41

N 21,092 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