4. Debt-equity choice, growth options and future stock returns
4.2 Results from cross-sectional regressions
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growth options, our results are consistent with the findings by BCGW (2010). However, after excluding firms that are most likely to be affected by the confounding effect related to the investment in growth options, we find firms issuing more equity relative to debt tend to have lower future stock returns even after controlling for the level of net external financing.
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Table 1.6. Regression of year-head stock returns on equity ratio and net external financing
This table reports the regression of year-ahead stock returns on net external financing and equity ratio. The dependent variable is the 12-month buy-and-hold return (BHARs). We calculate the independent variables at the end of June of each year t and match them with BHARs from July of year t to June of year t + 1. NF is net external financing, defined as the net amount of cash from issuing and repurchasing debt and equity securities scaled by lagged assets. ER is the decile ranking for equity ratio, defined as the proportion of net equity to net cash raised. The decile ranking is transformed to a value between -0.5 to 0.5. POSNF is an indicator variable that takes the value of one for firms with positive net external financing. NEGNF is an indicator variable that takes the value of one firms with negative net external financing. Log(MV) is the logarithm of the market value of equity at the end of June of year t. Log(B/M) is the logarithm of the book-to-market ratio, defined as the book value of equity as of the fiscal year end that occur in calendar year t – 1 scaled by the market value of equity at the end of December of year t - 1. Growth is the change in assets scaled by lagged assets. ROA is operating income before depreciation scaled by lagged book assets. The models are estimated using the Fama MecBeth procedure. The standard errors are calculated with the time series of quarterly coefficients, with the autocorrelation in the quarterly coefficients adjusted using the method in Abarranel and Bernard (2000). The t statistics are reported in the parentheses. The adjusted R2 statistics are the mean adjusted R2 of the annual regressions.
Intercept
POSNF
×NF
NEGNF
×NF
POSNF
×ERdec
NEGNF
×ERdec POSNF Log(MV) Log(BM) MOM Growth ROA R&D
High R&D
Adjusted R2 (%)
(1) 0.2739 -0.1801 -0.0702 -0.0199 0.0202 -0.0233 -0.0072 0.0378 0.0300 5.15
(2.26) (-8.80) (-0.69) (-0.97) (1.65) (-3.67) (-1.00) (4.67) (1.76)
(2) 0.2766 -0.0156 -0.0098 -0.0112 0.0155 -0.0258 -0.0092 0.0392 0.0274 -0.0780 0.1650 5.67 (2.37) (-0.61) (-0.10) (-0.61) (1.33) (-4.30) (-1.38) (4.80) (1.70) (-8.59) (3.88)
(3) 0.2436 -0.2071 -0.1000 -0.0469 0.0214 -0.0220 -0.0055 0.0474 0.0296 0.4923 5.82
(2.11) (-8.38) (-1.02) (-3.55) (1.77) (-3.14) (-0.81) (7.87) (1.76) (3.23)
(4) 0.2543 -0.0211 -0.0260 -0.0403 0.0151 -0.0259 -0.0089 0.0501 0.0264 -0.0861 0.2281 0.5989 6.33 (2.26) (-0.79) (-0.28) (-3.10) (1.27) (-3.99) (-1.36) (7.71) (1.61) (-9.51) (6.45) (3.93)
(5) 0.2515 -0.0077 -0.0246 -0.0384 0.0124 -0.0266 -0.0084 0.0468 0.0259 -0.0805 0.1935 0.0855 6.31 (2.24) (-0.33) (-0.25) (-3.07) (1.02) (-4.43) (-1.30) (7.10) (1.62) (-9.21) (5.32) (3.09)
(6) 0.2495 -0.0709 -0.0157 -0.0505 0.0237 -0.0289 -0.0085 0.0445 0.0339 -0.0662 0.2414 5.46 (2.14) (-2.69) (-0.16) (-2.97) (2.09) (-4.16) (-1.22) (5.15) (2.01) (-7.91) (4.97)
(7) 0.2755 -0.1234 0.0456 -0.0433 0.0227 -0.0401 -0.0093 0.0464 0.0352 -0.0552 0.2329 5.38
(2.21) (-2.61) (0.60) (-1.98) (1.10) (-3.91) (-1.21) (5.47) (1.86) (-2.65) (4.22)
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growth-options effect. For all three models, the coefficients for POSNF × ERdec turn statistically significant. For example, in Model (4), the coefficient for POSNF × ER is -0.0469, which is 3.6 standard errors away from 0. Therefore, a hedge portfolio formed by longing the capital raising firms in the lowest equity ratio decile and shorting those in the highest decile earn 4.69% per year.
This is a conservative estimate of the market timing effect related to firms’ debt-equity choices because R&D spending does not fully control for investment in growth options.
In Models (3), (4) and (5), we assume that set missing R&D value to zero, assuming that firms with missing R&D spend zero or negligible amount on research and development15. To make sure that our results are not driven by this assumption, we estimate two additional models that do not explicitly use R&D as a control variable. In Model (6), we estimate Model (2) after excluding firms that are known to have R&D expenditure higher than 5% of lagged assets. In Model (7), we estimate Model (2) after excluding firms that are known to have R&D expenditure higher than 5% of lagged assets and those with missing R&D. In Model (6), the coefficient for the POSNF × ERdec variable is -0.0505, which is 2.97 standard errors away from zero. In Model (7), the coefficient for the POSNF × ERdec variable is -0.0433, with a t statistic of -1.98. The results in these two models provide further evidence that firms issuing more equity relative to debt earn lower year-ahead stock returns after partially controlling for the investment-in-growth- options effect in the models.
The R&D related variables are significantly positive in all models where they are present.
For example, the coefficient for the high R&D dummy in Model (5) is 0.0855, indicating that high R&D firms earn 8.55% more per annum than low R&D firms. However, in Model (4) of Table 1.3, the coefficients for R&D are not significant, providing no evidence that investors are
15By the SEC rule adopted in 1972, firms are required to report estimated amount of R&D when (a) it is material, (b) it exceeds 1% of sales, or (c) a policy or deferral or amortization of R&D expenses is pursued. If firms consider their R&D spending immaterial and indicate this, e.g., by reporting 0 R&D in 10K, Compustat will record 0. A Comustat record of “not available” could happen in three situations: (a) firms say nothing about R&D in 10K, (b) firms’ R&D information is randomly missing, or (c) firms report R&D, but Compustat concludes that their definitions of R&D do not conform (Griliches, 1984). Julio, Kim and Weisbach (2008) suggests that it is “typical in the previous literature” to set missing R&D to 0.
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systematically surprised by high R&D firms at the information rich earnings announcement events. Thus, the higher year-ahead returns on R&D are more likely to be the rationally expected components of stock returns than the unexpected components related to the surprises to investors.
This is consistent with the view that investors require higher return for holding the equities of high R&D firms (Berk, Green and Naik (2004) and Li (forthcoming)). More importantly, this explains why the earnings announcement test can detect evidence of market timing without including R&D as a control variable. Since the R&D related stock returns effects, which we use as a proxy for the investment-in-growth-options effect, are rationally expected, they are spread more smoothly over the year. However, the market timing effect is more concentrated during the earnings announcement periods. Therefore, relative to the investment-in-growth-options effect, the market timing effect is stronger during the earnings announcement days. In other words, the market timing effect related to the debt-equity composition of external financing is more easily detected at earnings announcements, but offset by the new growth options effect during other time of the year.