Adjusted intercepts

Mitchell and Stafford (2000) claim that adjusting for risk using the Fama and French (1993) and the Carhart (1997) models may not be satisfactory. The argument is that these factor models cannot completely explain the cross-section of expected returns. For instance, Fama and French (1993) point out that three of the 25 portfolios formed based on size and book-to-market are associated with abnormal return estimates that are significantly different from zero. These portfolios are comprised of low book-to-market and small size firms, which is precisely the case of my event firms. In addition, Fama and French (1996) document a momentum bias for the three-factor model of equation (4.11). This evidence suggests that, in the worse case scenario, the estimate of the intercept under the null hypothesis of no abnormal performance may be biased when risk is adjusted by using the two factor models mentioned above.

Drawing on Boehme and Sorescu (2002) and Ikenberry and Ramnath (2002), I try to overcome this problem by estimating adjusted intercepts that are derived using an arbitrage portfolio that is long in the stock of bankrupt companies, and short in that of the control firms used earlier in this chapter. To be precise, I implement the following regressions:

(

)

, ,

ˆ

, p t control t p p t t p t p t p t

r

−r

=a

+b

rm

−rf

+s SMB

+h HML

+ε

(4.14)

(

)

, ,

ˆ

, p t control t p p t t p t p t p t p t

r

−r

=a

+b

rm

−rf

+s SMB

+h HML

+u UMD

+ε

(4.15)

where the parameters and variables of equation (4.14) have the same meaning as in equation (4.11), with the same applying to equations (4.15) and (4.13). The comparison between these two paired sets of equations shows that the main difference in this second estimation procedure is that the excess returns of the calendar-time portfolio are now calculated using the returns of a carefully selected control sample,

r

_{control t,} , and not the risk-free rate. In practice, I use the returns of the control samples based on size and book-to-market and size and momentum to estimate the values of the adjusted intercepts (

aˆ

_p). Such intercepts represent a measure of the

medium-term abnormal return performance that specifically accounts for the size and book-to- market (size and momentum) bias inherent to the traditional factor models.

4.4.3 Results71

Table 4.10 summarizes my results. Panel A reports what happens when the unadjusted Fama and French (1993) and Carhart (1997) models are used as benchmark. I find that irrespective of the holding period, all intercepts are negative and statistical significant at normal levels. This is in line with the BHAR evidence discussed above, indicating that a post-bankruptcy announcement drift is in place. For the one-year horizon and depending on the factor model, panel A shows an abnormal performance ranging from -3.37 to -2.69 percent per month. These monthly estimates imply a yearly underperformance between -39.9 and -32.2 percent, which is considerable higher than the point estimate of -24 percent obtained with the size and book-to- market risk-adjusted BHARs for the corresponding period (see panel C, table 3.3). As Ikenberry and Ramnath (2002) point out, these two approaches differ in several ways and differences are to be expected. However, as argued in section 4.4.2.2, the acute disparity in results may simply be due to a misspecification problem.

Panels B and C of table 4.10 show the results for the adjusted intercept technique. In this case, the point estimates for the intercepts are again negative and statistically significant at normal levels across the different holding periods. It is important to emphasize that, under this alternative method, the size and momentum adjusted results tend to be weaker than their size and book-to-market counterparts. For instance, the one-year post-event abnormal performance estimated using the Carhart (1997) model and the size and momentum adjustment is -1.71 percent, whereas its size and book-to-market equivalent is -2.52 percent. The former result implies a 12-month underperformance of -20.5 percent, which is significantly lower than the - 31.2 percent implied by the later. This suggests that failure to control for the momentum effect may result in incorrect estimates of the market’s post-event reaction to bankruptcy announcements.

Panels B and C of table 4.10 also favour the conclusion that the unadjusted intercepts reported on panel A of the same table are likely overestimating the true magnitude of the post-

71 _{I do not report the results for the alternative 4-month holding period because they are not reliable. In fact, in this} case, I have only 56 months to work with.

bankruptcy drift. In effect, when the adjusted intercept method is employed to compute the calendar-time results, the estimates of the market reaction to the announcement of bankruptcy are much closer to those obtained with the use of BHARs.

Overall, it seems safe to conclude that the post-bankruptcy drift uncovered in chapter 3 is robust to this alternative method for conducting longer-term event studies. Yet, a word of caution is now in order. This section started by emphasizing the idea that BHARs may fail to produce accurate estimates of the long-term abnormal performance, which justified the need to complement my initial analysis with the calendar-time portfolio approach. However, a number of scholars have also pointed out that this alternative is not without its own pitfalls. For instance, Lyon, Barber and Tsai (1999) show that the calendar-time method is generally incorrectly specified in non-random samples. Additionally, Barber and Lyon (1997) demonstrate that the arithmetic summation of returns (as it is done with calendar-time returns) does not precisely measure investors’ experience. More importantly, Loughran and Ritter (2000) argue that this approach has low power to detect abnormal performance. All in all, these critiques point to a simple conclusion: although presenting some potential advantages over the traditional BHARs, the calendar-time method does not guarantee the accuracy of results.

In addition, it should be noted that I have a limited number of months to work with when using the calendar-time portfolio technique. To be precise, for the 12-month holding period, I have a total of 204 months; for the 6-month holding period I have only 108 months available. Accordingly, the results presented in this section should be read with this caveat in mind.

Table 4.10

Calendar-time portfolio approach

Panel A - unadjusted intercepts: This panel reports abnormal stock returns for calendar-time portfolios formed using a sample of 351 non-finance, non-utility firms listed on the NYSE, AMEX or NASDAQ that filed for Chapter 11 between 01.10.1979 and 17.10.2005 and that remained listed on a major US stock exchange after their bankruptcy date. Firms are added to the portfolio at the end of the month following the Chapter 11 announcement and are held for 6 or 12 months. Portfolio returns are computed assuming an equally weighted investment strategy. Months where the portfolio holds less then ten stocks are deleted. Abnormal returns are determined using the Fama and French (1993) and the Carhart (1997) factor models, which are estimated using OLS. The regression intercept provides an estimate of monthly abnormal performance. Heteroskedasticy robust t-statistics are reported. N indicates the number of observations (months) included in the OLS estimation procedure.

Three-factors Four-factors Three-factors Four-factors

Intercept -0.0616 -0.0531 -0.0337 -0.0269 -5.37*** -4.08*** -4.21*** -2.70* b 1.0756 0.9422 1.0612 0.9716 4.10*** 3.53*** 5.55*** 4.75*** s 2.2973 2.4418 1.9375 2.0126 3.69*** 3.84*** 3.96*** 4.16*** h 1.0897 0.9712 1.0678 0.9200 1.70 1.50 2.27* 1.87 u - -0.8979 - -0.7035 - -1.95 - -1.71 N 108 108 204 204 Adj R2 _{0.1394 0.1672 0.1845 0.2156}

6-months holding period 12-months holding period

Table 4.10 (cont.): Calendar-time portfolio approach

Panel B – size and book-to-market adjusted intercepts: This panel reports abnormal stock returns for calendar-time portfolios formed using a sample of 351 non- finance, non-utility firms listed on the NYSE, AMEX or NASDAQ that filed for Chapter 11 between 01.10.1979 and 17.10.2005 and that remained listed on a major US stock exchange after their bankruptcy date. Firms are added to the portfolio at the end of the month following the Chapter 11 announcement and are held for 6 or 12 months. Portfolio returns are computed assuming an equally weighted investment strategy. Months where the portfolio holds less then ten stocks are deleted. Abnormal returns are determined using the Fama and French (1993) and the Carhart (1997) factor models, with the excess returns being adjusted with a control sample based on size and book-to- market. Specifically, for each sample company, I identify all CRPS firms with a market capitalization between 70 and 130 percent of its equity market value. The respective control firm is then selected as that firm with book-to-market closest to that of the sample firm. The models’ parameters are estimated using OLS. The adjusted regression intercept provides an estimate of monthly abnormal performance. Heteroskedasticidy robust t-statistics are reported. N indicates the number of observations (months) included in the OLS estimation procedure.

Three-factors Four-factors Three-factors Four-factors

Intercept -0.0548 -0.0501 -0.0280 -0.0252 -4.77*** -3.85*** -3.53*** -2.52* b 0.1632 0.0910 -0.0424 -0.0798 0.60 0.34 -0.22 -0.39 s 1.5190 1.5973 1.0417 1.0730 2.45* 2.48* 2.35* 2.34* h 0.6331 0.5689 0.3831 0.3215 0.98 0.89 0.87 0.70 u - -0.4864 - -0.2936 - -1.03 - -0.73 N 108 108 204 204 Adj R2 0.0408 0.0477 0.0335 0.0376

6-months holding period 12-months holding period

Table 4.10 (cont.): Calendar-time portfolio approach

Panel C: size and momentum adjusted intercepts: This panel reports abnormal stock returns for calendar-time portfolios formed using a sample of 351 non-finance, non- utility firms listed on the NYSE, AMEX or NASDAQ that filed for Chapter 11 between 01.10.1979 and 17.10.2005 and that remained listed on a major US stock exchange after their bankruptcy date. Firms are added to the portfolio at the end of the month following the Chapter 11 announcement and are held for 6 or 12 months. Portfolio returns are computed assuming an equally weighted investment strategy. Months where the portfolio holds less then ten stocks are excluded. Abnormal returns are determined using the Fama and French (1993) and the Carhart (1997) factor models, with the excess returns being adjusted with a control sample based on size and momentum. Specifically, for each sample company, I identify all CRPS firms with a market capitalization between 70 and 130 percent of its equity market value. The respective control firm is then selected as that firm with momentum closest to that of the sample firm. The models’ parameters are estimated using OLS. The adjusted regression intercept provides an estimate of monthly abnormal performance. Heteroskedasticidy robust t-statistics are reported. N indicates the number of observations (months) included in the OLS estimation procedure.

Three-factors Four-factors Three-factors Four-factors

Intercept -0.0388 -0.0343 -0.0171 -0.0171 -3.24** -2.38* -2.02$ -2.61* b 0.0398 -0.0219 -0.0766 -0.0768 0.14 -0.07 -0.39 -0.37 s 1.3612 1.4265 1.1330 1.1332 2.01$ 2.03$ 2.22$ 2.14$ h 1.5835 1.5313 1.0688 1.0685 2.47* 2.36* 2.11$ 2.00$ u - -0.4138 - -0.0013 - -0.74 - 0.11 N 108 108 204 204 Adj R2 0.0478 0.0519 0.0497 0.0469

6-months holding period 12-months holding period

4.5 Summary and limitations

This chapter explores to what extent the post-bankruptcy announcement drift documented in chapter 3 is robust to a number of other potential explanations already documented in the literature. The evidence points to a clear conclusion: the anomaly does not disappear after controlling for known confounding problems like the post-earnings announcement drift, the post-GCM drift, the book-to-market effect, industry clustering or the level of financial distress. Importantly, I find essentially the same results even after considering a range of alternative methodological combinations for undertaking a medium-term event study.

Early literature cautions about the dangers of putting market efficiency to the test. In fact, Fama (1970, 1991) strongly emphasizes that such line of research will always be clouded by the joint-hypothesis problem. In the particular case of my research, apprehension regarding how to calculate and make inferences about medium-term abnormal returns adds to this concern. As Kothari and Warner (2007) point out, the bottom line is that no correct method exists for conducting long-horizon event studies yet.72 _{Hence, the best practice is to use different} methodologies and verify the degree of stability across results. This is done here and, albeit some evidence suggesting that a momentum factor is present and that the anomaly is more pronounced for smaller, low-price firms, the overall results are very consistent. Accordingly, and even with the above-mentioned caveats in mind, I argue that there is enough evidence to conclude that the US equity market fails to appropriately react to news contained in Chapter 11 bankruptcy announcements.

72 _{Over the last few years a number of authors have tried to develop new methods that overcome the known} econometric problems with long-horizon event studies. See, for example, the paper by Jegadeesh and Karceski (forthcoming).

In document Market reaction to bad news: The case of bankruptcy filings. Luis Coelho (Page 108-117)