Diagnóstico

proach. After having defined the event as well as criteria for the classification of an observed incident as an event, the classical event-study approach involves three further steps: (i) esti- mation of “normal performance” during an estimation period used for parameter estimation, (ii) estimation of abnormal returns in the event period based on the parameter estimates ob- tained in the first step and (iii) performance of a statistical test against the null hypothesis (MacKinlay, 1997; Binder 1998; Johnston, 2007). Following MacKinlay (1997), Corrado (2011) and numerous other event studies, we estimate normal performance during a 250 day estimation period starting at day t = −265 (265 days prior to the event date) and ending at day t = −16 (16 days prior to the event date). Normal returns are estimated using a country- specific three factor model defined as follows:

Stock Market Reaction to Firm Withdrawal from State Sponsor of Terrorism Countries – Punishment for Foregone Business Opportunities or Reward for Ethical Behaviour?

(3) Rit= αi+ β1iRmt+ β2iSMBt+ β3iHMLt+ εit,

where Rit is the return on security i at time t, Rmtis the return on the country specific mar- ket portfolio (DataStream total market index) SMBt is the return on a country specific zero- investment portfolio long in small stocks and short in large stocks and HMLtis the return on a country-specific zero-investment portfolio long in high book-to-market stocks and short in low book-to-market stocks.

As depicted in Error! Reference source not found. and discussed in the data section, the distribution of events across industries leans towards the oil and gas industry. In order to ac- commodate this, we could include industry portfolio returns as controls into regression equa- tion (3). MacKinlay (1997) also mentions this possibility and deems the inclusion of appro- priate control factors advisable. This procedure would be similar to the one tested in Thomp- son (1988). Albeit its theoretical merits, Thompson (1988, p. 80) concludes that “(…) it doesn’t seem to matter which model is used” when comparing models with and without in- dustry factors. Building on this finding, we do not expect the inclusion of an industry factor to qualitatively improve our results. Nevertheless, we will check results obtained from esti- mating normal performance with the three-factor model as shown in regression (3) for ro- bustness with the help of a model augmented by an industry factor.

In a second step, based on the parameter estimates obtained in the estimation period, ab- normal returns are calculated. They are defined as the disturbance term of the factor model in event-time and are calculated as follows:

(4) ARiτ= Riτ– (αi+ β1iRmτ+ β2iSMBτ+ β3iHMLτ),

where τ denotes any day in the event window and αias well as βki(k = 1, 2, 3) are the pa- rameter estimates obtained from estimating regression (3). In a third step, we perform two statistical tests against the null hypothesis of zero abnormal return during the event window.

Stock Market Reaction to Firm Withdrawal from State Sponsor of Terrorism Countries – Punishment for Foregone Business Opportunities or Reward for Ethical Behaviour?

lined in Brown and Warner (1985). This test is frequently applied in event-studies and has, as Corrado (2011, p. 218) puts it, “come to eponymously define the genre”. Although the meth- od is still widely used (see e.g. Bargeron et al., 2008; Doidge et al., 2010 or Arena et al., 2011), it comes with two disadvantages. First, due to the use of non-standardised abnormal returns, securities with large variances are likely to dominate the test. Although Brown and Warner (1980, 1985) declare the use of standardised abnormal returns to be principally supe- rior, they conclude that standardisation makes little difference if event windows are suffi- ciently narrow. Since we aim to test event windows of up to thirteen days, standardisation is crucial in our case. Second, event-induced variance can bias test results towards rejecting the null. Brown and Warner (1985, p. 27) are well aware of this caveat, but do not conclusively discuss the issue, stating that “further research is necessary to fully understand the properties of alternative procedures for measuring abnormal performance in such situations”.

These two shortcomings of the test statistic introduced by Brown and Warner (1985), henceforth called the BW test, are accommodated with the introduction of an alternative test statistic advanced by Boehmer et al. (1991). The authors call this test statistic the “standard- ised cross-sectional method.” Henceforth, it will be called the BMP test. While this test statis- tic weighs each abnormal return by the inverse of its variance in the estimation period, the nominator standardises residuals. The test statistic is therefore less exposed to large variance securities. The denominator uses the cross-sectional standard deviation of the standardised abnormal returns during event-time and thus accounts for a potential increase in variance during event-time. Due to these advantages of the BMP test, we decide in favour of this test statistic among the many alternative parametric tests.

Albeit these merits, the BMP test shares one critical assumption with the BW test: the as- sumption of normally distributed returns. While Brown and Warner (1985, p. 25) ascertain that “the non-normality of daily returns has no obvious impact on event study methodolo-

Stock Market Reaction to Firm Withdrawal from State Sponsor of Terrorism Countries – Punishment for Foregone Business Opportunities or Reward for Ethical Behaviour?

gies”, Campbell and Wasley (1993) found standard parametrical event study procedures to be poorly specified. The analysis of NASDAQ returns seemed to pose problems unknown from NYSE data. Since return data from non-US exchanges is assumed to be equally difficult in the sense that parametric tests might be poorly specified, we make additional use of the Cor- rado (1989) rank test. MacKinlay (1997) also proposes this procedure, stating that it was worthwhile considering a non-parametric test in order to check the robustness of conclusions based on parametric results. We calculate the test statistic as proposed in Corrado (1989) adopted for a multiday event window according to Cowan (1992) and Campbell et al. (2010). Each security’s abnormal return is transformed into its respective rank among the total num- ber of abnormal returns in the combined estimation- and event period. Thus, the test is non- parametric and does therefore not rely on the assumption of normally distributed returns.

Methodologically, our approach is superior to the ones used in the three initial studies on withdrawal from South Africa. Neither Meznar et al. (1994), nor Wright and Ferris (1997), nor Posnikoff (1997) combine a parametrical test with a non-parametrical one. These anal- yses are therefore much more prone to committing a type I error (i.e. to rejecting a true null hypothesis) than ours. Possibly, this lack of methodological diversity has caused results of the studies to be incommensurable in the sense that they yielded conflicting results and interpre- tations.