DEPARTMENT OF ECONOMICS. Effect of oil price changes on the price of Russian and Chinese oil shares

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1 DEPARTMENT OF ECONOMICS Effect of ol prce changes on the prce of Russan and Chnese ol shares Stephen G. Hall, Unversy of Lecester, UK Amangeld Kenjegalev, Unversy of Lecester, UK Workng Paper No. 09/4 September 009

2 UNIVERSITY OF LEICESTER Effect of ol prce changes on the prce of Russan and Chnese ol shares Stephen G. Hall and Amangeld Kenjegalev

3 Effect of ol prce changes on the prce of Russan and Chnese ol shares Stephen G. Hall * Unversy of Lecester, Natonal Instute of Economc and Socal Research Amangeld Kenjegalev Unversy of Lecester 7/4/009 Abstract Do changes of ol prces have an effect on the stocks of ol companes n emergng markets? Do the shares of ol companes of emergng markets react to the prce news n a smlar way as those of the Western companes? Ths paper ams to answer these questons utlsng varous event study technques. As expected, the results of both parametrc and nonparametrc tests suggest that the fluctuatons of ol prce have an effect on the stock prces. However, an nterestng result s that the responses of stocks of Chnese and Russan ol companes are consderably dfferent from the shares of ther Western counterparts. Keywords: Event studes, abnormal return, parametrc t-test, non-parametrc rank test, ol companes. * Department of Economcs, Unversy of Lecester, Unversy Road, Lecester, LE 7RH, UK. Tel.: +44 (0)6 5 87, fax: +44 (0) , e-mal: s.g.hall@le.ac.uk (Correspondng author), Department of Economcs, Unversy of Lecester, Unversy Road, Lecester, LE 7RH, UK. e-mal: aak0@le.ac.uk

4 Introducton Do changes of ol prces have an effect on the stocks of major emergng economy ol companes? The answer to ths queston seems rather obvous. Yes, there must be a connecton between the ol prce changes and stock returns of ol companes shares. However, do the shares of ol companes of emergng markets react to the prce news n a smlar way as Western companes? Ths study ntends to provde answers for both questons wh partcular emphass on the latter utlsng event study methodology. The event study methodology nvestgates the mpact of news on secury prces. Dependng on the type of nformaton, announcements ncrease or decrease the value of stock on the market. It nvolves estmatng the normal return for a secury and calculatng the drecton and sze of the excess return attrbutable to unantcpated nformaton. Econometrc technques used n event studes are provded by Ball and Tourus (988), Bartholdy et al (007), Brown and Warner (980, 985), Campbell et al (997), Corrado (989), Corrado and Zvney (99), Dyckman et al (984), Jan (986), Krzman (994) and McWllams and Segel (997). The theory s stll growng and the number of economsts researchng n ths area s ncreasng, along wh the sophstcaton of studes. However, despe ths development, to the best of our knowledge, there are no emprcal event study nvestgatons on ol companes of Western and emergng markets and ol prces. Our paper ams to fll ths gap. The analyss s thus performed on twelve major ol companes. The companes represent the four key markets: (a) those n well establshed western economes such as the USA and Europe; and (b) those n emergng economes such as Russa and Chna. The remander of ths artcle s organsed as follows. Secton presents the desgn for the dervaton of abnormal returns and cumulatve abnormal returns. In ths Secton, two tests are presented: the parametrc t-test and the nonparametrc rank test. The results of ths research and an explanaton are gven n Secton 3. Fnally, Secton 4 concludes. 3

5 Theoretcal Backgrounds There are a large number of models that can be used to nvestgate event studes. These models consst of dentfyng the event and testng for excess prof. Tests are constructed n such a way that they detect abnormal performance. There are two broad approaches n conductng the tests: parametrc and non-parametrc. The former approach s usually based on a standard t-test. In the case of event studes, standard t- tests check whether the resduals are statstcally dfferent from the normal student t- dstrbuton. The numerator of the t-test represents the abnormal returns for a partcular date whle the denomnator scales the top part by the level of dsperson or the standard devaton of a gven tme seres. The parametrc test used n the paper s based on a tradonal t-test (Brown and Warner, 980, 985). Ths test s conducted for cumulatve abnormal return (MacKnley, 997) of ndvdual companes. In addon, a hypothetcal portfolo consstng of ol companes has been extensvely analyzed. The parametrc test deals drectly wh the resduals. However, the non-parametrc test has a dfferent approach to detect abnormal performances. Despe that, the test has a parametrc dstrbuton does not assume that the returns have a normal dstrbuton. For example, the nonparametrc sgn test, unlke the standard t-test, does not check the abnormal returns but the respectve sgns of ther resduals. The probably of a negatve and a posve sgn n ths test s assumed to be equal. Another approach to testng the presence or lack thereof of abnormal performances of stocks s the nonparametrc rank test. Ths test ranks the resduals n the tme seres and tests the rank of the return for abnormal performance. To measure the abnormal return, we use two statstcal models. These are the constant mean model and the market model. As prevously mentoned, despe ther relatve smplcy, these models can be effectvely used n the event studes methodology. These two models are presented below. R = µ + ε 4

6 E( ε ) = 0 and Var ( ε ) = σ where R represents the returns of secury at tme t, µ s the mean return for asset and ε s the error term for secury at tme t wh mean equal to zero and varance, σ. The second model utlsed n ths artcle s that of the market model. Ths model s a statstcal model relatng secury returns to market movements and s popular due to s ably to capture market movement n secury returns. R = α + β R + ε mt E( ε ) = 0 and Var ( ε ) = σ where R and R mt are secury and market returns at tme t, respectvely. α and are the coeffcents of the market model. Followng MacKnley, (997), the Standard & Poor s Index s used. β To detect the abnormal return, one needs to defne the event tme t,. In ths study, the event date s defned, as t = 0, the event wndow s represented by t = T + to t = T and, fnally, t = T + 0 to t = T s the estmaton wndow. The length of the event wndow and the estmaton wndow s gven by the followng equatons: L = T T and L = T T0, respectvely. The post event wndow s t = T + to t = T3.The length of the post event wndow s L3 = T3 T. Ths can be shown by the followng fgure: The dervaton of an estmaton perod.e., estmaton wndow, the event wndow and the post event wndow, s taken from MacKnley (997, p. 9). 5

7 The estmaton of the parameters of the market model (MacKnley, 997) for the th frm at the event tme nvolves dentfyng the beta, alpha and varance of the model. Computaton of the abnormal return n the case of the constant mean model s gven by the followng equaton AR = µ 3 R where AR s the abnormal return of the th secury at tme t, secury at tme t and µ s the mean return of the analysed secury. R s the return of the th Usng the parameters obtaned by OLS from the market model, s possble to estmate the abnormal return. Resduals n such cases represent excess returns. The expected return accordng to Bartholdy et al s gven by the followng equaton E( R ) = µ = ˆ α ˆ β R 4 mt The dscrepancy between the actual return and the expected return, adjusted for market movements over the analysed perod, s the abnormal return. AR = R ˆ α ˆ β R 5 mt In the above, the AR s the abnormal return or, n other words, the resdual for secury at tme t. R s the return for secury a tme t, R mt s the market return 6

8 and α and β are parameters of the OLS regresson. The abnormal return s thus the surprse term of the market model. In both cases, the constant mean model and the market model, the test statstc reles on the assumpton of a normal dstrbuton for returns. Under the null hypothess the dstrbuton of the abnormal returns s gven by: AR ~ N(0, σ ( )) 6 AR The null hypothess, H 0, tests that the event does not have an mpact on the abnormal return. To detect that the event has an mpact on the abnormal performance n the case of the secures portfolo, the excess returns of secures have to be aggregated. Aggregaton for abnormal returns of the secures can be made through secures and then through tme (MacKnlay, 997). In ths paper, the aggregaton s made through the secures at the same event dates. Dong ths, one obtans the average abnormal return across secures. Ths aggregaton s gven by the followng: AR = N N AR = 7 And the varance of the aggregated abnormal returns n ths case s: Var N ( AR) = ε N = σ 8 After calculatng the abnormal return for the secures, one can aggregate over the event wndow to fnd the overall mpact of the event. Cumulatng the abnormal returns through the event tme s needed n order to detect the excess returns. The cumulatve abnormal return (CAR) s the sum of the secures abnormal returns. In order to fnd the CAR, the followng equaton s used: 7

9 CAR t, t ) 9 ( t = AR t= t where T < t t T. The varance of the CAR s: ( t, t ) = ( t t ) σ ε σ + 0 It s assumed that the dstrbuton of the cumulatve abnormal return under the null hypothess s: CAR ( t, t ) ~ N(0, σ ( t, t )) The aggregaton of cumulatve abnormal return can be also done across secures and has smlar stages: t = CAR t, t ) AR t ( t= t The varance for above equaton n such condon s: t = Var CAR( t, t )) var( AR t ) 3 ( t= t The cumulatve abnormal return for the portfolo of secures s: N CAR( t, t ) = AR 4 N = and varance s: 8

10 Var( CAR( t 5 N, t )) = var( AR ) N = Testng for the sgnfcance of the abnormal returns can be performed along two broad avenues: parametrc and nonparametrc. The parametrc tests rely on the assumpton of normal dstrbuton. Lehman, (Hggns, 998) states that the t-test s optmal when the dstrbuton of the returns s normal. Thus, assumng that the resduals are normally dstrbuted, s possble to conduct a t-test such that under the null hypothess, the test statstc shows no abnormal return,.e., AR I = 6 var( AR ) where AR s the average abnormal return at a partcular event and var( AR ) s the varance of the abnormal return at ths event date. In addon, usng the cumulatve abnormal return and the varance of the cumulatve return, one can test for the null hypothess. CAR( t, t ) I CAR = ~ N(0, ) 7 var( CAR( t, t )) The prmary dsadvantage of parametrc tests s that they heavly rely on partcular dstrbutonal assumptons. Hence many studes (Bartholdy et al, 007; Brown and Warner, 985; Corrado, 989; Corrado and Zvney, 99; Cowan, 99; Cowan and Sergeant, 996) have utlzed nonparametrc tests. We use the nonparametrc rank test specfed n Corrado (989). Corrado employ a nonparametrc rank test for excess performance. Ths test has smlares wh the standard t-test. However, as opposed to the standard t-test, the rank of the abnormal return s used. 9

11 Consder a sample of observatons of abnormal returns n the event wndow for each of N frms. As stated above, to apply the nonparametrc rank test, the rank of the abnormal returns for the analysed perod for each secury s needed. The hghest rank s gven to the hghest prce of the secury for the analysed perod and vce versa for the lowest rank (Lehman, 975). The rank test transforms the secures excess returns nto a unform dstrbuton across ranks. Thus, n the case of the nonparametrc rank test, one should convert the gven tme seres nto s respectve ranks. Denotng K as the rank of the excess return, AR of the th frm and wh an estmaton wndow of 6 days, the event wndow comprsng 4 days, the followng defnon holds: K = rank( AR ), t = 46,..., The frst 6 days are used as the estmaton wndow and 0 days on each sde of day 0 as the event wndow. Corrado (989) reports that the average rank s obtaned by dvdng the number of observed returns by two. Thus, n ths case the average rank s The test statstc for the null hypothess s gven by the followng equaton: I = N N = ( K 83.5) SD( K) 9 where, ( K 83.5) s a proxy for the abnormal return (Corrado,989). The standard devaton of the rank test s determned by usng a sample perod of 67 days and follows as: + 0 N SD ( K) = ( K 83.5) 0 67 t= 46 N = Tests of the null hypothess can be mplemented usng the result that the asymptotc null dstrbuton s standard normal. The man feature of rank test conssts of rankng each observaton n order to brng them nto a unform dstrbuton. The rest of the procedure of the test does not consderably dffer from the t-test statstcs. 0

12 Emprcal Data and Results Ths artcle focuses on the twelve ol companes. These are companes of the USA - Chevron and Exxon Mobl; Western Europe - Brsh Petroleum, En, Total and Royal Dutch Shell; Russa - GazProm, LukOl and SurgutNefteGaz; and Chna - Chna Petroleum and Chemcals, Petrochna and Snopek. The tme seres s taken from DataStream. The ol prces are represented by crude ol prces whch are extracted from the Energy Informaton Admnstraton ( The tme seres of consst of the daly data for the perod spannng from the st January, 000 to the 8 th March, 008. The frst analysed event date starts n 00. The events are dvded nto types of news: good news and bad news. The followng creron was used to defne the news and s type. If the prce of ol on a partcular day was greater by 4.5% than the prce of the prevous day, then the news s classfed as good news. If, on the other hand, the day s ol prce s less than the prevous day s prce by 4.5% then the news s classfed as bad news. In addon, s nsured that there are at least 30 days between the consdered events (see Fgure ). The next analyss conssts of smulatng the random event dates. To do ths, we follow Brown and Warner (985). The dfference from Brown and Warner s experment s, however, that we do not ntroduce abnormal returns. The smulaton comprses of randomly generated events for each type of news. We defned them as news one and news two, respectvely. Thus on average, all two types of randomly generated news should reflect the absence of abnormal returns.

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14 Two models of abnormal return are used,.e., the constant mean model and the market model. In the frst part, the cumulatve abnormal return s analysed based on the parametrc t-test. In the second part, the abnormal return are analysed usng the nonparametrc rank test. In the last part the results for the random events are presented. Accordng to the t-test derved by constant mean model on the date zero there s no sgnfcant abnormal return for the good news. The abnormal return for that date s CAR 0. and varance s equal to The value of I s low, 034. However, at the event date the sgn of the cumulatve abnormal return changes from negatve to posve. In addon, the level of sgnfcance on the 5 th, 4 th and 3 th days pror the event accordng to I CAR s -3.0, -4.3 and -3.5 respectvely. The next sgnfcant negatve abnormal return s occurred a week before the event date wh -.7 for that date. CAR I equal to For the case of the bad news, the t-test does not show sgnfcance of abnormal return. Despe ths, one day before the event abnormal return was 0.3 whle at the CAR event date the level of cumulatve abnormal return fell to The I for the day before the event s.7. Statstcally sgnfcant abnormal return s observed from dates -8 and - wh statstcal sgnfcance rangng from 0.5% to 5%. The hghest level of cumulatve abnormal return s observed on the 4 th day before the event wh CAR I beng equal to 3.6. After the event date the paths for cumulatve abnormal return both for the bad news and the good news are strkngly smlar. 3

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16 Fgure 3 and Fgure 4 show that the constant mean and the market models show smlar trends. Moreover, the market model results are not markedly dfferent from those of constant mean model. It also shows the hgh level of abnormal return on the 4 th day pror the event for the bad news. The value of the t-test s -4.. The dssmlary n the cumulatve abnormal return can be attrbuted to the market movement. In other words, the dvergence s potentally caused by the changes n market preferences of nvestors after the sharp ncrease or decrease n ol prces. Accordng to the t-test, computed by the constant mean model, averaged abnormal return for ndvdual companes s nsgnfcant for the event date. The CAR I for the good news at the event date for the stocks of Brsh Petroleum, Chevron, Exxon Mobl, Total, En and Royal Dutch Shell are 0.7, 0.09, 0.75, -0.4, 0.63 and and the values of abnormal returns for that day are 0.4, 0.8, 0.8, 0.4, 0.9 and 0.4 respectvely. Despe that the t-test shows an exstence of abnormal returns two weeks before the event for ndvdual companes, s not sgnfcant. However, the value of the t-test s hgher for the 5 th, 4 th and 3 th days pror the event than those of other dates. The hghest but stll nsgnfcant level of the t-test s observed at the date -4. For the shares of Brsh Petroleum the value of the t-test s -.5, for Chevron s stock the t-test s -.4, for the secures of Exxon Mobl the t-test s equal to -.. The t-test for the stocks of Total s -.6, for the shares of En -.0 and -. for the stocks of Royal Dutch Shell. The sgn of the cumulated abnormal return for the most of the Western companes s negatve before the event. However, after the event the cumulatve return s posve. Exceptons are Total and Royal Dutch Shell. The secures of these two companes have negatve sgn all throughout perod. The t-test calculated by the market model at the event date s lower than the t-test computed by the constant mean model. The t-test for the stock s of Brsh Petroleum s -., for the share s of Chevron t-test s equal to -.6 and for secury s of Exxon Mobl s -.3. The values of the t-test for the stocks of the companes from the Contnental Europe at ths date are as follows: -.5, -.38 and -.77 for Total, En and Royal Dutch Shell secures, respectvely. The sgns of the cumulatve abnormal returns are roughly smlar to the ones obtaned by the constant mean model wh sporadc negatve sgns after the event date. 5

17 For the case of the bad news, the t-test does not show sgnfcance of abnormal return after the event date. The t-test ndcates statstcally sgnfcant posve abnormal returns for Total, En and Royal Dutch Shell s stocks two weeks before the event. The values of the t-test are.89,.4 and.70, respectvely. However, the market model does not report sgnfcant results for ths date. After the event the sgns of the cumulatve abnormal returns change to the negatve, although the tmng of ths change s dfferent for dfferent stocks. For Chnese s and Russan companes the t-test shows some surprsng outcomes whch are dffcult to explan by the changes n the ol prces. For example, Snopek s stocks have sgnfcant abnormal returns both for the good news and bad news. However, the cumulated abnormal return of good news s negatve, whle for the bad news the sgn of cumulated abnormal returns s posve. Accordng to the t- test, for the rest of the stocks the event date does not have any mpact. We conducted the second test, rank test, whch s nonparametrc n nature and does not rely on the assumpton of the normaly of the tme seres. Examnaton of the cumulatve abnormal return showed that the plot of the proxy of cumulatve abnormal return n case of the constant mean model has a smlar movement smlar to the actual abnormal returns for portfolo of companes. However, the test was conducted whout cumulatng proxy of the abnormal return both for constant mean model and market model. The proxy for the abnormal return for the event date s equal to and the varance s 6.6 for the good news. The rank test for the day zero (event day) s 3.5. The bad news show that the cumulatve abnormal return s -5.9 and the standard devaton s 7.4. The rank test n case of bad news s -9.. Another sgnfcant negatve abnormal return s observed 9 days before the event. The rank test for that day s -6.. Interestngly, bad news also ncludes posve abnormal returns. Sgnfcant posve abnormal returns are observed on the 5 th and 8 th days before the event. The abnormal returns for these days are 36. and 36.4 for date -5 and -8 respectvely. The rank test s equal to 5. for the date -5 and 8. for the date -8. The rank test based on the market model reveals the same trend n cumulatve abnormal return as prevous tests. The proxy for the abnormal return s and - 6

18 83.3 for the good news and bad news respectvely. The rank tests are 3.3 and - 6., respectvely and the null hypothess that the event has no mpact s strongly rejected. For the good news there s sgnfcant level of negatve abnormal return two weeks pror the event date. The rank tests are -5.0 and 5.3 for the 4 th day and th day before the event, respectvely. Another sgnfcant negatve abnormal return observed on the 8 th and 7 th days before the event, however on the 6 th day the sgn of abnormal return changes to posve one. The rank tests for those days are -7.9, -5.3 and 6.0 respectvely. The bad news has posve abnormal return 8 days before the event. The rank test showed that wh value 5.. The fgures for the cumulatve e abnormal returns and the proxy for the cumulatve abnormal return show smlares. The plots of proxy for cumulatve abnormal returns presented n Fgures 5 and 6. 7

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20 Yet agan, the tests undertaken for ndvdual companes showed mxed results. That s, the event date has consderable effect on Western companes. In case of Russan and Chnese ol companes, the effects of changes n ol prces do not changes the abnormal return markedly or, even, have an nverse effect. Compared wh the parametrc test, the nonparametrc test ndcates the strong sgnfcance for all Western stocks. The value of the rank test for the good news ranges from 5.9 for Royal Dutch Shell s stock to.79 for En s stocks on the event date. In addon, the rank test shows hgh negatve abnormal return approxmately two weeks before the event. However, a week before the event there are sgnfcant posve abnormal return for most of the secures. These are, especally, notceable wh stocks of Chevron and Exxon Mobl. The rank test for these secures are 8.5 and 6.38 respectvely. On the event date the rank test showed 8.9 for Chevron s stocks and 9.3 for the stocks of Exxon Mobl. Another sgnfcant date s 9 days after the event. The rank test detects consderable negatve abnormal returns on that date. For nstance, the value of the average proxy for abnormal returns of Brsh Petroleum s -8.4 whle on the 8 th day was equal to 3.5. For the rest of the secures the pcture s somewhat smlar to those of the shares of Brsh Petroleum. The bad news also affects the share prces. The lowest level of the rank test observed for the stocks of Royal Dutch Shell s -.78 and the greatest s for the shares of Exxon Mobl, whch s equal to The average rank test for all sx Western companes s Yet agan, the rank test ndcates statstcally sgnfcant posve abnormal returns 5 days before the event. The market model for the rank test supports the constant mean model. It ndcates the sgnfcance of abnormal returns at the event date and sgnfcant posve abnormal returns are observed 5 days before the event. The pcture s not clear for secures of Russan and Chnese companes. Overall, the changes n ol prces seem to have some effect on the share prces. However, n comparson wh the Western companes, s neglgble. For nstance good news s statstcally sgnfcant for the shares of followng companes: Lukol, Gazprom, Surgutneftegaz and Petrochna. Sgnfcance for the bad news observed for the stocks of Petrochna and Chna Petroleum and Chemcals. Despe ths, the level of 9

21 sgnfcance s much lower then those observed for the stocks of the Western companes. Interestngly, Chna Petroleum and Chemcals shares have sgnfcant negatve abnormal return ffteen days before the event for the good news. Whle at the same date for the most of the secures of the Western companes posve abnormal returns are observed. The rank test for the shares of Chna Petroleum and Chemcals s -9.5 and for the constant mean model and the market model, respectvely. Fnally, the nonparametrc rank test shows that overall the abnormal returns of ol companes shares for the analysed perod s affected by ol prce change. In addon, there are sgnfcant abnormal returns before and after the event. The results obtaned under the constant mean and the market models do not greatly dffer. Based on the rank test the outcome s dfferent from tradonal parametrc t-test. Whle the nonparametrc rank test strongly ndcates the exstence of abnormal returns, the parametrc test rejects the possbly of abnormal return. Bartholdy et al (007) pont out that n some nstances the test may show dfferent results. In such suatons, the nonparametrc test s more relable n detectng the abnormal return. Thus takng nto account the results obtaned by the nonparametrc rank test s possble to say that ol prce change affects the shares of Western ol companes. However, s mpact on Chnese and Russan companes s weak. The experment proves that on average random event dates do not show abnormal return and both the parametrc t-test and the nonparametrc rank test ndcate ths. Both tests show an absence of abnormal return. In the case of the constant mean model, the t-test shows 0.0 for news one and -0.0 for the news two. The market model ndcates the value of the t-test value for day zero equal to for news one and 0.00 for news two. For ndvdual companes the t-test shows a sporadc outcome. However these do not change the overall pcture of the test. The rank test as expected supports the t-test n ths experment. The value of the rank test for the proxy for the cumulatve abnormal return s 0.05 and for news one and news two respectvely. For the proxy obtaned by the market model, the value of the rank test s 0. and for the news one and news two respectvely. For the rest of the event wndow, the rank test does not ndcate any 0

22 abnormal return as well. Thus, ths experment confrms that randomly generated events on average should not show abnormal return. The analyss of ndvdual companes showed that the reactons on the ol prce news of shares of emergng and Western ol companes are dfferent. For nstance, the abnormal returns of most Western companes are negatve, rrespectve of news. The exceptons are Exxon Mobl and, to some extent, Chevron and En. In contrast, the nvestments nto Russan and Chnese ol companes generate posve abnormal return regardless of the type of news. The possble explanaton of ths result s that the ol companes are consdered as strategc companes n Chna and Russa and n many nstances prone to polcal nfluences. The experment wh random event dates ndcates a smlar outcome for ths experment both for the parametrc t-test and the nonparametrc rank test. They show that one cannot reject the null hypothess of no abnormal return. Concluson Ths analyss amed to fll the gap n the emprcal research of event studes of ol companes. It s focused on the queston of whether the changes n ol prces have an mpact on share prces of emergng and Western ol companes. The methodology s n lne, wh that of other researchers. The nonparametrc rank test results reveal consderable abnormal returns for ol companes. However, the tests also show the dscrepancy among the companes from dfferent economc areas. For example, whle ol prce change effects stock prces of Amercan and European ol companes as expected, the most atypcal behavour s observed for the secures of Chnese and Russan companes. A smulaton experment was addonally conducted based on the same tests but wh randomly generated event dates. Ths experment had the expected results both under the parametrc t-test and the nonparametrc rank test. Vz., one can not reject the null hypothess of no abnormal returns n the case of random event dates for the stock prces of ol companes.

23 It s also nterestng to note, and further research s requred n the future, to explan why the Russan and Chnese ol companes behave dfferently. Potental causes of such performances of the shares are, perhaps, nsde nformaton, polcal nfluences and corrupton.

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25 Krzman, M.P., (994), What Practoners Need to Know About Event Studes, Fnancal Analysts Journal, Vol. 50, pp MacKnlay, A.C., (997), Event studes n economcs and fnance, Journal of Economc Lerature, Vol. 35, pp McWllams, A., and Segel, D., (997), Event studes n management research: theoretcal and emprcal ssues, Academy of Management Journal, Vol. 40, No. 3, pp Lehman, E.L., and D Abrera, H.J.M., (975), Nonparametrcs: Statstcal Methods Based on Ranks, Holden-day Inc., San-Francsco. 4