taken, and for this first-announcer the combined two-day abnormal return for the period (-1,0) should be computed. Here 0 is the announcement date of the earnings release of the first-announcer and -1 is the day before the earnings announcement. In this example, Fiat is the first-announcer and the days in the window are January 27 and January 30 (January 28 – 29 is a weekend, and are not considered as trading days). For the (-1,0) window the ratio for the first-announcer (Fiat) should be calculated as follows: This formula is the same as the (AVAR) formula designed by Beaver (1968), which means that the values calculated in this formula give an indication about the abnormal stock price volatility. What is actually done in this formula is that the abnormal return of firm i on day t is compared with the ‘normal’ abnormal return during the year. This should give an indication if the abnormal return surrounding the earnings announcement of the first-announcer is bigger than normal, which implies a reaction to the earnings release. After calculating this ratio, this ratio should be classified in one of six mutually exclusive categories. The categories are named A till F and based on the value the earnings announcement of Fiat is placed in a certain category. The categories are divided as follows: A: > 6, B: > 4 but 6, C: > 3 but 4, D: > 2 but 3, E: > 1 but 2, F: 1. If we assume here that the value in the (-1,0) window = 2,5, then the earnings announcement of Fiat for January 2006 is placed in category D for the ‘announcing’ group of firms. After doing this, I will also compute the values for Fiat in the trading day periods surrounding the earnings announcement period. The two-day trading day windows surrounding the January 27-30 period for which an ratio will be
test if these abnormal return variances are normally distributed a Kolmogorov-Smirnov test is performed. This test is used to examine the distribution of Xit at the (-1,0) event date compared with the surrounding two-day trading periods. At the end a Spearman rank correlation test is performed. This test investigates if there is a correlation between the average abnormal return variances (Xit) of the announcing firms and the Xit values of the non-announcing firms, both in the (-1,0) event window. If the results of the Spearman rank correlation test are significant this implies that intra-industry information transfer occurs and that one determinant of their magnitude is the impact of the announcing firm’s release on its own share price. The Kolmogorov-Smirnov test and the Spearman rank correlation test are further described in the next chapter, where the results of this study are presented. Summary In this chapter the methodology used to measure intra-industry information transfer is described. First a datastream event study is performed for both the ‘announcing’ group of firms and the ‘non-announcing’ group of firms. The abnormal return values derived from the event study are then used to calculate the abnormal return variance. This is the amount of abnormal return surrounding the announcement date compared to the abnormal return in periods. A higher abnormal return variance indicates greater intra-industry information transfer. These abnormal return variances are categorized in the non-directional test. The results of the non-directional test contain the average abnormal return variances calculated for the event window (-1,0) and the surrounding estimation windows ranging from (-11,-10) till (9,10). These average abnormal return variances are tested on normality by performing a Kolmogorov-Smirnov test. At the end a Spearman rank correlation between the average abnormal return variances of the ‘announcing’ group of firms and the ‘non-announcing’ group of firms is performed. These test captures correlations in the abnormal returns of securities in the same industry (Foster, 1981). Results of these tests are discussed in the following chapter. Results Introduction In this section the results of the non-directional test are presented. The sample of this study consists of two parts. I consider the period 2001-2004 as the pre-IFRS period and the years 2005 till 2010 are considered as the post-IFRS period. To test if the magnitude of intra-industry information transfer has
Summary of literature related to intra-industry information transfers Author(s) Object of the study Sample Methodology Outcome Foster (1981) Intra-industry information transfers associated with earnings releases. Sample size: 75 firms in 10 SIC industries with announcement dates from 1963 till 1978 Analysis of the reaction of firm i’s stock price on the earnings announcement of firm j. The direction and magnitude of the impact of a firm’s earnings release on its own stock price is a determinant of both the direction and magnitude of the impact of that release on the stock prices of other firms in that industry. Freeman and Tse (1992) Reaction of late announcers’ prices on early announcements. Sample size: 10277 quarter observations. During 1979-1988 Analysis of industry and firm news on stock returns. Late announcers’ prices reacts best on first announcement. Information transfer is primarily positive. Thomas and Zhang (2008) The reaction of stock prices on intra-industry information transfers. Sample size: 245742 quarter observations. During 1973-2005 Analysis of stock price reactions on different announcements. Stock price tend to overreact at first and is corrected by the firm’s own earnings announcement Ramnath (2002) Investor and analyst reactions to earnings announcements of related firms. Sample size: 428 firms in 48 analyst based industry groups. 11 quarter observations from the first quarter of 1995 till the third of 1997. Predicting the error in subsequent announcer’s forecasts. The forecast error of the first announcing firm provides information about the error in the simultaneous earnings forecasts of other firms in the same industry, which still have to report their earnings.
Spearman's rho Announcing firms Correlation Coefficient 1,000 ,086 Sig. (2-tailed) . ,872 N 6 6 Non-announcing firms Correlation Coefficient ,086 1,000 Sig. (2-tailed) ,872 . N 6 6 Post-IFRS period: Correlations Announcing firms Non-announcing firms Spearman's rho Announcing firms Correlation Coefficient 1,000 ,143 Sig. (2-tailed) . ,787 N 6 6 Non-announcing firms Correlation Coefficient ,143 1,000 Sig. (2-tailed) ,787 . N 6 6