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The differential information hypothesis, firm size, and earnings information transfer
Journal of Business Research 53 (2001) 37 ± 47
The differential information hypothesis, firm size, and earnings information transfer An empirical investigation Sharad C. Asthanaa,*, Birendra K. Mishrab,1 a
Department of Accounting, Fox School of Business and Management, Temple University, Philadelphia, PA 19122, USA b Department of Accounting, School of Management, University of Texas at Dallas, Richardson, TX 75083 Accepted 18 August 1999
Abstract This study examines the effects of the sizes of the announcing and non-announcing firms on information transfers. Atiase's [Atiase RK. Predisclosure information, firm capitalization and security price behavior around earnings announcements. J Account Res 1985;23:21 ± 36 (Spring)] differential information hypothesis suggests that, relative to small firms, more pre-announcement information is available on large firms. An implication of the differential information hypothesis is that abnormal returns of large firms around earnings disclosures are caused by new information regarding the economy and industry. Thus, earnings disclosures by large firms may contain information that is useful for other firms in the industry. Consistent with our prediction, we find that information transfers within an industry are positively related to the announcing firm's size. The differential information hypothesis also suggests that information transfer will be inversely related to the non-announcing firm's size. However, our results support the null hypothesis that information transfer is not a function of the non-announcing firm's size. Possible explanations of this finding are discussed in the paper. D 2001 Elsevier Science Inc. All rights reserved. Keywords: Differential information; Firm size; Earnings
1. Introduction Information transfer studies suggest that unexpected information disclosures by a firm convey information to investors of related firms. Previous studies have documented information transfers in various settings such as earnings announcements (Foster, 1981; Freeman and Tse, 1992), sales announcements (Olsen and Dietrich, 1985), and management forecasts (Baginski, 1987; Clinch and Sinclair, 1987; Han et al., 1989). The evidence of those studies reflects an ``average finding'' across all firms. An interesting question is whether information transfers vary systematically across firm characteristics. The primary purpose of this study is to investigate firm size as a determinant of informa* Corresponding author. 105 Winchester Way, Harleysville, PA 194383604, USA. Tel.: +1-215-204-0489; fax: +1-215-204-5587. E-mail addresses: [email protected] (S.C. Asthana), [email protected] (B.K. Mishra). 1 Tel.: +1-602-967-6326; fax: +1-602-965-8392.
tion transfer in the light of Atiase's (1985) differential information hypothesis. Atiase (1985) suggests that the amount of private predisclosure information is an increasing function of firm size (capitalization value). Grant (1980) and Atiase (1987) also present evidence that there are fewer Wall Street Journal news items about small firms than about large firms. Similarly, empirical research by several authors suggests that relative to large firms' announcements, small firms' earnings announcements convey more ``unexpected'' information and so are associated with more intense and sustained market adjustments (Atiase 1985; Bamber 1987; Freeman, 1987). If the information supporting the security prices of large firms and small firms differs systematically, one would expect a systematic variation in information transfers to non-announcing firms because information transfers would be related to available information. Prior research in finance also suggests that large firms' reactions to common information leads those of small firms (Lo and MacKinlay, 1990; Brennan et al., 1993).
0148-2963/01/$ ± see front matter D 2001 Elsevier Science Inc. All rights reserved. PII: S 0 1 4 8 - 2 9 6 3 ( 9 9 ) 0 0 111 - 3
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We derive two hypotheses based on earlier research. The first hypothesis predicts that the magnitude of information transfer is positively related to the announcing firm size. The second hypothesis is a set of three sub-hypotheses that tests the null that information transfers are independent of the non-announcing firms' sizes against two directional alternative hypotheses. The hypotheses are tested using standard event-study methodology. Quarterly earnings announcements of firms in the same industry are used to test the hypotheses. Analyses are conducted using the absolute value of normalized 2-day announcement period excess returns of the announcing and non-announcing firms obtained from a single-index asset-pricing model. Two prior studies have examined the effects of firm sizes on earnings-related information transfers. Han and Wild (1999) show that non-announcing firms' price revaluations associated with other firms' earnings reports are inversely related to firm capitalization; where firm capitalization is defined in reference to both announcing and non-announcing firms. In other words, Han and Wild (1999) show that smaller earnings information transfers are associated with large announcing (non-announcing) firms in comparison to small announcing (non-announcing firms) firms. They interpret their results as reflective of differential predisclosure information production and dissemination by private parties, which is positively related to size, yielding earnings reports with price informativeness inversely related to firm sizes. Schoderbek (1995) explores the economic relations between dominant and fringe firms. He finds significant positive information transfers from dominant firms to fringe firms, indicating that the earnings of dominant firms are signaling industry-wide information to the fringe firms. In contrast, the information transfers around earnings announcements of fringe firms do not induce significant information transfers to the dominant firms. Contrary to Han and Wild (1999), Schoderbek's (1995) results suggest a positive relationship between the size of the announcing firms and the extent of information transfers. Thus, the implications of the differential information hypothesis for earnings-induced information transfers are not clear. We also discuss the implications of three factors Ð timing of announcements, clustering of announcements, and definition of industry groups in information transfer studies that make the results of Schoderbek (1995) and Han and Wild (1999) difficult to assess. A further problem with both studies is their limited sample sizes. Han and Wild's (1999) sample is confined to 1984 ± 1986 period and Schoderbek's (1995) sample includes only 12 dominant and 49 fringe firms. Generalization of such results usually poses problems of external validity. Our sample, on the other hand, covers 10 years of data (1984 ±1993) with 323 announcements. After controlling for these three factors, we show that information transfers around earnings disclosures are increasing in the size of the announcing firm and not significantly related to the size of the non-announcing firms.
The remainder of the paper is organized as follows. Section 2 discusses the differential information hypothesis, considers its implications for firm size and information transfers and develops the hypotheses tested in this study. Section 3 covers research design, sample selection, and test statistics. Section 4 presents analysis and results. Section 5 summarizes the findings and presents the conclusions. 2. The differential information hypothesis Atiase (1980) argues that information availability may vary systematically between large and small firms due to differential incentives for information search. In an ideal economic setting (risk-neutral investors and unlimited financing at a constant risk-free rate), aggregate trading profits for a given change in per share prices are proportional to the market value of the firm. This implies that a larger aggregate profit could be made from the knowledge of a fixedpercentage mispricing of common equity of a large firm than that of a small firm. An investor will search for information about a firm as long as the marginal cost of information search is less than or equal to the expected marginal profit from trading based on the incremental information available from the search. Hence, there is more incentive for information searches for large firms compared to small firms, i.e., a differential incentive for information searches exists. The above discussion implies that more information is available for large firms than for small firms due to the differential incentive for information search. Inter-firm information transfer occurs when market participants use the information released by firm i, an announcing firm (ieSa, where Sa is the set of announcing firms) to make inferences about the security return behavior of firm j, a non-announcing firm ( jeSn, where Sn is the set of nonannouncing firms), or equivalently f Rj =fj ; jeSn 6 f Rj =fj ; fi ; jeSn ; ieSa ; for all Sn and Sa belonging to S where f(./.) is a conditional density function, Rj is the security return of firm j, and fi (fj) is the available information (accounting or otherwise) for firm i (j), and S is the universe of firms. This is a definition of inter-firm information transfer in the broadest sense. However, for the purpose of this study, the universe of firms (S) is restricted to industry groups. Moreover, fi and fj are also restricted to the earnings announcements of firms i and j, respectively. In general, intra-industry information transfers reflect the similar conditions influencing the announcing and nonannouncing firms. Earlier accounting studies (Richardson, 1984; Atiase, 1985; Bamber, 1987; Freeman, 1987) suggest that more accurate firm-specific information is available for large firms than for small firms. Thus, market-adjusted returns of large firms during earnings announcements are more likely to be due to information pertaining to the overall
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trend in the economy or industry sector than due to firmspecific information. This implies that large firms' announcements have greater potential for conveying information about other firms in the industry than small firms' announcements. On the other hand, an announcement by a small firm may have substantial firm-specific information but may contain little relevant information about other firms in the industry. Thus, information transfers will be increasing in announcing firm size. This leads to our announcing firm hypothesis. H10. E(hj/fiL) E(hj/fiS), jeSn, ieSa; for all Sn and Sa belonging to S. H1a. E(hj/fiL) > E(hj/fiS), jeSn, ieSa; for all Sn and Sa belonging to S. where hj is a non-directional measure of abnormal return activity based on announcement period excess returns. The superscripts L and S on fi denote large and small announcing firms, respectively. Several competing hypotheses can be framed to test the effect of size of the non-announcing firm on information transfer. Availability of firm-specific information is an increasing function of the firm's size (Richardson, 1984; Atiase, 1985; Bamber, 1987; Freeman 1987). As a result, the earnings announcements of other firms have less (more) potential to convey firm-specific information about large (small) non-announcing firms (Han and Wild, 1999). This suggests that an announcement has more potential for information transfer to small non-announcing firms than to large non-announcing firms (H2a). Alternatively, large and small firms differ in market structures and macro-economic environment. Large firms generally have broader markets (compete in national or even international markets), diversified product lines, multiple business segments, and large numbers of shareholders. Small firms tend to concentrate on local and regional markets or may operate in niche
39
markets. This suggests that large firms could be affected by a wider range of factors than small firms. Hence, there is greater possibility of industry-specific information transfers to large non-announcing firms than to small non-announcing firms (H2b). Finally, it can be argued that the unexpected components of earnings disclosures contain industry- and economy-wide information as well as firm-specific information. The firmspecific component causes price reaction in the disclosing firm's stock but no information transfers. On the other hand, the industry- and economy-wide component causes information transfers. The latter component contains information that is equally valuable to all non-announcing firms irrespective of their sizes. Therefore, there is no differential response to an earnings disclosure among non-announcing firms (H20). This argument is depicted in Fig. 1. Based on the above arguments, we propose the following non-announcing firm hypotheses. H20. E(hjS/fi) = E(hjL/fi), jeSn, ieSa; for all Sn and Sa belonging to S. H2a. E(hjS/fi) > E(hjL/fi), jeSn, ieSa; for all Sn and Sa belonging to S. H2b. E(hjS/fi) E(hjL/fi), jeSn, ieSa; for all Sn and Sa belonging to S. where the variables are defined as previously, and the superscripts S and L on hj denote small and large nonannouncing firms, respectively. 3. Sample selection, methodology, and test statistics The sample consists of firms with the following information on the 1994 quarterly COMPUSTAT file: earnings announcement dates, number of shares used to compute
Fig. 1. Dissemination of earnings information.
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primary earnings per share, and closing per-share price in the current quarter. Sample firms also meet the following criteria: (1) December 31 year-end, to synchronize the fiscal quarters over which we examine information transfers; (2) earnings announcement within 90 days of the end of the fiscal quarter, to remove erroneous announcement dates and increase the likelihood that any information transfers are from announcing firms' results in corresponding quarters; (3) the earnings announcement is one of the first five announcements in that industry for that quarter, to control for the timing and clustering of announcements (discussed below); (4) there is no other earnings announcement in that industry in that quarter in the interval ( 1, 1) relative to the earnings announcement day, to control for contemporaneous announcements; (5) none of the non-announcing firms announces its own earnings in the interval ( 3, 3), to avoid confounding events. Increasing this interval to 15 days ( 7, +7) did not change the results qualitatively, even though the sample size is significantly reduced. Firms, satisfying the above criteria, are retained in the sample if (1) daily returns are available from CRSP 1994 daily return files (covering NYSE and AMEX); (2) at least 150 days of returns exist in the 200 trading days ( 240, 41) preceding the day of the earnings announcement, to facilitate the estimation of the asset pricing model; (3) there are no missing returns in the interval ( 40, 40), to facilitate the estimation of abnormal returns around the earnings disclosure; and (4) at least 75 firm-quarter observations are available during the entire sampling period. An announcement by a firm in any quarter is considered a firmquarter observation. The sample is restricted to at least 75 firm-quarter observations to increase the power of the test. This results in a sample of 323 announcements and 9645 firm-quarter observations for 1005 non-announcing firms from the first quarter of 1984 through the third quarter of 1993. The 323 announcements are distributed as follows: 172 first announcements, 72 second announcements, 30 third announcements, 25 fourth announcements, and 24 fifth announcements. The announcing and non-announcing firms belong to 18 industry groups. The timing of announcements can affect inferences in information transfer studies. Firm characteristics (for example, size) may vary systematically with the announcement sequence. Moreover, the amount of information that is useful for other firms may be a decreasing function of the announcement number, since the later announcements may contain redundant information (Freeman and Tse, 1992). Hypothetically if this is the case, both firm size and information transfer will be functions of the timing of announcement. Lack of control for the timing effect (Schoderbek, 1995; Han and Wild, 1999) could result in the spurious inference that size affects information transfer. Clustering of disclosures leads to simultaneous earnings announcements problem. Thus, release of earnings by different firms within 2 days of each other would cause such firms to be classified as announcing as well as
non-announcing on the same date. Such announcements are deleted from the sample. Another problem that arises due to simultaneous earnings release is unique to the purpose of this study. Since several firms may announce their earnings on the same date in an industry group, it is possible that one, some, or all of them together are responsible for information transfers to the non-announcing firms in that industry group. As a result, it is impossible to assign a unique announcing firm size to such announcements. To control for the timing and clustering effects, we restrict our sample to the first five announcements in an industry-quarter, as in Freeman and Tse (1992). Analysis of the announcement dates on COMPUSTAT reveals that the first five announcements in any industry-quarter are less clustered than the subsequent announcements. Most previous research, discussed above, confines the sample selection to four-digit SIC codes. If information transfers also occur to non-announcing firms in the same two-digit SIC codes (but different four-digit SIC codes), previous research may have problems with simultaneous announcements in the same two-digit but different fourdigit SIC codes. We conduct tests (not reported) by splitting the sample into two portfolios Ð one with same four-digit SIC codes and the other with different four-digit but same two-digit SIC codes. The amounts of information transfers within these two portfolios are not significantly different. Therefore, we use two-digit SIC codes to define industries. Estimates of the abnormal returns, ui,t for the period ( 40, 40) relative to day 0Ð the earnings announcement day, are obtained by using a single index asset pricing model in Eq. (1): ui;t Ri;t
ai bi RM;t
1
where the daily returns of the firm (Ri, t) are regressed against the value-weighted market returns (RM,t), during the period ( 240, 41) to estimate ai and bi. Since this study examines market activity associated with information transfers, it is necessary to control for the level of market activity in a non-report period. We use the average abnormal returns during a control period to standardize the firm's announcement period abnormal returns. The standardization endogenously controls for the firm-specific information during the non-report period and thereby any crosssectional differences in the average risk-adjusted returns. The approach taken here is similar to Atiase (1985), wherein revaluation indexes measure announcement period security price revaluations relative to average price revaluation during non-report periods. Our hypotheses are concerned with the magnitude (rather than the direction) of security price reactions. As a result, our revaluation index has to be based on a transformation that ignores the sign of the announcement period's unexpected return. Prior research (Foster, 1981; Chambers and Penman, 1984; Atiase et al., 1987; Han et al., 1989) has used several different transformations to
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develop non-directional measures of security return activity. We use the absolute standardized return residual (ABSR) metric since it has the least likelihood of biasing the results due to a few extreme values. ABSR is defined as the ratio of the absolute value of the 2-day announcement period excess returns to the mean absolute value of the non-report-period excess returns, which are calculated over 30, 2-day intervals (Eq. (2)). These 30 intervals consist of two sub-periods of 15 consecutive 2-day intervals. One is a pre-announcement period starting from day 11 and going backward through day 40, and the other is a post-announcement period starting from day +11 and going forward through day +40. jui;e j
ABSRi 1 30
15 P b1
jui;b j
15 P f 1
!
2
jui;f j
where ui,e is firm i's event period excess return, calculated over a 2-day interval ( 1, 0); ui,b is the excess return in the bth successive 2-day interval going backward; and ui,f is the excess return in the fth successive 2-day interval going forward. We use the window ( 1, 0) since earnings, earnings forecasts, and dividend announcements are reported in the Dow Jones News Service one trading day prior to publication in the Wall Street Journal (Patell and Wolfson, 1979). Previous researchers (Foster, 1981; Han et al., 1989) also used this accumulation period. In the rest of the paper ABSR(A) pertains to announcing firms and ABSR(NA) pertains to non-announcing firms. Intuitively, ABSR(NA) can be viewed as the ratio of the ``new'' information for the non-announcing firm conveyed to the market by the announcing firm's earnings announcement to the non-announcing firm's information over the non-report period. The expected value of ABSR(NA) is 1.0. A value greater than 1.0 for ABSR(NA) implies that an information transfer exists. That is, the announcement conveys information about the non-announcing firm beyond what was available outside the announcement period. Since each firm acts as its own control, this procedure explicitly controls for any volatility in returns that is correlated with firm size. We define firm size as the capitalized value, or market value of common shares outstanding, consistent with the underlying rationale for the firm size effect documented in the accounting literature (Atiase, 1985). For statistical analysis, the capitalized values are transformed into their natural logs (LCV). To control for the fact that firm size may vary from year-to-year, a relative measure of firm size is used. Each firm's LCV is standardized by the mean LCV of all the firms in the economy for each year as in Eq. (3) below: SIZEi
1 N
LCVi N P LCVi
i1
3
41
where SIZE is the standardized measure of size for the firm i; LCVi is the natural log of firm i's market value at the end of the quarter; and N is the number of firms in the economy (proxied by the population of firms on COMPUSTAT). Later in the paper, SIZE(A) denotes standardized size of the announcing firm and SIZE(NA) denotes the size of the non-announcing firm. The division by the denominator is just a scaling factor and does not change the results. Atiase (1980) uses the natural log of firm size in his regression analysis. Han and Wild (1999) also use scaled size variable in parts of their analysis. The compelling reason to use the log value of the firm size has certain econometric benefits compared to absolute size in linear regressions since it can linearize product and power form relations. The methodology used in this study is a variant of the one used by Foster (1981). The research design consists of examining the abnormal security returns of two groups of firms Ðan announcing group of firms and a non-announcing group of firms. A firm i, belonging to the set of feasible announcing firms (ieSa), is classified as an announcing firm for the date when its earnings report is released. Price revaluation indices (ABSR) are calculated from the cumulative abnormal returns for the 2 days ( 1, 0) for announcing firm i and non-announcing firms j belongs to set of feasible non-announcing firms ( jeSn). Each announcing firm i is placed in 1 of 20, equal, mutually exclusive and exhaustive portfolios formed separately for each of the first five announcements on the basis of the size of the announcing firm. All the corresponding non-announcing firms are assigned firm i's portfolio. Thus, for the first announcement of each quarter, portfolio 1 (20) contains the announcing firms with the smallest (largest) SIZE(A). The mean ABSR is estimated for each of these 100 (5 20) portfolio ± announcement number combinations. Next, the mean ABSR(NA) are ranked from 1 to 20 [RABSR(NA)] within each announcement number. Conversion of SIZE(A) into portfolios 1 ±20 and ranking of mean ABSR(NA) from 1 to 20 for each announcement number removes the effects of timing of announcement on SIZE(A) and ABSR(NA). Pearson's product ± moment correlation coefficient is calculated for portfolio number and RABSR(NA). If ABSR(NA) increases with SIZE(A) (after controlling for the timing of announcement), the Pearson's coefficient should be positive. Pearson's coefficients are also estimated for the portfolio number and mean/median RABSR(NA) (across all five announcements). These coefficients should also be positive. The test is repeated for portfolios formed on SIZE(NA). The next portfolio test compares the means of ABSR(NA) across two portfolios (with median SIZE(A) as the cut-off). First, the observations are ordered by announcement number. Within each announcement number, the set of observations with SIZE(A) less than or equal to the median value are placed in portfolio of small announcing firms and the remaining observations in portfolio of large announcing
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firms. The mean ABSR(NA) for these two portfolios are compared. If the hypothesized size effects exist, the mean ABSR(NA) for portfolio of large announcing firms should be greater than the mean ABSR(NA) for portfolio of small announcing firms. The test is repeated with portfolios formed on the basis of SIZE(NA). Linear regression analysis is also performed to analyze the effects of the magnitude of the announcing firm's price reaction, announcing firm size, and non-announcing firm size on information transfers. First, to confirm Foster's (1981) finding that the magnitudes of announcing firms' price reactions are positively correlated with the degree of information transfers and that the model is correctly specified, we run the following regression: ABSR NA a0 a1 ABSR A e0
The following regression is estimated to examine if information transfer is a function of the announcing and nonannouncing firms' sizes: ABSR NA b0 b1 ABSR A b2 ABSR A SIZE A b3 ABSR A SIZE NA e1 5 where b are the regression parameters and e1 is the error term. We expect b1 and b2 to be positive and b3 to be negative under H2a, positive under H2b, or insignificant under H20. We re-estimate regressions (4) and (5) with successive announcement numbers dropped from the regressions to insure that the results are not driven by the timing of announcements.
4
where a are the regression parameters and e0 is the error term. According to Foster (1981), a1 should be significant and positive. We use ABSR(A) as the explanatory variable instead of the unexpected earnings. Han and Wild (1990) show that the inferences using announcing firms' unsystematic stock returns as proxy for the financial reporting signal are similar to the inferences using unexpected earnings. Additionally, our model specification helps to control for information, other than earnings, contained in the disclosures (Schipper, 1990). One concern with such a model is that co-movements of returns can be wrongly interpreted as information transfers. However, since we use market-adjusted abnormal returns in computing ABSR(A) and ABSR(NA), the covariance of stock returns does not affect our conclusions.
4. Analysis and results Table 1 shows the distribution of the capitalization values (in millions of dollars) for the announcing and non-announcing firms by industry groups. The announcing firms' capitalization values range from US$29 million to US$249 billion, while those for non-announcing firms range from almost zero to US$161 billion. Table 2 provides the distribution of SIZE(A) and SIZE(NA) by industry groups. One advantage of using SIZE variable, instead of the capitalization value, is that it makes the variable across industries and years comparable. For example, a firm size of US$1 million in 1984 may have different implications for information transfers than a firm size of US$1 million in 1993. As a result of standardiza-
Table 1 Distribution of capitalization values of announcing and non-announcing firms by industry groups Where capitalization value is the price at the end of the quarter multiplied by the number of shares outstanding at that time. The range, mean, median, and standard deviation are in US$ million (rounded to the nearest million). Announcing firms
Non-announcing firms
SIC
Observations
Range
Mean
Median
Standard deviation
Observations
Range
Mean
Median
Standard deviation
13 27 28 30 33 34 35 36 37 38 48 49 50 60 61 63 67 73 Total
30 19 24 22 17 5 11 18 10 28 19 19 25 4 20 23 17 12 323
218 ± 16,190 484 ± 6082 45 ± 25,395 161 ± 3360 59 ± 6140 31 ± 825 1087 ± 95,697 29 ± 49,703 139 ± 1345 151 ± 5046 478 ± 5334 117 ± 6812 105 ± 2870 404 ± 3738 147 ± 249,036 166 ± 2564 55 ± 828 42 ± 2001 29 ± 249,036
3421 2717 12,167 969 2785 564 13,821 13,058 1110 3848 2813 1546 779 1352 14,394 1323 269 549 4523
858 2729 10,846 905 2908 593 6202 3689 966 4324 2632 1002 470 633 1539 1225 211 448 1279
4207 1442 7531 777 2445 322 27,313 17,443 920 1868 1373 1563 694 1601 55,266 712 201 559 15,805
782 293 1308 234 316 79 405 546 314 648 455 2319 158 242 210 789 392 155 9645
0 ± 16,350 14 ± 15,334 8 ± 51,201 6 ± 6535 23 ± 5661 21 ± 12,301 12 ± 93,362 10 ± 161,116 12 ± 22,253 10 ± 22,290 28 ± 34,867 11 ± 19,607 16 ± 4409 27 ± 15,409 8 ± 11,058 7 ± 27,296 4 ± 1817 19 ± 7562 0 ± 161,116
1051 2143 4233 1087 1057 1004 2443 1929 2077 1693 6426 1537 664 1435 2328 1967 196 555 2135
241 1840 1457 367 622 451 413 255 860 443 2782 825 119 814 1258 1211 135 326 700
2372 2117 6859 1396 1194 1838 10,239 7890 3273 3201 7132 2045 1015 1763 2637 2640 229 876 4717
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Table 2 Distribution of standardized sizes (SIZE) of announcing and non-announcing firms by industry groups Where standardized size of a firm (SIZE) is defined as the natural log of its capitalized value (price of a common share number of common shares outstanding) divided by the mean of log (capitalized value) of all firms on COMPUSTAT. Announcing firms
Non-announcing firms
SIC
Observations
Range
Mean
Median
Standard deviation
Observations
Range
Mean
Median
Standard deviation
13 27 28 30 33 34 35 36 37 38 48 49 50 60 61 63 67 73 Total
30 19 24 22 17 5 11 18 10 28 19 19 25 4 20 23 17 12 323
0.90 ± 1.66 1.05 ± 1.49 0.64 ± 1.73 0.88 ± 1.40 0.72 ± 1.50 0.59 ± 1.15 1.21 ± 1.95 0.57 ± 1.82 0.84 ± 1.37 0.86 ± 1.50 1.04 ± 1.47 0.84 ± 1.47 0.78 ± 1.33 1.02 ± 1.39 0.88 ± 2.09 0.88 ± 1.34 0.71 ± 1.13 0.65 ± 1.28 0.57 ± 2.09
1.24 1.33 1.52 1.12 1.22 1.01 1.50 1.37 1.13 1.36 1.34 1.19 1.07 1.16 1.26 1.20 0.91 0.98 1.24
1.18 1.37 1.59 1.15 1.34 1.13 1.53 1.38 1.20 1.44 1.34 1.17 1.06 1.12 1.28 1.21 0.90 1.04 1.21
0.24 0.14 0.25 0.14 0.28 0.24 0.19 0.35 0.18 0.16 0.11 0.15 0.16 0.17 0.27 0.11 0.14 0.22 0.25
782 293 1308 234 316 79 405 546 314 648 455 2319 158 242 210 789 392 155 9645
0.25 ± 1.71 0.45 ± 1.62 0.34 ± 1.87 0.31 ± 1.48 0.53 ± 1.53 0.51 ± 1.57 0.42 ± 2.00 0.40 ± 2.02 0.43 ± 1.70 0.40 ± 1.68 0.57 ± 1.80 0.42 ± 1.73 0.49 ± 1.41 0.56 ± 1.63 0.34 ± 1.58 0.34 ± 1.73 0.25 ± 1.28 0.53 ± 1.50 0.25 ± 2.02
0.96 1.22 1.23 1.04 1.09 1.01 1.04 1.00 1.13 1.05 1.33 1.14 0.90 1.16 1.16 1.16 0.82 0.97 1.11
0.93 1.29 1.23 1.00 1.09 1.05 1.03 0.94 1.16 1.04 1.36 1.15 0.83 1.15 1.21 1.21 0.83 1.00 1.12
0.28 0.21 0.28 0.25 0.19 0.26 0.28 0.30 0.28 0.28 0.29 0.21 0.27 0.18 0.28 0.23 0.17 0.19 0.27
tion, the variability in capitalization value is also reduced thereby strengthening the power of our tests. Note that in Table 1, the total standard deviations are more than twice the means while in Table 2, the total standard deviations are less than one-fourth of the means. The mean size of announcing firms (Table 2) is greater than the mean size of non-announcing firms. This occurs due to the fact that only the first five announcements in any industry-quarter
are included in the sample. However, there is a largeenough variation in the size of announcing firms (from 0.57 to 2.09) to analyze the effects of size of announcing firms on information transfers. The distribution of ABSR(A) and ABSR(NA) is presented in Table 3. ABSR(A) and ABSR(NA) have been winsorized in the interval ( 5, 5) to avoid the possibility of extreme values driving the results.
Table 3 Distribution of absolute standardized residuals (ABSR) of announcing and non-announcing firms by industry groups Where the absolute standardized residual (ABSR) is the announcement period price revaluation index. It is defined as the ratio of the absolute value of 2-day ( 1, 0) announcement period excess return to the mean absolute value of 2-day excess returns during a non-report period. Announcing firms
Non-announcing firms
SIC
Observations
Mean
Median
Standard deviation
Observations
Mean
Median
Standard deviation
13 27 28 30 33 34 35 36 37 38 48 49 50 60 61 63 67 73 Total
30 19 24 22 17 5 11 18 10 28 19 19 25 4 20 23 17 12 323
1.2886 0.9657 1.5778 1.1264 2.4270 0.9222 1.0281 2.0256 1.5110 1.2396 1.2893 1.2159 1.5291 1.7958 1.0571 1.9860 1.2668 1.4656 1.4305
1.0988 0.6209 1.1834 0.6672 1.3403 0.2033 0.6664 1.3835 1.5455 1.2159 1.3661 0.9622 1.0903 1.3613 0.6977 1.6632 1.4069 1.1316 1.0985
0.9731 1.0998 1.5238 1.5251 3.1774 1.1484 1.5052 1.9099 0.9547 0.8756 0.8809 1.0589 1.2705 1.8049 0.8351 1.6257 0.8882 1.0208 0.2522
782 293 1308 234 316 79 405 546 314 648 455 2319 158 242 210 789 392 155 9645
1.0149 0.9309 1.0361 1.0625 1.1190 0.8478 1.0407 1.1434 1.0662 1.0390 1.0740 1.0838 1.0276 0.9114 1.1031 1.1296 1.0209 1.0367 1.0589
0.8021 0.7126 0.7813 0.7666 0.8050 0.6954 0.8407 0.8816 0.7940 0.7839 0.8611 0.8374 0.7726 0.6644 0.7482 0.7871 0.8076 0.8139 0.8000
0.8864 0.8730 1.0498 0.9851 1.0284 0.6055 0.8843 01.0392 0.9574 1.0272 0.8901 1.0987 0.9274 0.8716 1.5237 1.2081 0.9064 0.8698 1.0342
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Table 4 Pearson's correlation matrix (Figures in parentheses are the p-values) Where postscript ``A'' implies announcing firm and ``NA'' implies non-announcing firm. The absolute standardized residual (ABSR) is the announcement period price revaluation index for the announcing or non-announcing firms. It is defined as the ratio of the absolute value of 2-day ( 1, 0) announcement period excess return to the mean absolute value of 2-day excess returns during a non-report period. Standardized size (SIZE) of a firm is defined as the natural log of its capitalized value (price of a common share number of common shares outstanding) divided by the mean of log (capitalized value) of all firms on COMPUSTAT. ABSR(NA) Announcement number SIZE(A) SIZE(NA) ABSR(A)
3.38% 1.94% 0.89% 6.87%
(0.0009) (0.0567) (0.3843) (0.0001)
ABSR(A)
SIZE(NA)
5.42% (0.0001) 2.30% (0.0238) 0.45% (0.6573)
As expected, the mean ABSR(A) is greater than 1 in 16 out of 18 industry groups, while mean ABSR(NA) is greater than 1 in 15 out of 18 industry groups. The correlation matrix is shown in Table 4. Announcement number and ABSR(NA) are positively correlated (3.38%, p = 0.0009) while announcement number and SIZE(A) are negatively correlated ( 18.96%, p = 0.0001). If this relationship is universally true (and not limited to the first five announcements as in our sample), then ABSR(NA) and SIZE(A) may appear to be inversely correlated (as in Han and Wild, 1999). This observed relationship might not be causal but a consequence of missing controls for timing of announcements. The correlation between SIZE(A) and ABSR(NA) is positive (1.94%, p = 0.0567) as predicted by H1a. The correlation between SIZE(NA) and ABSR(NA) is, however, not significant ( 0.89%, p = 0.3843). ABSR(A) and ABSR(NA) are positively correlated (3.38%, p = 0.0009) implying that the magnitude of information transfer varies directly with the magnitude of announcing firm's own price reaction. The positive correlation between SIZE(A) and SIZE(NA) may be an artifact of the variation of size within industry groups. In other words, if an industry is more capital intensive compared to rest of the industry groups, then both SIZE(A) and SIZE(NA) will be large for this industry group. The results of Pearson's correlation tests with portfolios formed on SIZE(A) are shown in Table 5. As predicted, the three correlation coefficients are significant and positive [r{PSIZE(A), RABSR(NA)} = 18.195%, p = 0.0700; r{PSIZE(A), mean RABSR(NA)} = 38.235%, p = 0.0962; r{PSIZE(A), median RABSR(NA)} = 38.925%, p = 0.0898]. Thus, information transfer increases with the size of the announcing firm, after controlling for the timing of announcement. A similar analysis with the portfolios formed on the basis of SIZE(NA) yielded insignificant correlation coefficients (results not reported). Thus, size of the non-announcing firm does not appear to be a determinant of information transfer. Table 6 presents the results of test on means of portfolios of small and large announcing firms for each announcement number. The mean ABSR(NA) for portfolio of large announcing firms is significantly greater than the mean ABSR(NA) for portfolio of small announcing firms for all
12.38% (0.0001) 10.47% (0.0001)
SIZE(A) 18.96% (0.0001)
announcements except the fourth (t for first announcement = 1.8051; t for second announcement = 3.7710; t for third announcement = 1.4579; t for fourth announcement = 1.0010; and t for fifth announcement = 2.8084). For the total sample also the results are in the predicted direction (t = 3.8289). Similar tests are also performed with portfolios formed on sizes of non-announcing firms (results not reported). The null of equal mean ABSR(NA) is not rejected for any announcement or the total sample. Overall, the above results support H1a and H20. Regression results of Eq. (4) are shown in panel A of Table 7. The coefficient of ABSR(A) for the full sample (0.0547, t = 5.989) is significantly positive as predicted. The results do not change qualitatively when the regression is reestimated with successive announcement numbers dropped. The test results are thus not an artifact of the timing of announcement. This finding supports Foster's (1981) contention that magnitude of information transfer is an increasing function of the magnitude of announcing firm's own price reaction. These results also provide evidence that variables ABSR(A) and ABRS(NA) are defined and measured appropriately and that the regression model is correctly specified. Panel B of Table 7 presents the results of Eq. (5). In the regression on the full sample, the coefficient of ABSR(A) ( 0.0078, t = 0.226) is insignificant; the coefficient of ABSR(A)*SIZE(A) is significant and positive (0.0713, t = 3.115); and the coefficient of ABSR(A)*SIZE(NA) is insignificant ( 0.0222, t = 1.155). An insignificant coefficient of ABSR(A) in Eq. (5) implies that when SIZE(A) and SIZE(NA) are zero, there is no reaction to ABSR(A) and hence, no information transfer. The overall results do not change qualitatively when Eq. (5) is re-estimated with successive announcement numbers dropped out. However, when regression (5) is run without the first announcement, the coefficient of ABSR(A) does become significantly negative. On further investigation, we found that this coefficient has significantly high collinearity (variance inflation factor of 16.14 which is more than the critical value of 10). Presence of collinearity in this variable makes the variance of its coefficient biased. Thus, the t-statistic of ABSR(A) is biased and unreliable for this particular regression. Overall, the regression results support H1a and H20.
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Table 5 Pearson's correlation analysis with control for timing of announcement: portfolios formed on SIZE(A) Where postscript ``A'' implies announcing firm and ``NA'' implies non-announcing firm. PSIZE(A) are the 20 portfolios formed on the basis of SIZE(A) for each announcement. SIZE(A) is the natural log of the capitalized value (price of a common share number of common shares outstanding) of the announcing firm, divided by the mean of log (capitalized value) of all firms on COMPUSTAT. RABSR(NA) is the rank of mean ABSR(NA) for a portfolio. ABSR(NA) is the announcement period price revaluation index for the non-announcing firms. It is defined as the ratio of the absolute value of 2-day ( 1, 0) announcement period excess return to mean absolute value of 2-day excess returns during non-report period. RABSR(NA) Announcement number PSIZE(A)
First
Second
Third
Fourth
Fifth
Mean RABSR(NA)
Median RABSR(NA)
Panel A: 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
9 4 12 15 3 1 7 6 20 18 13 14 11 17 2 16 19 5 10 8
3 12 6 2 1 4 17 11 18 9 20 7 19 8 16 14 10 13 15 5
4 8 9 14 13 10 18 6 15 17 5 3 19 7 1 2 11 16 20 12
14 7 17 19 6 8 9 2 12 18 16 20 11 5 15 13 10 3 4 1
3 13 2 15 8 10 11 6 9 7 1 4 5 12 16 19 20 18 17 14
6.6 8.8 9.2 13.0 6.2 6.6 12.4 6.2 14.8 13.8 11.0 9.6 13.0 9.8 10.0 12.8 14.0 11.0 13.2 8.0
4 8 9 15 6 8 11 6 15 17 13 7 11 8 15 14 11 13 15 8
Pearson's product moment correlations
Estimates (%)
p-Values
Panel B: r{PSIZE(A), RABSR(NA)} r{PSIZE(A), mean RABSR(NA} r{PSIZE(A), median RABSR(NA}
18.195 38.235 38.925
0.0700* 0.0962* 0.0898*
* Significance at the 10% level.
Table 6 Tests on mean's of portfolios formed on sizes of announcing firms Where the absolute standardized residual [ABSR(NA)] is the announcement period price revaluation index for the non-announcing firms. It is defined as the ratio of the absolute value of 2-day ( 1, 0) announcement period excess return to the mean absolute value of 2-day excess returns during a non-report period. The set of all observations, which pertain to announcing firms with standardized sizes less than or equal to the median value, is classified as the portfolio of small announcing firms, otherwise, as the portfolio of large announcing firms. Standardized size of a firm is defined as the natural log of its capitalized value (price of a common share number of common shares outstanding) divided by the mean of log (capitalized value) of all firms on COMPUSTAT. Announcement number
Number of observations
Mean ABSR(NA) for portfolio of small announcing firms
Mean ABSR(NA) for portfolio of large announcing firms
First Second Third Fourth Fifth Total
5673 2400 632 483 457 9645
0.9940 1.0116 1.0114 1.0929 1.0229 1.0079
1.0367 1.1614 1.1255 0.9063 1.2903 1.0802
* Significance at the 1% level. *** Significance at the 10% level. **** Significance at the 10% (one-sided) level.
t (equal means) 1.8051*** 3.7710* 1.4579**** 1.0010 2.8084* 3.8289*
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Table 7 Regressions of ABSR(NA) on announcing and non-announcing firms' characteristics See Table 4 for variable definitions. The results do not change qualitatively when White's (1980) procedure is used to estimate the heteroskedasticity corrected t-statistics (not reported here). Parameter estimates (t-statistic in parentheses) Without announcement #2
Without announcement #3
Without announcement #4
Without announcement #5
0.9674 (59.198*) 0.0467 (5.259*) 7245 27.659 0.0001 0.37%
0.9720 (64.580*) 0.0549 (6.426*) 9013 41.294 0.0001 0.45%
0.9720 (65.040*) 0.0540 (6.623*) 9162 43.870 0.0001 0.47%
0.9851 (66.170*) 0.0398 (4.813*) 9188 23.164 0.0001 0.24%
Panel B: regression of ABSR(NA) on ABSR(A), SIZE(A) and SIZE(NA) Intercept 0.9669 0.9311 0.9651 (65.755*) (36.834*) (58.880*) ABSR(A) 0.0078 0.1272 0.0092 ( 0.226) ( 2.304**) ( 0.236) ABSR(A)*SIZE(A) 0.0713 0.1902 0.0594 (3.115*) ((5.618*) (2.256**) ABSR(A)*SIZE(NA) 0.0222 0.0051 0.0167 ( 1.155) (0.168) ( 0.769) Observations 9645 3972 7245 F-value 18.735 30.767 10.998 Probability > F 0.0001 0.0001 0.0001 Adjusted R2 0.55% 2.20% 0.41%
0.9679 (63.917*) 0.0128 (0.347) 0.0635 (2.535**) 0.0318 ( 1.145) 9013 16.454 0.0001 0.51%)
0.9672 (64.392*) 0.0109 ( 0.312) 0.0756 (3.284*) 0.0248 ( 1.182) 9162 18.591 0.0001 0.57%
0.9828 (65.492*) 0.0203 (0.575) 0.0341 (1.439***) 0.0195 ( 1.000) 9188 8.676 0.0001 0.25%
Variable
Full sample
Without announcement #1
Panel A: regression of ABSR(NA) on ABSR(A) Intercept 0.9709 0.9314 (65.455*) (36.731*) ABSR(A) 0.0547 0.1071 (5.989*) (7.765*) Observations 9645 3972 F-value 45.654 60.288 Probability > F 0.0001 0.0001 Adjusted R2 0.46% 1.47%
* Significance at the 1% level. ** Significance at the 5% level. *** Significance at the 10% level (one-sided).
The regressions were also estimated with alternative definitions of SIZE(A) and SIZE(NA) defined within industries, rather than the economy (as in Han and Wild, 1999). The results (not reported) are qualitatively similar. White's (1980) test of heteroskedasticity rejects the null of homoskedastic errors at 1% level for Eqs. (4) and (5). When White's heteroskedasticity corrected t-statistics (not reported in the paper) are used for the tests, the conclusions do not change qualitatively. Belsley et al.'s (1980) test for multicollinearity does not reject the null of zero collinearity for either equation (full sample regression). Belsley et al.'s (1980) test for influential outliers is also conducted on both regressions. The regressions are run without the observations whose studentized residuals are more than two standard deviations away from their mean. The results (not reported in the paper) do not change significantly for either equation. 5. Summary and conclusion This study advances hypotheses about announcing and non-announcing firm sizes and intra-industry information transfers around earnings announcements. Our first hypothesis (the announcing firm hypothesis) states that the infor-
mation transfer is positively related to the announcing firm size. Atiase's (1985) differential information hypothesis suggests that relatively more information is available on large firms prior to their earnings announcements. One implication of this hypothesis is that abnormal price reactions of large firms around earnings disclosures are more likely due to information about overall trends in the economy and industry sector. As a result, the disclosures by large firms should contain more relevant information for nonannouncing firms in the same industry group and thereby cause more information transfers than small firms. Our empirical results support this hypothesis. These findings are contrary to Han and Wild (1999) who conclude that announcements by small firms cause more information transfers. Lack of controls for timing and clustering of announcements, and the definition of industry groups may have affected Han and Wild's (1999) results. Another possibility is that their results are peculiar to their sample period (1984 ± 1986). The second set of hypotheses examines if there are differences in information transfers to small and large non-announcing firms. The non-announcing firm hypotheses are a set of three mutually exclusive hypotheses (a null and two one-sided alternative hypotheses). Our tests support the null hypothesis that the non-announcing firm size does
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not affect information transfers. One explanation of this finding is that information transfers are caused mainly by industry-specific information that is equally valuable to all non-announcing firms irrespective of their sizes. This finding is different from the results of Han and Wild (1999) who report that information transfer is inversely related to nonannouncing firm sizes for their sample. The results in this study are consistent with Foster's (1981) finding that the magnitudes of announcing firms' price reactions are positively correlated with the degree of information transfers. Most of the information transfer literature has concentrated on documenting the different settings in which information transfers exist without considering the firm characteristics or timings of earnings announcements that may significantly affect information transfers. Our study extends prior research by providing additional evidence of intra-industry information transfers and identifying its determinants. Such evidence enhances our understanding of the information content of accounting earnings, the economics of security pricing within industries, and the sophistication of the securities markets in impounding publicly available information in stock prices. Acknowledgments We thank Steve Balsam, Michael Brennan, Ross Jennings, Jagan Krishnan, Charles Lee, Roland Lipka, Paul Newman, Eric Press, Heibatollah Sami, the editor, and an anonymous referee for their invaluable help. We owe a special debt of gratitude to Robert Freeman and Senyo Tse for their active guidance and encouragement. Last but not least, we are also grateful to many of our fellow doctoral students at Texas and participants at the workshop at Arizona for their constructive comments.
References Atiase RK. Predisclosure Informational Asymmetries, Firm Capitalization, Earnings Reports, and Security Price Behavior around Earnings Announcement. Unpublished PhD dissertation, University of California, Berkeley, 1980. Atiase RK. Predisclosure information, firm capitalization and security price
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behavior around earnings announcements. J Account Res (Spring) 1985;23:21 ± 36. Atiase RK. Market implications of predisclosure information: size and exchange effects. J Account Res (Spring) 1987;25:168 ± 76. Atiase RK, Bamber LS, Tse S. Timeliness of financial reporting, the firm size effect, and stock price reaction to annual earnings announcement. Contemp Account Res 1987;5:526 ± 52. Baginski SP. Intraindustry information transfers associated with management forecasts of earnings. J Account Res 1987;25:196 ± 216. Bamber LS. Unexpected earnings, firm size, and trading volume around quarterly earnings announcements. Account Rev 1987;62(3):510 ± 32. Belsley DA, Kuh E, Welsch RE. Regression Diagnostics: Identifying Influential Data and Sources of Collinearity. New York, NY: Wiley, 1980. Brennan MJ, Jegadeesh N, Swaminathan B. Investment analysis and the adjustment of stock prices to common information. Rev Financ Stud (Winter) 1993;6(4):799 ± 824. Chambers AE, Penman SH. Timeliness of reporting and the stock price reaction to earnings announcements. J Account Res (Spring) 1984;22: 21 ± 47. Clinch G, Sinclair N. Intra-industry information releases: a recursive systems approach. J Account Econ 1987;9:89 ± 106. Foster G. Intra-industry information transfers associated with earnings releases. J Account Econ 1981;3:201 ± 32. Freeman RN. The association between accounting earnings and security returns for large and small firms. J Account Econ 1987;9:195 ± 228. Freeman RN, Tse S. An earnings prediction approach to examining intercompany information transfer. J Account Econ (December) 1992;15: 509 ± 23. Grant EB. Market implications of differential amounts of interim information. J Account Res (Spring) 1980;255 ± 69. Han JCY, Wild JJ. Unexpected earnings and intraindustry information transfers: further evidence. J Account Res (Spring) 1990;18:211 ± 9. Han JCY, Wild JJ. Predisclosure information, firm capitalization and earnings information transfers. J Bus Res (in press). Han JCY, Wild JJ, Ramesh K. Managers' earnings forecasts and intra-industry information transfers. J Account Econ. 1989;28(11):3 ± 33. Lo A, MacKinlay C. When are contrarian profits due to stock market overreaction. Rev Financ Stud 1990;3:175 ± 206. Olsen C, Dietrich JR. Vertical information transfers: the association between retailers' sales announcements and suppliers' security returns. J Account Res 1985;23 (Supplement):144 ± 66. Patell JM, Wolfson MA. Anticipated information releases reflected in call option prices. J Account Econ 1979;2:117 ± 40. Richardson G. The information content of annual earnings for large and small firms: further empirical evidence. Working paper, University of British Columbia, 1984. Schipper K. Information transfers. Account Horiz (December) 1990;4(4): 97 ± 107. Schoderbek MP. Theory of the dominant firm: a capital market test. Rev Quant Finance Account 1995;5:253 ± 70. White H. A heteroskedasticity-consistent covariance matrix estimator and a direct test for heteroskedasticity. Econometrica (May) 1980;48: 817 ± 88.
The differential information hypothesis, firm size, and earnings information transfer An empirical investigation Sharad C. Asthanaa,*, Birendra K. Mishrab,1 a
Department of Accounting, Fox School of Business and Management, Temple University, Philadelphia, PA 19122, USA b Department of Accounting, School of Management, University of Texas at Dallas, Richardson, TX 75083 Accepted 18 August 1999
Abstract This study examines the effects of the sizes of the announcing and non-announcing firms on information transfers. Atiase's [Atiase RK. Predisclosure information, firm capitalization and security price behavior around earnings announcements. J Account Res 1985;23:21 ± 36 (Spring)] differential information hypothesis suggests that, relative to small firms, more pre-announcement information is available on large firms. An implication of the differential information hypothesis is that abnormal returns of large firms around earnings disclosures are caused by new information regarding the economy and industry. Thus, earnings disclosures by large firms may contain information that is useful for other firms in the industry. Consistent with our prediction, we find that information transfers within an industry are positively related to the announcing firm's size. The differential information hypothesis also suggests that information transfer will be inversely related to the non-announcing firm's size. However, our results support the null hypothesis that information transfer is not a function of the non-announcing firm's size. Possible explanations of this finding are discussed in the paper. D 2001 Elsevier Science Inc. All rights reserved. Keywords: Differential information; Firm size; Earnings
1. Introduction Information transfer studies suggest that unexpected information disclosures by a firm convey information to investors of related firms. Previous studies have documented information transfers in various settings such as earnings announcements (Foster, 1981; Freeman and Tse, 1992), sales announcements (Olsen and Dietrich, 1985), and management forecasts (Baginski, 1987; Clinch and Sinclair, 1987; Han et al., 1989). The evidence of those studies reflects an ``average finding'' across all firms. An interesting question is whether information transfers vary systematically across firm characteristics. The primary purpose of this study is to investigate firm size as a determinant of informa* Corresponding author. 105 Winchester Way, Harleysville, PA 194383604, USA. Tel.: +1-215-204-0489; fax: +1-215-204-5587. E-mail addresses: [email protected] (S.C. Asthana), [email protected] (B.K. Mishra). 1 Tel.: +1-602-967-6326; fax: +1-602-965-8392.
tion transfer in the light of Atiase's (1985) differential information hypothesis. Atiase (1985) suggests that the amount of private predisclosure information is an increasing function of firm size (capitalization value). Grant (1980) and Atiase (1987) also present evidence that there are fewer Wall Street Journal news items about small firms than about large firms. Similarly, empirical research by several authors suggests that relative to large firms' announcements, small firms' earnings announcements convey more ``unexpected'' information and so are associated with more intense and sustained market adjustments (Atiase 1985; Bamber 1987; Freeman, 1987). If the information supporting the security prices of large firms and small firms differs systematically, one would expect a systematic variation in information transfers to non-announcing firms because information transfers would be related to available information. Prior research in finance also suggests that large firms' reactions to common information leads those of small firms (Lo and MacKinlay, 1990; Brennan et al., 1993).
0148-2963/01/$ ± see front matter D 2001 Elsevier Science Inc. All rights reserved. PII: S 0 1 4 8 - 2 9 6 3 ( 9 9 ) 0 0 111 - 3
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We derive two hypotheses based on earlier research. The first hypothesis predicts that the magnitude of information transfer is positively related to the announcing firm size. The second hypothesis is a set of three sub-hypotheses that tests the null that information transfers are independent of the non-announcing firms' sizes against two directional alternative hypotheses. The hypotheses are tested using standard event-study methodology. Quarterly earnings announcements of firms in the same industry are used to test the hypotheses. Analyses are conducted using the absolute value of normalized 2-day announcement period excess returns of the announcing and non-announcing firms obtained from a single-index asset-pricing model. Two prior studies have examined the effects of firm sizes on earnings-related information transfers. Han and Wild (1999) show that non-announcing firms' price revaluations associated with other firms' earnings reports are inversely related to firm capitalization; where firm capitalization is defined in reference to both announcing and non-announcing firms. In other words, Han and Wild (1999) show that smaller earnings information transfers are associated with large announcing (non-announcing) firms in comparison to small announcing (non-announcing firms) firms. They interpret their results as reflective of differential predisclosure information production and dissemination by private parties, which is positively related to size, yielding earnings reports with price informativeness inversely related to firm sizes. Schoderbek (1995) explores the economic relations between dominant and fringe firms. He finds significant positive information transfers from dominant firms to fringe firms, indicating that the earnings of dominant firms are signaling industry-wide information to the fringe firms. In contrast, the information transfers around earnings announcements of fringe firms do not induce significant information transfers to the dominant firms. Contrary to Han and Wild (1999), Schoderbek's (1995) results suggest a positive relationship between the size of the announcing firms and the extent of information transfers. Thus, the implications of the differential information hypothesis for earnings-induced information transfers are not clear. We also discuss the implications of three factors Ð timing of announcements, clustering of announcements, and definition of industry groups in information transfer studies that make the results of Schoderbek (1995) and Han and Wild (1999) difficult to assess. A further problem with both studies is their limited sample sizes. Han and Wild's (1999) sample is confined to 1984 ± 1986 period and Schoderbek's (1995) sample includes only 12 dominant and 49 fringe firms. Generalization of such results usually poses problems of external validity. Our sample, on the other hand, covers 10 years of data (1984 ±1993) with 323 announcements. After controlling for these three factors, we show that information transfers around earnings disclosures are increasing in the size of the announcing firm and not significantly related to the size of the non-announcing firms.
The remainder of the paper is organized as follows. Section 2 discusses the differential information hypothesis, considers its implications for firm size and information transfers and develops the hypotheses tested in this study. Section 3 covers research design, sample selection, and test statistics. Section 4 presents analysis and results. Section 5 summarizes the findings and presents the conclusions. 2. The differential information hypothesis Atiase (1980) argues that information availability may vary systematically between large and small firms due to differential incentives for information search. In an ideal economic setting (risk-neutral investors and unlimited financing at a constant risk-free rate), aggregate trading profits for a given change in per share prices are proportional to the market value of the firm. This implies that a larger aggregate profit could be made from the knowledge of a fixedpercentage mispricing of common equity of a large firm than that of a small firm. An investor will search for information about a firm as long as the marginal cost of information search is less than or equal to the expected marginal profit from trading based on the incremental information available from the search. Hence, there is more incentive for information searches for large firms compared to small firms, i.e., a differential incentive for information searches exists. The above discussion implies that more information is available for large firms than for small firms due to the differential incentive for information search. Inter-firm information transfer occurs when market participants use the information released by firm i, an announcing firm (ieSa, where Sa is the set of announcing firms) to make inferences about the security return behavior of firm j, a non-announcing firm ( jeSn, where Sn is the set of nonannouncing firms), or equivalently f Rj =fj ; jeSn 6 f Rj =fj ; fi ; jeSn ; ieSa ; for all Sn and Sa belonging to S where f(./.) is a conditional density function, Rj is the security return of firm j, and fi (fj) is the available information (accounting or otherwise) for firm i (j), and S is the universe of firms. This is a definition of inter-firm information transfer in the broadest sense. However, for the purpose of this study, the universe of firms (S) is restricted to industry groups. Moreover, fi and fj are also restricted to the earnings announcements of firms i and j, respectively. In general, intra-industry information transfers reflect the similar conditions influencing the announcing and nonannouncing firms. Earlier accounting studies (Richardson, 1984; Atiase, 1985; Bamber, 1987; Freeman, 1987) suggest that more accurate firm-specific information is available for large firms than for small firms. Thus, market-adjusted returns of large firms during earnings announcements are more likely to be due to information pertaining to the overall
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trend in the economy or industry sector than due to firmspecific information. This implies that large firms' announcements have greater potential for conveying information about other firms in the industry than small firms' announcements. On the other hand, an announcement by a small firm may have substantial firm-specific information but may contain little relevant information about other firms in the industry. Thus, information transfers will be increasing in announcing firm size. This leads to our announcing firm hypothesis. H10. E(hj/fiL) E(hj/fiS), jeSn, ieSa; for all Sn and Sa belonging to S. H1a. E(hj/fiL) > E(hj/fiS), jeSn, ieSa; for all Sn and Sa belonging to S. where hj is a non-directional measure of abnormal return activity based on announcement period excess returns. The superscripts L and S on fi denote large and small announcing firms, respectively. Several competing hypotheses can be framed to test the effect of size of the non-announcing firm on information transfer. Availability of firm-specific information is an increasing function of the firm's size (Richardson, 1984; Atiase, 1985; Bamber, 1987; Freeman 1987). As a result, the earnings announcements of other firms have less (more) potential to convey firm-specific information about large (small) non-announcing firms (Han and Wild, 1999). This suggests that an announcement has more potential for information transfer to small non-announcing firms than to large non-announcing firms (H2a). Alternatively, large and small firms differ in market structures and macro-economic environment. Large firms generally have broader markets (compete in national or even international markets), diversified product lines, multiple business segments, and large numbers of shareholders. Small firms tend to concentrate on local and regional markets or may operate in niche
39
markets. This suggests that large firms could be affected by a wider range of factors than small firms. Hence, there is greater possibility of industry-specific information transfers to large non-announcing firms than to small non-announcing firms (H2b). Finally, it can be argued that the unexpected components of earnings disclosures contain industry- and economy-wide information as well as firm-specific information. The firmspecific component causes price reaction in the disclosing firm's stock but no information transfers. On the other hand, the industry- and economy-wide component causes information transfers. The latter component contains information that is equally valuable to all non-announcing firms irrespective of their sizes. Therefore, there is no differential response to an earnings disclosure among non-announcing firms (H20). This argument is depicted in Fig. 1. Based on the above arguments, we propose the following non-announcing firm hypotheses. H20. E(hjS/fi) = E(hjL/fi), jeSn, ieSa; for all Sn and Sa belonging to S. H2a. E(hjS/fi) > E(hjL/fi), jeSn, ieSa; for all Sn and Sa belonging to S. H2b. E(hjS/fi) E(hjL/fi), jeSn, ieSa; for all Sn and Sa belonging to S. where the variables are defined as previously, and the superscripts S and L on hj denote small and large nonannouncing firms, respectively. 3. Sample selection, methodology, and test statistics The sample consists of firms with the following information on the 1994 quarterly COMPUSTAT file: earnings announcement dates, number of shares used to compute
Fig. 1. Dissemination of earnings information.
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primary earnings per share, and closing per-share price in the current quarter. Sample firms also meet the following criteria: (1) December 31 year-end, to synchronize the fiscal quarters over which we examine information transfers; (2) earnings announcement within 90 days of the end of the fiscal quarter, to remove erroneous announcement dates and increase the likelihood that any information transfers are from announcing firms' results in corresponding quarters; (3) the earnings announcement is one of the first five announcements in that industry for that quarter, to control for the timing and clustering of announcements (discussed below); (4) there is no other earnings announcement in that industry in that quarter in the interval ( 1, 1) relative to the earnings announcement day, to control for contemporaneous announcements; (5) none of the non-announcing firms announces its own earnings in the interval ( 3, 3), to avoid confounding events. Increasing this interval to 15 days ( 7, +7) did not change the results qualitatively, even though the sample size is significantly reduced. Firms, satisfying the above criteria, are retained in the sample if (1) daily returns are available from CRSP 1994 daily return files (covering NYSE and AMEX); (2) at least 150 days of returns exist in the 200 trading days ( 240, 41) preceding the day of the earnings announcement, to facilitate the estimation of the asset pricing model; (3) there are no missing returns in the interval ( 40, 40), to facilitate the estimation of abnormal returns around the earnings disclosure; and (4) at least 75 firm-quarter observations are available during the entire sampling period. An announcement by a firm in any quarter is considered a firmquarter observation. The sample is restricted to at least 75 firm-quarter observations to increase the power of the test. This results in a sample of 323 announcements and 9645 firm-quarter observations for 1005 non-announcing firms from the first quarter of 1984 through the third quarter of 1993. The 323 announcements are distributed as follows: 172 first announcements, 72 second announcements, 30 third announcements, 25 fourth announcements, and 24 fifth announcements. The announcing and non-announcing firms belong to 18 industry groups. The timing of announcements can affect inferences in information transfer studies. Firm characteristics (for example, size) may vary systematically with the announcement sequence. Moreover, the amount of information that is useful for other firms may be a decreasing function of the announcement number, since the later announcements may contain redundant information (Freeman and Tse, 1992). Hypothetically if this is the case, both firm size and information transfer will be functions of the timing of announcement. Lack of control for the timing effect (Schoderbek, 1995; Han and Wild, 1999) could result in the spurious inference that size affects information transfer. Clustering of disclosures leads to simultaneous earnings announcements problem. Thus, release of earnings by different firms within 2 days of each other would cause such firms to be classified as announcing as well as
non-announcing on the same date. Such announcements are deleted from the sample. Another problem that arises due to simultaneous earnings release is unique to the purpose of this study. Since several firms may announce their earnings on the same date in an industry group, it is possible that one, some, or all of them together are responsible for information transfers to the non-announcing firms in that industry group. As a result, it is impossible to assign a unique announcing firm size to such announcements. To control for the timing and clustering effects, we restrict our sample to the first five announcements in an industry-quarter, as in Freeman and Tse (1992). Analysis of the announcement dates on COMPUSTAT reveals that the first five announcements in any industry-quarter are less clustered than the subsequent announcements. Most previous research, discussed above, confines the sample selection to four-digit SIC codes. If information transfers also occur to non-announcing firms in the same two-digit SIC codes (but different four-digit SIC codes), previous research may have problems with simultaneous announcements in the same two-digit but different fourdigit SIC codes. We conduct tests (not reported) by splitting the sample into two portfolios Ð one with same four-digit SIC codes and the other with different four-digit but same two-digit SIC codes. The amounts of information transfers within these two portfolios are not significantly different. Therefore, we use two-digit SIC codes to define industries. Estimates of the abnormal returns, ui,t for the period ( 40, 40) relative to day 0Ð the earnings announcement day, are obtained by using a single index asset pricing model in Eq. (1): ui;t Ri;t
ai bi RM;t
1
where the daily returns of the firm (Ri, t) are regressed against the value-weighted market returns (RM,t), during the period ( 240, 41) to estimate ai and bi. Since this study examines market activity associated with information transfers, it is necessary to control for the level of market activity in a non-report period. We use the average abnormal returns during a control period to standardize the firm's announcement period abnormal returns. The standardization endogenously controls for the firm-specific information during the non-report period and thereby any crosssectional differences in the average risk-adjusted returns. The approach taken here is similar to Atiase (1985), wherein revaluation indexes measure announcement period security price revaluations relative to average price revaluation during non-report periods. Our hypotheses are concerned with the magnitude (rather than the direction) of security price reactions. As a result, our revaluation index has to be based on a transformation that ignores the sign of the announcement period's unexpected return. Prior research (Foster, 1981; Chambers and Penman, 1984; Atiase et al., 1987; Han et al., 1989) has used several different transformations to
S.C. Asthana, B.K. Mishra / Journal of Business Research 53 (2001) 37±47
develop non-directional measures of security return activity. We use the absolute standardized return residual (ABSR) metric since it has the least likelihood of biasing the results due to a few extreme values. ABSR is defined as the ratio of the absolute value of the 2-day announcement period excess returns to the mean absolute value of the non-report-period excess returns, which are calculated over 30, 2-day intervals (Eq. (2)). These 30 intervals consist of two sub-periods of 15 consecutive 2-day intervals. One is a pre-announcement period starting from day 11 and going backward through day 40, and the other is a post-announcement period starting from day +11 and going forward through day +40. jui;e j
ABSRi 1 30
15 P b1
jui;b j
15 P f 1
!
2
jui;f j
where ui,e is firm i's event period excess return, calculated over a 2-day interval ( 1, 0); ui,b is the excess return in the bth successive 2-day interval going backward; and ui,f is the excess return in the fth successive 2-day interval going forward. We use the window ( 1, 0) since earnings, earnings forecasts, and dividend announcements are reported in the Dow Jones News Service one trading day prior to publication in the Wall Street Journal (Patell and Wolfson, 1979). Previous researchers (Foster, 1981; Han et al., 1989) also used this accumulation period. In the rest of the paper ABSR(A) pertains to announcing firms and ABSR(NA) pertains to non-announcing firms. Intuitively, ABSR(NA) can be viewed as the ratio of the ``new'' information for the non-announcing firm conveyed to the market by the announcing firm's earnings announcement to the non-announcing firm's information over the non-report period. The expected value of ABSR(NA) is 1.0. A value greater than 1.0 for ABSR(NA) implies that an information transfer exists. That is, the announcement conveys information about the non-announcing firm beyond what was available outside the announcement period. Since each firm acts as its own control, this procedure explicitly controls for any volatility in returns that is correlated with firm size. We define firm size as the capitalized value, or market value of common shares outstanding, consistent with the underlying rationale for the firm size effect documented in the accounting literature (Atiase, 1985). For statistical analysis, the capitalized values are transformed into their natural logs (LCV). To control for the fact that firm size may vary from year-to-year, a relative measure of firm size is used. Each firm's LCV is standardized by the mean LCV of all the firms in the economy for each year as in Eq. (3) below: SIZEi
1 N
LCVi N P LCVi
i1
3
41
where SIZE is the standardized measure of size for the firm i; LCVi is the natural log of firm i's market value at the end of the quarter; and N is the number of firms in the economy (proxied by the population of firms on COMPUSTAT). Later in the paper, SIZE(A) denotes standardized size of the announcing firm and SIZE(NA) denotes the size of the non-announcing firm. The division by the denominator is just a scaling factor and does not change the results. Atiase (1980) uses the natural log of firm size in his regression analysis. Han and Wild (1999) also use scaled size variable in parts of their analysis. The compelling reason to use the log value of the firm size has certain econometric benefits compared to absolute size in linear regressions since it can linearize product and power form relations. The methodology used in this study is a variant of the one used by Foster (1981). The research design consists of examining the abnormal security returns of two groups of firms Ðan announcing group of firms and a non-announcing group of firms. A firm i, belonging to the set of feasible announcing firms (ieSa), is classified as an announcing firm for the date when its earnings report is released. Price revaluation indices (ABSR) are calculated from the cumulative abnormal returns for the 2 days ( 1, 0) for announcing firm i and non-announcing firms j belongs to set of feasible non-announcing firms ( jeSn). Each announcing firm i is placed in 1 of 20, equal, mutually exclusive and exhaustive portfolios formed separately for each of the first five announcements on the basis of the size of the announcing firm. All the corresponding non-announcing firms are assigned firm i's portfolio. Thus, for the first announcement of each quarter, portfolio 1 (20) contains the announcing firms with the smallest (largest) SIZE(A). The mean ABSR is estimated for each of these 100 (5 20) portfolio ± announcement number combinations. Next, the mean ABSR(NA) are ranked from 1 to 20 [RABSR(NA)] within each announcement number. Conversion of SIZE(A) into portfolios 1 ±20 and ranking of mean ABSR(NA) from 1 to 20 for each announcement number removes the effects of timing of announcement on SIZE(A) and ABSR(NA). Pearson's product ± moment correlation coefficient is calculated for portfolio number and RABSR(NA). If ABSR(NA) increases with SIZE(A) (after controlling for the timing of announcement), the Pearson's coefficient should be positive. Pearson's coefficients are also estimated for the portfolio number and mean/median RABSR(NA) (across all five announcements). These coefficients should also be positive. The test is repeated for portfolios formed on SIZE(NA). The next portfolio test compares the means of ABSR(NA) across two portfolios (with median SIZE(A) as the cut-off). First, the observations are ordered by announcement number. Within each announcement number, the set of observations with SIZE(A) less than or equal to the median value are placed in portfolio of small announcing firms and the remaining observations in portfolio of large announcing
42
S.C. Asthana, B.K. Mishra / Journal of Business Research 53 (2001) 37±47
firms. The mean ABSR(NA) for these two portfolios are compared. If the hypothesized size effects exist, the mean ABSR(NA) for portfolio of large announcing firms should be greater than the mean ABSR(NA) for portfolio of small announcing firms. The test is repeated with portfolios formed on the basis of SIZE(NA). Linear regression analysis is also performed to analyze the effects of the magnitude of the announcing firm's price reaction, announcing firm size, and non-announcing firm size on information transfers. First, to confirm Foster's (1981) finding that the magnitudes of announcing firms' price reactions are positively correlated with the degree of information transfers and that the model is correctly specified, we run the following regression: ABSR NA a0 a1 ABSR A e0
The following regression is estimated to examine if information transfer is a function of the announcing and nonannouncing firms' sizes: ABSR NA b0 b1 ABSR A b2 ABSR A SIZE A b3 ABSR A SIZE NA e1 5 where b are the regression parameters and e1 is the error term. We expect b1 and b2 to be positive and b3 to be negative under H2a, positive under H2b, or insignificant under H20. We re-estimate regressions (4) and (5) with successive announcement numbers dropped from the regressions to insure that the results are not driven by the timing of announcements.
4
where a are the regression parameters and e0 is the error term. According to Foster (1981), a1 should be significant and positive. We use ABSR(A) as the explanatory variable instead of the unexpected earnings. Han and Wild (1990) show that the inferences using announcing firms' unsystematic stock returns as proxy for the financial reporting signal are similar to the inferences using unexpected earnings. Additionally, our model specification helps to control for information, other than earnings, contained in the disclosures (Schipper, 1990). One concern with such a model is that co-movements of returns can be wrongly interpreted as information transfers. However, since we use market-adjusted abnormal returns in computing ABSR(A) and ABSR(NA), the covariance of stock returns does not affect our conclusions.
4. Analysis and results Table 1 shows the distribution of the capitalization values (in millions of dollars) for the announcing and non-announcing firms by industry groups. The announcing firms' capitalization values range from US$29 million to US$249 billion, while those for non-announcing firms range from almost zero to US$161 billion. Table 2 provides the distribution of SIZE(A) and SIZE(NA) by industry groups. One advantage of using SIZE variable, instead of the capitalization value, is that it makes the variable across industries and years comparable. For example, a firm size of US$1 million in 1984 may have different implications for information transfers than a firm size of US$1 million in 1993. As a result of standardiza-
Table 1 Distribution of capitalization values of announcing and non-announcing firms by industry groups Where capitalization value is the price at the end of the quarter multiplied by the number of shares outstanding at that time. The range, mean, median, and standard deviation are in US$ million (rounded to the nearest million). Announcing firms
Non-announcing firms
SIC
Observations
Range
Mean
Median
Standard deviation
Observations
Range
Mean
Median
Standard deviation
13 27 28 30 33 34 35 36 37 38 48 49 50 60 61 63 67 73 Total
30 19 24 22 17 5 11 18 10 28 19 19 25 4 20 23 17 12 323
218 ± 16,190 484 ± 6082 45 ± 25,395 161 ± 3360 59 ± 6140 31 ± 825 1087 ± 95,697 29 ± 49,703 139 ± 1345 151 ± 5046 478 ± 5334 117 ± 6812 105 ± 2870 404 ± 3738 147 ± 249,036 166 ± 2564 55 ± 828 42 ± 2001 29 ± 249,036
3421 2717 12,167 969 2785 564 13,821 13,058 1110 3848 2813 1546 779 1352 14,394 1323 269 549 4523
858 2729 10,846 905 2908 593 6202 3689 966 4324 2632 1002 470 633 1539 1225 211 448 1279
4207 1442 7531 777 2445 322 27,313 17,443 920 1868 1373 1563 694 1601 55,266 712 201 559 15,805
782 293 1308 234 316 79 405 546 314 648 455 2319 158 242 210 789 392 155 9645
0 ± 16,350 14 ± 15,334 8 ± 51,201 6 ± 6535 23 ± 5661 21 ± 12,301 12 ± 93,362 10 ± 161,116 12 ± 22,253 10 ± 22,290 28 ± 34,867 11 ± 19,607 16 ± 4409 27 ± 15,409 8 ± 11,058 7 ± 27,296 4 ± 1817 19 ± 7562 0 ± 161,116
1051 2143 4233 1087 1057 1004 2443 1929 2077 1693 6426 1537 664 1435 2328 1967 196 555 2135
241 1840 1457 367 622 451 413 255 860 443 2782 825 119 814 1258 1211 135 326 700
2372 2117 6859 1396 1194 1838 10,239 7890 3273 3201 7132 2045 1015 1763 2637 2640 229 876 4717
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43
Table 2 Distribution of standardized sizes (SIZE) of announcing and non-announcing firms by industry groups Where standardized size of a firm (SIZE) is defined as the natural log of its capitalized value (price of a common share number of common shares outstanding) divided by the mean of log (capitalized value) of all firms on COMPUSTAT. Announcing firms
Non-announcing firms
SIC
Observations
Range
Mean
Median
Standard deviation
Observations
Range
Mean
Median
Standard deviation
13 27 28 30 33 34 35 36 37 38 48 49 50 60 61 63 67 73 Total
30 19 24 22 17 5 11 18 10 28 19 19 25 4 20 23 17 12 323
0.90 ± 1.66 1.05 ± 1.49 0.64 ± 1.73 0.88 ± 1.40 0.72 ± 1.50 0.59 ± 1.15 1.21 ± 1.95 0.57 ± 1.82 0.84 ± 1.37 0.86 ± 1.50 1.04 ± 1.47 0.84 ± 1.47 0.78 ± 1.33 1.02 ± 1.39 0.88 ± 2.09 0.88 ± 1.34 0.71 ± 1.13 0.65 ± 1.28 0.57 ± 2.09
1.24 1.33 1.52 1.12 1.22 1.01 1.50 1.37 1.13 1.36 1.34 1.19 1.07 1.16 1.26 1.20 0.91 0.98 1.24
1.18 1.37 1.59 1.15 1.34 1.13 1.53 1.38 1.20 1.44 1.34 1.17 1.06 1.12 1.28 1.21 0.90 1.04 1.21
0.24 0.14 0.25 0.14 0.28 0.24 0.19 0.35 0.18 0.16 0.11 0.15 0.16 0.17 0.27 0.11 0.14 0.22 0.25
782 293 1308 234 316 79 405 546 314 648 455 2319 158 242 210 789 392 155 9645
0.25 ± 1.71 0.45 ± 1.62 0.34 ± 1.87 0.31 ± 1.48 0.53 ± 1.53 0.51 ± 1.57 0.42 ± 2.00 0.40 ± 2.02 0.43 ± 1.70 0.40 ± 1.68 0.57 ± 1.80 0.42 ± 1.73 0.49 ± 1.41 0.56 ± 1.63 0.34 ± 1.58 0.34 ± 1.73 0.25 ± 1.28 0.53 ± 1.50 0.25 ± 2.02
0.96 1.22 1.23 1.04 1.09 1.01 1.04 1.00 1.13 1.05 1.33 1.14 0.90 1.16 1.16 1.16 0.82 0.97 1.11
0.93 1.29 1.23 1.00 1.09 1.05 1.03 0.94 1.16 1.04 1.36 1.15 0.83 1.15 1.21 1.21 0.83 1.00 1.12
0.28 0.21 0.28 0.25 0.19 0.26 0.28 0.30 0.28 0.28 0.29 0.21 0.27 0.18 0.28 0.23 0.17 0.19 0.27
tion, the variability in capitalization value is also reduced thereby strengthening the power of our tests. Note that in Table 1, the total standard deviations are more than twice the means while in Table 2, the total standard deviations are less than one-fourth of the means. The mean size of announcing firms (Table 2) is greater than the mean size of non-announcing firms. This occurs due to the fact that only the first five announcements in any industry-quarter
are included in the sample. However, there is a largeenough variation in the size of announcing firms (from 0.57 to 2.09) to analyze the effects of size of announcing firms on information transfers. The distribution of ABSR(A) and ABSR(NA) is presented in Table 3. ABSR(A) and ABSR(NA) have been winsorized in the interval ( 5, 5) to avoid the possibility of extreme values driving the results.
Table 3 Distribution of absolute standardized residuals (ABSR) of announcing and non-announcing firms by industry groups Where the absolute standardized residual (ABSR) is the announcement period price revaluation index. It is defined as the ratio of the absolute value of 2-day ( 1, 0) announcement period excess return to the mean absolute value of 2-day excess returns during a non-report period. Announcing firms
Non-announcing firms
SIC
Observations
Mean
Median
Standard deviation
Observations
Mean
Median
Standard deviation
13 27 28 30 33 34 35 36 37 38 48 49 50 60 61 63 67 73 Total
30 19 24 22 17 5 11 18 10 28 19 19 25 4 20 23 17 12 323
1.2886 0.9657 1.5778 1.1264 2.4270 0.9222 1.0281 2.0256 1.5110 1.2396 1.2893 1.2159 1.5291 1.7958 1.0571 1.9860 1.2668 1.4656 1.4305
1.0988 0.6209 1.1834 0.6672 1.3403 0.2033 0.6664 1.3835 1.5455 1.2159 1.3661 0.9622 1.0903 1.3613 0.6977 1.6632 1.4069 1.1316 1.0985
0.9731 1.0998 1.5238 1.5251 3.1774 1.1484 1.5052 1.9099 0.9547 0.8756 0.8809 1.0589 1.2705 1.8049 0.8351 1.6257 0.8882 1.0208 0.2522
782 293 1308 234 316 79 405 546 314 648 455 2319 158 242 210 789 392 155 9645
1.0149 0.9309 1.0361 1.0625 1.1190 0.8478 1.0407 1.1434 1.0662 1.0390 1.0740 1.0838 1.0276 0.9114 1.1031 1.1296 1.0209 1.0367 1.0589
0.8021 0.7126 0.7813 0.7666 0.8050 0.6954 0.8407 0.8816 0.7940 0.7839 0.8611 0.8374 0.7726 0.6644 0.7482 0.7871 0.8076 0.8139 0.8000
0.8864 0.8730 1.0498 0.9851 1.0284 0.6055 0.8843 01.0392 0.9574 1.0272 0.8901 1.0987 0.9274 0.8716 1.5237 1.2081 0.9064 0.8698 1.0342
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Table 4 Pearson's correlation matrix (Figures in parentheses are the p-values) Where postscript ``A'' implies announcing firm and ``NA'' implies non-announcing firm. The absolute standardized residual (ABSR) is the announcement period price revaluation index for the announcing or non-announcing firms. It is defined as the ratio of the absolute value of 2-day ( 1, 0) announcement period excess return to the mean absolute value of 2-day excess returns during a non-report period. Standardized size (SIZE) of a firm is defined as the natural log of its capitalized value (price of a common share number of common shares outstanding) divided by the mean of log (capitalized value) of all firms on COMPUSTAT. ABSR(NA) Announcement number SIZE(A) SIZE(NA) ABSR(A)
3.38% 1.94% 0.89% 6.87%
(0.0009) (0.0567) (0.3843) (0.0001)
ABSR(A)
SIZE(NA)
5.42% (0.0001) 2.30% (0.0238) 0.45% (0.6573)
As expected, the mean ABSR(A) is greater than 1 in 16 out of 18 industry groups, while mean ABSR(NA) is greater than 1 in 15 out of 18 industry groups. The correlation matrix is shown in Table 4. Announcement number and ABSR(NA) are positively correlated (3.38%, p = 0.0009) while announcement number and SIZE(A) are negatively correlated ( 18.96%, p = 0.0001). If this relationship is universally true (and not limited to the first five announcements as in our sample), then ABSR(NA) and SIZE(A) may appear to be inversely correlated (as in Han and Wild, 1999). This observed relationship might not be causal but a consequence of missing controls for timing of announcements. The correlation between SIZE(A) and ABSR(NA) is positive (1.94%, p = 0.0567) as predicted by H1a. The correlation between SIZE(NA) and ABSR(NA) is, however, not significant ( 0.89%, p = 0.3843). ABSR(A) and ABSR(NA) are positively correlated (3.38%, p = 0.0009) implying that the magnitude of information transfer varies directly with the magnitude of announcing firm's own price reaction. The positive correlation between SIZE(A) and SIZE(NA) may be an artifact of the variation of size within industry groups. In other words, if an industry is more capital intensive compared to rest of the industry groups, then both SIZE(A) and SIZE(NA) will be large for this industry group. The results of Pearson's correlation tests with portfolios formed on SIZE(A) are shown in Table 5. As predicted, the three correlation coefficients are significant and positive [r{PSIZE(A), RABSR(NA)} = 18.195%, p = 0.0700; r{PSIZE(A), mean RABSR(NA)} = 38.235%, p = 0.0962; r{PSIZE(A), median RABSR(NA)} = 38.925%, p = 0.0898]. Thus, information transfer increases with the size of the announcing firm, after controlling for the timing of announcement. A similar analysis with the portfolios formed on the basis of SIZE(NA) yielded insignificant correlation coefficients (results not reported). Thus, size of the non-announcing firm does not appear to be a determinant of information transfer. Table 6 presents the results of test on means of portfolios of small and large announcing firms for each announcement number. The mean ABSR(NA) for portfolio of large announcing firms is significantly greater than the mean ABSR(NA) for portfolio of small announcing firms for all
12.38% (0.0001) 10.47% (0.0001)
SIZE(A) 18.96% (0.0001)
announcements except the fourth (t for first announcement = 1.8051; t for second announcement = 3.7710; t for third announcement = 1.4579; t for fourth announcement = 1.0010; and t for fifth announcement = 2.8084). For the total sample also the results are in the predicted direction (t = 3.8289). Similar tests are also performed with portfolios formed on sizes of non-announcing firms (results not reported). The null of equal mean ABSR(NA) is not rejected for any announcement or the total sample. Overall, the above results support H1a and H20. Regression results of Eq. (4) are shown in panel A of Table 7. The coefficient of ABSR(A) for the full sample (0.0547, t = 5.989) is significantly positive as predicted. The results do not change qualitatively when the regression is reestimated with successive announcement numbers dropped. The test results are thus not an artifact of the timing of announcement. This finding supports Foster's (1981) contention that magnitude of information transfer is an increasing function of the magnitude of announcing firm's own price reaction. These results also provide evidence that variables ABSR(A) and ABRS(NA) are defined and measured appropriately and that the regression model is correctly specified. Panel B of Table 7 presents the results of Eq. (5). In the regression on the full sample, the coefficient of ABSR(A) ( 0.0078, t = 0.226) is insignificant; the coefficient of ABSR(A)*SIZE(A) is significant and positive (0.0713, t = 3.115); and the coefficient of ABSR(A)*SIZE(NA) is insignificant ( 0.0222, t = 1.155). An insignificant coefficient of ABSR(A) in Eq. (5) implies that when SIZE(A) and SIZE(NA) are zero, there is no reaction to ABSR(A) and hence, no information transfer. The overall results do not change qualitatively when Eq. (5) is re-estimated with successive announcement numbers dropped out. However, when regression (5) is run without the first announcement, the coefficient of ABSR(A) does become significantly negative. On further investigation, we found that this coefficient has significantly high collinearity (variance inflation factor of 16.14 which is more than the critical value of 10). Presence of collinearity in this variable makes the variance of its coefficient biased. Thus, the t-statistic of ABSR(A) is biased and unreliable for this particular regression. Overall, the regression results support H1a and H20.
S.C. Asthana, B.K. Mishra / Journal of Business Research 53 (2001) 37±47
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Table 5 Pearson's correlation analysis with control for timing of announcement: portfolios formed on SIZE(A) Where postscript ``A'' implies announcing firm and ``NA'' implies non-announcing firm. PSIZE(A) are the 20 portfolios formed on the basis of SIZE(A) for each announcement. SIZE(A) is the natural log of the capitalized value (price of a common share number of common shares outstanding) of the announcing firm, divided by the mean of log (capitalized value) of all firms on COMPUSTAT. RABSR(NA) is the rank of mean ABSR(NA) for a portfolio. ABSR(NA) is the announcement period price revaluation index for the non-announcing firms. It is defined as the ratio of the absolute value of 2-day ( 1, 0) announcement period excess return to mean absolute value of 2-day excess returns during non-report period. RABSR(NA) Announcement number PSIZE(A)
First
Second
Third
Fourth
Fifth
Mean RABSR(NA)
Median RABSR(NA)
Panel A: 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
9 4 12 15 3 1 7 6 20 18 13 14 11 17 2 16 19 5 10 8
3 12 6 2 1 4 17 11 18 9 20 7 19 8 16 14 10 13 15 5
4 8 9 14 13 10 18 6 15 17 5 3 19 7 1 2 11 16 20 12
14 7 17 19 6 8 9 2 12 18 16 20 11 5 15 13 10 3 4 1
3 13 2 15 8 10 11 6 9 7 1 4 5 12 16 19 20 18 17 14
6.6 8.8 9.2 13.0 6.2 6.6 12.4 6.2 14.8 13.8 11.0 9.6 13.0 9.8 10.0 12.8 14.0 11.0 13.2 8.0
4 8 9 15 6 8 11 6 15 17 13 7 11 8 15 14 11 13 15 8
Pearson's product moment correlations
Estimates (%)
p-Values
Panel B: r{PSIZE(A), RABSR(NA)} r{PSIZE(A), mean RABSR(NA} r{PSIZE(A), median RABSR(NA}
18.195 38.235 38.925
0.0700* 0.0962* 0.0898*
* Significance at the 10% level.
Table 6 Tests on mean's of portfolios formed on sizes of announcing firms Where the absolute standardized residual [ABSR(NA)] is the announcement period price revaluation index for the non-announcing firms. It is defined as the ratio of the absolute value of 2-day ( 1, 0) announcement period excess return to the mean absolute value of 2-day excess returns during a non-report period. The set of all observations, which pertain to announcing firms with standardized sizes less than or equal to the median value, is classified as the portfolio of small announcing firms, otherwise, as the portfolio of large announcing firms. Standardized size of a firm is defined as the natural log of its capitalized value (price of a common share number of common shares outstanding) divided by the mean of log (capitalized value) of all firms on COMPUSTAT. Announcement number
Number of observations
Mean ABSR(NA) for portfolio of small announcing firms
Mean ABSR(NA) for portfolio of large announcing firms
First Second Third Fourth Fifth Total
5673 2400 632 483 457 9645
0.9940 1.0116 1.0114 1.0929 1.0229 1.0079
1.0367 1.1614 1.1255 0.9063 1.2903 1.0802
* Significance at the 1% level. *** Significance at the 10% level. **** Significance at the 10% (one-sided) level.
t (equal means) 1.8051*** 3.7710* 1.4579**** 1.0010 2.8084* 3.8289*
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Table 7 Regressions of ABSR(NA) on announcing and non-announcing firms' characteristics See Table 4 for variable definitions. The results do not change qualitatively when White's (1980) procedure is used to estimate the heteroskedasticity corrected t-statistics (not reported here). Parameter estimates (t-statistic in parentheses) Without announcement #2
Without announcement #3
Without announcement #4
Without announcement #5
0.9674 (59.198*) 0.0467 (5.259*) 7245 27.659 0.0001 0.37%
0.9720 (64.580*) 0.0549 (6.426*) 9013 41.294 0.0001 0.45%
0.9720 (65.040*) 0.0540 (6.623*) 9162 43.870 0.0001 0.47%
0.9851 (66.170*) 0.0398 (4.813*) 9188 23.164 0.0001 0.24%
Panel B: regression of ABSR(NA) on ABSR(A), SIZE(A) and SIZE(NA) Intercept 0.9669 0.9311 0.9651 (65.755*) (36.834*) (58.880*) ABSR(A) 0.0078 0.1272 0.0092 ( 0.226) ( 2.304**) ( 0.236) ABSR(A)*SIZE(A) 0.0713 0.1902 0.0594 (3.115*) ((5.618*) (2.256**) ABSR(A)*SIZE(NA) 0.0222 0.0051 0.0167 ( 1.155) (0.168) ( 0.769) Observations 9645 3972 7245 F-value 18.735 30.767 10.998 Probability > F 0.0001 0.0001 0.0001 Adjusted R2 0.55% 2.20% 0.41%
0.9679 (63.917*) 0.0128 (0.347) 0.0635 (2.535**) 0.0318 ( 1.145) 9013 16.454 0.0001 0.51%)
0.9672 (64.392*) 0.0109 ( 0.312) 0.0756 (3.284*) 0.0248 ( 1.182) 9162 18.591 0.0001 0.57%
0.9828 (65.492*) 0.0203 (0.575) 0.0341 (1.439***) 0.0195 ( 1.000) 9188 8.676 0.0001 0.25%
Variable
Full sample
Without announcement #1
Panel A: regression of ABSR(NA) on ABSR(A) Intercept 0.9709 0.9314 (65.455*) (36.731*) ABSR(A) 0.0547 0.1071 (5.989*) (7.765*) Observations 9645 3972 F-value 45.654 60.288 Probability > F 0.0001 0.0001 Adjusted R2 0.46% 1.47%
* Significance at the 1% level. ** Significance at the 5% level. *** Significance at the 10% level (one-sided).
The regressions were also estimated with alternative definitions of SIZE(A) and SIZE(NA) defined within industries, rather than the economy (as in Han and Wild, 1999). The results (not reported) are qualitatively similar. White's (1980) test of heteroskedasticity rejects the null of homoskedastic errors at 1% level for Eqs. (4) and (5). When White's heteroskedasticity corrected t-statistics (not reported in the paper) are used for the tests, the conclusions do not change qualitatively. Belsley et al.'s (1980) test for multicollinearity does not reject the null of zero collinearity for either equation (full sample regression). Belsley et al.'s (1980) test for influential outliers is also conducted on both regressions. The regressions are run without the observations whose studentized residuals are more than two standard deviations away from their mean. The results (not reported in the paper) do not change significantly for either equation. 5. Summary and conclusion This study advances hypotheses about announcing and non-announcing firm sizes and intra-industry information transfers around earnings announcements. Our first hypothesis (the announcing firm hypothesis) states that the infor-
mation transfer is positively related to the announcing firm size. Atiase's (1985) differential information hypothesis suggests that relatively more information is available on large firms prior to their earnings announcements. One implication of this hypothesis is that abnormal price reactions of large firms around earnings disclosures are more likely due to information about overall trends in the economy and industry sector. As a result, the disclosures by large firms should contain more relevant information for nonannouncing firms in the same industry group and thereby cause more information transfers than small firms. Our empirical results support this hypothesis. These findings are contrary to Han and Wild (1999) who conclude that announcements by small firms cause more information transfers. Lack of controls for timing and clustering of announcements, and the definition of industry groups may have affected Han and Wild's (1999) results. Another possibility is that their results are peculiar to their sample period (1984 ± 1986). The second set of hypotheses examines if there are differences in information transfers to small and large non-announcing firms. The non-announcing firm hypotheses are a set of three mutually exclusive hypotheses (a null and two one-sided alternative hypotheses). Our tests support the null hypothesis that the non-announcing firm size does
S.C. Asthana, B.K. Mishra / Journal of Business Research 53 (2001) 37±47
not affect information transfers. One explanation of this finding is that information transfers are caused mainly by industry-specific information that is equally valuable to all non-announcing firms irrespective of their sizes. This finding is different from the results of Han and Wild (1999) who report that information transfer is inversely related to nonannouncing firm sizes for their sample. The results in this study are consistent with Foster's (1981) finding that the magnitudes of announcing firms' price reactions are positively correlated with the degree of information transfers. Most of the information transfer literature has concentrated on documenting the different settings in which information transfers exist without considering the firm characteristics or timings of earnings announcements that may significantly affect information transfers. Our study extends prior research by providing additional evidence of intra-industry information transfers and identifying its determinants. Such evidence enhances our understanding of the information content of accounting earnings, the economics of security pricing within industries, and the sophistication of the securities markets in impounding publicly available information in stock prices. Acknowledgments We thank Steve Balsam, Michael Brennan, Ross Jennings, Jagan Krishnan, Charles Lee, Roland Lipka, Paul Newman, Eric Press, Heibatollah Sami, the editor, and an anonymous referee for their invaluable help. We owe a special debt of gratitude to Robert Freeman and Senyo Tse for their active guidance and encouragement. Last but not least, we are also grateful to many of our fellow doctoral students at Texas and participants at the workshop at Arizona for their constructive comments.
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