Level of information asymmetry and market impact of seasoned equity announcement: a comparison of market reaction across industry groups

ABSTRACT

This paper examines the market reaction to seasoned equity issues on industry groups of Banks, Utilities, and Industrial& Due to information asymmetry between investors and the management of a company, market generally reacts negatively to seasoned equity issues [Myers and Majluf (1984)]. We hypothesized that since Industrials raise capita/from the equity market more frequently than other groups, they will experience more negative market reactions. Results from this study support this hypothesis, though differences in abnormal returns between the groups, on and around the equity issue announcement date were not significant. Raymar (1993) argued that leverage of firm is negatively correlated to information asymmetry. To test this each industry group was subdivided into high and low levered samples. Low-levered group experienced more negative market reaction than the high- levered groups, but the differences were not significant. Overall, low-levered industrial group suffered the most.

1. INTRODUCTION

Equity issues (IPO and Seasoned) are the major sources of external financing for corporations. Lee, et.al. (1996), documented that the number of seasoned equity issue events surpass the combined total numbers of convertible and straight debt issues. During the same period, the number of seasoned equity issues was 1.5 times the number of straight issuing events. The puzzling part of these findings was that previous empirical evidence indicated that the market reaction to equity offerings was significantly negative and more importantly value decreasing for the issuing corporations. Some studies [Barclay and Litzenberger (1988), Bradford (1987), Choi, Masulis, and Nanada (1992), and Varma (1995)] reported positive market response around the event date of a seasoned equity offerings, but none of them was statistically significant. Smith (1986) summarized the impact of external financing on the value of a firm that previous empirical research had discovered: 1) External financing does not increase the value of a firm; 2) Equity financing is more value decreasing than debt or preferred stock financing; 3) Securities which are convertible into common stock carry a greater negative impact than those which are not convertible.

Model developed by Myers and Majluf (1984) holds that equity financing always has negative consequences due to the existence of information asymmetry between management and the existing shareholders with respect to the value of firm's assets in place and also with respect to the net present value (NPV) of the new investment projects. Under the theory of separation of ownership and management [Berle and Means (1931)] investors do not have direct access to the inside information regarding the value of the firm. Investors learn about the value of the business from signals management provide from time to time. In Myers-Majluf model as new investment opportunities become available, management will favor a seasoned equity issue if the firm is overvalued. Investors being aware of this behavior of the management will protect their own interests by revaluing the stock thus leading to a negative market reaction at the announcement of seasoned equity offering. Thus, according to information asymmetry theory firms never issue equity if it has the opportunity to use other means of financing. Information asymmetry theory also suggests a positive correlation between the level of information asymmetry and the level of value drop in the vent of equity financing (Dierkerns, 1991).

Identification of the factors affecting the level of information asymmetry was the subject of several studies. Issuer's leverage, growth opportunities, and level of diversification were found to impact the level of information asymmetry. Raymar (1993), Dierkerns (1991) found a negative correlation between the leverage of the issuer and level of information asymmetry. A fully levered firm should suffer minimal value loss at seasoned equity announcement, whereas an unlevered firm should suffer the most. Barclay and Litzenberger (1988), Dennis (1994), Piolotte (1992), Ambarish, John, and Willam (1987) found negative correlation between expected growth and information asymmetry. Morck, Shleifer, and Vishny (1990), Lang and Stultz (1994), Berger and Ofek (1995), Ahmed and Rahim (2003) found that more diversified firms experience greater losses than the less diversified ones.

In this study we attempted to study the differential market reaction to seasoned equity offerings by different sectors of the market. Instead of looking at the characteristics of individual issuer, we examined the levels of information asymmetry for different industry groups and computed the market reaction around the seasoned equity announcement by that group. The sample was divided into three groups: Industrials, Bank, and Utilities. Abnormal market return on and around the announcement date of equity issuance was estimated and compared.

The remaining part of this paper is organized as follows: section II: hypotheses, section III: data and methodology, section IV: results, section V: conclusion, and section VI: bibliography.

2. HYPOTHESES

Hypothesis I: Since the level of information asymmetry is negatively correlated to the level of equity and firms are expected to issue equity only when they are overvalued, an industry group that issues equity more frequently will suffer more value loss at the announcement of seasoned equity issue.

Hypothesis II: Level of leverage is negatively correlated to the level of information asymmetry. So, reaction to equity issuance by a high-levered firm will be more positive than that by a low-levered firm.

3. DATA AND METHODOLOGY

3.1 Data

The seasoned equity offerings from 11983 through the end of 1994 were collected from Investment Dealer's Digest. The data collected from the above source included: the offering date, the offering price, and the number of shares offered. Initially, 4876 issue events were identified with the criteria that the issue must be either a pure primary seasoned issue (henceforth Seasoned Issue) or a combination of primary and secondary seasoned issue (henceforth called combined issue). The following criteria were set for each event to be a part of the sample:

a. For financial data, Compustat annual data tape of 1996 (hereinafter, Compustat) was used. If the sample firm was not in the data tape, then events related to that company was deleted from the sample.

b. For market date, the daily return data tape of the Center for Research in Security Price (CRSP) for period ending December 31, 1996 was used. Abnormal return calculation methodology requires that each sample event must have return data for -187 days from the event date up to +15 days from the vent date. Sample events failing to meet the criteria were also dropped from the sample.

c. In order to avoid the confounding effect, sample events which had seasoned or combined issues within the last 12 months or within the subsequent 12 months of the event date were also deleted. In case of firms making multiple issues within the event period (1983-1994), the earliest event was first considered provided there was no seasoned or combined offering in the past months of such offering. For a second event to be considered in the sample from the same company, the event had to be at least 12 months apart from the first offering provided no similar offering took place within the subsequent 12 month period.

d. To avoid possible information contamination around the sample event date from offerings other than seasoned or combined offerings (such as offerings of debt, convertibles etc.) sample events were also eliminated where such events were present within 30 days surrounding the sample event date.

e. Also, deleted were those events associated with firms which were not listed on the American Stock Exchange (AMEX), New York Stock Exchange (NYSE) or NASDAQ Market System.

The above selection criteria brought the sample down to 1353 sample events for 984 companies over a period 12 years (1983-1994). Table 1 shows the sample collection and elimination process.

Table 2 shows the distribution of sample events and sample firms by the Industry. In defining industry we used Compustat supplied Standard Industry Classification (SIC) code. If the first two digits of the SIC was 49 then the firm was classified as Utilities, if the first two digits were between 60 and 90 (both inclusive), then firms were Financial and Banking, and all others were classified as Industrials (Slovin, Sushka, and Polonchek, 1992). For 1353 sample events, 926 (715 sample firms) events belonged to the industrial category, 236 (164 sample firms) events were from the Financial and Banking group. The balance of 191 sample events (105 sample firms) were from the Utilities group.

3.2 Methodology

Day 0 was the event day in the time line. The estimation period was t= -162 to -36 relative to the event day was used to calculate normal return of the event window, which was -15 to +15 (31 days) relative to the event day. The market model was used to estimate normal or expected returns of the common stocks of the sample events. In this ordinary least squares (OLS) model, returns on a given security were regressed against the concurrent returns on the market. The CRSP equally weighted index was used as a proxy for the market portfolio.

[R.sub.jt] = [[alpha].sub.j] + [[beta].sub.j][R.sub.mt] + [[epsilon].sub.jt]

Where,

t = day measured relative to the event,

[R.sub.jt] = return on security j on day t,

[R.sub.mt] = daily equally-weighted index for all common stocks on NYSE & AMEX and NASDAQ f firms on the CRSP tape on the event date t (a proxy for the market portfolio of the risky assets)

[[alpha].sub.j] = estimated period intercept of firm j

[[beta].sub.j] = Ordinary Least Square (OLS) estimates of firm j's market model parameters.

[[epsilon].sub.j] = the error term of security j on the sample event day t

The abnormal returns for the sample event was the difference between the actual returns on the common stock and the contemporaneous expected return generated by the market model. The abnormal returns (AR) for each sample event 'j' on day 't' was obtained as follows:

A[R.sub.jt] = [R.sub.jt] - ([[alpha].sub.j] - [[beta].sub.j][R.sub.mt])

Where

t = day measured relative to the event,

A[R.sub.jt] = excess return to security j for day t,

[R.sub.jt] = return on security j during day t,

[R.sub.mt] = daily equally-weighted index for all common stocks on NYSE & AMEX and NASDAQ firms on the CRSP tape on the event date t (a proxy for the market portfolio of the risky assets)

[[alpha].sub.j] = estimated intercept of firm j

[[beta].sub.j] = OLS estimates of firm j's market model parameters.

Daily abnormal or excess returns were calculated for each sample event for each sample event. For a sample of N events, the daily average abnormal return for each day was estimated as:

A[R.sub.t] = N[N.summation over j=1]A[R.sub.jt]/N

The expected value of A[r.sub.jt] is zero by definition.

Analysis of statistical significance of the abnormal returns calculated above requires the standardization of abnormal return to reflect statistical errors in the determination of expected returns. To determine whether the average daily abnormal return was significantly different from zero, the average standardized abnormal return (ASA[R.sub.t]) was calculated as:

ASA[R.sub.t] = 1/N[N.summation over j=1]A[R.sub.jt]/[S.sub.jt]

Where,

[S.sub.jt] = [[S.sub.jt][[S.sup.2.sub.j][1 + 1/T + [([R.sub.mt] - [R.sub.m]).sup.2]/[T.summation over t=1][([R.sub.mi] - [R.sub.m]).sup.2]]].sup.1/2]

and

[S.sub.jt] = Standard error of the forecast for security j in period t in the event period;

[S.sup.2.sub.j] = The residual variance for security j from the market model regression;

N = The number of observations in the estimation period;

[R.sub.m] = The average return of market portfolio for the estimation period

[R.sub.mt] = The returns on the market portfolio for the day t

[R.sub.mi] = The market return for period jwithin the estimation period;

T = Number of periods employed in the regression equation for parameter estimation (126 days).

T = Number of periods in the event window/period (31 days).

I = Sub-script for estimation period.

J = Sub-script for the event window/period.

Assuming the normality and the independence of the distribution of the calculated abnormal returns the t-statistics of the estimated were calculated for each day as:

t = [square root of (N(ASA[R.sub.t]))]

The cumulative abnormal return (CAR) for each security j is calculated by summing average abnormal returns over the event period as follows:

CA[R.sub.j,K,L] = [L.summation over t=K]A[R.sub.jt]

Where the CA[R.sub.j,k,L] was for the period from t = day k to t = day L.

The cumulative average abnormal return (CAAR) over the vent period from day k to day L was calculated as:

CAA[R.sub.K,L] = 1/N[N.summation over j=1]CA[R.sub.j,K,L]

The average of the above standardized cumulative abnormal return over the interval k to L were obtained as follows:

ASCA[R.sub.K,L] = [L.summation over K]ASA[R.sub.K,L]/[square root of (K - L + 1)]

Finally, the t-statistics for the average standardized cumulative abnormal return were calculated as:

t(ASCA[R.sub.KI,L]) = [square root of (N(ASCA[R.sub.K,L]))]

We also calculated the leverage of sample firms and grouped them into high and low levered sub-samples. Leverage was defined as the ratio of the book value of total debt to the sum of book value of total debt, market value of equity, and liquidating value of preferred stock (Pilotte, 1992). Issuing firm's leverage in the last year (i.e., the year before the issue announcement took place) was adjusted by industry mean (calculated using all the available firms on the COMPUSTAT data base). If the adjusted leverage ratio was positive, then the firm was classified as high-levered. Out of 1353 sample events, in 687 instances the issuers were identified as high-levered, and the rest(666) as tow-levered The mean leverage ratios for low and high levered firms were 0.137 and 0.358 respectively.

4. EMPIRICAL RESULTS

Table 3 contains the average abnormal returns on the event date (CA[R.sub.0.0]), cumulative abnormal returns for 3 days around the event date (CA[R.sub.-1.1]), and cumulative abnormal returns for 5 days around the event date (CA[R.sub.-2.]) along with their respective t-statistics. In the overall sample it was found that the average abnormal for event day was -0.8416% (t=-11.8163), which was significant at 0.01 level. The 3-day CAR was -2.1626% (t= -1.93871) and 5-day CAR was -2.4328% (t= -1.7085). Both of these CARs, though negative, were not statistically significant. For these three time periods (0,0; -1,1; and -2,2) CAR for industrial sample were negative and very highly significant (level of significance = 0.01). CARs for the Bank and Utilities samples were also highly significant, but their t-statistics were lower than that of the industrial sample. Similarly t-statistics for utilities were lower than the bank sample. This supports the first hypothesis that industry group that raises capital more often in the equity market will suffer more value loss due to greater information asymmetry. Of the total sample had 34.885 percent of the events reported positive return on the event date. For Industrial, Bank, and Utilities groups this ratio was 33.153%, Banks 41.949%, and 34.555%. Again, we see that the industrial group had the higher percentage of negative responses.

We also examined the cumulative abnormal returns for high and low-levered firms. Higher leverage reduces information asymmetry, because for those firms market will be closely followed by both creditors and shareholders. So, reaction to equity issuance by high levered firms will be less negative than that of low-levered firms. The event day abnormal returns for the high and low-levered groups were -0.779306% and -0.901994% respectively. Though low levered group lost more (supporting hypothesis 2) , the difference in return between the groups were not significant. Same results were observed (i.e., low-levered firms suffering more than the high-levered ones), but again in none of the cases difference in abnormal returns were statistically significant.

The abnormal returns were also examined for each industry group. In all time periods, low levered groups' CAR was more negative than their high-levered counterparts (except for period 0,0 for Bank sample), but the differences were not significant.

Comparison of the abnormal returns for samples with similar leverages showed different results. We took the differences in abnormal returns of high-levered groups (Table 5) and also low-levered groups (Table 6) from each industry. Table 5 shows that 3-day and 5-day cumulative abnormal returns for high-levered industrial sample were significantly lower (level of significance = 0.10) than those of Bank and Utilities groups. Table 6 demonstrates that cumulative abnormal returns for the industrial sample for 3-day and 5-day periods were significantly lower than that of the Bank sample. These findings support our hypothesis that it is possible to predict market reaction to seasoned equity offerings by observing the characteristics of an industry.

This paper examines if market reacts differently to announcement of seasoned equity issue by different industry groups. According to Myers and Majluf (1984) due to the information asymmetry between the firm and investors, firms will issue equity only when it is overvalued. So, whenever an issue of equity announcement is made investor will be push the value of the stock downwards and the negative market reaction greater as the level of information asymmetry increase. We hypothesized that the negative reaction will greater for the industry group that raise capital in the equity market more often. Between 1983 to 1994 (sample period), of the 1353 equity issues were made, 715 were by the Industrial sample, 164 by Banks and 105 by Utilities. It was observed that on the average, cumulative abnormal returns for the Industrial sample were more negative other groups for all three estimation periods (event-day, 3 days around the event date, and 5 days around the event date) and they were statistically significant (level of significance = 0.01). CARs for the Bank and Utilities group were also significant, but absolute value of their t-statistics were much lower than the Industrial sample. This evidence supports hypothesis 1. We also classified each industry group into high and low levered samples. According to existing literature, leverage has negative correlation information asymmetry. So, at the announcement of equity announcement, high-levered firms will suffer less than low levered ones. For each group, Car for low-levered samples were more negative than the high-levered group (supports hypothesis 2), but the difference was not significant. After sub-grouping each industry sample into high and low-levered classes, it was found that 3-day and 5-day abnormal returns of the high-levered industrial group was significantly lower than those Bank and Utilities sample of similar leverages. Over the same periods (3 and 5-day) abnormal returns of the low-levered industrial group was significantly lower than that of the Bank sample.

TABLE 1

THE DESCRIPTION OF THE DATA COLLECTION PROCESS FROM THE PRIMARY
SOURCE--HALF YEARLY PUBLICATION FROM THE INVESTMENT DEALERS DIGEST
(A, B, C, D, AND E, REFERS TO THE SELECTION CRITERIA DISCUSSED IN THE
DATA SECTION OF METHODOLOGY)

Primary (1) Seasoned and Combined (2) Seasoned Equity
offerings from the Primary source                           4876 events
LESS(a & b):
Sample firms NOT available in Compustat & CRSP data tape
for 1996.                                                   2012 events
LESS(c & d):
Sample Events(Other seasoned equity issues) within the
past 12 months subsequent 12 month period. Other
issue(e.g., debt etc., within the days around event date.   915 events
LESS(b):
Sample Event does not have daily return data data for
-162 through +15 days of event date AND, Sample Firms
change inAccounting Reporting date between 1983 and
1994.                                                       559 events
Sample events available for study                           1463 events
LESS(e):
Sample Firms NOT listed in New York Stock Exchange,
American Stock Exchange, or
NASDAQ Market System.                                       36 events
TOTAL Sample Events used in the Study                       1353 events
TOTAL Sample Firms used in the Study                        984 events

(1.) Primary Seasoned offering refers to the pure seasoned
equity offering where no other issue is involved.

TABLE 2

THE DISTRIBUTION OF SAMPLE EVENTS (1) AND SAMPLE FIRMS (2) BY INDUSTRY
CLASSIFICATION (3)--INDUSTRIAL (IND), BANKS AND FINANCIAL INSTITUTIONS
(BNK) AND UTILITIES (UTL) AND BY THE EVENT YEAR.

YEAR          SAMPLE FIRMS BY               SAMPLE FIRMS BY
                 INDUSTRY                      INDUSTRY

        IND    BNK    UTL    TOTAL    IND    BNK    UTL    TOTAL

1983    179     11     17      207    179     11     17      207
1984     25     09     16       50     22      8     15       45
1985     67     30     13      110     56     30      9       95
1986     85     31     10      126     73     22      6      101
1987     65     10     10       85     45      6      5       56
1988     25     05     06       36     15      3      3       21
1989     50     16     20       86     43      8     14       65
1990     41     08     12       61     29      5      5       39
1991    107     36     22      165     74     22      5      101
1992     98     27     22      147     64     13     11       88
1993    117     35     35      187     69     20     13      102
1994     67     18     08       93     46     16      2       64

TOTAL   926    236    191    1353     715    164    105      984

(1) Sample Events are the Seasoned Equity issue events between 1983 and
1994.

(2) Sample Firms are the Seasoned Equity issuing firms.

(3) Industry classification is done using the two-digit SIC code,
available in the Compustat data Tape.

TABLE 3

EVENT-DAY ABNORMAL RETURN, 3-DAYS CUMULATIVE ABNORMAL RETURN, AND
5-DAYS CUMULATIVE ABNORMAL RETURN AROUND THE EVENT DATE. EVENT DATE
IS THE SEASONED EQUITY OFFERING DATE. SAMPLES ARE BROKEN DOWN BY
INDUSTRY. NUMBERS IN THE PARENTHESIS ARE THE T-STATISTICS FOR THE
ABNORMAL RETURNS.

                      % OF POSITIVE    [CAR.sub.0,0]   [CAR.sub.-1,1]
                       ON EVENT-DAY

Total Sample (1353)      34.885%          -0.008416       -0.021626
                                        (-11.8163)       (-1.9387)
Banks (236)              41.949%         -0.0061584       -0.0174887
                                        (-4.2232)        (-7.0945)
Industrial (926)         33.153%         -0.0097866       -0.0249151
                                        (-9.6227)       (-14.4787)
Utilities (191)          34.555%         -0.0045605       -0.017934
                                        (-3.8706)        (-5.3319)

                      [CAR.sub.-2,2]

Total Sample (1353)     -0.024328
                       (-1.7085)
Banks (236)             -0.020792
                       (-6.1614)
Industrial (926)        -0.027326
                      (-12.3350)
Utilities (191)         -0.014165
                       (-5.2658)

TABLE 4

EVENT-DAY ABNORMAL RETURNS, AND 3-DAYS CUMULATIVE ABNORMAL RETURN, AND
5-DAYS CUMULATIVE ABNORMAL RETURN FOR THE OVERALL SAMPLE AROUND THE
EVENT DATE OF THE HIGH-LEVERED FIRMS AND LOW LEVERED FIRMS GROUPS BY
THE INDUSTRY CLASSIFICATION. HIGH-LEVERED FIRMS ARE THOSE WHOSE EVENT
YEAR MINUS ONE LEVERAGE IS GREATER THAN THE INDUSTRY AND EXCHANGE
ADMUSTED MEDIAN LEVERAGE

                                    [CAR.sub.0,0]    [CAR.sub.-1,1]

Total Sample   Low-Levered (687)     -0.00901994      -0.02184419
               High-Levered (666)    -0.00779306      -0.02140140
                    Differences      -0.00122688      -0.00044279
Banks          Low-Levered (110)     -0.00860890      -0.01784080
               High-Levered (126)    -0.00401903      -0.01718135
                    Differences      -0.00458987      -0.00065945
Industrial     Low-Levered (502)     -0.00993390      -0.02428773
               High-Levered (424)    -0.00961230      -0.02565793
                    Differences      -0.0003216        0.0013702
Utilities      Low-Levered (75)      -0.00350533      -0.01136035
               High-Levered (116)    -0.00524280      -0.01042687
                    Differences       0.00173747      -0.00093348

                                    [CAR.sub.-2,2]

Total Sample   Low-Levered (687)     -0.02375360
               High-Levered (666)    -0.02492123
                      Differences     0.00116763
Banks          Low-Levered (110)     -0.02067191
               High-Levered (126)    -0.02089591
                     Differences      0.00022400
Industrial     Low-Levered (502)     -0.02549117
               High-Levered (424)    -0.02949860
                     Differences      0.00400743
Utilities      Low-Levered (75)      -0.01664321
               High-Levered (116)    -0.01256249
                     Differences     -0.00408072

TABLE 5

INTER-INDUSTRY DIFFERENCES IN EVENT-DAY ABNORMALR ETURNS, 3-DAY
CUMULATIVE ABNORMAL RETURNS, AND 5-DAY CUMULATIVE ABNORMAL RETURNS FOR
HIGH-LEVERED GROUPS

                     [CAR.sub.0,0]    [CAR.sub.-1,1]    [CAR.sub.-2.2]

Industrials (424)    -0.0096123       -0.02565793       -0.0294986
Banks (126)          -0.00401903      -0.01718135       -0.02089591
Difference           -0.00559327      -0.00847658 *     -0.00860269 *

Industrials (424)    -0.0096123       -0.02565793       -0.0294986
Utilities (116)      -0.0052428       -0.01042687       -0.01256249
Difference           -0.0047695       -0.01523106 *     -0.01693611 *

* Significant at 0.10 level

TABLE 6

INTER-INDUSTRY DIFFERENCES IN EVENT-DAY ABNORMAL RETURNS, 3-DAY
CUMULATIVE ABNORMAL RETURNS, AND 5-DAY CUMULATIVE ABNORMAL RETURNS
FOR LOW-LEVERED GROUPS

                    [CAR.sub.0,0]    [CAR.sub.-1,1]    [CAR.sub.-2.2]

Industrials (502)    -0.0099339       -0.02428773       -0.02549117
Banks (110)          -0.0086089       -0.0178408        -0.02067191
Difference           -0.001325        -0.00649693       -0.00481926

Industrials (502)    -0.0099339       -0.02428773       -0.02549117
Utilities (75)       -0.00350533      -0.01136035       -0.01664321
Difference           -0.00642897      -0.01292738 *     -0.00884796 *

* Significant at 0.10 level

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Author Profile

Dr. Niazur Rahim earned his Ph.D. in Finance at Virginia Commonwealth University, Richmond, Virginia in 1994. Currently he is an Associate Professor of Finance at Christopher Newport University, Newport News, Virginia.

Dr. Mojib Ahmed earned his Ph.D. in Finance at Old Dominion University, Norfolk, Virginia in 1998. Currently he is an Associate Professor of Finance at SUNY-Empire State College, New York.

Mr. A. J. Ananaba earned his MBA from Norfolk State University, Norfolk, Virginia. Currently he is an Associate Professor at Atlanta Metropolitan College, Atlanta, Georgia.

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