Evidence and Implications of Increases in Trading Volume around Seasoned Equity Offerings

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1 Evidence and Implications of Increases in Trading Volume around Seasoned Equity Offerings Surendranath R. Jory *, Assistant Professor University of Michigan at Flint Thanh N. Ngo, Assistant Professor University of Texas Pan American ABSTRACT This paper examines whether managers time their seasoned equity offering (SEO) when the level of trading volume of the firm s stock significantly increases relative to normal months. We hypothesize that once management of the issuing firm observes a significant positive increase in their stock s trading volume, they interpret this increase in liquidity as an opportune time for the firm to float new shares. Using four different measures of abnormal trading volume, we find evidence that supports our hypothesis. Our results are robust even after controlling for various factors that have been previously found to be significant determinants of the SEO decision. Keywords: Seasoned Equity Offerings; Trading Volume; Market Timing; Event Study JEL Classification Code: G12; G14 INTRODUCTION While the results in the extant literature suggest that seasoned equity offering (SEO) firms sell overvalued equity (see, for example, Loughran and Ritter, 1995), the question of whether firms carry out their SEO during a period in which there is a spur in their volume is not addressed. Even though the ideas of timing and marketability are not new to the SEO literature, to the best of our knowledge the question of how much liquidity is an influence in a firm s decision to issue seasoned equity has not been studied. This paper fills this void in the literature. We examine the connection between liquidity and SEOs. We hypothesize that the likelihood of a firm filing and announcing a SEO is greatest when there is a significant increase in the stock trading volume. The increase in trading volume implies that the stock is in favor and market sentiment towards the stock is positive. In particular, we hypothesize that the typical SEO is preceded by positive abnormal trading volume. Using monthly trading volume data of SEO firms from January 1995 to December 2005, we find that a stock s turnover, measured as the natural log of trading volume divided by the natural log of the number of shares outstanding, in the twelve months immediately preceding a SEO filing and announcement is significantly higher than (i) the past average monthly turnover of the stock itself; (ii) the average monthly market stock turnover, (iii) the average monthly share turnover of a portfolio of firms similar in size and book to market, and (iv) the monthly share turnover of a matching firm based on size and book to market. Abnormally high share turnover prior to the SEO exists after controlling for market, industry, size and growth factors. We also observe that the magnitude of the positive abnormal share turnover increases as we approach the month of the SEO filing and announcement. The role of trading volume in the twelve months preceding the SEO announcement in explaining the decision to carry out the SEO is statistically significant after controlling for other variables, which have been found by previous studies to influence the SEO decision. LITERATURE REVIEW The negative market reaction to firms announcements of SEO is well documented in finance literature. Direct and indirect evidence from previous studies indicates that firms engage in actions to * The authors s are sjory@umflint.edu and ngotn@utpa.edu respectively.

2 avoid negative price reactions to their SEO announcement. Korajczyk, Lucas, and Macdonald (1991) report less negative returns for equity issues conducted subsequent to earnings announcement. D Mello, Tawatnuntachai, and Yaman (2003) hypothesize that firms split stocks before a SEO in an attempt to increase marketability of the shares at reduced prices as individual investors are constrained by limited capital. They report that approximately 14 percent of the equity issues they cover are preceded by at least one stock split announcement. This marketability hypothesis has been suggested by managers of splitting firms (see Baker and Gallagher (1980), and Baker and Powell (1993)). The implication of these results is that equity issues are not randomly distributed in time, and that a set of conditions exist to facilitate a SEO. The findings of Butler, Grullon, and Weston (2003) support the hypothesis that investment bankers view high trading volume as maximizing the likelihood of a SEO being successful. They hypothesize that when firms access the external equity capital market, the liquidity of their stock has an influence on the transaction costs specifically, the investment banking fees associated with floating new equity. Using a large sample of SEO, they test this hypothesis and find that, ceteris paribus, investment banks fees are substantially lower for firms with more liquid stocks. This result has an important implication for the role of volume in the decision and timing of a SEO, as we explain next. If investment banks are charging lower fees for firms with more liquid stocks, it means that confidence in floating a SEO for firms with higher volume activity pre SEO is higher. Firms with low volume activity pre SEO pay higher investment banking fees. It is easier for the investment bank to place a SEO for a company with more liquid stock than for a firm with illiquid stock. Hence, the pre SEO volume is an important criterion in the decision to float a SEO for investment banks. While the results in the existing literature suggest that SEO firms are selling overvalued equity, Jindra (2000) finds a small portion of firms in his sample, about 6.15 percent are issuing equity when they are undervalued. One natural question is: Why would firms issue equity when they are undervalued? Analysis of this subsample reveals that undervalued issuers are older and larger firms that issue proportionately less equity, have higher leverage and lower operating income at the time of the offering. That is, these firms are in general financially constrained. It is possible that given the high leverage of these firms coupled with the fact that they are financially constrained, they are tapping the equity market for funds because other sources of funds have been exhausted. Then the question is: On what criteria do these firms base their decision to float new equity? Is it the volume activity in their stocks which is supporting their decision to float new equity? To our knowledge, Eckbo, Masulis, and Norli ((2000); hereafter EMN) is the only study with results that provide support for our hypothesis. In one part of their study, they examine the average monthly level of share turnover for issuers and a matched sample prior to and following the SEO public offering date. In the five year period prior to the SEO offer date, they find that SEO issuer common stocks exhibit higher share turnover ratios than their risk matched control sample. While the results of EMN indicate that issuing firms are significantly more liquid than nonissuers, we approach the hypothesis from a different direction. We hypothesize that it is the trading volume immediately prior to a SEO announcement that is an important determinant in the firm s decision to float new equity. We do not consider high liquidity in the full five year period prior to a SEO offer date as particularly important in the firm s decision. While high liquidity during the full five year period prior to the SEO inarguably places the firm in a better position to float new shares, it could also be the case that a firm s shares are illiquid in the first four years, become liquid in the final year, and the firm decides to float new shares based on this prior year liquidity. Thus, the 12 month liquidity of the stock prior to the SEO is more important than the 5 year liquidity pre SEO. Moreover, since EFM examine turnover prior to the SEO public offering date, their results do not necessarily provide direct evidence that a share s liquidity is an important factor in floating new shares. The reason is that the SEO decision is made well before its public offering date. As a result, the relevant event date to be considered is the SEO announcement date rather than its public offering date. Since, an examination of turnover up to the SEO public offering date will include the effect of the SEO announcement; we cannot conclude, based on such evidence alone, that high volume pre SEO offering

3 date provides proof that liquidity is a major determinant in the SEO decision making process, especially if the announcement significantly increases stock liquidity. The relevant volume to be considered in the SEO decision making process should end before the SEO announcement date and not before the SEO offering date. Additionally, to provide strong evidence that indeed liquidity matters in the SEO decision making process, any test must be robust to benchmarks other than matched firm adjusted trading volume. In short, we recognize that it is not the objective of the EMN study to examine the importance of liquidity in the SEO decision making process; they focus only on issuer underperformance. The rest of the paper proceeds as follows. We present our hypothesis in section III. The data and methodology are described in section IV. The results are reported in Section V, and conclusion is made in section VI. HYPOTHESIS Lee and Swaminathan (2000) put forth the momentum/expectation life cycle hypothesis. According to this hypothesis, trading volume serves as a useful indicator of the level of investor interest in a stock. When a stock falls in disfavor or neglect, the number of sellers tends to exceed the number of buyers. Generally, this leads to falling share prices and dwindling trading activity. Conversely, when a stock is popular or glamorous, the number of buyers exceeds the number of sellers, so prices tend to rise and trading activity increases. As a result, a firm s turnover ratio is a proxy for investor interest relatively low turnover is an indication of a firm near the bottom of its momentum/expectation life cycle, while relatively high turnover is an indication of a firm close to the top of this cycle. Their explanation is consistent with their empirical findings. Based upon the momentum or expectation life cycle hypothesis suggested by Lee and Swaminathan (2000), we hypothesize that a firm files and announces a SEO only when its stock is in favor and that market sentiment towards the stock is positive. More precisely, we hypothesize that the signal for the go ahead with the SEO depends on the state of the stock s trading volume at that point in time: positive abnormal trading volume paves the way for the typical SEO If favorable volume activity is a precondition for a SEO, then we would expect to find positive abnormal volume, at least, in the period immediately preceding a firm s SEO announcement. Evidence supporting substantial positive abnormal volume preceding SEO announcement would support the notion that favorable volume activity is a pre condition for a SEO. DATA AND METHODOLOGY Data We obtain our initial SEO sample from the Securities Data Corporation (SDC) Global Issues Database. The initial sample consists of completed SEOs by public firms between January 1995 and December For each issue, we obtain the filing and offer dates from the SDC database. Thus, we have an initial sample of 1149 SEOs. Volume data is obtained from the University of Chicago Center for Research in Security Prices (CRSP) database. The final sample meets the following restrictions: i. Issuer common stock is listed on the NYSE, AMEX or NASDAQ at the time of the filing date. This precludes IPOs from entering the sample. All issuer stocks are found in the CRSP monthly stock return database at the time of the SEO filing date. The offer must be an offer of only common stock. This sample requirement excludes, among other securities, issues by closed end funds, unit investment trusts, Real Estate Investment Trusts (REITS), and American Deposit Rights (ADRs), and eliminating 107 such SEOs. We also eliminate financial institutions (SIC codes 6000 through 6999), a screen which eliminates 207 SEOs, and public utilities (SIC codes 4910 through 4939), eliminating 21 such SEOs. The exclusion of financial institutions and utilities is primarily due to the fact that they operate in regulated environment and that their characteristics differ substantially from non regulated firms.

4 ii. For the SEO, there must be no simultaneous offers of debt, preferred stock or warrants. All issuers are U.S. domiciled and all issues made publicly in the U.S. market. All private placements, exchange offers of stock, 144A shelf registered offers, pure secondary offerings and canceled offers are excluded. iii. SEOs with a difference greater than 1 year between the filing and offer dates are excluded (103 such SEOs). For the purpose of testing our hypothesis, it is difficult to interpret why a company will take in excess of a year post filing date to issue shares if it is basing its SEO decision on excess volume prior to the filing date. Over a year, the momentum in volume might have dissipated. This sub sample forms less than 1 percent of our original sample. iv. To avoid problems associated with confounding effects, we also require that the firm performed no SEO(s) at least over the year preceding the file date and over our estimation period which is the ( 36, 13) window in months. We include only the earlier offering of firms with multiple offerings in our sample period. This requirement results in 101 SEOs being excluded from the sample. v. The company has at least 36 months of volume data prior to the SEO filing month. The final sample consists of 382 SEOs. The number of SEOs for each year of the sample period is reported in Table 1. Methodology We test our hypothesis by comparing abnormal volume in the twelve months prior to the SEO filing date. We use the SEO filing dates reported by SDC. We use SDC filing dates as opposed to the Wall Street Journal s (WSJ) announcement dates because WSJ reporting of offering announcements after 1984 is infrequent (see D Mello, Tawatnuntachai, and Yaman 2003). Clarke, Dunbar, and Kahle (2001), Jegadeesh, Weinstein, and Welch (1993), and D Mello, Schlingemann, and Subramaniam (2002) also use the SEO filing date as the initial announcement date in their respective studies Since our sample contains NYSE, AMEX, and NASDAQ firms, the construction of trading volume presents some problems. In dealer markets, trades are often immediately turned around by the market maker and thus double counted, making it hard to compare with volume in auction markets. Thus, we follow the common approach of dividing NASDAQ trading volume by two to correct for double counting (see Butler, Grullon, and Weston (2003)). In addition, since raw trading volume data are hardly standardized across the sample firms, we take the natural logarithm of trading volume and divide it by the natural logarithm of the number of shares outstanding, and use this ratio in place of raw trading volume. We label this ratio as TURNOVER, and this method for transforming trading volume is used in Ajinkya and Jain (1989), Cready and Ramanan (1991), Campbell, Grossman and Wang (1993) and Campbell and Wasley (1996). TURNOVER = ln( TradingVolume) / ln( NumberofSharesOutstanding ) (1) Abnormal TURNOVER (ABTURNOVER) is defined as the turnover ratio of the firm in excess of a benchmark s turnover ratio over the 12 months window leading to the SEO announcement month. ABTURNOVER = TURNOVER minus a BENCHMARK (2) We use four different benchmarks as presented in Table 2. (i) Comparison period mean adjusted abnormal turnover ABTURNOVER 1 We calculate the average monthly TURNOVER for each firm over the window ( 36, 13) in months prior to the SEO announcement. We subtract that average from the monthly TURNOVER in the window ( 12. 1) in months prior to the SEO announcement to obtain SEO firms ABTURNOVER 1.

5 (ii) Market adjusted abnormal turnover ABTURNOVER 2 Campbell and Wasley (1996) suggest using the following market model for abnormal trading volume: AV it = V it (α i + β i V mt ) (3) where α i and β i are obtained via ordinary least squares (OLS) estimation, and V mt is a market volume index. We apply Campbell and Wasley s model but use the market s TURNOVER in place of trading volume. Market model parameters are estimated in the window ( 36, 13) in months prior to the SEO announcement, using the CRSP equally weighted and value weighted index as market index, alternately. Monthly ABTURNOVER 2 in the window ( 12, 1) prior to the SEO announcement equals to actual TURNOVER minus market model estimated TURNOVER. (iii) Matching portfolio adjusted abnormal turnover, ABTURNOVER 3 Fama and French (1992) find that two variables, market equity (ME) and the ratio of book equity to market equity (BE/ME) capture much of the cross section of average stock returns. Firms with low market equity are more likely to have poor prospects while large stocks are more likely to be firms with strong prospects. Low BE/ME is typical of firms with high average returns on capital (growth stocks), whereas high BE/ME is typical of firms that are relatively distressed. Given that Lee and Swaminathan (2000) demonstrate that trading volume and price momentum are interlinked, the effect of the two factors ME and BE/ME may influence our results. To control for this possibility, we create six portfolios based on ME and BE/ME, and compare the turnover of each SEO stock with the turnover of its corresponding portfolio. Such an examination is important because if positive abnormal trading volume is a precondition for a SEO, then we would expect to find its presence within each of the subdivisions/quantiles based on size and BE/ME. We break down the whole universe of firms in COMPUSTAT into six portfolios (to be used as matching portfolios for SEO firms) based upon their size and book to market ratio as in Fama and French (1992). Matching portfolios are constructed at the end of June in the year prior to a SEO. We use the median NYSE market equity as the breakpoint to form two portfolios based on size. Then, in each of the two size based portfolios, we use BE/ME breakpoints at the 30th and 70th NYSE percentiles to form another three portfolios. This procedure yields six matching portfolios. BE values are obtained at fiscal year ends, while ME values are obtained at calendar year ends. Data for the size and BE/ME breakpoints are downloaded from Kenneth R. French s Data Library available from his homepage.1 A SEO firm s monthly ABTURNOVER 3 in the window ( 12, 1) prior to the SEO announcement is equal to its TURNOVER for the month minus the equally weighted average TURNOVER of its matching non SEO portfolio (i.e., the portfolio its size and BE/ME belong to). (iv) Matching firm adjusted abnormal turnover ABTURNOVER 4 The use of the matching firm or control firm as a benchmark is motivated by the results in Barber and Lyon (1997), who show empirically that the use of the matching firm as a benchmark yields wellspecified test statistics. Thus, for every SEO firm in our sample, we obtain a matched non SEO firm based on size and book to market. We select matching firms as follows: To control for survivorship bias, our matching firms come from the universe of firms in COMPUSTAT and have stock data in CRSP. We then generate a list of all companies that are within 30 percent of the issuer s market value of equity at year end prior to the SEO announcement. We then select from that list the firm with the BE/ME ratio that is closest to the issuer s. We check the SDC database to make sure that the matching firm is not a SEO firm. We use the inverse of COMPUSTAT s market to book ratio as a proxy for the firm s BE/ME ratio. A SEO firm monthly 1

6 ABTURNOVER 4 in the window ( 12, 1) in months prior to the SEO announcement is equal to its TURNOVER minus the TURNOVER of its matching non SEO firm. RESULTS Abnormal turnover, ABTURNOVER Table 3 provides the comparison period mean adjusted abnormal turnover, ABTURNOVER 1 in each of the 12 months preceding the month of the SEO announcement. The monthly comparison period mean adjusted abnormal turnover is statistically significant and positive from month 8 up to one month preceding the SEO announcement month. This suggests that the monthly share turnover in the eight months prior to the SEO announcement is higher than the firm s average monthly share turnover in the two years ending a year prior to the SEO announcement. Moreover, the mean abnormal share turnover is per cent in month 8, and gradually increases to per cent in month 1. Freund and Webb (1999) find that NASDAQ volume always exceeds NYSE/AMEX volume over their sample period from 1983 to More importantly, NASDAQ volume grew substantially relative to NYSE/AMEX volume during the later part of their sample period. The higher volume associated with NASDAQ stocks are often attributed to double counting by the financial press, which we already take into account when constructing our measure of turnover. Freund and Webb show that it is unlikely that double counting can explain the large and almost continuous increase in volume on NASDAQ relative to the NYSE/AMEX. Thus, it is possible that the positive abnormal turnover from Table 3 is driven by relatively higher NASDAQ volume. To control for this NASDAQ effect, we perform our analysis by stock markets. To converse space we do not report the results by stock markets. The results show that the phenomenon of positive abnormal turnover pre SEO announcement is present in both the NYSE and NASDAQ markets. The results between the two markets are quite similar: positive abnormal turnover pre SEO is observed as early as 10 months prior to the SEO announcement month in both markets and increases as we approach the month of the SEO announcement. Therefore, our results from Table 3 are not driven by the relative excess volume in the NASDAQ market. Given the small sample size of AMEX stocks, we are unable to offer any reliable results for such stocks. In Table 4, we present the results from analyzing market adjusted abnormal turnover, i.e., ABTURNOVER 2. We use two index for market turnover: (i) CRSP equally weighted market index in Panel A, and (ii) CRSP value weighted market index in Panel B. We observe that the average monthly market adjusted turnover is significant in each of the 12 months prior to the SEO announcement in both Panels. Moreover, the abnormal turnover increases monotonically as we approach the month of the SEO announcement. We would expect that a bullish market would cause share turnover in general to be higher than normal. More precisely, if we assume that our event period coincides with a bullish market then we would find positive abnormal share turnover and this coincidence would render our conclusion that positive abnormal share turnover is a precondition for a SEO weak. Using the market model, we control for such bullish effects in Table 4, if they exist. Thus, after controlling for market wide factors, we still find that the SEO announcement follows months of positive abnormal turnover. In Table 5 we present the analyses of matching portfolio adjusted abnormal turnover, i.e. ABTURNOVER 3. Panel A of Table 5 reports the results for large growth, large neutral, and large value firms. Panel B reports the results for small growth, small neutral, and small value firms. The results show that positive abnormal turnover (relative to the matching portfolio turnover) before SEO announcement is statistically significant among growth and value firms. This result applies for both small and large firms. We observe positive abnormal turnover for growth firms, both large and small in each of the 12 months prior to the SEO announcement. We observe consistent positive monthly abnormal turnover starting month 7 for large and small value firms. In Table 6, we report the results of the matching firm adjusted abnormal turnover, i.e. ABTURNOVER 4. Once again we observe that SEO firms experience significant positive average monthly

7 abnormal turnover relative to their matching firms starting month 9 prior to the SEO announcement, and they increase monotonically as we approach the SEO announcement. Therefore, all the results, so far, suggest that positive abnormal turnover precedes the announcement of a SEO and support our liquidity hypothesis. Logistic regression of SEO decisions Previous literature identifies several factors that are significantly related to the decision to issue equity (see Jindra (2000)). Jung, Kim, and Stulz (1996) and Opter and Titman (1996) study firm s debtequity choice. They show that firm with lower operating income, higher Tobin s Q, and higher momentum are more likely to issue equity. McLaughlin, Safieddine, and Vasudevan (1996) study equity issuance decision and document that leverage and firm size are also important determinants of the decision to issue. In short, the existing literature suggests that firms with higher momentum, leverage, growth opportunities, lower operating income and smaller in size are more likely to issue equity. In this section, we test to see if our liquidity hypothesis holds in the existence of these firm characteristics. More specifically, we run a logistic regression on the SEO sample and their matched non SEO firms, whereby the dependent variable takes a value of 1 for SEO firms and 0 for matched non SEO firms. The independent variables are: MOMENTUM the six month raw stock return prior to the month of the SEO announcement from CRSP; MKBK the previous fiscal year s market to book ratio computed as the ratio of the market value of equity, book value of preferred stock, and book value of debt to the book value of assets; all values downloaded from COMPUSTAT; lnasset the natural logarithm of last fiscal year s Total Assets obtained from COMPUSTAT; DAT the previous fiscal year s total debt ratio from COMPUSTAT; OPERINC/ASSET the previous fiscal year s ratio of Operating Income to Total Assets; both values downloaded from COMPUSTAT; CUMTURNOVER_12MONTHS the cumulative log share turnover over the window ( 12, 1) preceding the month of the SEO announcement. Therefore, we run the following logistic regression: SEO = α + α MOMENTUM + α MKBK + α ln ASSET + α DAT i 0 1 i 2 i 3 i 4 i + α ln OPERINC / ASSET + α CUMTURNOVER _12 MONTHS + ε 5 i 6 i i (4) where SEO is a dummy equal to 1 for SEO firms (the sample firm) and 0 for non SEO matching firm. Matching firms are matched on size and book to market ratio as explained earlier in the methodology section. Since MOMENTUM and CUMTURNOVER_12MONTHS are highly correlated, based upon our correlation examination, we test the effects of MOMENTUM and CUMTURNOVER_12MONTHS separately in Models 1 and 2, and simultaneously in Model 3. We present our findings in Table 7. We find that the models are highly fit. Consistent with previous studies on SEO decision, MOMENTUM has a significant effect on the decision to conduct a SEO. MKBK also marginally contributes to explaining the SEO decision. There is also some evidence that firms with higher leverage, DAT are more likely to perform SEOs. Interestingly, the results show that the cumulative turnover ratio, CUMTURNOVER_12MONTHS also significantly predicts future SEO announcements. Thus, our liquidity hypothesis holds even in the presence of other explanatory variables. CONCLUSION Trading volume provides information on the degree of interest (or neglect) of a stock. The purpose of this paper is to examine whether a firm files and announces a SEO when its stock is in favor. Our examination reveals that positive abnormal share turnover precedes the announcement of a SEO and supports our hypothesis that liquidity is an important determinant of whether a firm conducts a SEO. Our

8 results, which are robust to alternative benchmarks are strong, and present among small, big, value and growth firms. Our evidence is robust even after controlling for various factors, which are found to be significant determinants of SEO decisions. Our result should not be interpreted that any firm experiencing abnormal increase in share turnover is going to perform a SEO. It only implies that the increase in trading volume provides a positive condition to managers of firms who already have the intention to perform a SEO to go ahead with the offering.

9 Table 1 Distribution of SEO Announcements by Year Descriptive statistics for the sample of seasoned equity offering (SEO) firms. For each SEO announcement year, the number of SEO announcements is presented. The percentage of each year s observations of the total sample is also reported. SEO firms are identified in the Securities Data Corporation (SDC) Global Issues Database. SEO Announcement Year N % % % % % % % % % % % % Total % Table 2 Benchmarks used to calculate Abnormal Turnover, ABTURNOVER Benchmark Acronym Short Description Long Description of Benchmark The sample firms average turnover Comparison Period ratio starting 36 months and ending 1 ABTURNOVER 1 Mean Adjusted 13 months before the month of the Abnormal Turnover SEO announcement. 2 ABTURNOVER 2 Market Adjusted Abnormal Turnover 3 ABTURNOVER 3 Adjusted Abnormal Matching Portfolio Turnover 4 ABTURNOVER 4 Adjusted Abnormal Matching Firm Turnover The corresponding turnover ratio for the market. The corresponding turnover ratio for an equally weighted portfolio of matching firms. The corresponding turnover ratio for matching firms.

10 Table 3 Comparison Period Mean Adjusted Abnormal Turnover Pre SEO This table reports comparison period mean adjusted abnormal turnover, ABTURNOVER 1,t for SEO firms. Means are obtained in the window ( 36, 13) months prior to the SEO. The symbols ****, ***, ** and * denote significance at the 0.1, 1, 5, and 10 percent levels based on a two tailed test, respectively. Months prior to SEO N ABTURNOVER 1,t Patell Z Positive:Negative % : % : % : % : % 2.70*** 203: % 2.60*** 197: % 2.80**** 201: % 3.80**** 211: % 4.50**** 220: % 5.40**** 222: % 5.20**** 212: % 6.00**** 219:163 ( 12, 1) % 4.50**** 243:120 ( 6,0) % 6.00**** 257:125 ( 3,0) % 7.00**** 262:120

11 Table 4 Market Adjusted Abnormal Turnover Pre SEO This table reports average market adjusted abnormal turnover, ABTURNOVER 2,t for SEO firms. Abnormal turnover equals to actual turnover minus turnover predicted by a market model. The symbols ****, ***, ** and * denote statistical significance at the 0.1, 1, 5, and 10 per cent levels based on a twotailed test, respectively. Panel A Market Index is the CRSP Equally Weighted Market one Months prior to SEO N ABTURNOVER 2,t Positive:Negative Patell Z % 166: **** % 163: **** % 162: * % 180: ** % 181: **** % 186: **** % 192: **** % 199: **** % 203: **** % 221: **** % 209: **** % 219: **** ( 12, 1) % 218: **** ( 6,0) % 239: **** ( 3,0) % 242: **** Panel B Market Index is the CRSP Value Weighted Market one % 163: **** % 159: **** % 168: ** % 182: **** % 182: **** % 185: **** % 198: **** % 193: **** % 209: **** % 223: **** % 210: **** % 216: **** ( 12, 1) % 231: **** ( 6,0) % 247: **** ( 3,0) % 254: ****

12 Table 5 Matching Portfolio Adjusted Abnormal Turnover Pre SEO The table reports average matching portfolio adjusted abnormal turnover, ABTURNOVER 3,t for SEO firms. Large firms have market values of equity in excess of the median NYSE firm. Growth firm have book to market ratios below the 30th percentile NYSE firm. Value firms have book to market ratios above the 70th percentile NYSE firm. Neutral firms are in between. Matching portfolio is the portfolio of firms in the same size and book to market quantile as the SEO firm. Patell Z test statistic is reported in parentheses. The symbols ****, ***, ** and * denote significance at the 0.1, 1, 5, and 10 percent levels based on a twotailed test, respectively. Months before SEO Panel A Large Firms Panel B Small Firms Growth (N = 154) Neutral (N = 79) Value (N = 32) Growth (N = 39) Neutral (N = 45) Value (N =33) % 0.08% 1.18% 3.55% 1.39% 0.24% (3.54***) ( 0.17) (0.99) (3.37***) (1.56) (0.20) % 0.43% 1.81% 3.87% 1.71% 1.23% (2.98***) (0.83) (1.45) (3.48***) (2.14**) ( 0.73) % 0.56% 1.94% 3.57% 1.00% 0.41% (2.72***) (0.35) (1.99*) (2.96***) (0.99) ( 0.19) % 0.72% 0.40% 3.92% 1.61% 1.67% (3.66***) (1.32) (0.35) (3.46***) (1.40) (1.02) % 1.00% 2.33% 2.84% 1.44% 1.79% (4.22****) (1.89*) (2.28**) (2.64***) (1.42) (0.86) % 0.82% 2.72% 3.44% 1.14% 2.71% (3.57***) (1.43) (2.58**) (3.36***) (1.30) (2.05**) % 0.82% 1.67% 3.11% 0.42% 2.99% (3.32***) (1.34) (1.72*) (2.70***) (0.33) (1.72*) % 1.16% 2.16% 3.73% 0.53% 2.52% (3.59***) (1.93*) (2.16**) (5.10****) (0.72) (1.29) % 1.01% 2.99% 3.69% 1.44% 5.07% (3.92***) (1.41) (2.68***) (3.00***) (1.90*) (2.74***) % 0.54% 3.02% 3.20% 2.48% 6.15% (3.57***) (0.63) (2.23**) (2.83***) (5.52***) (3.46***) % 0.79% 2.48% 4.02% 2.76% 5.60% (3.12***) ( 0.64) (2.68***) (5.32****) (3.42***) (3.21***) % 0.10% 1.50% 3.93% 3.15% 5.78% (2.07**) ( 0.05) (2.55**) (2.31***) (4.01****) (3.36***) ( 12, 1) 21.37% 7.99% 25.82% 46.94% 16.34% 16.16% (1.96*) (1.56) (1.89*) (2.00**) (1.82*) (2.33**) ( 6,0) 12.79% 4.60% 13.14% 22.56% 7.20% 27.60% (1.96*) (1.54) (1.97*) (2.03**) (1.80*) (2.08**) ( 3,0) 8.30% 0.01% 8.50% 11.64% 7.20% 16.56% (1.83*) (0.20) (2.47**) (2.54**) (2.21**) (2.57**)

13 Table 6 Matching Firm Adjusted Abnormal Turnover Pre SEO This table presents matching firm adjusted abnormal turnover, ABTURNOVER 4,t for SEO firms. A matching firm s size is within 30 per cent of the SEO firm and is closest in the book to market ratio. The symbols ****,***,** and * denote significance at the 0.1, 1, 5 and 10 per cent levels based on a twotailed test, respectively. Months prior to SEO N AABTURNOVE R 4, t Patell Z Positive:Negative % : % : % 2.38** 178: % 2.68*** 180: % 3.15*** 183: % 4.11**** 185: % 4.24**** 186: % 4.24**** 184: % 5.05**** 189: % 5.96**** 195: % 7.27**** 203: % 8.77**** 212:120 ( 12, 1) % 4.56**** 192:140 ( 6,0) % 6.13**** 204:128 ( 3,0) % 7.29**** 212:120

14 Table 7 Logistic Regression for SEO Decision This table presents results of running logistic regressions. The dependent variable in each model equals to 1 for SEO firms, and 0 for matching non SEO firms. MOMENTUM is the 6 month raw return from month 6 till month 1 prior to SEO announcement; MKBK is the previous fiscal year ratio of the sum of the market value of equity, book value of preferred stock, and book value of debt to the book value of assets; lnasset is the natural logarithm of the previous fiscal year total assets; DAT is the previous fiscal year ratio of total debt to total assets; OPERINC/ASSET is the previous fiscal year ratio of operating income to total assets; and CUMTURNOVER_12MONTHS is the cumulative log share turnover over the window ( 12, 1) in months prior to the SEO. T statistics are reported in parentheses. The symbols ****, ***, ** and * denote significance at the 0.1, 1, 5, and 10 percent levels based on a two tailed test, respectively. Independent Variables Model 1 Model 2 Model 3 Intercept (4.04**) (5.11**) (4.03**) MOMENTUM (15.20****) (15.85****) MKBK (0.12) (3.18*) (0.16) lnasset (0.04) (0.03) (0.03) DAT (3.51*) (1.89) OPERINC/ASSET (0.19) (0.15) (0.15) CUMTURNOVER_12MONTHS (6.73**) (4.05**) Likelihood ratio test statistic 20.39*** 21.73*** 20.37*** Score test statistic 19.80*** 21.07*** 19.77*** Wald test statistic 18.68*** 19.88*** 18.68*** N

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