Measuring the Short-Term Effect on Return of Unexpected Trading Volume (15 March 2005)

Transcription

1 Measuring the Short-Term Effect on Return of Unexpected Trading Volume (15 March 2005) Peyton Foster Roden * University of North Texas Abstract This paper examines the relationship between unexpected trading volume and short-term abnormal returns in the market for seasoned equity offerings. After using ARIMA modeling with intervention analysis to measure unexpected volume, the author builds a categorical regression model to explain the cross-sectional relationship between a three-day cumulative abnormal prediction error, unexpected volume, conditions in the market at time of issue (hot markets), and the market in which the issue trades. Analysis reveals that unexpected volume is significantly positively correlated with abnormal return, consistent with the price-pressure hypothesis. Keywords: hot markets; price-pressure hypothesis, unexpected volume. JEL classification G140 * Corresponding author is Peyton Foster Roden, College of Business Administration, University of North Texas, PO Box , Denton, Texas ; (940) ; fax (940)

2 Measuring the Short-Term Effect on Return of Unexpected Trading Volume Relative prices in financial markets serve as an invisible hand to allocate finance between firms with high growth prospects and those with low growth prospects. The market s task of allocating finance is done efficiently if shares are perfect substitutes for each other and the demand curve facing an issuer is essentially horizontal. Each stock trading in such a market is a candidate for inclusion in a portfolio. Actions by buyers and sellers will lead to price changes such that expected rates of returns on investment in shares will be the same for each risk class. In such a market, price changes surrounding a new issue reflect changes in the prospects for the company or other influences which would occur even in the absence of an offering. An alternative view of markets and stock offerings is that shares are not close substitutes and the demand curve facing an issuer is downward sloping. Investors view shares as uniquely representing the underlying company perhaps because of brand identity, company location, or some other company characteristic. As Shleifer noted (1986), when a firm issues shares in such a market, additional shares must be offered at a discount from face value to attract new buyers to the specific offering. Equilibrium is reinstated at a new, lower price. A view of markets less restrictive than (though related to) the downward-sloping demand curve is the price-pressure hypothesis. It proposes that returns during an offering change as the demand for the shares changes with the signal provided by the new offering. In this less-restrictive framework, a new offering leads to a new equilibrium, either higher or lower. Whether or not price pressure leads to a new equilibrium is not a trivial issue because it influences the distribution of wealth between pre-offering and post-offering shareholders through its impact on the distribution of returns and risk between these two groups. [Lutz and Lutz, 1951, pp ] Assume that the number of shares obtained by new investors in an offering is and

3 the number of shares held by pre-offering shareholders,. New shareholders will receive of the issuer s expected cash flows and of any component of the accompanying probability distribution. Corresponding values for pre-offering shareholders after an offering is. The ratio is an increasing function of the volume of new shares offered if the demand curve for shares is downward sloping. It is an increasing function if upward sloping. In this case, a low (high) issue price reduces (increases) both the return and dispersion to pre-offering shareholders. After examining the literature and developing a model, this paper finds that the abnomal returns and unexpected increases in volume surrounding an offering are positively related, consistent with the price-pressure hypothesis. The results are useful to present and prospective shareholders concerned with the short-term impact of a new offering on the value of shares. Shareholders should expect an impact from the announcement of an offering and the possibility that post-offering returns and dispersion will differ from their pre-offering levels. Financial managers will use the results to plan for a change in stock price surrounding an issue, and thus to condition accordingly their calculations of the cost of capital. This paper is organized as follows: Part One addresses the literature surrounding volume and new issues. Part Two discusses the sample and data used to examine the statistical relationship between returns and abnormal volume. Part Three describes the variables used in the subsequent categorical regression model, with particular emphasis on the measure of unexpected volume. Part Four presents the statistical results, and Part Five is a summary and conclusion. 1 Volume, Stock Returns, and Price Pressure Scholes (1972) examined the relationship between daily returns and secondary offerings to determine the amount of price change surrounding an new issue. He looked at daily returns and secondary offerings and contrasted the price-pressure and substitution hypotheses: The price- Page 2 of 24

4 pressure hypothesis suggests that price change will be a function of the size of an offering, and the substitution hypothesis that return is unaffected by issue volume. Scholes found the prediction errors from his event study failed to support the price-pressure argument. In fact, his analysis of daily prediction errors of 345 secondary distributions sorted by size of offering was consistent with the substitution hypothesis because... larger distributions do not experience a larger abnormal return than the smaller distributions.... (1972, p. 199) Barclay and Litzenberger (1988) examined intra-day trades and issue volume of 218 new stock offerings and 85 of debt offerings and also found support for the substitution hypothesis. Examining the price-pressure effect of stocks added to the S&P 500, Shleifer (1986) changed the definition of volume from the number of shares offered to the number of shares traded. He measured abnormal daily trading volume on the announcement date as the difference between the announcement-date volume and the average daily volume in the previous six months as a fraction of the shares outstanding. He noted (p. 585) that the significantly positive relationship between abnormal returns and abnormal trading volume is consistent with the pricepressure hypothesis. Masulis and Korwar (1986) used the percentage change in the shares of common stock outstanding as an explanatory variable in their study of seasoned equity offering (SEOs). They found returns to be significantly correlated with the volume of shares offered. Korajczyk, Lucas, and McDonald (1990) found mixed results in their study of SEOs and issue volume. They measured issue volume by the number of new shares issued divided by the pre-offering number of shares outstanding. Gallant, Rossi, and Tauchen (1992) reverted to the measure of volume used by Shleifer. They used a time-trend measure of trading volume and dummy variables to conclude (p. Page 3 of 24

5 230) that large stock price changes accompanied with high trading volume have an asymmetric effect on subsequent volatility. Later studies continued to find conflicting results when using different measures of volume, some the volume of shares issued and some, trading volume. For example, Akhigbe and Madura (2001) modeled liquidity of closed-end funds as the total trading volume during the previous month relative to the number of shares outstanding 20 days before an issue. Their crosssectional analysis disclosed price-pressure effects as issuers experienced negative post-offering valuation effects. Brooks and Patel (2000) used bid ask spreads and issue volume. Elyasiani, et. al (2000) introduced several measures of liquidity to explain the observed performance of SEOs. Their non-parametric tests disclosed that any of several measures (percentage bid-ask spread, volume, Hasbrouck s s, and variance as market liquidity) accounted for resulting postannouncement performance. Eckbo, Masulis, and Norli (2000) measured trading volume as the turnover of shares (shares traded divided by shares outstanding) to examine the performance of SEOs using matched firms. They found no difference in the Nasdaq sample, but a large increase in turnover of issuers in the NYSE and AMEX sample. Widespread acceptability of the price-pressure hypothesis is implicit in the way the cost of external equity is calculated in managerial finance books. [Gitman, pp ] For the cost of external equity, the equations are adjusted to reflect the reduced price associated with the offering and necessary flotation costs. In this way, the cost of external equity is greater than that of internal equity, resulting in an increased cost of capital to the issuer and fewer projects selected for investment. This paper examines trading volume and its relationship with the short-term conditional return on shares surrounding seasoned equity offerings (SEOs). The market in which the security Page 4 of 24

6 trades and conditions in the market at the time of announcement also will be included in the analysis. 2 Sample and data description 2.1 Sample selection Data selection began by finding 1,159 seasoned equity offerings for the 10-year period in Investment Dealer s Digest. To be included in the sample for statistical analysis, issuers met the following characteristics: (1) The issuer was listed on the Center for Research in Security Prices (CRSP) daily NYSE/AMEX or Nasdaq tape at the time of the announcement; (2) the issue was a seasoned offering; (3) the issue involved only common stock; (4) an offering had no confounding events during the four weeks preceding and following the announcement. The above criteria excluded combined offerings in which some of the shares were from a secondary offering, preferred-stock offering, or debt offering because of the potential for the market to react to mixed signals for example, common-stock investors may interpret preferredstock or debt offerings as positive signals because of the increased scrutiny associated with such an offering by rating agencies. Shelf registrations of equity offerings were excluded. Utility companies were excluded from the sample to avoid the confounding effects of regulation the regulatory approval process reduces the ability of utilities to time an offering to exploit overpricing conditions in the market for their shares. Finally, consistent with Healy and Palepu (1990) and Loughran and Ritter (1997), omitted were new-equity announcements by the same firm during the five years following an SEO. Applying these criteria led to a sample of 779 announcements. 2.2 Data description Exhibit 1 summarizes the 779 offerings included in the sample for statistical analysis. The largest number of offerings occurred in 1992 (113) and 1993 (112), and the fewest in 1988 (29). Nasdaq Page 5 of 24

7 offerings (340) dominated the sample with AMEX offerings a distant third (62). The most dominant year for the Nasdaq was 1993 (66 offerings to 37 for NYSE and nine for AMEX). 3 Model Building (Insert Exhibit 1 from Page 21 About Here) Categorical regression with optimal scaling (CATREG) was used to examine the statistical relationship between conditional returns (measured by cumulative abnormal prediction errors) and unexpected volume. CATREG extends loglinear modeling by quantifying categorical variables. * The procedure assigns scale values simultaneously to independent variables reflecting ** nominal, ordinal, and numerical variables to reflect characteristics of the original categories. It maximizes the squared correlation between the transformed response and the weighted combination of transformed independent variables. Using ordinal qualitative regressors instead of 0 1 dichotomous (dummy) variables may produce substantial efficiency gains and reduction of bias in the estimation of qualitative shifts in the regression line and simplifies interpretation. (Meulman, 2001; Terza, 1987) * I rejected standard linear regression analysis for several reasons. First, estimating one coefficient for each variable w ould reflect arbitrary values assigned w ithin categories. Second, standard regression requires numerical independent variables, regression w ith optimal scaling offers three scaling levels for each variable. Third, dealing in standard linear regression w ith the nonlinear relationship betw een a categorical variable (for example, differing classifications of markets based on liquidity) and other variables is problematic w hen an independent variable has both high and low values associated w ith one value of the dependent variable. The independent variable receives only one w eight, w hich cannot reflect the same amount of change in the predicted response for both large and small values of the independent variable. Fourth, recoding categorical variables as a series of 0 1 interval variables (for example, three markets classified into tw o 0 1 dichotomous variables) means that the model contains a separate intercept and/or slope coefficient for each combination of the levels of the categorical variables. That results in a large number of parameters to interpret. Fifth, it s a nifty model and almost fun to use. ** The level of optimal scaling determines the optimality properties of the quantifications. The SPSS 12 optimal-scaling program used in this paper orders ordinal variables (for example, the market in w hich the stock trades at announcement) in the same manner as original values. Scaling all variables at the numerical level w ould lead to analysis analogous to standard principal component analysis. Page 6 of 24

8 3.1 Variables Dependent variable The dependent variable was the cumulative abnormal prediction error over a three-day period surrounding the SEO announcement, CAPE ( 1, +1). CAPEs over a 3-day period ( 1, +1) were used rather than only on day 0 to account for the possibility that issue information may be available before the opening or after the close of the announcement date. Moreover, the three-day estimation (rather than some longer period) permitted efficient estimation of the effects of the announcement on returns because a longer measurement period would introduce sources of variability in returns attributable to extraneous factors unrelated to announcements. Using data from the Center for Research in Security Prices at the University of Chicago, an Eventus program extracted daily returns (including dividends) of the 779 offerings for 316 trading days. The estimation period consisted of 255 trading days ending 30 days before the announcement day (Eventus day 286) to avoid statistical bias. The validation period (the event window) consisted of the remaining 61 trading days. Return specification used the single index market model (SIMM) in the estimation period. An estimated prediction error j,t was computed during the validation period: where R was the CRSP return to security j on day t, R the return on the value-weighed CRSP j,t index on day t, and and were estimated coefficients of the market model from the estimation period. The prediction error in equation 1 differs from the residual because predicted observations were not included in the estimation period. Prediction errors for the validation period were then m,t Page 7 of 24

9 accumulated for the cumulative average prediction error of each security j as the sum of prediction errors over the interval to yield the cumulative abnormal prediction error, or conditional return. Exhibit 2 presents a summary of the results classified for convenience by exchange listing. Three-day CAARs were on average negative irrespective of the exchange on which market the issuer s stock traded. However, the Exhibit shows that the distribution of offerings classified by exchange listing was roughly even between positive and negative returns over the period (46 percent on average were positive). (Insert Exhibit 2 from Page? About Here) The categorical regression procedure holds constant the widely-held or commonknowledge determinants of stock valuation and examines whether abnormal returns differ in relationship to specified independent variables. Thus, a finding of significant changes in CAPE ( 1, +1) to unexpected volume indicates that returns are influenced by volume and consistent with the price-pressure hypothesis Independent variables Independent variables included in the analysis are the cumulative three-day unexpected volume, market conditions (hot markets), and the exchange on which the stock traded at time of the announcement Unexpected Trading Volume Karpoff (1986) used simulations to show that financial theory supports the use of volume as a variable is explaining market behavior. Because trading volume data are in the form of time series in which observations are serially dependent and may be nonstationary, it is appropriate to model the process using time series methodology developed by Box and Jenkins (1970 ) and Box and Tiao (1975). Page 8 of 24

10 ARIMA modeling of unexpected trading volume in this paper began by examining residual autocorrelations of a subsample of offerings using daily trading volume for 29 days preceding an announcement. The results suggested that the series was stationary. Moreover, autocorrelation (ACF) and partial autocorrelation (PACF) plots suggested a first-order autoregressive AR(1) model, which was then applied to each offering in the validation period (the event window). Exhibit 3 is a sequence chart of cumulative fitted values of days 12 through +5 from the ARIMA model applied to the total offerings each day in the data set. Upper and lower bands reflect ±3. The reference line indicates that the volume of shares for the sample average on Day 0 was beyond 3 standard deviations of the average. (Insert Exhibit 3 from Page 23 About Here) The measure of unexpected trading volume in this paper is comparable with that of Gallant, Rossi, and Tauchen (1992), who used a set of dummy variables and time-series modeling to develop their measures. I use intervention analysis developed by Wichern and Jones (1977) to quantify unexpected volume on three trading days centered on the announcement date. Intervention analysis used three step indicators in the ARIMA model to capture unexpected trading volume: S 2 became 1 on the announcement date (day 0) and stayed at that level thereafter. S 1 and S 3 became 1 one trading day before ( 1) and one trading day after the announcement (+1), respectively, and remained 1 thereafter. Page 9 of 24

11 This estimate of trading volume surrounding an event is more powerful than simple measures for two reasons: Volume measures based on simple comparisons of before- and afteroffering indicate neither the rate of change in volume due to the announcement nor the nature of the effect. In addition, simple measures often use a student t test or nonparametric test of volume changes, thus relying on the assumption of independent observations. ARIMA modeling with intervention analysis overcomes these issues. Trading volume data were accumulated for all 779 issues, and intervention analysis applied as discussed above. Sample statistics were as follows: Total 3 Day Unexpected Volume on Day Unexpected Volume ,748 33,883* 28,112* 10,519 Unexpected average trading volume on day 1 increased by a statistically insignificant 4,748 shares. However, on the announcement date the volume attributable to the announcement increased by a significant (0.01 as indicated by *) 33,883 shares and then fell by a significant (0.01) 28,112 the following trading day. In all, then, unexpected volume of shares traded increased by 10,519 shares (4, ,883 28,112) over these three days. For statistical analysis, three-day unexpected volume of each of the 779 offerings was categorized into quartiles and coded for analysis as follows: Category Count Average Minimum Maximum Page 10 of 24

12 Hot Markets Several studies suggest using market conditions to explain abnormal returns. Bayless and Chaplinsky (1996) use the term window of opportunity (p. 257) to describe a hot market as one in which adverse selection from an offering is minimized. Investors during such periods are less fearful of over valuation perhaps in part (as Bayless and Chaplinsky note) because they (investors) believe issuers have more projects with positive net present value than at other times. Bayless and Chaplinsky (1996) used a three-month moving average of equity-issue volume segmented into quartiles and designated hot markets as at least three contiguous months where equity volume exceeds the upper quartile. They found evidence using a two-day announcement period that conditional returns are more volatile in hot-issue markets than in other periods. Brous, Datar, and Kini (2001) defined hot markets as the year in which occurred the most SEOs in their data set They examined a three-day earnings-announcement period and found no evidence that hot markets explained conditional returns. Eckbo, Masulis, and Norli (2000) included the... well-known hot issue periods and 1993 (p. 255). They found in their long-term study of SEOs no evidence of abnormal conditional returns during these two periods relative to other periods in their sample. The measure of hot markets in this paper began with calculating the mean and quartiles of announcements by month. Hot markets were defined as at least three contiguous months in the fourth quartile of the monthly announcements over the period, resulting in four hot-market periods (scaled ordinally). The number and dates were as follows: Page 11 of 24

13 Hot Market Inclusive Period Number of Announcements 11/85 07/ /91 10/ /92 09/ /93 11/ Exchange Listing Karpoff (1986) noted his simulations indicated that the relation between information and volume is affected by the institutional design of the market. Consequently several studies have used exchange-listing to explain mispricing surrounding new issues. They note that liquidity has positive value so that new issues on an illiquid market should lead to mispricing. During the period examined in this study, Nasdaq listed issuers had securities trading in an illiquid market because such firms were likely to be smaller in size with less-stringent exchangerelated listing and disclosure requirements, fewer analysts following the shares, lower institutional ownership, and lower frequency of voluntary disclosures than issuing firms on the NYSE or AMEX. Moreover, announcements were likely to be more informative to investors in Nasdaqlisted issuers than for NYSE- and AMEX-listed issuers because the former had fewer means through which information was conveyed. Spiess and Affleck-Graves (1995) created subsamples of offerings based on exchange listing at time of offering and found greater mispricing for Nasdaq offerings than for those on the NYSE and AMEX. Elyasiani, Hauser, and Lauterbach (2000); McConnell and Sanger(1987); and Dharan and Ikenberry (1995) generally found positive conditional returns during periods prior to an SEO depending upon the listing (or change in listing) and reversals afterward, suggesting that substitution effects (rather than price-pressure) prevailed in their sample. Eckbo, Masulis and Norli (2000) in a discussion of model misspecification (arising from cohort methodology and from accounting numbers) found conflicting results when examining SEOs and market listing. Indeed, Page 12 of 24

14 in one model and sample period, they found a weak tendency for overpricing of SEOs for issuers on the NYSE and AMEX. Brous, Datar, and Kini (2001) found no relationship between mispricing and market listing. Exhibit 2 on page 21 shows the distribution among markets of the SEOs in this study. The most offerings were Nasdaq issues, and the least, AMEX. Even though that number differed, the CAPE ( 1, +1) and percentage declining were substantially similar. The specific listings were categorized and scaled ordinally to reflect the differing liquidity of each market: Nasdaq least liquid and NYSE most liquid. The four variables are used to test for the relationship between unexpected trading volume and return in a multivariate model: 1 = 2 = 3 = Unexpected trading volume for the three trading days centered on the announcement date, coded 1 4 and scaled ordinally Market condition at time of announcement coded 1= ordinary and 2= hot, scaled ordinally Categorical variable for the exchange listing coded (frequencies in parentheses) 1=NYSE (341), 2=AMEX (62), 3=Nasdaq (377), scaled ordinally Signs above the coefficients reflect the hypothesized correlation between each transformed independent variable and transformed CAPE ( 1,+1) as suggested in the literature. Of special interest is the sign above unexpected volume. It reflects either price-pressure or substitution effects after controlling exchange listing and market conditions. Following Shleifer (1986, p. 585), a positive sign is consistent with a causative influence of volume on return and so would support the price-pressure hypothesis. Page 13 of 24

15 4 Results Model summary and analysis of variance are in Exhibit 4. Although lacking in analytical power 2 (adjusted R =3.6%), the F statistic (7.205) is significant at the 0.01 level. Tolerance values after transformation suggest that the level of multicollinearity in the model is minimal. 4.1 Model Evaluation (Insert Exhibit 4 from Page 24 About Here) To check for robustness, coefficients were reestimated using deciles of unexpected trading volume rather than quartiles without a substantial change in results. Moreover, using the measures of hot market of 1990 from Eckbo, Masulis, and Norli (2000) and of 6/85 8/97 and 4/88 9/88 from Bayless and Chaplinsky (1996) left results substantially unchanged. That was not the case when the model was reestimated using dichotomous exchange classifications of Eckbo, Masulis, and Norli (2000), Brous, Datar, and Kini (2001), and Korajczyk, Lucas, and McDonald (1990). Combining NYSE and AMEX issues in one category and Nasdaq in another resulted in a not significant coefficient on the transformed exchange variable (classified ordinally) although the sign remained positive. Moreover, the coefficient for hot market fell in significance to 0.10 from (0.05 in Exhibit 4). Intuitively, it seems as though lumping the NYSE and AMEX into one category is in error because the two markets were substantially different during this period in terms of quantity and quality of listings and of media coverage. Consequently, Exhibit 4 presents results from the three-category classification. 4.2 Coefficients Each coefficient is statistically significant, quartiles of unexpected trading volume at the 0.01 level, and hot markets and exchange listing at the 0.05 level. The sign of each coefficient is as hypothesized: As transformed quantifications for unexpected volume, market Page 14 of 24

16 conditions, and exchange listing increase by one standard deviation, transformed classifications of CAPE ( 1,+1) increase by 0.158, 0.075, and standard deviations respectively. Square of the partial coefficients in Exhibit 4 corresponds to the proportion of the variance in the CAPE ( 1,+1) explained relative to the residual response remaining after removing the effects of other two independent variables. After removing the effects of market 2 conditions and market listing, unexpected trading volume explains 2.5% (0.158 ) of the variation in the CAPE ( 1,+1). Similar measures for market conditions and exchange listing are 0.6% 2 2 (0.076 ) and 0.61% (0.078 ) respectively. Unexpected trading volume unambiguously explains the preponderance of abnormal return (as measured by the CAPE). The sign on the coefficient of unexpected volume ( 1) is positive and significant, so that transformed values of unexpected volume and of CAPE covary significantly. Thus, increased unexpected trading volume during periods surrounding a specific announcement contributes to changes in return as investors sort out the portfolio implications of the announcement. The significant coefficient is consistent with the price-pressure hypothesis (rather than the substitution hypothesis) as noted in Shleifer (1986). Investors during the three-day period were rebalancing their portfolios in light of the announcement so that unexpected trading volume propelled returns away from equilibrium of pre-offering levels. Investors were compelled to await the arrival of new information to make further portfolio adjustments. Returns surrounding the announcement were evidently influenced by trading volume. 5 Summary and Conclusion Analysis in this paper applied a time series model of volume similar to that of Gallant, Rossi, and Tauchen (1992) and the cross-sectional method of Shleifer (1986) to examine the statistical relationship between unexpected trading volume and conditional returns. Page 15 of 24

17 5.1 Summary Data collection began with 1,160 seasoned equity offerings by firms with shares trading in three markets (Nasdaq, NYSE, and AMEX) during the 10-year period The final sample for statistical analysis was 779 offerings. Model building began with determining the conditional return surrounding an SEO announcement date. The dependent variable in subsequent cross-sectional analysis was the cumulative abnormal prediction error for a three-day period centered on the announcement date CAPE ( 1, 1). Dependent variables were hot markets, exchange listing, and unexpected trading volume, the variable of particular interest. Unexpected trading volume was measured for each of the 779 announcements by ARIMA modeling with intervention analysis. Three step variables captured unexpected trading volume: one day before the announcement (day 1), the day of the announcement (day 0), and one day after the announcement (day +1). Categorical regression with optimal scaling tested the hypothesis that returns are statistically related to unexpected trading volume. Although the model lacked strong analytical power, the F statistic for the 779 observations was statistically significant at the 0.01 level. Each coefficient was as hypothesized. The coefficient of unexpected trading volume was positive and statistically significant, thus supporting the price pressure hypothesis and the downward sloping demand for stocks surrounding the announcement date. 5.2 Conclusion This paper extended previous analysis of the relationship between trading volume and returns surrounding an announcement of a seasoned equity offering. It examined the literature to show that we should expect a statistically significant relationship between trading volume and conditional returns. The results suggest the present of a short-term downward-sloping demand for Page 16 of 24

18 stocks reflecting market inefficiencies and suggests that stock prices change as a result of an increase in the number of shares traded. Those interested in market efficiency and its implications for investors and issuers can extend this study in several direction. First, the data can be updated to include the recent period and its increased flow of information arising from increased efficiencies in financial markets. Second, conflicting results referred to above from changing the specifications of hot markets and of exchange listing suggest that refinement and perhaps consensus are needed in the definitions of these variables. Third, the link between volume and returns may be mitigated by companyspecific issues as measured by accounting numbers. This paper avoided using accounting numbers to concentrate on only market measures. In this regard, the paper reflected the adage that the only thing that can predict the market is the market, itself. Finally, it would be interesting to see the long-term influence of unexpected volume on returns. Here, the ambitious reader can use a longer period for the CAPE and a longer period for unexpected volume. The analysis here used a short period to minimize extraneous influences. Page 17 of 24

19 References Akhigbe, A., and J. Madura. Motivation and Performance of Seasoned Offerings by Closed-End Funds, The Financial Review, 38 (2001), pp Barclay, M. J., and R. H. Litzenberger. "Announcement Effects of New Equity Issues and the Use of Intraday Price Data," Journal of Financial Economics 21 (1988), pp Bayless, M., and S. Chaplinsky. Is there a Window of Opportunity for Seasoned Equity Issuance? Journal of Finance 51 (1996), pp Brooks, R. M., and A. Patel. Information Conveyed by Seasoned Offerings: Evidence from Components of Bid Ask Spread, Review of Financial Economics 9 (2000), pp Brous, P. A., V. Datar, and O. Kini. Is the Market Optimistic about the Future Earnings of Seasoned Equity Offerings Firms? Journal of Financial and Quantitative Analysis 36 (2001), pp Dharan, B. and D. Ikenberry. The Long-Run Negative Drift of Post-Listing Stock Returns, Journal of Finance 50 (1995), pp Eckbo, B. E., R. W. Masulis, and Ø. Norli. Seasoned Public Offerings: Resolution of the New Issues Puzzle, Journal of Financial Economics 56 (2000), pp Elyasiani, E., S. Hauser, and B. Lauterbach. Market Response to Liquidity Improvement: Evidence from Exchange Listing, The Financial Review 41 (February, 2000), pp Gallant, A. R., P. E. Rossi, and G. Tauchen. "Stock Prices and Volume, The Review of Financial Studies 5 (1992), pp Gitman, Lawrence J. Principles of Managerial Finance 10/e, Boston: Addison-Wesley, Healy, P., and K. Palepu. Earnings and Risk Changes Surrounding Primary Stock Offers. Journal of Accounting Research 32 (Spring 1990), pp Page 18 of 24

20 Karpoff, J.M. A Theory of Trading Volume, The Journal of Finance XLI (December, 1986), pp. 1069=1087. Korajczyk, R. A., D. Lucas, and R. L. McDonald. Understanding Stock Price Behavior around the Time of Equity Issues, in Asymmetric Information, Corporation Finance, and Investment, edited by R. Glenn Hubbard, The University of Chicago Press, 1990, pp, Loughran, T., and J. R. Ritter. The Operating Performance of Firms Conducting Seasoned Equity Offerings. Journal of Finance 52 (December 1997), pp Lutz, Friedrich, and Vera Lutz. The Theory of Investment of the Firm, Greenwood Press, Westport, Connecticut, Masulis, R. W., and A. Korwar. Seasoned Equity Offerings: an Empirical Investigation. Journal of Financial Economics 15 (January/February 1986), pp McConnell, J., and G. Sanger. The Puzzle in Post-Listing Common Stock Returns, Journal of Finance 42 (1987), pp Meulman, J. J. Nonlinear Scaling Techniques for the Analysis of Large and Complicated Data Sets, MSRI Workshop, Berkeley, CA, March 19-29, 2001 ( Shleifer, Andrei. "Do Demand Curves for Stocks Slope Down?" Journal of Finance XLI (July, 1986), pp Scholes, M. S. "The Market for Securities: Substitution versus Price Pressure and the Effects of Information on Share Prices," The Journal of Business 45 (1972), pp Page 19 of 24

21 Spiess, D. K., and J. Affleck-Graves. Underperformance in Long-Run Stock Returns Following Seasoned Equity Offerings. Journal of Financial Economics 38 (July 1995), pp Terza, J. V. Estimating Linear Models with Ordinal Qualitative Regressors, Journal of Econometrics, 34 (1987), pp Wichern, D. W., and R. H. Jones. Assessing the Impact of Market Disturbances Using Intervention Analysis, Management Science, 24 (1977), pp Page 20 of 24

22 Exhibit 1. Sample Offerings by Year and Exchange Listing Year of Offering Exchange Listing Row Total Percent of Total Cumulative Percent NYSE Nasdaq AMEX Total Totals Page 21 of 24

23 Exhibit 2. Cumulative Average Abnormal Returns for Three Days Centered on the Announcement Date Values for Exchange Listing NYSE Nasdaq AMEX Average Offerings Cumulative average prediction error( 1,+1) Percentage positive 47.0% 46.0% 45.0% 46.0% Page 22 of 24

24 Exhibit 3. Average Daily Volume of Shares Traded Relative to Announcement Date Total shares 1,778,142 Minimum 85,802 Maximum 134,947 Average 98,785 Standard deviation 12,824 Page 23 of 24

25 Exhibit 4. REGRESSION MODEL SUMMARY AND ANALYSIS OF VARIANCE Standardized Coefficients Correlations Tolerance Beta F Zero-Order Partial Part Pratt s Importance After Transformation Before Transformation Unexpected volume * Hot market ** Exchange ** R F* * Significant at the.01 level, two-tailed test. ** Significant at the.05 level, two-tailed test. Page 24 of 24