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  1. 1. The long-run performance of mergers and acquisitions : Evidence from the Canadian stock market Paul André Jean-François L’Her a Very preliminary, please do not quote j July 月曜日 JEL Classification: G12, G14, G34 Keywords: Mergers and acquisitions, four factor pricing model, Canadian stock market a The authors gratefully acknowledge financial support from the Social Sciences and Humanities Research Council of Canada. The usual disclaimer applies. Corresponding author: Paul André, École des Hautes Études Commerciales, phone : (514) 340-6528, fax : (514) 340-5633 , paul.andre@hec.ca.; Jean- François L'Her, Caisse de dépôt et placement du Québec, Gestion des comptes des déposants, phone: (514) 847-2601, Fax : (514) 847-5443, e-mail: jlher@cdp.ca.
  2. 2. The long-run performance of mergers and acquisitions : Evidence from the Canadian stock market Abstract: We study the long run performance of mergers and acquisitions from 267 Canadian firms over the period 1980 to 2000. Using various methodologies including the Fama-French three factor pricing model and a four factor pricing model, we document a significant underperformance when returns of a portfolio of Canadian acquirors are equally-weighted. This result is robust under certain conditions when returns are value-weighted 2
  3. 3. 1 Introduction The international wave of merger and acquisitions of the last decade is unprecedented notwithstanding the recent slowdown. Just in the U.S. in each of the years 1999 and 2000, there were more than 9,000 transactions worth more than a trillion dollars (Mergerstat review). Canada is no stranger to this trend. In 2000, a record breaking year, Crosbie & Co. reported close to 1,300 transactions for a total value of almost 235 billion dollars. Beyond all this activity however, there is a growing concern about the prices that are being paid and about the impact of these major transactions on future corporate performance. Understanding the value creation process related to these transactions and the factors that drive it has been an important focus of accounting and finance research. This research can generally be divided in three different broad approaches. First, researchers (e.g., Jensen and Ruback 1983, Andrade, Mitchell and Stafford 2001) have examined the stock market reaction to mergers and acquisitions announcements. Most researchers generally agree that these transactions seem to create some value for shareholders overall. However, the gains accrue almost entirely to target shareholders, acquiring shareholders gaining nothing and sometimes losing. More recently, researchers have been concerned with a second question, that is, the post acquisition operational performance of these transactions. Kaplan and Weisbach (1992) point out that 44% of transactions are actually undone inside a ten year period. Starting with the Healy, Palepu and Ruback (1992) paper, number of researchers have concluded that on average cash flow returns improve in the post acquisition period and that this improvement is not artificially generated by short term decisions such a cutting investments. However, more recent papers are challenging these results and suggesting that there may not be any improvements on average (Ghosh, 2001 and Bécotte, 2002). Our paper use a third approach to examine value creation following mergers and acquisitions. We examine the long term post acquisition returns of mergers and acquisitions. Recent studies question the ability of markets to fully interpret the consequences of major transactions such as mergers and acquisitions at their announcement. Results however have been mixed, these studies being very sensitive to the methodology used. We adopt various methodologies to examine the issue and examine various explanations for the long run performance of Canadian acquirors related to firm-specific and transactions specific attributes. 3
  4. 4. Overall, our results suggest that on average Canadian acquirors underperform a match control sample. The remainder of the paper is organized as follows. In Section II, we present the literature on long-run performance. In Section III, we present the data and the methodology used to estimate the average long-run abnormal performance of Canadian acquirors. In Section IV, we expose the methodology and our results. In Section V, we present deal variables and acquiror/acquired characteristics can explained cross-sectional differences in long-run abnormal performance of acquirors. Section IV presents concluding remarks. 2 Post-merger performance Empirical research on mergers and acquisitions has generated a good deal of results as to their trends and characteristics over the last decades. A large number of event studies has demonstrated that mergers and acquisitions appear to create shareholder value but with most of the gains accruing to the target shareholders. Large scale studies in the U.S. by Jensen and Ruback (1983), Jarrell, Brickley and Netter (1988), Schwert (1996) and more recently by Andrade, Mitchell and Stafford (2001) that examine the average abnormal stock reaction in traditional short-window event studies all suggest that target shareholders are clearly winners in merger transactions. Andrade et al. (2001) examine some 3,688 transactions over the 1973-1998 period and find combined abnormal returns over the three-day period surrounding the announcement of 1.8% (statistically significant at the 5 percent level). Target shareholder abnormal returns are 16.0% while acquirer abnormal returns are not significantly different from zero. Target shareholders do even better when there is no equity financing while abnormal returns to acquiring shareholders in equity financed transactions are –1.5% and significant. These results hold for the sub-periods 1973-79, 1980-89 and 1990-98. Eckbo (1986) finds similar non significant results for successful Canadian bidders and so does Betton and Williams (2001) for a more recent sample of Canadian bidders and independent of transaction types. b b However, the results of empirical studies in Canada do not confirm the superiority of abnormal returns obtained by the shareholders for the transactions financed with payment in cash. For example, Eckbo, Giammarino and Heinkel (1990) analyzed abnormal returns obtained by the shareholders of the acquiring company of 182 acquisitions over the period 1964-1982. 92 of these acquisitions are completely financed with cash, 34 financed by stock while the last 56 acquisitions are financed with a combination of the two. Eckbo, Giammarino and Heinkel show that the shareholders of the acquiring company obtain abnormal returns of 5.7 % when the grouping is financed with a mixed payment of actions and sorts. However, these abnormal returns are only 2.7 % when the acquisitions are completely financed by stock 4
  5. 5. More recent studies have examined the long-run performance and cast some doubt on the interpretation of more traditional short-window event study findings. These studies generate a certain number of concerns with respect to market efficiency. While most authors accept that stock prices quickly adjust to incorporate the available information around merger and acquisition announcements, many believe that markets have some difficulty in properly measuring the immediate strategic fit of the combinations even more so when it is known that mergers and acquisitions are followed by important restructurings and frequent divestitures in almost half the merger and acquisition cases (Kaplan and Weisbach, 1992). 2.1 MEASURE OF LONG - RUN PERFORMANCE Since Fama and French's (1992) article, financial literature considers that systematic risk, beta, is not a sufficient statistics to measure risk. Two easily recognizable measures: size (market capitalization of companies) and the book to market ratio explain a larger part of the future stock returns. As a consequence, methodologies used to measure the abnormal returns have to take into account these two risk factors. Several methodologies are possible to control for these forms of risk. The first used by Loughran and Vijh (1997) bases itself on the comparison of the geometrical return over 5 years on the acquiring company and on that of a comparable company in terms of size and book to market ratio. Rau and Vermaelen (1998) criticize this method and propose an alternative methodology. The first limitation of Loughran and Vijh's procedure is that the acquiring company and the matched firm are going to evolve during the 5 years examined in terms of size and book to market ratio, and so is the level of risk. Rau and Vermaelen (1998) suggest that risk factors appropriate for two companies vary a lot after the initial matching. In their sample, only 19 % of companies are classified in the same portfolios of size and book to market ratio after 3 years. The second inconvenience of this methodology concerns statistical tests on the long-term abnormal performance. Indeed, Barber and Lyon (1997), Kothari and Warner (1997) as well as Lyon, Barber and Tsai (1999) showed the limits of tests relative to arithmetical or geometrical abnormal returns over a long periods. They favor a technique of bootstrap aiming to generate under the hypothesis of no abnormal returns so as to determine if observed abnormal returns are statistically different from zero. This technique consists essentially in classifying month after month the acquiring companies in portfolios of stocks having about the same characteristics in and not significant when they are completely paid in cash. Recently, the study of Eckbo and Thorburn (2000) indicates that returns obtained by the shareholders of the acquiring company are on average 3.1 % for deals financed in its entirety with payment in cash, 3 % for deals financed exclusively by stock and 5.1 % for deals financed with a mixed payment (cash and stock). 5
  6. 6. terms of size and book to market ratio. The returns on the acquiring companies are then compared with those of these reference portfolios. Abnormal returns are then calculated as in the case of event studies. Statistical tests consist in generating a distribution of abnormal returns under the null hypothesis, that is to make several random draws of stocks composing the reference portfolios and to keep in every iteration the long-term abnormal return on this stock with regard to its reference portfolio. p values associated to the abnormal return are then inferred from the empirical distribution of abnormal returns. 2.2 R ESULTS OF THE LONG - RUN PERFORMANCE OF MERGERS AND ACQUISITIONS Control for systematic risk and size, but not for the book to market ratio: Franks, Harris and Titman (1991) study 399 acquisitions over the period from 1975 to 1984. They find positive and significant long-term abnormal returns only for those leading to a weaker post-grouping market capitalization. On 304 mergers studied over the period from 1965 to 1986, Loderer and Martin (1992) observe a negative but not significant abnormal return over the 5 subsequent years. Abnormal returns are however significant when measured over 3 years. On 155 examined acquisitions, they obtain a positive, but not significant abnormal return. However, when they subdivide their sample in three sub- periods, they notice that long-term abnormal returns over 3 years are significant during the sixties, when the objective of high growth was most likely more important. On the contrary, Agrawal, Jaffe and Mandelker (1992) over the period from 1955 to 1987 find for 937 studied mergers a negative and significant abnormal return on 10.3 % over the five subsequent years. Abnormal returns on 227studied tender offers are positive, but not significant. Control for systematic risk, size and book to market: Loughran and Vijh (1997) find that long- term abnormal returns (5 years) in the case of mergers are 15.9 % while in the case of tender offers they are +43 %. In the cases they call ambiguous, these abnormal returns are 20.8 %. Loughran and Vijh perform the same tests on a sample where there is no overlapping of events by the same company and they find for these 534 transactions that long-term abnormal returns in the case of mergers are 14.2 % while 61.3 % in the case of acquisitions. Rau and Vermaelen (1998) confirm the fact that long-term abnormal returns are respectively negative and significant in the case of mergers and positive and significant in the case of acquisitions. However, they criticize the methodology used by Loughran and Vijh and obtain results sharply lower in absolute value, even though their results are over three years rather than five. When one compares the performance of the acquiring companies in the case of mergers with that of a portfolio of companies characterized by the same risk factors in terms of size and book to market value, it is lower by 4 % and significantly different from zero. On the contrary, the acquiring 6
  7. 7. companies in the cases of tender offers present a return superior to that of the portfolios of companies of the same level of risk. Abnormal returns are however only 8.9 %. Finally, Mitchell and Stafford (2000), as well as Ikenberry, Lakonishok and Vermaelen (2000) examine in a more general way long-term financial performance further to three types of events entailing a significant change of the number of shares in circulation : share repurchases, equity offerings and mergers and acquisitions. Mitchell and Stafford (2000) analyzes over the period 1961 to 1993 a sample of 2068 transactions. They do not distinguish the mergers of acquisitions but report a negative mean abnormal monthly returns over three years of –0.14 % and -0.04 % for equal weight and value weight portfolios respectively using calendar time abnormal returns based on the Fama-French three factor model. Ikenberry, Lakonishok and Vermaelen (2000) examine in Canada a sample of 27 acquisitions over the period from 1989 to 1995 for which more than a third of the financing were shares. Abnormal returns are negative, but not significantly different from zero over the subsequent three years. 2.3 A LAST WORD ON LONG TERM PERFORMANCE Loughran and Vijh ( 1997 ) is the only study, to our knowledge, to examine collectively the short-term and long-term performance of the acquisitions using US data. It allows us to attempt to answer the following question: do the acquisitions of companies create of the value for the shareholders ? While the answer was not ambiguous in the short term, one can imagine that long-term loss of value in the case of mergers can cancel short-term returns. Loughran and Vijh take the point of view of a shareholder of the target company and make the following hypotheses: 1) in the case of mergers, the short- term abnormal returns on the shareholders are calculated as in all event studies and they calculated long run returns by making the hypothesis that the shareholder keeps the shares during the 5 subsequent years; 2) in the case of tender offers, the short-term abnormal return for the shareholders are calculated as in all event studies and the long run returns are calculated by making the hypothesis that the shareholders reinvest the cash in the shares of the acquiring company. In the case of mergers, short-term return is 25.8 % while that in the long run is of 14.8 %. Combined abnormal return would be 29.6 % from the date of the announcement to 5 years after. In the case of tender offers, short-term return is 24.5 % while that in the long run is of 61.5 %. Combined abnormal return would be 126.9 % from the date of announcement to 5 years after. Abnormal performance remains positive for mergers, but this last one is more of 4 times weaker than that of the tender offers. There is average value creation for the shareholders when one observes an acquisition. Loughran and Vijh (1997) note only one exception : in the case of the acquisitions characterized by high ratio of the size of the target over the acquiror, they find negative 7
  8. 8. abnormal returns of 47.4 %on the full period considered (pre and post acquisition periods). In conclusion, most of studies suggest abnormal long-term returns are negative in the case of mergers. However, in the case of tender offers and with the exception of the Loughran and Vijh ( 1997) study, abnormal returns are positive but hardly significant. However, these differences show clearly that methodologies used to determine abnormal long-term returns have a major impact on results. We therefore examine long- run performance of mergers and acquisitions in Canada using a variety of methodologies. 3 Data 3.1 DATA Our data sets of mergers and acquisitions of assets was obtained from the Securities Data Corporation Worldwide Mergers and Acquisitions database. The data meet the following criteria: 1) Given that the subject of this research is post acquisition performance of merger and acquisition in Canada we begin by considering all transactions made by Canadian firms. 2) Collected observations are for 1980 – 2000 period inclusively. 3) Mergers should be completed to analyze an effect to its full extent. 4) Deal must be a merger, exchange offer or acquisition of majority interest. 5) We do not reject companies with several merger or acquisition announcements made during period. 6) We only include transactions greater than 10 million $ Canadian. Selection criteria Request Hits Request Description 0 - DATABASES: All Mergers (MA, OMA, IMA) 1 - Date Announced: 1/1/1962 to 31/12/2000 (Custom) 2 18656 Acquiror Nation : CA 3 4277 Deal Value ($ Mil): 10 to HI 4 2752 Deal Status : C 5 2208 Deal Type : 1,2,4 6 971 Form of the Deal : A, AM, EO, M 7 497 Target Public Status : P 8
  9. 9. 8 383 Acquiror Public Status : P 9 373 Percent of Shares Owned after Transaction: 50 to 100 As a result from the approximate 18,000 acquisitions (completed and uncompleted, public and private of all types including acquisitions of partial and non controlling interests) by Canadian companies, we obtained an initial sample of 373 transactions. We retain the companies which had a correspondence in the Research Insight Compustat database over the April 1980-April 2001 period. The final sample comprises 267 events (176 companies). We impose the following criteria to keep the event in the final sample: to have at least 24 monthly returns available in the post acquisition period. Descriptive statistics [Insert Table 1 and Figure 1] Panel A of Table 1 reports the annual number , aggregate value and mean value of target stock for acquisitions completed during 1980-2000. Our sample includes a total of 267 acquisitions with a market value over 100 billion dollars. A noticeable trend is present in the data. Few transactions took place before 1994. During the late 1990s, the number of acquisition has continuously increased. Figure 1 plots the number of acquisitions and the total dollar value of the transactions by year. Panel B presents a breakdown by primary SIC code. As could be expected, the large majority (over 38%)of the 267 transactions and 176 firms are in the resource industries (SIC 1000). The rest of the transaction are fairly distributed across industries. Panel C presents the Top 10 transactions in our sample. Panel D categorizes the sample by different characteristics. We find 124 (46.4%) tender offers. The smaller number of tender offers is consistent with prior studies (Loughran and Vijh 1997, Rau and Vermalean 1998). Also consistent with prior studies is the breakdown between Friendly and Hostile transactions. Only 20 (7.5%) transactions are classified by SDC Thomson Financial as being hostile or neutral, whereas, 247 (92.5%) are classified as friendly. Schwert (2000) cautions researcher of the difficulties in properly identifying the targets attitude toward the transaction. We also find 120 (44.5%) cash payments, 55 stock payments (20.6%) and 92 (34.5%) mixed payments. Further, out of the 90 (33.7%) cross-border transactions, the vast majority, 68 (25.5%), involved a US target as could be expected while 22 are with firms in other countries. We also note that few transactions, 8 (4.5%) are accounted for using the pooling of interest method of accounting for acquisitions given the much stricter rules in Canada as compared to the US. Finally, 199 (74.4%) of all transactions are between firms 9
  10. 10. with the same primary SIC code, i.e., most of the transactions are in related business with 110 (41.2%) in the same 4 digit SIC Code. 4 Methodology The literature about the methodologies used to measure long-run abnormal performance has grown substantially in the past few years (see, Barber and Lyon, 1997, Kothari and Warner 1997, Lyon, Barber and Tsai 1999, Mitchell and Stafford 2000) and so has the sensitivity of the results to different methodologies (see amongst the more recent studies, Brav, Gezcy and Gompers 2000, Eckbo, Masulis and Norli 2000, Jegadeesh 2000). Since no consensus has yet to emerge as to what is the best methodology, we use for the sake of robustness several models. We first examine the performance of acquirors over the period (-12,+36) using an event-time approach. The month 0 corresponds to the effective date of the acquisition. Second, we adopt a calendar-time approach, using both equally- weighted and value-weighted returns. 4.1 EVENT -TIME APPROACH To test the null hypothesis of no post-acquisition long-run abnormal performance, we first use an event-time approach while using different methods of determining abnormal performance. 4.1.1 Raw performance We first computed the equally-weighted raw returns of a portfolio of Canadian acquirors over the period (-12,+36). We then calculated the associated cumulative performance. As presented in Table 2, the cumulative average return over the pre-acquisition period, (-12,0), is 23.09% whereas the cumulative average return over the post-acquisition period, (+1,+36), is 16.80%. The cumulative performance is significantly different from zero at the 95% confidence level from t=-7 to t=0. However the post-acquisition raw performance is never significantly different from zero. Figure 2 shows the cumulative and abnormal returns. In this figure, we calibrate the cumulative returns relative to the initial month following the acquisitions for both the pre- and post-acquisition period. The figure indicates that the cumulative raw returns present a net valuation effect over 48 months of about 40% (23% pre-acquisition run-up and 17% post-acquisition increase). 10
  11. 11. 4.1.2 Market-adjusted performance To estimate the abnormal performance, we first computed the market-adjusted performance. As reposted in Table 2, the cumulative average market-adjusted performance of a portfolio of Canadian acquirors over the period (-12,0) is 5.25%. By contrast, the cumulative average market-adjusted performance over the period (+1,+36) is -25.44%. None of them are significantly different from zero. Figure 2 indicates that the cumulative market-adjusted returns present a net valuation effect over 48 months of about -20% (5% pre-acquisition run-up and 25% post-acquisition decline). 4.1.3 Matching firm-adjusted performance The literature about long-run abnormal performance has shown the importance of accounting for risk associated to size and book-to-market (Fama and French 1996a and b). To ensure robust results, we also compute CAR long-run returns using a matching firm approach similar to Loughran and Vijh (1997) and Foerster and Karolyi (2000). We identify the firm that has a book-to-market ratio within 25% of that of the sample firm, and as a second criteria, a market value as close as possible to the sample firm. If no match on market-to-book was feasible, a market match was used, and, if the market value of the match firm was too small (less than 90% of the sample firm), no match was declared. As can be seen in Figure 2, the cumulative average matching firm-adjusted performance of a portfolio of Canadian acquirors over the period (-12,0) is 1.91%. By contrast, the cumulative average market-adjusted performance over the period (+1,+36) is -18.53% for a cumulative 48 month return of about –16%. [Insert Table 2 and Figure 2] 4.1.4 Buy and hold abnormal returns Kothari and Warner (1997) and Barber and Lyon (1997) among others point out a number of problems in making statistical inferences from the traditional measure of CARs. We further investigate the post-acquisition CAR performance 11
  12. 12. by first examining the cross-sectional differences using buy-an-hold abnormal returns (BHARs) for each of the post-acquisition returns horizons to 12, 24, and 36 months and for each of two benchmarks. Beginning with Ritter (1991), BHARs have become one of the most popular estimators of long-run abnormal performance. Barber and Lyon (1997) argue that BHARs are the appropriate estimator because they “precisely measure investor experience”. Buy-and-hold abnormal returns measure the average multiyear returns from a strategy of investing in all firms that complete an acquisition and selling at the end of a prespecified holding period versus a comparable strategy using a benchmark. We calculate 1 year, 2-year and 3-year BHARs for each of firm having completed an acquisition using both the market and a control portfolio as benchmarks: BHAR iT = ∏ (1 + R it ) - ∏ (1 + E(R benchmark , t )) BHAR = ∑ w i BHAR iT The benchmark portfolios are constructed in the same manner as for the previous event-time methodology, i.e., using the market and a matching firm. The BHAR are then equally weighted. In Table 3, post-acquisition buy-and-hold abnormal returns are –0.42% over the 36 months for the market benchmark and –25.79% for the control portfolio benchmark, and only the latter estimate is significantly different from zero. In fact, each of the BHARs relative to the control portfolio from 12-36 months is statistically different from zero (abnormal returns of –20.88% over the first 12 months and –27.01% over the first 24 months). Loughran and Vijh (1997) document a –6,5% 5 year equally weighted BHAR while Mitchell and Stafford (2000) also find negative postevent BHARs. [Insert Table 3] However, Kothari and Warner (1997), Barber and Lyon (1997), Lyon, Barber and Tsai (1999) and Mitchell and Stafford (2000) point out a number of concerns with the measurement of long-run abnormal returns including BHARs that hamper the statistical inferences that can be made from these measures of abnormal returns. We further investigate the issue using the alternative calendar-time approach. 12
  13. 13. 4.2 C ALENDAR -TIME APPROACH To test the null hypothesis of no post-acquisition long-run abnormal performance, several authors recommend a calendar-time approach. Indeed, it allows to simulate an investment strategy which could be implemented by a portfolio manager. Fama (1998) recommends the construction of monthly portfolios in calendar time to measure the average abnormal long-run performance for the following reasons: First, monthly returns are less subject to «The bad model problem ». Second, monthly portfolios allow taking into consideration the cross-correlation between the firms in the sample. Third, the portfolio returns allow better statistical inferences. c The first acquisition in the sample occurred in April 1980 while the last occurred in December 2000. The portfolio of acquiror firms is formed in May 1980, and revised monthly until April 2001. The first date of this interval corresponds to the month following that of the first acquisition of our sample, while the latest date corresponds to the last month for which we have the monthly returns of the firms in the sample (i.e., April 2001). Hence, the portfolio composition is reviewed at the beginning of each month such as : - We include the firms that completed an acquisition in the previous month. - We withdraw those firms that completed an acquisition during the last thirty six months but for whom we have no returns for the prevailing and the subsequent months. By construction, the candidate firms have a minimum of 24 observations after the acquisition. Over a total of N firms, M have a series of returns over a horizon of 36 months. - We exclude the firms that completed an acquisition exactly 36 months ago. We used both equally-weighted and value-weighted returns. We use the three asset pricing models to ensure the robustness of the inferences, namely the CAPM, the Fama-French three factor model (Fama and French, 1993) and the four-factor model which corresponds to the three-factor model of Fama and French (1993) with an additional factor related to momentum. The models are presented in equations (1), (2) and (3), respectively. CAPM : rp ,t − r f ,t = α p + β p RMRFt + e p ,t (1) c This approach was adopted by Loughran and Ritter (1995), Brav, Geczy and Gompers (2000) and Jegadeesh (2000) to measure the average abnormal long run performance of US issuers. The Canadian study by Ikenberry, Lakonishok and Vermaelen (2000), also use calendar time formed portfolios of issuing firms. 13
  14. 14. 3FM : rp ,t − r f ,t = α p + β p RMRFt + s p SMBt + h p HMLt + e p ,t (2) 4 FM : rp ,t − r f ,t = α p + β p RMRFt + s p SMBt + h p HMLt + u p UMDt + e p ,t (3) According to the first model, the excess return of a portfolio of firms (r p,t - r f,t ) is only function of its sensitivity to the market factor (RMRF t ). However, Fama and French (1993) add two other factors to explain the cross-section of returns, namely a size factor (SMB t ) and a factor related to the book-to-market equity ratio BE/ME (HML t ). In the same vein, Carhart (1997) constructs a four-factor model by adding the momentum effect (UMD t ) over the last twelve months. The loadings for each factor ( β p , sp , h p , u p ) are estimated in a time-series fashion (see also Appendix). The dependent variable of the regression is the excess returns of the monthly portfolios (r p,t - r f,t ), which corresponds for a given month t, to the returns of the portfolio of acquirors (r p,t) less the risk free rate (30-days Treasury bills of the Canadian Government (r f,t ). The independent variables correspond to zero-investment portfolios constructed such as to mimic the risk factors common to the sand of securities. The three factor model has also been used by Loughran and Ritter (1995), Brav, Geczy and Gompers (2000), Mitchell and Stafford (2000) and Jegadeesh (2000) to measure the average abnormal performance for various events (stock issues, IPOs, acquisitions, repurchases) in the US. Among these studies, only that of Brav, Geczy and Gompers (2000) also uses the four-factor model. As for Ikenberry, Lakonishok and Vermaelen (2000), they only used the three factor model for their sample of Canadian acquirors. 4.2.1 Construction of the risk factors We examine the monthly returns from the four factors : R M -R F , SMB, HML and WML on the Canadian stock market over the April 1980-April 2001 period. Data relative to financial statements come from the Financial Post database (from 1979 to 1986; 1992 version) and from Research Insight Compustat (from 1987 to 2000; 2001 version). d Monthly stock returns and firms' market equity (number of shares outstanding times the stock price) come from the TSE-Western tape (from d To avoid a look-ahead bias, the book equity corresponding to the end of the fiscal year y is assumed to be available in June from year y+1. Consequently, as in Fama and French, returns from factors are measured form July of year y+1 to June of year y+2. 14
  15. 15. 1980 to June 1987; 1998 version) and from Research Insight Compustat (from July 1988 to April 2001; 2001 version). The market return is a value-weighted return computed from the sample. e Returns from the risk-free asset are estimated from the Scotia Capital 91 day Canadian Treasury Bills series. Book equity (BE) is computed as the book value of stockholders' equity, plus balance sheet deferred taxes and investment tax credit (if available), minus the book value of preferred stock (see Fama and French, 1992). All observations with a negative BE are excluded from the sample. Using data from July 1960 to April 2001, we derive the time-series of the market, size, book-to-market and momentum premiums which are described in the Appendix. 4.2.2 Results Tables 4, 4A, 4B and 4C present the CAPM, three factor and four factor regressions for various horizons. Results show significant underperformance for the equally weighted two-factor and three-factor model. For example, the 3-year equally-weighted portfolio exhibits statistically significant average abnormal returns of –1.06% (or –38.2% after three years, -1.06% x 36 months) with the CAPM and –1.21% (or -44% after three years) with the three-factor model. Once returns are value weighted, however, intercepts in all models are statistically insignificant. Since the abnormal returns are only significant when the event firms receive equal weights in the portfolio, it appears that small acquirers are more prone to underperformance in the post-event period. Mitchell and Stafford (2000) find similar results for US acquisitions and these findings are similar to the previously documented results by Brav and Gompers (1997) and Brav et al. (2000) for equity issuers. A similar explanations can be derived from the four-factor model. The four factor results show that even the equal weight portfolio can be priced. Intercepts are insignificant in all four-factor regressions but the momentum factor negative and significant. Given that the acquirers have high past returns but in the period immediately following the acquisition, their returns look like returns of low past return stocks. A sentiment based interpretations would argue that UMD is just picking up the acquisition mispricing. [Insert Table 4, 4A, 4B and 4C] e The correlation between our sample market return and the TSE 300 index is more than 96%. 15
  16. 16. 5 Determinants of long-run performance While we know that on average mergers and acquisitions create overall value when examining the window around the announcement date and that these transaction seem to underperform over the years following the transactions, results vary a lot when we control for different factors. These firm-specific, transaction-specific and market-specific factors are discussed in greater detail in André and L’Her (2000), André, Ben Amar and L’Her (2000) and L’Her and Magnan (2000). Among the most often cited factors, researchers (Loughran and Vijh 1997, Mitchell and Stafford 2000) find that acquirers that use stock to finance the merger perform worse than those that abstain from equity financing. Loughran and Vijh (1997) also find that acquirers that undertake mergers perform worse than those that use a tender offer process. Rau and Vermalean (1998) and Mitchell and Stafford (2000) present some evidence that glamour or growth firms explain part of the post-acquisition underperformance. Results are mixed with respect to the type of strategy (horizontal, vertical or conglomerate mergers). We examine our results by way of a cross sectional regression of post-acquisition abnormal return performance that control for transaction-specific factors. These include dummy variables for the form of the transaction (100% or less than 100%), mode of payment (anything without voting shares, voting shares, mixed), attitude (friendly, hostile, not determined, not sollicitated), crossborder transaction and target nation (Canada, US, Other). Results are presented in Table 5. To facilitate the interpretation of the coefficients, we restrict the intercept to equal the average CAR. Thus, the coefficient represent the difference of each sub-group with respect to the overall mean. While some of the signs are as expected, unfortunately, these preliminary results do not show any of these transaction-specific factors as being significant in explaining the post-acquisition underperformance except the coefficient for transactions paid with voting shares which is negative and significant as expected. [Insert Table 5] 16
  17. 17. 6 Conclusion This study investigates the long-run performance of Canadian acquirors. Overall, our sample of 267 acquisitions over the period of 1980 and 2000 underperform various benchmarks, including a comparable matched firm by size and book-to-market, using various methodologies to calculate long-run abnormal returns. The sample underperforms by as 25% over the three years following issuance when examing buy- and-hold returns compared to a matching firm and by as much as 35% to 40% when using a calendar time approach that controls for various risk factors. Our results are generally consistent with many US studies, Agrawal et al. (1992), Loughran and Vijh (1997), Rau and Vermalean (1998) and Mitchell and Stafford (2000) that find negative results overall and very negative results for mergers (85% of own sample consists of mergers). They differ from those of Betton and Williams (2001) that finds generally positive but insignificant results also using Canadian data. Our contributions are twofold. First, this is one of the few studies examining Canadian acquisition. Further, we study the most recent wave of acquisitions, the wave of the late 1990s, the most significant of all times both in number and values. Second, we hope that future work will allow to pin point the sources of the underperformance. This papers adds to a growing body of literature that questions whether short-run measurements of abnormal returns can capture the full effects of complex transactions such as mergers and acquisitions. Recent work by Bécotte (2002) also notes negative abnormal operating results in the period following mergers and acquisitions in Canada. While methodoligical questions with respect to the measure and testing of long-run abnormal returns remain important given the wide range of results that can be obtained, understanding the value creation process related to what are most likely the most important investment decisions made by organizations deserves future research. Acknowledgements We appreciate the comments from our colleagues. We have also received helpful comments from seminar participants. We thank Walid Ben-Amar for his research assistance. This paper was completed while Paul André was visiting Concordia University and the CIRANO research center. 17
  18. 18. Appendix We construct the size factor, SMB and the book-to-market factor, HML as Fama and French (1993) and the momentum factor is constructed as UMD (Up minus Down) on Kenneth French’s website. For each month t from July of year y-1 to June of year y, we rank the stocks based on their size and book-to-market ratio of June y-1. We then use these two rankings to calculate a 50 percent breakpoint for size, and 30 percent and 70 percent breakpoints for book-to-market. The stocks are subsequently sorted into two size groups and three book-to-market groups based on these breakpoints. In addition, the stocks above the 50 percent size breakpoint are designated B (for big) and the remaining 50 percent are designated S (for small). Finally, the stocks above the 70 percent book-to-market breakpoint are designated H (for high), the middle 40 percent are designated M and the firms below the 30 percent book-to-market breakpoint are designated L (for low). We form six value-weight portfolios, S/L, S/M, S/H, B/L, B/M and B/H as the intersection of size and book-to-market groups. Note that the number of firms in each of the six portfolios varies. SMB (Small minus Big) is the equal-weight average of the returns on the small stock portfolios minus the returns on the big stock portfolios: SMB = (( S/L − B/L) + ( S/M − B/M) + ( S/H − B/H))/3 . (2) Similarly, HML (High minus Low) is the equal-weight average of the returns on the value stock portfolios minus the returns on the growth stock portfolios: HML = (( S/H − S/L) + ( B/H − B/L))/2 . (3) For each month t from July of year y-1 (beginning in July 1990) to June of year y, we rank the stocks based on their size on June y-1 and their performance between t-12 and t-2. We then use these two rankings to calculate a 50 percent breakpoint for size, and 30 percent and 70 percent breakpoints for prior performance. The stocks are subsequently sorted into two size groups and three prior performance groups based on these breakpoints. Moreover, the stocks above the 50 percent size breakpoint are designated B (for big) and the remaining 50 percent S (for small). Finally, the stocks above the 70 percent prior performance breakpoint are designated W (for winner), the middle 40 percent are designated M and the firms below the 30 percent prior performance breakpoint are designated L (for loser). As previously, we form six value-weight portfolios, S/L, S/M, S/W, B/L, B/M and B/W, as the intersection of size and prior performance groups. UMD is the equal- 18
  19. 19. weight average of the returns on the winner stock portfolios minus the returns on the loser stock portfolios: f UMD = ((S/W − S/L) + ( B/W − B/L))/2 . (4) Finally, we also determine the market risk premium, which is the capitalization- weighted return of all the securities considered in excess of the one-month Canadian Treasury bill rate (R M - R f ). f Liew and Vassalou (2000) used three sequential sorts, Carhart (1997) did not construct size-neutral momentum portfolios and Brav et al. (2000) used 50 percent breakpoint for momentum. 19
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  22. 22. Table 1: Descriptive statistics Table 1a Distribution by year Year # transactions Total value Average value 1980 1 235.4 235.4 1981 1 597.6 597.6 1982 1 1675.0 1675.0 1983 3 630.8 210.3 1984 2 43.3 21.7 1985 4 390.3 97.6 1986 4 350.3 87.6 1987 6 4288.7 714.8 1988 10 2606.1 260.6 1989 13 5666.0 435.8 1990 4 2826.6 706.7 1991 3 93.3 31.1 1992 5 895.5 179.1 1993 8 1617.4 202.2 1994 16 5895.4 368.5 1995 20 4559.7 228.0 1996 23 9570.0 416.1 1997 31 11218.0 361.9 1998 36 32499.0 902.8 1999 35 16457.1 470.2 2000 41 31719.7 773.7 267 133835.2 501.3 Table 1b Distribution by Primary SIC Code SIC # firms # transactions Total value Average value 1000 Minerals 68 102 34964.73 342.79 2000 Manufacturing 20 29 14946.23 515.39 3000Manufacturing 20 31 20315.46 655.34 4000 Communications 24 36 35446.14 984.61 5000 Trade 7 12 15213.36 1267.78 6000 Financial 19 35 10767.60 307.65 7000 Services 14 16 1720.08 107.50 8000 Services 4 6 461.70 76.95 176 267 133835.29 501.26 22
  23. 23. Table 1c Top 10 transactions Value of Date Date Transaction Announced Effective Acquiror Name Target Name ($mil US) 15-06-1998 31-08-1998 Northern Telecom Ltd(BCE Inc) Bay Networks Inc 9268.60 28-07-2000 05-10-2000 Nortel Networks Corp Alteon Websystems Inc 7056.89 15-06-1998 10-11-1998 Teleglobe Inc Excel Communications Inc 6407.24 15-02-2000 01-11-2000 Bell Canada Enterprises Inc Teleglobe Inc 5065.54 26-01-1998 02-07-1998 TransCanada Pipelines Ltd NOVA Corp of Alberta Ltd 4905.54 11-02-2000 22-06-2000 Abitibi-Consolidated Inc Donohue Inc 4817.52 11-08-1999 11-10-2000 Alcan Aluminum Ltd Alusuisse Lonza Group Ltd 4789.17 21-08-2000 12-01-2001 Telus Corp Clearnet Communications Inc 4532.33 19-01-1989 12-05-1989 Imperial Oil Ltd(Exxon Corp) Texaco Canada Inc(Texaco Inc) 4126.29 13-04-1987 01-09-1988 Amoco Canada Petroleum(Amoco) Dome Petroleum Ltd 3615.79 Table 1d Frequency distribution # % Related Yes 199 74.43 No 68 25.47 Crossborder Yes 90 33.71 No 177 66.29 Target Nation Canada 177 66.29 US 68 25.47 Other 22 8.24 Form 100% 226 84.64 Less than 100% 41 15.36 Paiement Cash only 120 44.94 Mixed 55 20.60 Stock only 92 34.46 Attitude Friendly 247 92.51 Hostile 14 5.24 Neutral 6 2.25 Pooling Yes 8 4.5 No 169 95.5 Tender offer Yes 124 46.4 No 143 53.6 23
  24. 24. 45 40000,0 40 35000,0 # of firms Number of transactions 35 Dollar value (millions) 30000,0 Value of transaction 30 25000,0 25 20000,0 20 15000,0 15 10000,0 10 5 5000,0 0 0,0 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 Year Figure 1. The number of transactions and the dollar value of transactions in millions of dollars. The sample consists of 267 completed Canadian mergers and acquisitions between two public companies during the 1980-2000 period. The data is obtained from SDC Thomson Financial. The value of the transaction is greater or equal 10 millions dollars US. Acquiring firms must be listed on Compustat. 24
  25. 25. Table 2: Event-time approach Cumulative Cumulative Number Cumulative Abnormal Month Raw return abnormal Cumulative Abnormal abnormal of firms return return Raw return return return return return t N RR CR AR CAR t(RR) t(CR) t(AR) t(CAR) -12 199 2,23% 2,23% 1,36% 1,36% 2,26 1,45 -11 199 2,23% 4,51% 0,72% 2,09% 1,95 1,27 0,67 0,61 -10 201 1,58% 6,16% 1,11% 3,22% 1,67 1,42 1,25 0,77 -9 201 2,04% 8,32% 1,04% 4,29% 2,52 1,66 1,41 0,89 -8 202 1,79% 10,26% 0,36% 4,67% 2,18 1,84 0,48 0,87 -7 202 2,22% 12,71% 1,22% 5,95% 2,69 2,08 1,66 1,01 -6 205 1,01% 13,84% -0,55% 5,37% 1,37 2,11 -0,76 0,85 -5 207 2,16% 16,30% 0,57% 5,97% 2,82 2,34 0,78 0,89 -4 207 1,97% 18,59% 0,63% 6,63% 2,21 2,51 0,74 0,93 -3 207 1,23% 20,05% 0,07% 6,71% 1,43 2,57 0,09 0,89 -2 207 0,68% 20,86% -0,49% 6,19% 0,70 2,55 -0,56 0,79 -1 207 1,60% 22,80% 0,19% 6,38% 1,85 2,67 0,23 0,78 0 207 0,23% 23,09% -1,07% 5,25% 0,28 2,60 -1,36 0,61 1 209 2,03% 2,03% 0,84% 0,84% 1,96 0,78 0,87 0,33 2 211 0,31% 2,35% -0,72% 0,12% 0,40 0,45 -0,95 0,02 3 211 2,15% 4,56% 0,26% 0,37% 2,28 0,66 0,29 0,06 4 211 0,09% 4,65% -1,18% -0,81% 0,09 0,56 -1,18 -0,10 5 211 1,82% 6,56% -0,14% -0,95% 1,95 0,70 -0,16 -0,10 6 211 1,72% 8,39% 0,23% -0,72% 2,23 0,80 0,32 -0,07 7 211 -0,41% 7,95% -1,77% -2,48% -0,40 0,70 -1,89 -0,22 8 211 -0,52% 7,39% -1,49% -3,94% -0,61 0,60 -1,89 -0,33 9 211 2,20% 9,75% 0,48% -3,47% 2,28 0,75 0,51 -0,27 10 211 -1,00% 8,65% -1,92% -5,33% -1,17 0,63 -2,27 -0,39 11 211 0,34% 9,02% -0,69% -5,99% 0,38 0,62 -0,83 -0,42 12 211 -0,05% 8,96% -1,40% -7,30% -0,05 0,59 -1,37 -0,49 13 206 -0,05% 8,90% -1,39% -8,60% -0,06 0,55 -1,54 -0,55 14 202 0,95% 9,94% 0,52% -8,12% 0,99 0,59 0,60 -0,49 15 202 -0,17% 9,76% -1,67% -9,65% -0,18 0,56 -1,93 -0,56 16 200 1,58% 11,49% 0,14% -9,53% 1,46 0,63 0,14 -0,53 17 198 0,63% 12,19% -0,12% -9,63% 0,58 0,65 -0,11 -0,52 18 194 -0,38% 11,77% -1,95% -11,39% -0,38 0,60 -1,88 -0,59 19 191 1,40% 13,34% 0,31% -11,12% 1,46 0,66 0,33 -0,56 20 185 -0,29% 13,00% -1,72% -12,64% -0,29 0,61 -1,81 -0,61 21 183 0,03% 13,04% -1,10% -13,60% 0,03 0,60 -1,08 -0,63 22 178 2,30% 15,64% 1,04% -12,70% 1,47 0,69 0,67 -0,57 23 174 1,93% 17,88% 0,47% -12,29% 1,80 0,76 0,46 -0,53 24 170 -0,51% 17,28% -1,80% -13,87% -0,56 0,71 -2,04 -0,58 25 166 1,23% 18,72% -0,44% -14,25% 0,96 0,75 -0,35 -0,58 26 162 -0,40% 18,25% -1,85% -15,84% -0,34 0,70 -1,63 -0,62 27 160 -1,55% 16,41% -2,06% -17,57% -1,68 0,62 -2,09 -0,67 28 159 -0,75% 15,54% -1,94% -19,17% -0,64 0,57 -1,70 -0,72 29 156 -1,03% 14,35% -2,52% -21,21% -0,95 0,51 -2,32 -0,77 30 153 -2,87% 11,07% -4,22% -24,53% -2,57 0,39 -3,90 -0,87 31 149 0,48% 11,60% -0,82% -25,15% 0,37 0,39 -0,71 -0,87 32 147 0,44% 12,09% -0,41% -25,45% 0,35 0,40 -0,33 -0,86 33 143 0,03% 12,12% -0,48% -25,81% 0,02 0,39 -0,32 -0,84 34 140 2,31% 14,72% 2,29% -24,11% 1,49 0,46 1,50 -0,77 35 131 0,72% 15,55% -0,62% -24,58% 0,49 0,46 -0,45 -0,75 36 128 1,08% 16,80% -1,14% -25,44% 1,02 0,49 -1,06 -0,75 25
  26. 26. Figure 2 Pre- and Post-Acquisition Return Performance 30,00% Cumulative Holding Returns Period 10,00% -10,00% 16 2 12 20 24 28 32 36 -8 -4 0 4 8 -1 -30,00% Months relative to the issue raw returns abnormal returns (market) abnormal returns (match) 26
  27. 27. Table 3 Post-acquisition return performance using buy-and-hold abnormal returns (BHAR) for various horizons and two benchmarks 12-month holding period 24-month holding period 36-month holding period BHAR BHAR BHAR BHAR BHAR BHAR market control portfolio market control portfolio market control portfolio Mean -6.31% -20.88% 2.75% -27.01% -0.42% -25.79% Standard deviation 54.82% 46.53% 94.35% 61.87% 88.29% 70.71% T -1.509 -5.115 0.382 -5.110 -0.062 -4.142 25% quantile -33.54% -50.96% -50.58% -73.49% -57.19% -80.27% 75% quantile 8.29% 3.19% 24.95% -1.38% 23.29% -6.05% N 172 130 172 137 172 129 27
  28. 28. Table 4 : Abnormal returns following mergers and acquisitions in the Canadian stock market The sample consists of 267 mergers and acquisitions initiated by firms listed on the Toronto stock exchange during the 1980-2000 period. Excess returns are regressed on the CAPM's, TFPM's and FFPM's factor(s) in a calendar-time framework where the 12 pre merger months are considered . E.W. and V.W. respectively stand for equally-weighted and value-weighted. Factor CAPM TFPM FFPM loadings E.W. V.W. E.W. V.W. E.W. V.W. µp 0,31% 0,57% 0,08% 0,45% 0,48% 0,68% 0,65 1,13 0,17 0,89 0,94 1,24 bp 1,11 1,13 1,24 1,20 1,26 1,21 1,05 1,11 2,12** 1,64 2,26** 1,72* sp 0,40 0,25 0,42 0,25 2,74** 1,54 2,86** 1,55 hp 0,41 0,26 0,35 0,22 3,06** 1,74 2,59** 1,38 up -0,21 -0,13 -2,10** -1,14 adjusted. R 2 33% 31% 36% 31% 37% 31% Table 4A : Abnormal returns following mergers and acquisitions in the Canadian stock market The sample consists of 267 mergers and acquisitions initiated by firms listed on the Toronto stock exchange during the 1980-2000 period. Excess returns are regressed on the CAPM's, TFPM's and FFPM's factor(s) in a calendar-time framework where the 12 post merger months are considered . E.W. and V.W. respectively stand for equally-weighted and value-weighted. Factor CAPM TFPM FFPM loadings E.W. V.W. E.W. V.W. E.W. V.W. µp -0,97% -0,77% -1,06% -0,73% -0,21% -0,28% -1,82* -1,59 -1,99** -1,50 -0,36 -0,52 bp 1,11 1,14 1,13 1,08 1,16 1,09 0,96 1,34 0,99 0,67 1,24 0,80 sp 0,38 0,08 0,42 0,10 2,26** 0,49 2,56** 0,62 hp 0,10 -0,16 -0,02 -0,22 0,62 -1,07 -0,11 -1,47 up -0,40 -0,22 -3,60** -2,05** adjusted. R 2 29% 35% 30% 35% 34% 36% * significant at the 90% confidence level ** significant at the 95% confidence level H0 for the beta coefficient is beta=1 28
  29. 29. Table 4B : Abnormal returns following mergers and acquisitions in the Canadian stock market The sample consists of 267 mergers and acquisitions initiated by firms listed on the Toronto stock exchange during the 1980-2000 period. Excess returns are regressed on the CAPM's, TFPM's and FFPM's factor(s) in a calendar-time framework where the 24 post merger months are considered . E.W. and V.W. respectively stand for equally-weighted and value-weighted. Factor CAPM TFPM FFPM loadings E.W. V.W. E.W. V.W. E.W. V.W. µp -0,62% -0,42% -0,78% -0,33% -0,10% -0,04% -1,15 -0,87 -1,47 -0,68 -0,19 -0,08 bp 0,92 1,00 0,94 0,87 0,94 0,88 -0,70 -0,03 -0,50 -1,12 -0,47 -1,09 sp 0,57 0,00 0,62 0,02 3,45** 0,03 3,81** 0,13 hp 0,10 -0,34 0,05 -0,38 0,67 -2,44** 0,31 -2,67** up -0,33 -0,15 -3,17** -1,48 adjusted. R 2 21% 28% 25% 30% 28% 30% * significant at the 90% confidence level ** significant at the 95% confidence level H0 for the beta coefficient is beta=1 29
  30. 30. Table 4C : Abnormal returns following mergers and acquisitions in the Canadian stock market The sample consists of 267 mergers and acquisitions initiated by firms listed on the Toronto stock exchange during the 1980-2000 period. Excess returns are regressed on the CAPM's, TFPM's and FFPM's factor(s) in a calendar-time framework where the 36 post merger months are considered . E.W. and V.W. respectively stand for equally-weighted and value-weighted. Factor CAPM TFPM FFPM loadings E.W. V.W. E.W. V.W. E.W. V.W. µp -1,06% -0,69% -1,21% -0,58% -0,60% -0,22% -2,07** -1,44 -2,42** -1,22 -1,13 -0,42 bp 0,93 0,99 0,95 0,87 0,95 0,87 -0,60 -0,08 -0,44 -1,14 -0,43 -1,11 sp 0,58 -0,01 0,63 0,01 3,69** -0,04 4,03** 0,08 hp 0,08 -0,34 0,03 -0,39 0,60 -2,41** 0,23 -2,73** up -0,31 -0,19 -3,05** -1,91* adjusted. R 2 23% 27% 27% 29% 29% 30% * significant at the 90% confidence level ** significant at the 95% confidence level H0 for the beta coefficient is beta=1 30
  31. 31. Table 5 Cross-Sectional Regression of Post-Acquisition Return Performance Parameter Standard Variable DF Estimate Error t Pr > Intercept 1 -0.00371 0.00210 -1.77 0.0784 M 1 0.00089 0.00099 0.90 0.3677 AMI 1 -0.00427 0.00474 -0.90 0.3677 AWVS 1 0.00286 0.00243 1.18 0.2404 VS 1 -0.01003 0.00435 -2.31 0.0221 Mix 1 0.00185 0.00272 0.68 0.4976 NCB 1 0.00072 0.00457 0.16 0.8741 CB 1 -0.00129 0.00811 -0.16 0.8741 F 1 0.00013 0.00065 0.21 0.8334 H 1 -0.00146 0.00691 -0.21 0.8334 CDA 1 0.00050 0.00437 0.11 0.9088 US 1 -0.00678 0.00871 -0.78 0.4370 OTH 1 0.01387 0.01039 1.33 0.1836 REL 1 0.00004 0.00113 0.04 0.9660 UNREL 1 -0.00017 0.00399 -0.04 0.9660 Sum of Mean Source DF Squares Square F Value Pr > F Model 11 0.02900 0.00264 2.39 0.0085 Error 196 0.21634 0.00110 Corrected Total 207 0.24534 Root MSE 0.03322 R-Square 0.1182 Dependent Mean 0.00331 Adj R-Sq 0.0687 Coeff Var 1002.21334 The dependant variable is the market-adjusted 36 month abnormal returns. The independant dummy variables are for the form of the transaction (merger=M or acquisition of majority interest=AMI); mode of payment (anything without voting shares=AWVS. voting shares=VS. mixed=MIX); attitude (friendly=F. hostile=H. not determined=NA. not sollicitated=NS); sollicitated=S or not=UNS; crossborder transaction=CB or not=NCB; target nation (Canada=CDA. US. Other=OTH) and target in a related sector (REL) or unrelated (UNREL) . 31