Value Creation of Mergers and Acquisitions in IT industry before and during the Financial Crisis

Fang Chen, Suhong Li 175 Value Creation of Mergers and Acquisitions in IT industry before and during the Financial Crisis Fang Chen 1*, Suhong Li 2 1 Finance Department University of Rhode Island, Kingston, RI, USA 2 Computer Information Systems Department Bryant University, Smithfield, RI, USA * EMAIL: fchen@mail.uri.edu Abstract: Most prior researches suggest that average change in market value of acquiring firms varies closely around zero on average and many acquirers experience negative returns. Extending previous studies on mergers and acquisitions, this study focuses on value creation of the mergers and acquisitions of Tech 100 companies in the US before and during the recent financial crisis. Results of the event study show that all mergers and acquisitions in leading IT companies in the US have no significant cumulative abnormal return before the financial crisis (between 2004 and 2006), which is in consistent with previous literature. However, during the financial crisis (between 2007 and 2009), acquirers that pursued an unrelated/low related diversification strategy received significant cumulative abnormal return, while acquirers that pursued a highly related synergy strategy received no significant return. In addition, the results also show that among the three maor sections of IT industry (hardware, software and services), companies in both hardware and services sectors have a significant cumulative abnormal return while software companies have no significant cumulative abnormal return during the financial crisis. Keywords: Mergers and Acquisitions, Information Technology Industry, Event Study I. Introduction Mergers and acquisitions (M&As) are important mechanism for increasing market power, overcoming barriers to entry, and expanding externally [9]. However, most empirical research has concluded that acquiring firm shareholders have nothing or little gain while the target firm shareholders have positive return [9]. Schwert [8] found 1.4% cumulative abnormal return from 6666 M&As between 1975 and 1991. Explanations for these outcomes include integration cost, distraction of mangers and resources from other activities, strategic realignment and managerial hubris [8]. In addition, the performance of the acquiring firm was also affected by the market conditions at the time of the merger and whether the acquiring firm was an early-mover. Some studies have also considered relatedness between the acquiring firm and target firm as an important factor influencing market reaction to M &As. For example, Wade and Gravill [10] found related acquisitions outperform unrelated ones. In contrast, Park [6] finds related acquirers were not better than the unrelated acquirers on the market value creation. In general, most studies on M & As have not focused on a single industry. It is possible that market reaction to M&As in a single industry may be different based on its characteristics. Among all industries, information technology (IT) industry has undertaken many M&As during 1990s and 2000s [7]. Today, IT industry has been widely integrated into almost all industry sectors and constitutes a significant part of all maor economies of the world. IT companies provide hardware, software and consulting services to automate/integrate business process and are driving forces for all business transformation and innovation. Therefore, market reaction to M&As in IT industry may be different because of its unique nature. Several recent studies have focused on M&As in IT industry. For example, Kohers and Kohers [5] found that 1.26% abnormal return for acquiring firms among high-tech firms. Cloodt et al. [2] studied the impact of M&As on the performance of acquiring firms and concluded that they should acquire target firms that are neither too similar nor unrelated to their knowledge base. Wilcox et al. [11] examined the short-run stock market response to acquiring firm shareholders and found that M&As involving narrow diversification created more value than those with broad diversification. Rheaume and Bhabra [7] found that related acquisition strategies brought synergistic gains resulting in positive wealth change for the acquiring firm s shareholders. The firms that pursued an unrelated diversification strategy that lowered operating risk experienced no significant change in shareholder wealth. Michael and Schwert [8] found that firms diversifying into unrelated areas outperformed other firms. It can be seen that the results of M&As in IT industry is mixed and the performance of acquirers is influenced by acquisition strategy (diversification versus synergy). It is known that market and economic condition at the time of the merger will impact the gains of acquiring firm and target firm [7]. One most significant event is recent financial crisis starting at 2007 that has a dramatic impact on the whole economy and is commonly considered as an economical catastrophe [3] [4]. The crisis stemmed from the subprime mortgage market and swept over the whole economy. From early 2007, the economy slowed down and

176 Fang Chen, Suhong Li the subprime crisis starting in July 2007 is the onset of financial crisis. The bankruptcy of Bear Stearns in March 2008 and Lehman Brothers in September 2008 are signs of a deep trouble. In June of 2009, the bankruptcy case of General Motors Corporation indicated the struggling economy. In 2010, some optimist views the worst time is over and some pessimist thinks the recession will last longer. A maor problem of the financial crisis is the diminishing liquidity market. The banks were unwilling to lending and the firms were not interested in borrowing. The value of many firms went down. To the firms who want to merge or acquire other firms during the financial crisis, it is a good opportunity. A comparison between the number of M&As in the US in a three year period before and during the financial crisis support this argument. Table 1 shows that the total number of U.S. M &As between 2004 and 2006 is 31,255. This number slightly increased to 32,344 from 2007 to 2009. It is of interest to see whether market reaction to M&As differs before and during the financial crisis. However, few studies have focused on this issue, especially in IT industry. To fill this gap, the purpose of this study is to investigate market reaction to acquiring companies in IT industry before and during the financial crisis using an event study. We focus our study on leading IT companies, which are identified as including all Tech 100 companies in the US ranked by Business Weeks magazine in 2009. We collected all M&As by tech 100 companies in a three-year period before and during the financial crisis. Therefore, before the financial crisis period refers to between 2004 and 2006 and after the financial crisis period is from 2007 to 2009. We also investigate the impact of acquisition strategy and type of IT sectors on the performance of acquiring firms. Specifically, we addressed the following questions: (1) Does market react differently to the acquiring IT firms before and during the financial crisis? (2) Does market react differently to the acquiring IT firms that acquire a target firm in the same industry (synergy strategy) or in different industry (diversification strategy) before and during the financial crisis? (3) Does market react differently to the acquiring firm in each of three IT sectors (hardware, software and services) before and during the financial crisis? The reminder of this paper is organized as follows: section 2 discusses data and methodology. Section 3 presents the results of our data analysis. Concluding remarks and future research are provided in section 4. Table 1. Distribution of Mergers and Acquisitions by Years U.S M&A by Informational Technology Firms Hardware Software Service Total U.S. M&A by All Industries Year 2004 52 22 21 95 9435 2005 61 32 22 115 10221 2006 70 37 36 143 11599 Sub Total Before Financial 183 91 59 353 31255 Crisis 2007 79 39 37 155 12619 2008 42 36 30 108 10957 2009 43 25 24 92 8768 Sub Total After Financial Crisis Percentage Change(before/after) 164 100 91 355 32344 90% 110% 154% 101% 103%

Fang Chen, Suhong Li 177 II. Research Methodology Sample Description We obtained the list of tech 100 in 2009 ranked by Business Week magazines. The firms were selected based on the ranking of four criteria: return on equity, shareholder return and revenue growth (which were given equal weight), and total revenues (which were weighted). We then eliminated the non-us firms and the final data sample includes 47 U.S IT firms in 8 industries: computer, communication, telecommunication, semiconductor, software, net, service and distribution. To simplify the data analysis, the firms are classified into three sectors: hardware (computer, communication, telecommunication, semiconductor), software (software) and service (net, service and distribution). The SDC database from Thomson Reuters contains 72134 U.S. mergers and acquisitions from January 01, 2004 to December 31, 2009. We then matched the sample with the 47 IT firms and obtained 708 U.S. mergers and acquisitions by those firms in the sample period. For each firm, we obtained the stock information from Center for Research in Security Prices (CRSP) database. Event Study Methodology Abnormal return (AR) is the measurement of the market value of the mergers and acquisitions. AR is computed using standard event-study methodology following Brown and Warner (1985). Market model parameters are estimated using days -301 to -46 relative to the M&A announcement day. The daily abnormal returns are summed to get the cumulative abnormal return (CAR). For each security, the market model is used to calculate an abnormal return (AR) for event day t as follows: AR t = R t - (a + R mt ) (1) where R t is the rate of return on security for event day t, and R mt is the rate of return on the CRSP equally-weighted portfolio on event day t. 1 The coefficients and are the ordinary least squares estimates of the intercept and slope, respectively, of the market model regression. The cumulative abnormal return for firm (CAR ) from day T 1 to day T 2 is defined as: CAR = T 2 (2) t = T 1 AR We cumulate over various intervals around the announcement date. For a sample of N securities, the mean CAR is defined as: 1 We get qualitatively similar results using value-weighted CRSP market index. These are available upon request. CAR = (3) =1 CAR / N The expected value of CAR is zero in the absence of abnormal performance. The test statistic described by Dodd and Warner (1983) is the mean standardized cumulative abnormal return. To compute this statistic, the abnormal return AR t is standardized by its estimated standard deviation S t. The value of S t is: S 2 1 = S (1 D +( R - R D )2/ ( R t = 1 2 t mt m where S 2 D R mt R m R mt SAR = AR mt - R = residual variance for security from the market model regression = number of observations during the estimation period = rate of return on the market index for date of the event period = mean rate of return on the market index during the estimation period = rate of return on the market of day t of the estimation period / S t t t (4) The SARs are cumulated and Patell Z-statistics for the sample of N securities is employed to evaluate the significance of the abnormal return for the acquiring firms. III. Empirical Results Due to the information leakage of the maor events like M&As, the reaction of market lasts a long period. We focus on the cumulative average abnormal return for the event windows (-30, +30) where day 0 is the M&As announcement day. If the announcement day is in the nontrading day or after the closing of the market, we move the day to the next available trading day. Abnormal Return for the Acquiring Firms before and after the Financial Crisis The results for the abnormal return for the acquiring firms before and after the financial crisis are shown in Table 2. It can be seen that M&As do not result in any wealth gain for the acquiring firm shareholders during the period of 2004 to 2006, which is before the financial crisis. In contrast, the cumulative abnormal average return during the period of 2007 to 2009, which is during the financial crisis, was 2.73% (significant at the 0.01% level). The evidence indicates the investors in the market interpret the M&As in leading IT companies before and during the crisis differently. m ) 2 )

178 Fang Chen, Suhong Li In the financial crisis, the M&As may be a strong signal of positive cash flow and strong profit ability. The signal is verified by our results. The Impact of Relatedness on Abnormal Return for the Acquiring Firms before and during the Financial Crisis In our study, we consider an M&A to be highly related if the acquiring firm has the same 4 digit SIC code as the target firm, medium related if the acquiring firm has the same 2 or 3 digit SIC code as the target firm, and low/unrelated if the acquiring firm has the same 1 or 0 digit SIC code as the target firm. The abnormal return for the acquiring firms in those three acquisition strategies before and during the financial crisis is presented in Table 3. The results show that all M&As have no significant cumulative abnormal return before the financial crisis. However, during the financial crisis, low/unrelated M&As have a significant positive cumulative abnormal return 3.46%, medium related M&As have a significant positive cumulative abnormal return 3.94%. It is also worth noting that high related M&As have no significant cumulative abnormal return during the financial crisis. These results are in inconsistent with most of previous studies that reported that narrow diversification or related acquisitions created more value for the acquiring firms (Cloodt et al., 2006; Wilcox et al., 2001; Rheaume and Bhabra, 2008). The different results may be explained by the time period of M&As. During the financial crisis, reducing risk is more important. The low related/unrelated acquisition strategy may diversify the industry level risk for the acquiring firms. Therefore, the investors rewarded the low or medium related M&As, not the highly related M&A, during the financial crisis. The Impact of IT Sectors on Abnormal Return for the Acquiring Firms before and during the Financial Crisis Table 4 shows that the abnormal return for the acquiring firms before and during the financial crisis in three traditional IT sectors: hardware, software and services. The results show that all three IT sectors have no significant cumulative abnormal return before the financial crisis. During the financial crisis, both hardware and service sectors have a significant cumulative abnormal return of 2.20% and 4.47% respectively. Software sector had no significant cumulative abnormal return during the financial crisis. IV. Conclusion This study focuses on value creation of the mergers and acquisitions of Tech 100 companies in the US before and during the recent financial crisis. Results of an event study show that all mergers and acquisitions in leading IT companies in the US have no significant cumulative abnormal return before the financial crisis (between 2004 and 2006), which is in consistent with previous literature. However, during the financial crisis (between 2007 and 2009), acquirers that pursued an unrelated/low related diversification strategy received significant cumulative abnormal return, while acquirers that pursued a highly related synergy strategy received no significant return. In addition, the results also show that among the three maor sections of IT industry (hardware, software and services), companies in both hardware and services sectors have a significant cumulative abnormal return while software companies have no significant cumulative abnormal return during the financial crisis. The findings of our study show that financial crisis does have an impact on market reaction to acquiring firms in IT industry. This result demonstrates the importance of market condition on M&As. In addition, the results also show that market reaction to M&As are influenced by the acquisition strategy of an acquiring firm. An IT company that pursues a diversification strategy is more likely to receive positive return during the financial crisis. The type of IT sector is also found to impact market reaction to acquiring firms in IT industry. Table 2. Abnormal Return of the Acquirers Acquirers before Financial Crisis (2004-2006) Acquirers During Financial Crisis (2007-2009) Day relative to announcement Number AR Z-statistics Number AR Z-statistics -10 342 0.01% 0.01% 354 0.10% 1.048-9 342 0.09% 1.094 354-0.04% -0.475-8 342-0.03% -0.142 354 0.06% 1.437-7 342-0.09% -2.145* 354-0.09% -1.227-6 342-0.11% -1.759$ 354 0.13% 1.536-5 342 0.00% 0.247 354-0.03% -0.598-4 342 0.10% 0.314 354 0.03% 0.124-3 342 0.03% 0.521 354-0.07% -0.752-2 342 0.20% 2.592** 354 0.12% 1.831$ -1 342-0.01% 0.516 354-0.06% -0.705

Fang Chen, Suhong Li 179 0 342 0.08% 0.869 354 0.05% 1.045 1 342 0.03% 0.440 354 0.24% 2.927** 2 342-0.24% -2.574* 354 0.23% 2.821** 3 342 0.06% 0.492 354-0.06% -1.649$ 4 342 0.12% 1.310 354 0.02% -0.156 5 342 0.08% 0.763 354-0.17% -1.834$ 6 342-0.22% -2.472* 354 0.05% 1.197 7 342 0.10% 1.061 352 0.09% 1.509 8 342 0.07% 0.440 351 0.01% 0.242 9 342-0.09% -0.559 351-0.05% -0.087 10 342 0.13% 1.119 350 0.15% 1.980* Windows: (-10,+10) 342 0.29% 0.531 354 0.71% 2.226* (-30,-1) 342 0.72% 0.760 354 1.25% 2.358* (0,30) 342-0.36% -1.509 354 1.48% 3.940*** (-30,+30) 342 0.36% -0.543 354 2.73% 4.454*** The symbols $,*, **, and *** denote statistical significance at the 0.10, 0.05, 0.01 and 0.001 levels, respectively, using a generic two-tail test. Table 3. Cumulative Abnormal Returns for Acquirers for Within and Cross Industry Samples Acquirers before Financial Crisis (2004-2006) Acquirers During Financial Crisis (2007-2009) Relatedness Between CAR Number Z-stat CAR Number Z-stat Acquirer and Target High(4 digit SIC code 0.08% 115-0.352 1.43% 105 1.43% match) Medium (2 or 3 digit SIC 0.59% 95-0.449 3.94% 77 3.688*** Low (0 or 1 digit SIC code match) 0.44% 132-0.164 3.46% 80 2.773** The symbols $,*, **, and *** denote statistical significance at the 0.10, 0.05, 0.01 and 0.001 levels, respectively, using a generic two-tail test. Table 4. Cumulative Abnormal Returns for Acquirers by Sector Acquirers before Financial Crisis (2004-2006) Acquirers after Financial Crisis (2007-2009) Sectors CAR Number Z-stat CAR Number Z-stat Hardware 0.67% 181 0.006 2.20% 163 3.002** High(4 digit SIC 0.98% 34 0.320-2.22% 30-0.306 Medium (2 or 3 digit SIC -0.15% 47-0.578 4.64% 33 3.321*** Low (0 or 1 digit SIC 0.95% 100 0.218 2.71% 100 2.090* Software -0.23% 91-0.518 2.03% 100 1.538 High(4 digit SIC -0.23% 66-0.523 2.27% 56 1.104 Medium (2 or 3 digit SIC 1.63% 18 0.253-0.22% 15 0.057 Low (0 or 1 digit SIC -5.01% 7-0.667 2.75% 29 1.285 Service 0.32% 70-0.620 4.47% 91 3.168**

180 Fang Chen, Suhong Li High(4 digit SIC -0.61% 15-0.361 4.73% 19 1.262 Medium (2 or 3 digit SIC 1.11% 30-0.272 5.30% 29 2.427* Low (0 or 1 digit SIC -0.07% 25-0.460 3.79% 43 1.759$ This study only focuses on the M&As on leading IT companies, future study can extend the sample to include all IT companies to see whether the findings hold true. In addition, future study can investigate market reaction to not only acquiring firms but also target firms. Future study can also explore the impact of other factors, such as mode of payment (case, stock and debt) and nature of M&As (domestic or cross-border), on the performance of acquirers. Another possible direction is to look at the impact of M&As on the whole supply chain, including acquiring firms suppliers and customers, which have received increased attention in the literature. Suhong Li is an Associate Professor of Computer Information Systems at Bryant University. She earned her Ph.D. from the University of Toledo in 2002. She has published in academic ournals including Journal of Operations Management, OMEGA: the International Journal of Management Science, Decision Support Systems, Journal of Computer Information Systems, International Journal of Operations & Production Management and others. Her research interests include supply chain management, electronic commerce, and adoption and implementation of IT innovation. References [1] Business Cycle Dating Committee of the National Bureau of Economic Research, 2008. Determination of the December 2007 Peak in Economic Activity. Available at : http://www.nber.org/cycles/dec2008.html. Accessed on April 2010. [2] Cloodt, M., Hagedoorn, J., Kranenburg, H. V. 2006. Mergers and Acquisitions: Their Effect on the Innovative Performance of Companies in High-Tech Industries, Research Policy, 35: 642-654. [3] Helwege, Jean, 2010. Financial Firm Bankruptcy and systemic Risk. Journal of International Financial Markets, Institutions and Money. Vol. 20, Issue. 1, pp.1-12. [4] Jorion, Philippe and Zhang, Gaiyan, 2009. Credit Contagion from Counterparty Risk. The Journal of Finance. Vol. LXIV, No. 5, pp.2053-2087. [5] Kohers, N. and T. Kohers, 2000, The Value Creation Potential of High-Tech Mergers. Fianacial Analysts Journal. Vol.53, No.3, pp.40-48. [6] Park, C. 2003. Prior performance characteristics or related and unrelated acquirers. Strategic Management Journal. Vol.24, pp. 471 480. [7] Rheaume, L. and H. S. Bhabra, 2008. Value creation in informationbased industries through convergence: a study of U.S. mergers and acquisitions between 1993 and 2005. Information and Management. Vol.45, pp.304-311. [8] Schwert, G.W., 1996, Markup Pricing in Mergers and Acquisitions, Journal of Financial Economics. Vol.41, No. 2, pp85-120. [9] Uhlenbruck, K., Hitt, M. A., Semadeni, M., 2006. Market Value Effects of Acquisitions Involving Internet Firms: A Resource-Based Analysis, Strategic Management Journal, 27: 899-913. [10] Wade, M.R., Gravill, J.I. 2003. Diversification and performance of Japanese IT subsidiaries: a resource based view. Information and Management. Vol.40, pp. 305 316. [11] Wilcox, D., Chang, K. C., Grover, V. 2001. Valuation of Mergers and Acquisitions in the Telecommunications Industry: A study on Diversification and Firm Size. Information and Management 38:459-417. Background of Authors Fang Chen is a Ph. D student in the Finance Department at University of Rhode Island. His research interests focus on contagion effect along the supply chain, proxy contest and risk management.