nach oben

Review of Quantitative Finance and Accounting

Erschienen in:

01.08.2014 | Original Research

The impact of technology-motivated M&A and joint ventures on the value of IT and non-IT firms: a new examination

verfasst von: Thomas G. Canace, Steven V. Mann

Erschienen in: Review of Quantitative Finance and Accounting | Ausgabe 2/2014

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

We extend Lee and Lim (Rev Quant Financ Account 27:111–123, 2006) who provide empirical evidence on the impact of mergers and acquisitions (M&As) and joint ventures on the value of information technology (IT) and non-IT firms. Using technology-motivated transactions, we examine whether there are differences in market response to the announcement of M&As and joint ventures, and we consider the long-term performance of such firms. We find the market provides no (positive) reaction to joint ventures (M&As) at the announcement. We also present new evidence suggesting the market reacts more favorably to the announcement of technology M&As relative to joint ventures for our full sample, IT sample and non-IT sample. However, our examination of these firms’ long-term performance suggests the initial reaction is not fully supported. The findings suggest improved (declining) operating performance for joint venture (M&A) firms, and evidence to conclude joint venture firms achieve superior long-term performance changes for both accrual- and cash-based measures. To explain these inconsistencies, we employ a set of control variables previously documented as determinants of the innovation ownership decision. For joint venture firms, we find that, while the market fails to consider the importance of the firms’ R&D intensity and growth prospects in its initial reaction, these are ultimately key indicators of their future performance. The evidence also suggests the market overreacts to M&A announcements because it over-weights the impact of R&D intensity on the firms’ future performance in its initial response.

Vorheriger Artikel Tail risk in pension funds: an analysis using ARCH models and bilinear processes

Nächster Artikel Investor sentiment and interest rate volatility smile: evidence from Eurodollar options markets

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Technology-motivated partnerships are primarily associated with product innovation and development to provide a firm with new sources of competitive advantage and competencies (Das et al. 1998; Piachaud and Muresan 2004; Kallunki et al. 2009).

Traditionally, the choice of business partnership has been determined by four dimensions: commitment, degree of amalgamation, future view, and financial arrangement.

Our use of a 3 year horizon is consistent with prior literature (Danzon et al. 2007; Ornaghi 2009). Further, de Man and Duysters (2005), in their review of over forty studies, report that the median time horizon of studies is 3 years, and there appears to be no significant impact of the choice of time horizon on the research results.

We end the sample period at December 2003 to provide 3 years of available data for assessing subsequent performance. We recognize that some announcements may not be reported through the news portals we examine, that such results may not be covered through press releases for all firms, or that firms may not announce changes in the status of their partnerships following the initial announcement. It is also possible, although we believe unlikely, that our data selection criteria may have caused sample-induced biases. These represent limitations of our study.

Similar to prior literature (Jain and Kini 1994; Carline et al. 2009; Kallunki et al. 2009), we require that the initial agreements result in completed partnerships that are not terminated 3 years after the announcement. To make this assessment, we search for subsequent news announcements by firm for our sample firms.

To make this assessment, we extend our examination of press releases for our sample firms to the period 3 years before and after our sample period. Moreover, although we do not control for firms entering into multiple technology partnerships within the same ownership structure, we note that such instances comprise a very small portion of our sample. When we exclude these cases, our results remain qualitatively and quantitatively similar.

We subsequently match the sample with the annual Compustat file to obtain long-term financial variables.

We utilize a value-weighted market benchmark from CRSP in all testing. In subsequent testing, we use an equal-weighted market benchmark. The results (untabulated) and conclusions remain unchanged.

We calculate these measures by firm. The ratio of net income to average total assets is computed as (COMPUSTAT item #172) divided by (average COMPUSTAT item #6). The ratio of operating income to average total assets is computed as (COMPUSTAT item #13) divided by (average COMPUSTAT item #6). The ratio of operating cash flow to average total assets equals operating income minus capital expenditures (COMPUSTAT item #13 less item #128) divided by (average Compustat item #6). Average Compustat item #6 is calculated as [(end of current fiscal year value + end of prior fiscal year value)/2].

Tobin’s q is computed as q = (market value of equity + book value of preferred stock + book value of long-term debt) divided by (book value of total assets), with all values measured at year end.

The sample is reduced after matching with the annual Compustat file to test the four long-term measures and after removing extreme observations. See Sect. 5.2 and Tables 3 and 4.

The non-IT sample is primarily constituted by firms in the pharmaceutical/biotechnology industry.

As a robustness measure, we therefore re-perform testing after removing announcements occurring in these 3 years. The results (untabulated) are qualitatively similar.

Our design requires firms with three (one) year leading (lagged) data for the four performance measures. We require that the firm have data available for the third subsequent year, and then separately for the first three subsequent years to calculate the average 3 year post-announcement performance. Also, for calculating the industry-adjusted performance we require a matching industry, and that the third year and 3 year average post-announcement performance are available for both the event firm and matched industry. Further, for M&A firms, to ensure a consistent comparison before and after the transaction, we require either restated operating data from Compustat or pre-M&A operating data from Compustat for both the target and acquirer to compare performance to the combined post-M&A firm. These requirements result in a reduced sample as indicated in Tables 3 and 4.

We are grateful to the reviewer for offering suggestions to aid our design of transaction characteristics.

Healy et al. (1992) discuss that it is also important to consider whether the transaction is financed by cash or debt because this may contribute to lower post-M&A performance as compared to stock deals. In further analysis we partition the sample by considering whether the deal is financed, at least partly, with cash, debt or some combination (“cash”) as opposed to consisting of all stock (“stock”). We obtain a sample of 41 and 52 cash and stock transactions, respectively. We find no significant differences between deal types for our accrual and cash measures. Our findings that cash flow return is unaffected by financing choice are consistent with Healy et al. (1992).

For quantitative variables, we use a one period lag by calculating each control at the prior fiscal year end before the announcement date (t − 1). We winsorize variables at the top and bottom 1% of their respective distributions.

We include changes controls in the long-term regressions because they are comprised in the information set determining the change in performance, whereas these measures are not relevant to our short-term market reaction regressions.

The results (firm and industry-adjusted) for the change in performance for the average 3 year post-announcement period relative to the 1 year pre-announcement period (untabulated) are qualitatively and quantitatively similar to those reported in Tables 6 and 7.

Patell (1976) uses a test statistic where the abnormal returns for the event period are standardized by the standard deviation of the estimation period abnormal returns. The Boehmer et al. (1991) test statistic provides a standardized cross-sectional test where event period abnormal returns are standardized by estimation period standard deviation.

Andrade G, Mitchell M, Stafford E (2001) New evidence and perspectives on mergers. J Econ Perspect 15(2):103–120CrossRef

Bertrand O, Zuniga P (2006) R&D and M&A: are cross-border M&A different? An investigation on OECD countries. Int J Ind Organ 24(2):401–424

Boehmer E, Poulsen A, Musemeci J (1991) Event Study methodology under conditions of event-induced variance. J Financ Econ 30:253–272

Carline N, Linn S, Yadav P (2009) Operating performance changes associated with corporate mergers and the role of corporate governance. J Bank Financ 33:1829–1841

Chakrabarti A, Hauschildt J, Sueverkruep C (1994) Does it pay to acquire technological firms? R&D Manag 24:47–56

Chan S, Kensinger J, Keown A, Martin J (1997) Do strategic alliances create value? J Financ Econ 46:199–221

Chiang C, Mensah Y (2004) The determinants of investor valuation of R&D expenditure in the software industry. Rev Quant Financ Account 22:293–313

Chung K, Pruitt S (1994) A simple approximation of Tobin’s q. Financ Manag 23(3):70–74

Danzon P, Epstein A, Nicholson S (2007) Mergers and acquisitions in the pharmaceutical and biotech industries. Manag Decis Econ 28:307–328

Das S, Sen P, Sengupta S (1998) Impact of strategic alliances on firm valuation. Acad Manag J 41:27–41

de Man A, Duysters G (2005) Collaboration and innovation: a review of the effects of mergers, acquisitions, and alliances on innovation. Technovation 25:1377–1387

Dushnitsky G, Lenox M (2006) When does corporate venture capital investment create firm value? J Bus Invest 21:753–772

Gemba K, Kodama F (2001) Diversification dynamics of the Japanese industry. Res Policy 30(8):1165–1184

Ghosh A (2001) Does operating performance really improve following corporate acquisitions? J Corp Financ 7:151–178

Hagedoorn J (1993) Understanding the rationale of strategic technology partnering: interorganizational modes of cooperation and sectoral differences. Strateg Manag J 14:371–385

Hall B (1988) The effect of takeover activity on corporate research and development. In: Auerbach AJ (ed) Corporate takeovers: causes and consequences, 3rd edn. University of Chicago Press for NBER, Chicago

Healy P, Palepu K, Ruback R (1992) Does corporate performance improve after mergers? J Financ Econ 31(2):135–175

Hennart J, Reddy S (1997) The choice between mergers/acquisitions and joint ventures: the case of Japanese investors in the United States. Strateg Manag J 18:1–12

Higgins M, Rodriquez D (2006) The outsourcing of R&D through acquisitions in the pharmaceutical industry. J Financ Econ 80:351–383

Himmelberg C, Peterson B (1994) R&D and internal finance: a panel study of small firms in high-tech industries. Rev Econ Stat 76:38–51

Holmstrom B (1989) Agency costs and innovation. J Econ Behav Organ 12:305–327

Jain B, Kini O (1994) The post-issue operating performance of IPO firms. J Financ 49(5):1699–1726

Jensen M (1986) Agency costs of free cash flow, corporate finance and takeovers. Am Econ Rev 76:323–329

Kallunki J, Pyykko E, Laamanen T (2009) Stock market valuation, profitability and R&D spending of the firm: the effect of technology mergers and acquisitions. J Bus Financ Account 36(7):838–862

Koh J, Venkatraman N (1991) Joint venture formations and stock market reactions: an assessment in the information technology sector. Acad Manag J 34(4):869–892

Lee S, Lim K (2006) The impact of M&A and joint ventures on the value of IT and non-IT firms. Rev Quant Financ Account 27:111–123

Lin B (2011) Knowledge diversity as a moderator: inter-firm relationships, R&D investment and absorptive capacity. Technol Anal Strateg Manag 14(3):331–343

Madhavan R, Prescott J (1995) Market value impact of joint ventures: the effect of industry information-processing load. Acad Manag J 38(3):900–915

McConnell J, Nantell T (1985) Corporate combinations and common stock returns: the case of joint ventures. J Financ 40(2):519–536

Myers S, Majluf N (1984) Corporate financing and investment decisions when firms have information that investors do not. J Financ Econ 13:187–221

Neill J, Pfeiffer G, Young-Ybarra C (2001) Technology R&D alliances and firm value. J High Technol Manag Res 12:227–237

Ornaghi C (2009) Mergers and innovation in big pharma. Int J Ind Organ 27:70–79

Patell J (1976) Corporate forecasts of earnings per share and stock price behavior: empirical tests. J Account Res 14(2):246–276

Piachaud B, Muresan E (2004) A study of shareholder reaction to technology motivated joint ventures and strategic alliances: strategic and financial perspectives. Int J Technol Manag 27(4):343–356

Porter M, Fuller M (1986) Coalitions and global strategy. In: Porter M (ed) Competition in global industries. Harvard Business School Press, Boston, pp 315–344

Powell W (1990) Neither market nor hierarchy: network forms of organization. In: Staw B, Cummings L (eds) Research in organization behavior, vol 12. JAI Press, Greenwich, pp 295–336

Ramaswamy K, Waegelein J (2003) Firm financial performance following mergers. Rev Quant Financ Account 20:115–126

Ravenscraft D, Scherer F (1987) Mergers, selloffs and economic efficiency. Brookings Institution, Washington, DC

Singh H, Kogut B (1989) Industry competitive effects on the choice of entry mode. Acad Manag Proc 116–120

Tsung-Ming Y, Hoshino Y (2000) The effects of mergers and acquisitions on Taiwanese corporations. Rev Pac Basin Financ Mark Policies 3(2):183–199

Wang Y, Wu C (2004) The share price responses and determinants of strategic alliances in Taiwan’s hi-tech industry: a quantile regression approach. Rev Pac Basin Financ Mark Policies 7(3):355–378

Xue Y (2007) Make or buy new technology: the role of CEO compensation contract in a firm’s route to innovation. Rev Account Stud 12(4):659–690

Titel: The impact of technology-motivated M&A and joint ventures on the value of IT and non-IT firms: a new examination
verfasst von: Thomas G. Canace
Steven V. Mann
Publikationsdatum: 01.08.2014
Verlag: Springer US
Erschienen in: Review of Quantitative Finance and Accounting / Ausgabe 2/2014
Print ISSN: 0924-865X
Elektronische ISSN: 1573-7179
DOI: https://doi.org/10.1007/s11156-013-0374-8

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Wirtschaft"

Weitere Artikel der Ausgabe 2/2014

The impact of prompt corrective action on the default risk of the U.S. commercial banking sector

Earnings management and corporate spinoffs

Investor protection and corporate cash holdings around the world: new evidence

Investor sentiment and interest rate volatility smile: evidence from Eurodollar options markets

Tail risk in pension funds: an analysis using ARCH models and bilinear processes

Information asymmetry and accounting restatement: NYSE-AMEX and NASDAQ evidence