Mergers and Acquisitions in the Indian Context : A Valuation Perspective for the Indian Pharmaceutical Industry

Authors

  •   Chintan Gala PGP 2019 – 2021 Student, Indian Institute of Management Shillong (IIM Shillong), Shillong, Meghalaya - 793 014
  •   Mousumi Bhattacharya Associate Professor, Indian Institute of Management Shillong (IIM Shillong), Shillong, Meghalaya - 793 014

DOI:

https://doi.org/10.17010/ijf/2022/v16i4/169173

Keywords:

Mergers And Acquisitions

, Synergy Valuation, Artificial Neural Network, Pharmaceutical Sector, Event Study.

JEL Classification Codes

, G34, G30, L65.

Paper Submission Date

, June 5, 2021, Paper Sent Back for Revision, February 15, 2022, Paper Acceptance Date, March 15, Paper Published Online, April 15, 2022.

Abstract

Mergers and acquisitions (M&A) as a strategy has been used increasingly by the corporate sector to gain a competitive advantage. Mergers and acquisitions (M&A) help a firm gain new customer bases, enter new markets, gain access to new technologies, and help achieve cost reduction. In today’s time, firms try to achieve synergistic gains through M&As. This study aimed to: a) understand if prices paid by acquiring firms reflected synergistic gains and b) observe value created for shareholders of the acquiring firms. The study used secondary data for the purpose of research. A neural network model was built to scrutinize the impact of the selected variables on the value of the mergers and acquisitions (M&A) deal. An event study was carried out to assess value creation for shareholders of the acquiring firm. The study found that an acquiring firm generally paid more during the M&As compared to the synergistic gains it realized post-acquisition. Additionally, there was no value creation for the acquiring firm’s shareholders. The neural network model from this study, with appropriate variables, can be used to predict the price of an M&A deal. The study will benefit the stakeholders of pharmaceutical firms to make informed decisions regarding mergers and acquisitions.

Downloads

Download data is not yet available.

Author Biographies

Chintan Gala, PGP 2019 – 2021 Student, Indian Institute of Management Shillong (IIM Shillong), Shillong, Meghalaya - 793 014

ORCID iD : https://orcid.org/0000-0003-0394-1642

Mousumi Bhattacharya, Associate Professor, Indian Institute of Management Shillong (IIM Shillong), Shillong, Meghalaya - 793 014

ORCID iD : https://orcid.org/0000-0002-6506-6524

Downloads

Published

2022-04-13

How to Cite

Gala, C., & Bhattacharya, M. (2022). Mergers and Acquisitions in the Indian Context : A Valuation Perspective for the Indian Pharmaceutical Industry. Indian Journal of Finance, 16(4), 31–46. https://doi.org/10.17010/ijf/2022/v16i4/169173

Issue

Section

Articles

References

Beena, S. (2006). Mergers and acquisitions in the Indian pharmaceutical industry: Nature, structure and performance (MPRA Paper No. 8144). https://mpra.ub.uni-muenchen.de/8144/1/MPRA_paper_8144.pdf

Chatterjee, S. (1986). Types of synergy and economic value: The impact of acquisitions on merging and rival firms. Strategic Management Journal, 7(2), 119–139. https://doi.org/10.1002/smj.4250070203

Damodaran, A. (2005). The value of synergy. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.841486

Danzon, P. M., Epstein, A., & Nicholson, S. (2007). Mergers and acquisitions in the pharmaceutical and biotech industries. Managerial and Decision Economics, 28(4–5), 307–328. https://doi.org/10.1002/mde.1343

Duggal, N. (2015). Post merger performance of acquiring firms: A case study on Indian pharmaceutical industry. International Journal of Research in Management & Business Studies, 2(3), 24–28. http://ijrmbs.com/vol2issue3/neha.pdf

Mishra, P. (2006). Mergers, acquisitions, market structure and industry performance: Experience of Indian pharmaceutical industry. Review of Development and Change, 11(2), 135–164. https://doi.org/10.1177/0972266120060202

Prakash, S. (2017). The impact of mergers and acquisitions on shareholders’ value: An empirical analysis of select Indian companies. Indian Journal of Finance, 11(9), 22–38. https://doi.org/10.17010/ijf/2017/v11i9/118087

Raghuvanshi, A., & Raghuvanshi, A. (2014). Determinants of shareholder gains in acquisitions: An empirical study in the Indian corporate sector. Indian Journal of Finance, 8(2), 37–48. https://doi.org/10.17010/ijf/2014/v8i2/71979

Ranju, P. K., & Mallikarjunappa, T. (2017). Do acquisitions create value for acquirer companies in India? An empirical study. Indian Journal of Research in Capital Markets, 4(1), 7–18. https://doi.org/10.17010/ijrcm/2017/v4/i1/112880

Sarangi, P., Sinha, D., Sinha, S., & Dubey, M. (2019). Financial modeling using ANN technologies: Result analysis with different network architectures and parameters. Indian Journal of Research in Capital Markets, 6(1), 21–33. https://doi.org/10.17010/ijrcm/2019/v6/i1/144039

Shelton, L. M. (1988). Strategic business fits and corporate acquisition: Empirical evidence. Strategic Management Journal, 9(3), 279 – 287. https://doi.org/10.1002/smj.4250090307

Tripathy, S., & Prajapati, V. (2014). Mergers and acquisitions: Trends in Indian pharmaceutical industry. Journal of Medical Marketing: Device, Diagnostic and Pharmaceutical Marketing, 14(4), 182–190. https://doi.org/10.1177/1745790415577854

Vyas, V., Narayanan, K., & Ramanathan, A. (2012). Determinants of mergers and acquisitions in Indian pharmaceutical industry. Eurasian Journal of Business and Economics, 5(9), 79–102. https://www.ejbe.org/EJBE2012Vol05No09p079VYAS-NARAYANAN-RAMANATHAN.pdf

Willmott, C., Ackleson, S., Davis, R., Feddema, J., Klink, K., & Legates, D., O’Donnell, J., & Rowe, C. M. (1985). Statistics for the evaluation and comparison of models. Journal of Geophysical Research, 90(C5), 8995 – 9005. https://doi.org/10.1029/jc090ic05p08995

Willmott, C., & Matsuura, K. (2005). Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance. Climate Research, 30, 79–82. https://doi.org/10.3354/cr030079

Zhu, M., & Meng, Z. (2021). Fuzzy comprehensive evaluation model of M&A synergy based on transfer learning graph neural network. Computational Intelligence and Neuroscience, 1–12. https://doi.org/10.1155/2021/6516722