Beyond Binary Decisions : Modeling the Frequency of Mergers and Acquisitions in the Indian IT Industry Using Count Data Models

Authors

  •   Muhammadriyaj Faniband Assistant Professor (Corresponding Author), Symbiosis Centre for Corporate Education, Pune, Symbiosis International (Deemed University), Pune - 411 016, Maharashtra ORCID logo https://orcid.org/0000-0001-6775-3582
  •   Seema Singh Professor and Director, Symbiosis Centre for Corporate Education, Pune, Symbiosis International (Deemed University), Pune - 411 016, Maharashtra ORCID logo https://orcid.org/0000-0003-0816-909X

DOI:

https://doi.org/10.17010/ijf/2026/v20i1/175222

Keywords:

macroeconomic factors, mergers, acquisitions, IT industry, count data, India.
JEL Classification Codes : C58, F62, G34
Publication Chronology: Paper Submission Date: August 5, 2025 ; Paper sent back for Revision : December 19, 2025 ; Paper Acceptance Date : December 22, 2025 ; Paper Published Online : January 15, 2026

Abstract

Purpose : This paper investigated the impact of macroeconomic factors, inflation, exchange rate, interest rate, GDP, and economic policy uncertainty (EPU) on mergers and acquisitions (M&A) activity of the Indian IT industry.

Design/Methodology/Approach : This paper used the count data models, Poisson and Negative Binomial models, for a period ranging from 2007 to 2023.

Findings : We found several important findings: First, inflation showed no significant impact at any lag, while exchange rates had a significant and negative effect in the contemporary period and one lag, but lost significance after two lags. Second, GDP had a significant negative impact in both models, strongest at one lag, but lost significance in the Negative Binomial model at two lags. Third, interest rates had no significant effect on M&A for all periods. Fourth, EPU did not significantly affect the contemporary and one-lag periods but revealed a significant negative relationship in the two-lag period in the Poisson model.

Practical Implications : The findings offered valuable implications for policymakers, business leaders, and investors who are involved in or considering M&A activities in this dynamic industry.

Originality/Value : Despite comprehensive research on M&A decision-making, the literature lacked studies applying discrete outcome models like Poisson or Negative Binomial to model M&A activities, particularly in the Indian IT industry. Therefore, this research filled the methodological and contextual gap.

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Published

2026-01-15

How to Cite

Faniband, M., & Singh , S. (2026). Beyond Binary Decisions : Modeling the Frequency of Mergers and Acquisitions in the Indian IT Industry Using Count Data Models. Indian Journal of Finance, 20(1), 72–85. https://doi.org/10.17010/ijf/2026/v20i1/175222

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