Divers of Merger Waves: The Indian Context

Abstract

The question of what drives waves of merger activity is a dated one. The past decade has witnessed authors as Shleifer and Vishny (2003) and Rho des-Kropf and Vishwanathan (2004) developing behavioural models explaining the observed relation between stock market valuations and merger activity. Harford (2005) has tried to answer whether the mergers at the aggregate level are caused due to a combination of industry shocks (which creates a favourable environment for mergers to occur) or market timing. Several hypotheses emerge out with focus on both behavioural and neoclassical models. But most of these works have been limited to t1le context of developed countries like US where the markets are closer to the assumptions of high market efficiency. But the markets ill developing countries may or may not follow the similar trend due to macroeconomic factors like trade restrictions. poor liquidity in capital markets._ etc. As has been discussed in the above mentioned studies, market timing driven hypothesis has little explanatory power relative to an economic model encompassing these additional factors. Hence._ there is a need for an independent analysis of developing markets like India. In Indian context, since rapid globalization has taken place and several sectors have been opened only in early 2000s, data is analyzed from 2005. Regressions equations will be made specific to Indian industries which would be able to predict merger waves. Also, correlation coefficients thus obtained will be used to comment upon the ability of the traditional variables to fully explain the variation ill Indian context. The project aims at understanding the drivers of merger waves in Indian context. International experience suggests that factors like industry shocks and liquidity in markets have been used to model the merger wave occurrence. Data for mergers in Indian companies over the past 8 years have been analysed to see the occurrence of merger waves. Factors like shock, cash flows, Tobin’s Q etc. have also been calculated for all the industries. 8 industry groups were taken and data was analysed to make separate predictions for each of the industry. For all the industries, separate regressions were run to predict probability of occurrence of merger waves, expected number of mergers and probability of a particular merger triggering a merger wave.