Hybrid investment motivations lead to heavy tails in M&A dynamics: Empirical evidence from China’s stock market

Rui Hou, Zhi Luo, Mingbao Cheng, Yu-xiao Zhu and Jia-wen Wu

Physica A: Statistical Mechanics and its Applications, 2019, vol. 528, issue C

Abstract: The time interval distribution law of mergers and acquisitions (M&As) is a key breakthrough in understanding the M&As dynamics in financial markets. Using the stock composite index (SCI) data of the Shanghai Stock Exchange (SSE) and the merger and acquisition (M&A) activities data in China’s stock market (1996–2014) as samples, we analyze the heavy-tailed distribution characteristics of the M&As time intervals and test the positive correlation between the SCI volatility and M&A activity fluctuations. We then establish an M&A dynamics model based on mixed investment motives and verify the correctness of the model through simulations. The result shows that the different volatility signals of SCI will trigger different investment motivations on short-term speculative and long-term value M&A, which are the direct reason for the heavy-tailed distribution phenomenon of M&A time intervals in financial markets.

Keywords: Hybrid investment motivations; Heavy-tailed distribution; Merger and acquisition; Time interval; Empirical mode decomposition (EMD) (search for similar items in EconPapers)
Date: 2019
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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:528:y:2019:i:c:s0378437119308192

DOI: 10.1016/j.physa.2019.121399

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