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Professional thesis
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The Impact of M&A Events of Chinese Listed Companies on Stock Prices and Long-Term Performance

ContributorsZhao, Jihong
Number of pages71
Imprimatur date2022
Defense date2022
Abstract

Using the event study methodology, this thesis analyses the impact of mergers and acquisitions (M&A) on Chinese listed companies. The thesis evaluates the long- and short- term performance of the firms from 2000 to 2019. It then focuses on the form of acquirers’ long-term market performance after acquisitions.

When studying short-term stock market performance, this thesis evaluates the cumulative abnormal returns (CAR) around the merger events focusing on market performance within an event window of ten days before to thirty days after the merger announcement (-10,30]). The analysis then splits the sample according to industry, years and organizational form of the firm. When studying the long-term market performance, this study examines the buy-and-hold abnormal return (BHAR) of mergers and acquisitions within a 36-month period after the merger event. When studying the impact of organizational form on long-term performance of M&A transactions, the thesis examines whether acquisitions initiated by state-owned holding companies have differential long-term performance. The thesis also examines if the long-term performance of acquisitions involving state-owned acquirers has changed since 2015.

The empirical results show that for most companies, M&A events will bring good market performance in the short- and long-term. Furthermore, the long-term market performance of state-owned enterprises is inferior to that of non-state-owned enterprises. Additionally, with the progression of market-oriented reform, the long-term performance of state-owned enterprises M&A is in turn improving.

eng
Keywords
  • M&A
  • Event Research Method
  • Market Performance
  • Synergistic Effect
  • State-Owned Enterprise
Citation (ISO format)
ZHAO, Jihong. The Impact of M&A Events of Chinese Listed Companies on Stock Prices and Long-Term Performance. 2022.
Main files (1)
Thesis
accessLevelPublic
Identifiers
  • PID : unige:178883
  • Thesis number : 0053
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