Review of the Literature on Merger Waves

Cho, Sangjun; Chung, Chune Young

doi:10.3390/jrfm15100432

Cited by 12 publications

(5 citation statements)

References 82 publications

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“…In recent times, the rapid growth of mergers and acquisitions has raised the interest of academics and professionals. While mergers and acquisitions continue to be the subject of careful examination, many questions still surround the merger process (Cho & Chung, 2022). Therefore, Question 1 was about the merger of large companies in Europe, which always raises many doubts for society about the monopoly and the maintenance of jobs (Chiriac, 2021).…”

Section: Methodsmentioning

confidence: 99%

Generative AI for Business Decision-Making: A Case of ChatGPT

Chuma

Oliveira

2023

MSBD

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ChatGPT (Generative Pretrained Transformer) is a chatbot using artificial intelligence (AI) launched by OpenAI, which is an AI research and deployment company. The ChatGPT has taken the technology world by storm. The ChatGPT is a trained AI model that can chat almost like a human. The dialog format allows the ChatGPT to answer follow-up questions, admit mistakes, challenge incorrect premises, and reject inappropriate requests. The ChatGPT can be utilized for compiling research, drafting marketing content, brainstorming ideas, delivering aftercare services, increasing customer engagement, and many others. The ChatGPT can provide enormous opportunities for companies leveraging this breakthrough technology strategically. Thus, we evaluate ChatGPT as a tool in common business decision-making cases in the current study. For example, the ChatGPT was asked about the impacts of a hypothetical merging of two supermarket chains in Sweden. In another example, the ChatGPT was asked about recommendations for investment in a Brazilian oil company. Finally, it was asked about the factors that influence online shopping behavior. The results are significant and demonstrate the tremendous potential of the ChatGPT in revolutionizing the corporate world.

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Section: Methodsmentioning

confidence: 99%

Generative AI for Business Decision-Making: A Case of ChatGPT

Chuma

Oliveira

2023

MSBD

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“…In the merger wave aspect, the late movers, when acquisition fury declines, may be able to evaluate the true value of potential target and undertake the merger more rationally (McNamara et al, 2008). Cho and Chung (2022) consider acquisition decision as a long-term investment with shareholders expecting benefits over an extended period, thus emphasising the importance of reassessing the impact of mergers and acquisitions on long-term post-merger performance. Based on the above analysis, we start with testing the existence of early mover/late mover advantage and present the following hypothesis: H1: Early movers and late movers both tend to show advantages in merger waves and outperform their peers in terms of higher buy-and-hold return and higher ROA in the post-merger period.…”

Section: Late Mover Advantagementioning

confidence: 99%

Industry Concentration, Firm Size and Entry-Timing in Merger Waves

Lin,

Pun,

2023

AAMJAF

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As early mover advantage and late mover advantage have been discussed for decades, the conditions for the best use of them are yet unclear. Using the U.S. merger data during 2000–2019, this study examines the entry-timing and the post-merger performance of 1,376 bidders in the merger waves recognised. We find that early movers have advantages with higher post-merger performance in a merger wave, while the effect of late mover advantage is not significant. Moreover, early mover advantage can only be implemented in competitive industries. Our analysis further provides the entry-timing strategies for large firms and small/mid enterprises, respectively. Our results indicate that late mover advantage can only be adopted in large companies, while small/mid firms should be followers, not early or late movers, in high concentration industries. Overall, our study sheds new light on the entry-timing in a merger wave with the consideration of different market concentration environment and different firm size.

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“…Seven waves have been identified. While it is not a discrete cutover to the next wave, multiple events have triggered the beginning and end of a phase (Cho & Chung, 2022).…”

Section: Introductionmentioning

confidence: 99%

“…There have been seven waves of M&A identified, and the cyclical pattern was observed by Golbe and White (1988). The waves are abstract patterns to understand the history and evolution of mergers; they are not meant to be precise start and endpoints (Cho & Chung, 2022). In India, the latter part of the 1990s was when M&A activity gained momentum.…”

Section: Introductionmentioning

confidence: 99%

Multiple Models in Predicting Acquisitions in the Indian Manufacturing Sector: A Performance Comparison

Vinod,

S K

2023

IJIKM

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Aim/Purpose: Acquisitions play a pivotal role in the growth strategy of a firm. Extensive resources and time are dedicated by a firm toward the identification of prospective acquisition candidates. The Indian manufacturing sector is currently experiencing significant growth, organically and inorganically, through acquisitions. The principal aim of this study is to explore models that can predict acquisitions and compare their performance in the Indian manufacturing sector. Background: Mergers and Acquisitions (M&A) have been integral to a firm’s growth strategy. Over the years, academic research has investigated multiple models for predicting acquisitions. In the context of the Indian manufacturing industry, the research is limited to prediction models. This research paper explores three models, namely Logistic Regression, Decision Tree, and Multilayer Perceptron, to predict acquisitions. Methodology: The methodology includes defining the accounting variables to be used in the model which have been selected based on strong theoretical foundations. The Indian manufacturing industry was selected as the focus, specifically, data for firms listed in the Bombay Stock Exchange (BSE) between 2010 and 2022 from the Prowess database. There were multiple techniques, such as data transformation and data scrubbing, that were used to mitigate bias and enhance the data reliability. The dataset was split into 70% training and 30% test data. The performance of the three models was compared using standard metrics. Contribution: The research contributes to the existing body of knowledge in multiple dimensions. First, a prediction model customized to the Indian manufacturing sector has been developed. Second, there are accounting variables identified specific to the Indian manufacturing sector. Third, the paper contributes to prediction modeling in the Indian manufacturing sector where there is limited research. Findings: The study found significant supporting evidence for four of the proposed hypotheses indicating that accounting variables can be used to predict acquisitions. It has been ascertained that statistically significant variables influence acquisition likelihood: Quick Ratio, Equity Turnover, Pretax Margin, and Total Sales. These variables are intrinsically linked with the theories of liquidity, growth-resource mismatch, profitability, and firm size. Furthermore, comparing performance metrics reveals that the Decision Tree model exhibits the highest accuracy rate of 62.3%, specificity rate of 66.4%, and the lowest false positive ratio of 33.6%. In contrast, the Multilayer Perceptron model exhibits the highest precision rate of 61.4% and recall rate of 64.3%. Recommendations for Practitioners: The study findings can help practitioners build custom prediction models for their firms. The model can be developed as a live reference model, which is continually updated based on a firm’s results. In addition, there is an opportunity for industry practitioners to establish a benchmark score that provides a reference for acquisitions. Recommendation for Researchers: Researchers can expand the scope of research by including additional classification modeling techniques. The data quality can be enhanced by cross-validation with other databases. Textual commentary about the target firms, including management and analyst quotes, provides additional insight that can enhance the predictive power of the models. Impact on Society: The research provides insights into leveraging emerging technologies to predict acquisitions. The theoretical basis and modeling attributes provide a foundation that can be further expanded to suit specific industries and firms. Future Research: There are opportunities to expand the scope of research in various dimensions by comparing acquisition prediction models across industries and cross-border and domestic acquisitions. Additionally, it is plausible to explore further research by incorporating non-financial data, such as management commentary, to augment the acquisition prediction model.

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Review of the Literature on Merger Waves

Cited by 12 publications

References 82 publications

Generative AI for Business Decision-Making: A Case of ChatGPT

Generative AI for Business Decision-Making: A Case of ChatGPT

Industry Concentration, Firm Size and Entry-Timing in Merger Waves

Multiple Models in Predicting Acquisitions in the Indian Manufacturing Sector: A Performance Comparison

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