Categorization of Mergers and Acquisitions in Japan Using Corporate Databases: A Fundamental Research for Prediction

Shao, Bohua; Asatani, Kimitaka; Sakata, Ichiro

doi:10.1109/ieem.2018.8607408

Cited by 7 publications

(2 citation statements)

References 10 publications

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“…Lastly, we can also support the institutional theory hypothesis by the institutional indicators, including the culture and law factor, and information factor. (14) Culture and Law Factor. Culture issues are of great importance in the coordination of the acquirer and target companies to fulfill the post-merger synergy for long-term development.…”

Section: Methodsmentioning

confidence: 99%

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Application of Machine Learning Models for Predictions on Cross-Border Merger and Acquisition Decisions with ESG Characteristics from an Ecosystem and Sustainable Development Perspective

et al. 2022

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From an ecosystem perspective, mergers and acquisitions (M&A) are one of the key paths for firms to foster complementary sectors and gain complementary assets. From the perspective of sustainable development, M&A can reallocate resources from target to asset to achieve better synergy and prolong the operation of a merged firm. However, M&A activities are characterized by high risk due to the high cost and uncertainty. Thus, a prediction model of M&A decisions is valuable for firms’ strategy design from an ecosystem and sustainable development perspective. By adopting a machine learning technique, this study measured the cross-border M&A decisions by analyzing firm-level cross-sectional data of the global financial marketplace under ecosystem mapping for the application of various country, deal and firm-level indicators related to sustainable development. Our paper can support the hypotheses of corporate governance, ecosystem stakeholder theory, ecosystem risk and institution theory in explaining that firms can increase their success rate of M&A to achieve sustainable development. Methodologically, we used AdaBoost to train several weak classifiers (decision trees) to achieve a strong decision-making model with a large financial transaction database of 215,160 deal activities. Results achieved 80.1% prediction accuracy by using the AdaBoost model through 10-fold cross validation. We found that differences exist on prediction features of M&A with different characteristics of sustainable development. For a robustness check, comparable results were obtained with a support vector machine (SVM) model. By analyses of the features during the cross-border M&A decision-making processing, this study is expected to contribute to the utilization of machine learning in ecosystem studies.

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Section: Methodsmentioning

confidence: 99%

“…Currently, increasing studies have paid attention to M&A predictions [7,8,14]. Several limitations still exist for the predictions of M&A, especially from an ecosystem and sustainable development perspective.…”

Section: Introductionmentioning

confidence: 99%

Application of Machine Learning Models for Predictions on Cross-Border Merger and Acquisition Decisions with ESG Characteristics from an Ecosystem and Sustainable Development Perspective

et al. 2022

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Predicting acquirers of US food and agribusiness firms

Mukhopadhyay,

Adelaja

2023

Agribusiness

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To fill an important gap in the literature on acquirer prediction, this paper investigates which factors contribute to the likelihood that a firm will choose to acquire a target company from the United States (US) food and agribusiness industry (FABI). It relies on existing literature and a conceptual model in identifying the determinants of the choice to be an acquirer. Rejecting the pooled ordinary least squares and random‐effects models, the paper estimates a fixed‐effects logistic regression model based on panel data on acquirers and nonacquirers of US FABI (USFABI) target companies. Contrary to the study's various hypotheses, the results suggest that (1) efficiency, profitability, and liquidity do not affect the likelihood of being an acquirer, but that (2) less‐solvent, less‐leveraged, and less‐attractive firms are more likely to become acquirers (possibly to improve their solvency, profitability, and attractiveness). These contradictory results are attributed to the uniqueness of the USFABI sector. The mergers and acquisitions process may therefore be a way for acquirers of USFABI firms to strengthen themselves, not necessarily an expression of their financial strength. This analysis is useful to target companies, acquirers, transaction advisers, investors, regulators, and other merger and acquisition stakeholders. [EconLit Citations: D22, G24, G34, L66, Q14].

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Predict industry merger waves utilizing supply network information

Qu,

Wang,

et al. 2024

J Ambient Intell Human Comput

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Categorization of Mergers and Acquisitions in Japan Using Corporate Databases: A Fundamental Research for Prediction

Cited by 7 publications

References 10 publications

Application of Machine Learning Models for Predictions on Cross-Border Merger and Acquisition Decisions with ESG Characteristics from an Ecosystem and Sustainable Development Perspective

Application of Machine Learning Models for Predictions on Cross-Border Merger and Acquisition Decisions with ESG Characteristics from an Ecosystem and Sustainable Development Perspective

Predicting acquirers of US food and agribusiness firms

Predict industry merger waves utilizing supply network information

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