|
| Authors & Year |
Focus/Objective |
Methodology |
Key Findings |
Tools/Techniques |
Limitations and Future Work |
|
|
| Yan, Nie, and Fan (2020) |
Verify the mechanism of due diligence impact on M&A performance using data from Chinese A-share market (2011–2018). |
Multivariate Regression Model |
Higher diligence level in due diligence positively impacts M&A performance. Performance commitments affect the relationship by encouraging truthful disclosures and active integration. |
Statistical analysis, Multivariate regression models |
Limited to Chinese A-share market; future studies can explore cross-market comparisons and long-term impacts of performance commitments. |
|
| Shi and Wang (2020) |
Develop an M&A matching method using DEA-based performance perspective that incorporates irrational behavior. |
Data Envelopment Analysis (DEA) with a prospect value optimisation goal and cross-efficiency modelling |
Proposed method effectively optimises two-sided M&A matching and can be used in third-party platforms for recommendation services. |
DEA, Prospect value, Cross-efficiency modelling |
Assumes availability of comprehensive data; future work could test the method's applicability in different industries and regions. |
|
| Li et al. (2021) |
Investigate GNN application in M&A forecasting by quantifying statistical associations among firms, founders, and investors. |
Graph Neural Networks (GNN), Text analysis, Feature engineering |
Achieved an AUC of 0.952 and an 83% true positive rate in predicting M&A outcomes. |
Graph Neural Networks, Text analysis, Statistical analysis |
Focused on specific data sources, future studies could incorporate more diverse datasets and assess scalability in real-time predictions. |
|
| Lang (2021) |
Evaluate technical and potential efficiency in virtual M&A using listed commercial banks in China as case studies. |
Three-stage DEA and Bootstrap DEA |
Virtual M&A is generally beneficial, but efficiency of M&A does not guarantee technical or potential efficiency post-M&A. |
Three-stage DEA, Bootstrap DEA |
Limited to banking sector; future research could apply the methodology to other industries and refine efficiency evaluation criteria. |
|
| Wang (2021) |
Explore characteristics of enterprises and industries adopting M&A strategies in the U.S. (1978–2007) and their growth post-M&A. |
Statistical analysis of historical M&A data using Stata |
M&A strategic activities can lead to growth in scale, capital, and output value, varying by enterprise and industry. |
Stata statistical analysis |
Covers a specific timeframe; future work could explore post-2007 trends and technological influences on M&A strategies. |
|
| Venuti (2021) |
Utilise Graph SAGE framework to predict M&A of enterprise companies. |
Graph SAGE (Graph-based Machine Learning Framework) |
Achieved 81.79% prediction accuracy, showcasing the effectiveness of graph-based ML for financial data science and M&A predictions. |
Graph SAGE, Graph-based ML |
Relies on graph structure assumptions; future research could test performance with more complex graph datasets and hybrid ML models. |
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