{"title":"预测竞争性行业中的并购:基于时间动态和行业网络的模型","authors":"Dayu Yang","doi":"arxiv-2404.07298","DOIUrl":null,"url":null,"abstract":"M&A activities are pivotal for market consolidation, enabling firms to\naugment market power through strategic complementarities. Existing research\noften overlooks the peer effect, the mutual influence of M&A behaviors among\nfirms, and fails to capture complex interdependencies within industry networks.\nCommon approaches suffer from reliance on ad-hoc feature engineering, data\ntruncation leading to significant information loss, reduced predictive\naccuracy, and challenges in real-world application. Additionally, the rarity of\nM&A events necessitates data rebalancing in conventional models, introducing\nbias and undermining prediction reliability. We propose an innovative M&A\npredictive model utilizing the Temporal Dynamic Industry Network (TDIN),\nleveraging temporal point processes and deep learning to adeptly capture\nindustry-wide M&A dynamics. This model facilitates accurate, detailed\ndeal-level predictions without arbitrary data manipulation or rebalancing,\ndemonstrated through superior evaluation results from M&A cases between January\n1997 and December 2020. Our approach marks a significant improvement over\ntraditional models by providing detailed insights into M&A activities and\nstrategic recommendations for specific firms.","PeriodicalId":501139,"journal":{"name":"arXiv - QuantFin - Statistical Finance","volume":"48 13 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Predicting Mergers and Acquisitions in Competitive Industries: A Model Based on Temporal Dynamics and Industry Networks\",\"authors\":\"Dayu Yang\",\"doi\":\"arxiv-2404.07298\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"M&A activities are pivotal for market consolidation, enabling firms to\\naugment market power through strategic complementarities. Existing research\\noften overlooks the peer effect, the mutual influence of M&A behaviors among\\nfirms, and fails to capture complex interdependencies within industry networks.\\nCommon approaches suffer from reliance on ad-hoc feature engineering, data\\ntruncation leading to significant information loss, reduced predictive\\naccuracy, and challenges in real-world application. Additionally, the rarity of\\nM&A events necessitates data rebalancing in conventional models, introducing\\nbias and undermining prediction reliability. We propose an innovative M&A\\npredictive model utilizing the Temporal Dynamic Industry Network (TDIN),\\nleveraging temporal point processes and deep learning to adeptly capture\\nindustry-wide M&A dynamics. This model facilitates accurate, detailed\\ndeal-level predictions without arbitrary data manipulation or rebalancing,\\ndemonstrated through superior evaluation results from M&A cases between January\\n1997 and December 2020. Our approach marks a significant improvement over\\ntraditional models by providing detailed insights into M&A activities and\\nstrategic recommendations for specific firms.\",\"PeriodicalId\":501139,\"journal\":{\"name\":\"arXiv - QuantFin - Statistical Finance\",\"volume\":\"48 13 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-04-10\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"arXiv - QuantFin - Statistical Finance\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/arxiv-2404.07298\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"arXiv - QuantFin - Statistical Finance","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/arxiv-2404.07298","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Predicting Mergers and Acquisitions in Competitive Industries: A Model Based on Temporal Dynamics and Industry Networks
M&A activities are pivotal for market consolidation, enabling firms to
augment market power through strategic complementarities. Existing research
often overlooks the peer effect, the mutual influence of M&A behaviors among
firms, and fails to capture complex interdependencies within industry networks.
Common approaches suffer from reliance on ad-hoc feature engineering, data
truncation leading to significant information loss, reduced predictive
accuracy, and challenges in real-world application. Additionally, the rarity of
M&A events necessitates data rebalancing in conventional models, introducing
bias and undermining prediction reliability. We propose an innovative M&A
predictive model utilizing the Temporal Dynamic Industry Network (TDIN),
leveraging temporal point processes and deep learning to adeptly capture
industry-wide M&A dynamics. This model facilitates accurate, detailed
deal-level predictions without arbitrary data manipulation or rebalancing,
demonstrated through superior evaluation results from M&A cases between January
1997 and December 2020. Our approach marks a significant improvement over
traditional models by providing detailed insights into M&A activities and
strategic recommendations for specific firms.