互联网企业海外并购财务风险大数据预警研究——基于Stacking集成学习

Research on Big Data Early Warning of Financial Risks of Overseas Mergers and Acquisitions of Internet Enterprises Based on Stacking Integrated Learning

数智化时代中国互联网企业海外并购强势崛起但风险巨大,机器学习与非财务信息可为此类风险预警提供新思路。在已有研究基础上,选取2013—2020年45家中国互联网上市公司56起海外并购事件的大数据,构建Stacking集成学习模型进行大数据财务风险预警因子挖掘。结果表明,Stacking集成学习模型相比其他机器学习模型的大数据预警效果更好;运营能力等传统型财务指标依然是互联网企业海外并购财务风险大数据预警的首选指标,但股吧评论等创新型非财务指标也具有重要预警价值。研究结论提供了Stacking机器学习与利益相关者大数据信息有助于预警互联网企业海外并购财务风险的经验证据,为互联网企业、投资者、监管者等进行海外并购财务风险管控决策提供了重要参考。

In the era of digital intelligence,China’s Internet enterprises’overseas mergers and acquisitions have risen strongly,but the risks are huge.Machine learning and non‐financial information can provide new ideas for such risk early warning.Based on existing research,selecting the big data of 56 overseas mergers and acquisitions of 45 Chinese Internet listed companies from 2013 to 2020,the Stacking integrated learning model was used to mine the big data financial risk early warning factors.The results show that the stacking integrated learning model has better big data early warning effect than other machine learning models.Traditional financial indicators such as operational capacity are still the preferred indicators for big data early warning of financial risks of overseas mergers and acquisitions of Internet enterprises,but innovative non‐financial indicators such as stock bar reviews also have important early warning value.The research conclusions provides empirical evidence that Stacking machine learning and stakeholder big data information can help to early warning the financial risks of overseas mergers and acquisitions of Internet enterprises,and provides important reference for Internet enterprises,investors,regulators,etc.to make financial risk control decisions of overseas mergers and acquisitions.