论文标题
监督学习预测公司动态的学习
Supervised learning for the prediction of firm dynamics
论文作者
论文摘要
由于颗粒状，高维，公司水平数据的可用性日益增加，机器学习（ML）算法已成功应用于解决与公司动态有关的多个研究问题。尤其是监督学习（SL），这是涉及标记结果预测的ML分支，已被用来更好地预测公司的绩效。在这项贡献中，我们将说明一系列用于预测任务的SL方法，在公司生命周期的不同阶段相关。我们将重点关注的阶段是（i）启动和创新，（ii）公司的增长和绩效以及（iii）公司退出市场。首先，我们审查SL实施以预测成功的创业公司和研发项目。接下来，我们描述如何使用SL工具来分析公司的增长和绩效。最后，我们审查SL应用程序，以更好地预测财务困境和公司失败。在总结部分中，我们根据有针对性的政策，可解释性和因果关系扩展了SL方法的讨论。
Thanks to the increasing availability of granular, yet high-dimensional, firm level data, machine learning (ML) algorithms have been successfully applied to address multiple research questions related to firm dynamics. Especially supervised learning (SL), the branch of ML dealing with the prediction of labelled outcomes, has been used to better predict firms' performance. In this contribution, we will illustrate a series of SL approaches to be used for prediction tasks, relevant at different stages of the company life cycle. The stages we will focus on are (i) startup and innovation, (ii) growth and performance of companies, and (iii) firms exit from the market. First, we review SL implementations to predict successful startups and R&D projects. Next, we describe how SL tools can be used to analyze company growth and performance. Finally, we review SL applications to better forecast financial distress and company failure. In the concluding Section, we extend the discussion of SL methods in the light of targeted policies, result interpretability, and causality.
