Corporate cash policy and double machine learning
首次运用双机器学习技术，识别出资产有形性和研发支出是企业现金增加的两大驱动因素，并发现交易成本模型和长期债务再融资风险在样本初期更相关，而预防性动机在当代更突出。
Abstract We are the first to explore the role of firm‐level drivers in corporate cash policy applying cutting‐edge double machine learning technique. We identify tangibility of assets and R&D spending as two main driving forces behind the cash increase when they are considered both independently and jointly. Furthermore, our findings support the relevance of the transaction cost model and the refinancing risk of long‐term debt at the beginning of the sample period. In contrast, precautionary motive emerges as more pertinent in contemporary times. Our results are robust to alternative machine learners, cash proxies and estimation methods.