Corporate cash policy and double machine learning

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.