The effects of AI research innovation on firms' capital financing decisions

• Main Authors: Tan, Ryan Swee Keat, Wong, Justin Yu Shuen, Koo, Andreas Wei Kang
• Other Authors: Guangzhi Ye
• Format: Final Year Project
• Language: English
• Published: Nanyang Technological University 2024
• Subjects: Social Sciences
• Online Access: https://hdl.handle.net/10356/175425
• Institution: Nanyang Technological University

In this paper, we seek to expand upon existing capital structure theories such as the MM proposition and pecking order theory, to understand if the recent waves of Artificial Intelligence (AI) investments and research would impact a firm’s capital structure. As of current, AI has demonstrated immense potential and flexibility in usage across a wide range of industrial sectors. We therefore aim to investigate the relationship between investing in AI and a firm’s capital financing decisions, in our paper. To answer this question, we utilised AI patents data and financial ratios extracted from WRDS Compustat in our analysis. Our results highlighted that AI innovation is positively associated with debt-to-asset ratio. We also observed that AI innovation had a negative association with equity-to-asset ratio. Given that the ratios are the inverse of the other, these results do check out. There are a couple of explanations for the results. 1) AI innovation may provide enough improvements that outweigh the added financial risk from increased debt financing. 2) Due to the importance of intellectual property for such firms and the firms’ rapid growth potential, they may be more sensitive to equity dilution. 3) The results reflect the pecking order theory. 4) Control rights approach applies to firms investing in AI due to their growing proportion of intangible assets like patents.