Discovering causal relationships among financial variables associated with firm value using a dynamic Bayesian network - Ji Young Choi; Chae Young Lee; Man-Suk Oh (Data Science in Finance and Economics)
This study investigated the causal relationships among financial variables associated with firm value using a Causal Dynamic Bayesian Network (CDBN), which is an extension of the basic Bayesian network that captures both temporal and contemporaneous causal relationships. The CDBN model was constructed using a panel dataset of listed manufacturing companies in Korea over a 14-year period (2009–2022). By visualizing the interactions between financial factors, the model makes it easy to understand their dynamic and instantaneous relationships, offering valuable insights into corporate finance. Key findings in the model include evidence of autocorrelation in all dynamic variables, a lagged feedback loop between the intangible assets ratio and firm value, the widespread impact of the COVID-19 pandemic on the financial sector, and important causal relationships involving key financial metrics such as the fixed assets ratio, firm value, and return on assets ratio.