A study on financing decisions of Indian firms using machine learning algorithm “LASSO”

This research paper utilizes machine learning methodology, specifically the least absolute shrinkage and selection operator (LASSO), to analyze the key determinants of the financing mix and performance of Indian companies. By examining a sample of 1034 listed nonfinancial companies in India from 1999 to 2019, the study identifies the crucial factors influencing their financing decisions. The findings reveal notable variations between the factors influencing long-term and short-term financing choices. Long-term financing decisions appear to be primarily influenced by the availability of internal funds, while short-term financing decisions are driven through the analysis of costs and benefits associated with the level of debt. Furthermore, the study highlights a relationship between the ownership structure, market share and leverage levels of Indian firms. Companies with concentrated ownership and a higher market share tend to exhibit lower leverage ratios compared to firms with diverse ownership structures and lower market shares.