Political connections and investment inefficiency: a machine learning approach
研究印尼上市公司中前政治家担任监事会成员对投资效率低下的影响，使用多种机器学习算法分析，发现前政治家能显著缓解真实投资效率低下问题，随机森林算法表现最佳。
This study examined the impact of political connections on corporate investment inefficiency, focusing on the governance role of former politicians serving on the board of commissioners within a two-tier board structure. Various advanced machine learning algorithms were employed to analyse data from publicly listed Indonesian firms (a laboratory in this context), the results of which indicate that the presence of former politicians on a company’s board of commissioners significantly mitigates real investment inefficiency concerns. The Random Forest algorithm emerged as the best estimator, demonstrating the lowest root relative squared error level, closely followed by the Bootstrap aggregation (Bagging) technique. These findings significantly advance empirical aspects of the literature on political connections, highlighting the potential of former politicians on a two-tier board to effectively mitigate investment inefficiency. This study provides robust evidence of the advantages that former politicians bring to supervisory boards in Indonesia. The results provide valuable insights for regulators and capital market authorities and, moreover, have the potential to reshape the academic discourse on political connections and investment inefficiency.