Deep Study of the Impact of ESG Indicators on Company Value on the Example of the Indonesian Market: An AI Models View
Abstract
The article analyzes the impact of a wide range of deep ESG components (individual ESG practices) on the value of companies, measured by the Q-Tobin coefficient. Empirical estimates are shown on a sample of public companies in the Indonesian emerging capital market, for the period from 2018 to 2023. We apply an original authors’ approach, in which at the first stage machine learning models are built to identify the key determinants of company value, and at the second stage Explanatory AI (Shapley vectors) is used to rank by these determinants by importance and identify the direction of their influence. We build and compare a number of machine learning models according to the RMSE quality metric: lasso regression, ridge regression, decision tree, random forest, CatBoost regression, gradient boosting, extreme gradient boosting, multilayer perceptron, bagging regressor (ensemble model). CatBoostRegressor is recognized as the best model.
The results show that the following ESG indicators have the greatest influence on company value in Indonesia: board diversity, product quality management, and quality of management regarding water consumption. The direction of influence of these indicators on company value is positive, as expected. At the same time, we obtain some unexpected findings regarding the negative impact of such indicators as the aggregate social responsibility indicator, the indicator of measures taken to reduce adverse impact on the climate, the quality of waste management, and the quality of the audit system. The hypothesis that, in an emerging market, ESG indicators play a smaller role in influencing company value compared to fundamental indicators is confirmed. The hypothesis that environmental responsibility plays a greater role in forming company value compared to social responsibility is not confirmed.
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