Forecasting Corporate Financial Performance Using Deep Learning with Environmental, Social, and Governance Data

Abstract

In recent years, extensive research has focused on the relationship between corporate social responsibility (CSR) and financial performance. While past studies have explored this connection, they often faced challenges in quantitatively assessing the effectiveness of CSR initiatives. However, advancements in research methodologies and the development of Environmental, Social, and Governance (ESG) measurement dimensions have led to the creation of more robust evaluation criteria. These criteria use ESG scores as primary reference indicators for assessing the effectiveness of CSR activities. This study aims to utilize ESG indicators from the ESG InfoHub website of the Taiwan Stock Exchange Corporation (TSEC) as benchmarks, comprising 15 items from the environmental (E), social (S), and governance (G) dimensions to form the CSR effectiveness indicators and predict financial performance. The data cover the years 2021–2022 for listed companies, using return on assets (ROA) and return on equity (ROE) as measures of financial performance. With the rapid development of artificial intelligence in recent years, the applications of machine learning and deep learning (DL) have proliferated across many fields. However, the use of machine learning to analyze ESG data remains rare. Therefore, this study employs machine learning models to predict financial performance based on ESG performance, utilizing both classification and regression approaches. Numerical results indicate that two deep learning models, Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN), outperform other models in regression and classification tasks, respectively. Consequently, deep learning techniques prove to be feasible, effective, and efficient alternatives for predicting corporations’ financial performance based on ESG metrics.

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