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Applying sustainable development goals in financial forecasting using machine learning techniques

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  • Ariana Chang
  • Tian‐Shyug Lee
  • Hsiu‐Mei Lee

Abstract

This study seeks to identify the impact of sustainable development goals (SDGs) in predicting corporate financial performance (CFP) in the information communications technology (ICT) industry. Data over the period of 2016–2020 that are relevant to financial reporting and corporate social responsibility (CSR) reporting have been extracted for 208 firms in the ICT industry. Important variables have been identified to help predict the financial performance in the following years upon the publication of CSR reports. Drawing on resource‐based view and stakeholder theory, the purpose of this study is to find the quintessential variables that influence the prediction accuracy of financial performance. To better forecast earnings per share (EPS), machine learning feature selection methods have been implemented. The findings suggest that certain variables such as return on total assets, SDGs adoption and whether the firm has established KPI for SDGs achievements can help enhance EPS prediction. With the various predictive models, the artificial neural network model is the most effective in predicting CFPs. Most importantly, the adoption of SDGs can be utilized to sharpen the forecast on financial performance as it enables firms to bolster stakeholder engagement and evaluate environmental, social, and corporate governance efforts.

Suggested Citation

  • Ariana Chang & Tian‐Shyug Lee & Hsiu‐Mei Lee, 2024. "Applying sustainable development goals in financial forecasting using machine learning techniques," Corporate Social Responsibility and Environmental Management, John Wiley & Sons, vol. 31(3), pages 2277-2289, May.
    • Handle: RePEc:wly:corsem:v:31:y:2024:i:3:p:2277-2289
    DOI: 10.1002/csr.2694
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