A Data-Driven Review of the Financial Performance and Environmental Compliance of Shariah-Compliant Businesses

Yıl 2024, , 39 - 54, 15.01.2024

Klemens Katterbauer , Rahmi Deniz Özbay , Hassan Syed , Sema Yılmaz Genç

https://doi.org/10.55237/jie.1340830

Cited By: 1

Öz

In order to analyze their investment choices and achieve better impact investments, investors are increasingly considering environmental, social, and Governance aspects. Investors are under increasing pressure from society to make sure that, in addition to profitability reasons, the environment's effect, society's impact, and corporate governance standards are taken into consideration when allocating funds. As a result, there has been an increase in the divestment of firms that use forced labor, lack diversity in their workforces, and operate in highly polluting sectors. Islamic banking incorporates Shariah law's guiding principles, which place a heavy emphasis on protecting the environment and advancing society. It can be difficult to determine if firms are Shariah-compliant in terms of the environment since environmental ESG ratings could not accurately reflect all of a corporation's environmental effects or its compliance with Shariah. In addition to evaluating a company's financial success, this article introduces a new data-driven approach for assessing its Shariah-compliant environmental performance. The deep learning system uses an unsupervised-random forest learning method to classify environmental compliance while also measuring these firms' financial performance. Large Islamic-compliant US listed firms were the subject of an investigation, which revealed high clustering performance and a difference between Islamic environmental compliance and non-compliance.

Anahtar Kelimeler

Islamic Compliance, ESG, Environmental Shariah Compliance, Deep Learning, Data Analysis

Kaynakça

• Abdelzaher, D. M., & Abdelzaher, A. (2017). Beyond environmental regulations: Exploring the potential of “eco-Islam” in boosting environmental ethics within SMEs in Arab markets. Journal of Business Ethics, 145(2), 357-371.
• Armstrong, A. (2020). Ethics and ESG. Australasian Accounting, Business and Finance Journal, 14(3), 6-17.
• Audemard, G., Bellart, S., Bounia, L., Koriche, F., Lagniez, J. M., & Marquis, P. (2022). On the explanatory power of Boolean decision trees. Data & Knowledge Engineering, 102088.
• Devie, D., Liman, L. P., Tarigan, J., & Jie, F. (2019). Corporate social responsibility, financial performance and risk in Indonesian natural resources industry. Social Responsibility Journal.
• Emari, H., Vazifehdoust, H., & Nikoomaram, H. (2017). Islam and environmental consciousness: a new scale development. Journal of religion and health, 56(2), 706-724.
• Folqué, M., Escrig‐Olmedo, E., & Corzo Santamaría, T. (2021). Sustainable development and financial system: Integrating ESG risks through sustainable investment strategies in a climate change context. Sustainable Development, 29(5), 876-890.
• Fränti, P., & Sieranoja, S. (2018). K-means properties on six clustering benchmark datasets. Applied intelligence, 4743-4759.
• Gada, M. Y. (2014). Environmental ethics in Islam: Principles and perspectives. World Journal of Islamic History and Civilization, 4(4), 130-138.
• Ghatasheh, N. (2014). Business analytics using random forest trees for credit risk prediction: a comparison study. International Journal of Advanced Science and Technology, 72, 19-30.
• Ghernaout, D. (2017). Environmental principles in the Holy Koran and the Sayings of the Prophet Muhammad. American Journal of Environmental Protection, 6(3), 75-79.
• Islam, M. A., Yousuf, S., Hossain, K. F., & Islam, M. R. (2014). Green financing in Bangladesh: challenges and opportunities–a descriptive approach. International Journal of green economics, 8(1), 74-91.
• Lindgreen, A., & Swaen, V. (2010). Corporate social responsibility. International journal of management reviews, 12(1), 1-7.
• Nelson, T. (2018). ESG, climate change risk and disclosure. Governance Directions, 70(11), 705-709.
• Pavlidis, N. G., Plagianakos, V. P., Tasoulis, D. K., & Vrahatis, M. N. (2006). Financial forecasting through unsupervised clustering and neural networks. Operational Research, 103-127.
• Schonlau, M., & Zou, R. Y. (2020). The random forest algorithm for statistical learning. . The Stata Journal , 20(1), 3-29.
• Singhania, M., & Saini, N. (2021). Institutional framework of ESG disclosures: comparative analysis of developed and developing countries. Journal of Sustainable Finance & Investment, 1-44.
• Tang, L., Cai, F., & Ouyang, Y. (2019). Applying a nonparametric random forest algorithm to assess the credit risk of the energy industry in China. Technological Forecasting and Social Change , 144, 563-572.
• Williams, C. A., & Nagy, D. M. (2020). ESG and Climate Change Blind Spots: Turning the Corner on SEC Disclosure. Tex. L. Rev., 99, 1453.
• Yoon, A. S., & Serafeim, G. (2022). Understanding the Business Relevance of ESG Issues. Journal of Financial Reporting.
• Zhang, T., Ramakrishnan, R., & Livny, M. (1997). BIRCH: A new data clustering algorithm and its applications. Data mining and knowledge discovery, 141-182.