Volume 4 , Issue 1 , PP: 29-35, 2024 | Cite this article as | XML | Html | PDF | Full Length Article
Noura Metawa 1 * , Saad Metawa 2
This study explores the integration of machine learning methodologies in stock analysis to enhance the understanding of the relationship between sustainable business practices and financial performance. Against the backdrop of a shifting investment landscape that emphasizes responsible and informed decision-making, our research addresses the need for innovative approaches in evaluating stocks within a sustainability framework. Leveraging a combination of Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and linear regression, we systematically analyze a dataset comprising sustainability metrics and stock performance. The DBSCAN clustering identifies distinct groups of stocks based on sustainability profiles, offering novel insights into market segmentation. Concurrently, linear regression models quantitatively reveal the impact of sustainability metrics on stock outcomes. The results affirm the significance of sustainability considerations in investment decisions, presenting a compelling case for the adoption of machine learning techniques in responsible investing strategies.
Sustainable finance , Responsible investing , Machine learning , Business sustainability , Data-driven decision-making , Algorithmic trading , Market trend analysis.
[1] Khan, Muhammad Anees, Kumail Abbas, Mazliham Mohd Su’ud, Anas A Salameh, Muhammad Mansoor Alam, Nida Aman, Mehreen Mehreen, Amin Jan, Nik Alif Amri Bin Nik Hashim, and Roslizawati Che Aziz. 2022. “Application of Machine Learning Algorithms for Sustainable Business Management Based on Macro-Economic Data: Supervised Learning Techniques Approach.” Sustainability 14 (16): 9964.
[2] Fedeli, Morris D, and Florian Lüdeke-Freund. n.d. “The Classification of Sustainable Business Model Patterns Using Machine Learning.”
[3] Toniolo, Korinzia, Eleonora Masiero, Maurizio Massaro, and Carlo Bagnoli. 2020. “Sustainable Business Models and Artificial Intelligence: Opportunities and Challenges.” Knowledge, People, and Digital Transformation: Approaches for a Sustainable Future, 103–17.
[4] Wang, Dongling, and Yuming Zhang. 2020. “Implications for Sustainability in Supply Chain Management and the Circular Economy Using Machine Learning Model.” Information Systems and E-Business Management, 1–13.
[5] Vaio, Assunta Di, Rosa Palladino, Rohail Hassan, and Octavio Escobar. 2020. “Artificial Intelligence and Business Models in the Sustainable Development Goals Perspective: A Systematic Literature Review.” Journal of Business Research 121: 283–314.
[6] Bocken, Nancy, Frank Boons, and Brian Baldassarre. 2019. “Sustainable Business Model Experimentation by Understanding Ecologies of Business Models.” Journal of Cleaner Production 208: 1498–1512.
[7] Wenninger, Simon, Can Kaymakci, Christian Wiethe, Jörg Römmelt, Lukas Baur, Björn Häckel, and Alexander Sauer. 2022. “How Sustainable Is Machine Learning in Energy Applications?--The Sustainable Machine Learning Balance Sheet.”
[8] Minatogawa, Vinicius Luiz Ferraz, Matheus Munhoz Vieira Franco, Izabela Simon Rampasso, Rosley Anholon, Ruy Quadros, Orlando Durán, and Antonio Batocchio. 2019. “Operationalizing Business Model Innovation through Big Data Analytics for Sustainable Organizations.” Sustainability 12 (1): 277.
[9] Kumar, Satish, Dipasha Sharma, Sandeep Rao, Weng Marc Lim, and Sachin Kumar Mangla. 2022. “Past, Present, and Future of Sustainable Finance: Insights from Big Data Analytics through Machine Learning of Scholarly Research.” Annals of Operations Research, 1–44.
[10] Broccardo, Laura, Adrián Zicari, Fauzia Jabeen, and Zeeshan A Bhatti. 2023. “How Digitalization Supports a Sustainable Business Model: A Literature Review.” Technological Forecasting and Social Change 187: 122146.
[11] Mohamed, M. (2023) “Agricultural Sustainability in the Age of Deep Learning: Current Trends, Challenges, and Future Trajectories”, Sustainable Machine Intelligence Journal, 4, p. (2):1−20. doi: 10.61185/SMIJ.2023.44102.
[12] Nosratabadi, Saeed, Amir Mosavi, Shahaboddin Shamshirband, Edmundas Kazimieras Zavadskas, Andry Rakotonirainy, and Kwok Wing Chau. 2019. “Sustainable Business Models: A Review.” Sustainability 11 (6): 1663.
[13] Budak, Ay\csenur, and Peiman Alipour Sarvari. 2021. “Profit Margin Prediction in Sustainable Road Freight Transportation Using Machine Learning.” Journal of Cleaner Production 314: 127990.
[14] Braun, Simon, and Venkata Ratan Deep Eli. 2020. “Designing a Business Model for Machine Learning Based Predictive Maintenance.”
[15] Prause, Gunnar. 2015. “Sustainable Business Models and Structures for Industry 4.0.” Journal of Security \& Sustainability Issues 5 (2).
[16] Ifaei, Pouya, Morteza Nazari-Heris, Amir Saman Tayerani Charmchi, Somayeh Asadi, and ChangKyoo Yoo. 2023. “Sustainable Energies and Machine Learning: An Organized Review of Recent Applications and Challenges.” Energy 266: 126432.
[17] Muthuswamy, M. and M. Ali, A. (2023) “Sustainable Supply Chain Management in the Age of Machine Intelligence: Addressing Challenges, Capitalizing on Opportunities, and Shaping the Future Landscape”, Sustainable Machine Intelligence Journal, 3. doi: 10.61185/SMIJ.2023.33103.
[18] Brenner, Barbara. 2018. “Transformative Sustainable Business Models in the Light of the Digital Imperative—A Global Business Economics Perspective.” Sustainability 10 (12): 4428.
[19] Reis, Carolina, Pedro Ruivo, Tiago Oliveira, and Paulo Faroleiro. 2020. “Assessing the Drivers of Machine Learning Business Value.” Journal of Business Research 117: 232–43.
[20] la Torre, Rocio de, Canan G Corlu, Javier Faulin, Bhakti S Onggo, and Angel A Juan. 2021. “Simulation, Optimization, and Machine Learning in Sustainable Transportation Systems: Models and Applications.” Sustainability 13 (3): 1551.
