Ethical Tenets of Stock Price Prediction Using Machine Learning Techniques: A Sustainable Approach

Abstract

When Machine Learning (ML) algorithms take over decision-making from human conscience and cognition, the essence of ethics gets saturated. The visible decline of ethics generally gets reflected in financial markets, as it portrays human actions and sentiments in numerical terms than any sector. Accuracy in stock market prediction remains inefficient due to many known and unknown variables. Academia and industry recently relied on ML at large to track the market and monetize the movements. The norms of fairness, accuracy, dependability, and transparency in financing is left unattended in ML prediction models with assumptions far from reality. The integration of the tenets proposed in this study can emphasize and reconfigure the sustainable side of investing with concepts already in place but not connected to the network of prediction models. This study focuses on the ethical dimension of Machine Learning models and generates a sustainable framework for investors. Specifically, the Sustainable Development Goals (SDG) can enhance the prediction models in ML with improved efficiency. Along with SDG, this research broadens the variables' horizon of prediction in ML of computer science domain with concepts of Socially Responsible Investing (SRI), Environmental Social and Corporate Governance (ESG), and carbon footprints. With 115 articles reviewed, the proposed framework ensures sustainability in investments at the grassroots level. When adding the sustainability quotient backed by recognized norms, the research can be a breakthrough in rational investing based on ethical judgments aided by initiatives like the United Nation's SDG goals.