Algorithmic Finance and Financial Literacy: Social and Educational Implications of AI-Driven Portfolio Optimization

Abstract

The rapid integration of artificial intelligence into financial markets has created sophisticated investment tools that consistently outperform traditional strategies yet remain largely inaccessible to ordinary investors. This study examines the social and educational implications of advanced portfolio optimization algorithms through the lens of Science and Technology Studies and critical financial literacy research. While our empirical analysis demonstrates that Transformer-based reinforcement learning approaches achieve superior risk-adjusted returns compared to conventional methods, we argue that the proliferation of such opaque and computationally intensive technologies may exacerbate existing inequalities in wealth accumulation. Drawing on sociological theories of technological inequality and critical pedagogical frameworks, we analyze how algorithmic finance reshapes power dynamics in capital markets and creates new challenges for financial education at all levels. The paper concludes with comprehensive policy recommendations for educational reform and regulatory oversight aimed at promoting more equitable access to algorithmic investment tools while fostering the critical capacities needed to navigate an increasingly automated financial landscape.