Using AI to Predict and Mitigate Green Asset Bubbles in Financial Markets

Abstract

The global financial system is undergoing a profound transformation, driven by the rapid advancement of Artificial Intelligence (AI) and the escalating urgency of climate change. AI technologies—including machine learning, deep learning, and natural language processing—are redefining financial processes by improving efficiency, risk assessment, and decision-making in areas such as algorithmic trading, ESG scoring, and portfolio management. Concurrently, green finance has gained momentum as capital flows increasingly align with environmental sustainability goals, leading to the proliferation of green financial instruments. This study explores the intersection of AI and green finance, with a particular focus on AI’s capacity to detect and mitigate green asset bubbles—instances where environmentally themed assets are overvalued due to speculative investment behavior. The research demonstrates that AI, through tools such as multi-scale confidence indicators and the Phillips–Shi–Yu (PSY) test, offers superior predictive accuracy over traditional econometric models by identifying complex market patterns and shifts in investor sentiment. Theoretically, the findings challenge the Efficient Market Hypothesis and expand agency and behavioral finance theories by illustrating AI’s role in reducing information asymmetry and interpreting market psychology. Practically, AI strengthens risk management frameworks, improves internal controls, and ensures more responsible allocation of sustainable capital. Policy implications include the urgent need for clear AI governance structures, explainable AI mandates, and integration of AI-based risk tools into financial regulation. Addressing concerns around algorithmic bias, privacy, and energy consumption is essential to ensure AI contributes meaningfully to both financial stability and sustainable development.