Artificial intelligence-driven undervalued stock crypto-funding strategy: coping with risk-reward asymmetry in speculative markets

AI-driven cryptocurrency fund management strategies are emerging, aiming to use pattern recognition and large-scale data analytics to identify undervalued stocks, similar to AI’s successful experience in drug development and software engineering. While these models are able to find insignificant correlations and asymmetric opportunities in speculative markets, they lack a coherent understanding of the world and have the risk of failure in structural changes such as regulatory changes. Investors are advised to adopt a hybrid approach that combines artificial intelligence automation with manual supervision to manage liquidity and ethical issues, including the significant environmental impacts of training large language models.