AI-Enhanced Cryptocurrency Trading Algorithm for Optimal Investment Strategies - Koehler, Samuel; Dhameliya, Niravkumar; Patel, Bhavik; Anumandla, Sunil Kumar Reddy (Asian Accounting and Auditing Advancement, 2018)
This study aims to understand better how to use AI-enhanced bitcoin trading algorithms in the dynamic and turbulent cryptocurrency market to optimize investing strategies. The primary goals are to determine future trends and policy implications, assess the impact on investment strategies and risk management, and investigate the efficacy of AI techniques. Methodologically, a thorough analysis of the body of research on AI-enhanced trading algorithms is carried out, emphasizing machine learning, deep learning, and natural language processing approaches. Important discoveries demonstrate how AI can improve automation, efficiency, and decision-making in bitcoin trading. However, there are restrictions, including poor data quality, unclear regulations, and market manipulation, that need to be considered by regulators and policymakers. The policy implications underscore the necessity of unambiguous regulatory frameworks, risk management protocols, and investor education campaigns to guarantee responsibility, equity, and openness in algorithmic trading operations. Overall, this study emphasizes how AI-enhanced cryptocurrency trading algorithms have the potential to completely transform risk management and investment strategies in the cryptocurrency market. It also emphasizes how crucial it is for stakeholders to work together to address related issues.