Applying Artificial Intelligence in Cryptocurrency Markets: A Survey
Survey on applying AI in cryptocurrency markets, focusing on price prediction, research trends, and future directions.
What it examines
This survey reviews the application of artificial intelligence, particularly supervised and reinforcement learning models, in cryptocurrency price prediction. It aims to highlight current research trends, identify research gaps, and suggest potential areas for improvement in the volatile and complex cryptocurrency market.
What it concludes
The study concludes that AI can significantly enhance cryptocurrency price prediction and trading strategies. Future research should focus on integrating cryptocurrencies with traditional markets and improving feature engineering. Potential applications include automated trading systems and risk management tools.
Evidence objects
The study concludes that AI can significantly enhance cryptocurrency price prediction and trading strategies. Future research should focus on integrating cryptocurrencies with traditional markets and improving feature engineering. Potential applications include automated trading systems and risk management tools.
key_findings bullet 1 · key_findings · validation V0
Raw abstract and provenance
The total capital in cryptocurrency markets is around two trillion dollars in 2022, which is almost the same as Apple’s market capitalisation at the same time. Increasingly, cryptocurrencies have become established in financial markets with an enormous number of transactions and trades happening every day. Similar to other financial systems, price prediction is one of the main challenges in cryptocurrency trading. Therefore, the application of artificial intelligence, as one of the tools of prediction, has emerged as a recently popular subject of investigation in the cryptocurrency domain. Since machine learning models, as opposed to traditional financial models, demonstrate satisfactory performance in quantitative finance, they seem ideal for coping with the price prediction problem in the complex and volatile cryptocurrency market. There have been several studies that have focused on applying machine learning for price and movement prediction and portfolio management in cryptocurrency markets, though these methods and models are in their early stages. This survey paper aims to review the current research trends in applications of supervised and reinforcement learning models in cryptocurrency price prediction. This study also highlights potential research gaps and possible areas for improvement. In addition, it emphasises potential challenges and research directions that will be of interest in the artificial intelligence and machine learning communities focusing on cryptocurrencies.
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