TY - JOUR
T1 - Ascertaining price formation in cryptocurrency markets with Machine Learning
AU - Fang, Fan
AU - Chung, Waichung
AU - Ventre, Carmine
AU - Basios, Michail
AU - Kanthan, Leslie
AU - Li, Lingbo
AU - Wu, Fan
PY - 2021/4/5
Y1 - 2021/4/5
N2 - The cryptocurrency market is amongst the fastest-growing of all the financial markets in the world. Unlike traditional markets, such as equities, foreign exchange and commodities, cryptocurrency market is considered to have larger volatility and illiquidity. This paper is inspired by the recent success of using machine learning for stock market prediction. In this work, we analyze and present the characteristics of the cryptocurrency market in a high-frequency setting. In particular, we applied a machine learning approach to predict the direction of the mid-price changes on the upcoming tick. We show that there are universal features amongst cryptocurrencies which lead to models outperforming asset-specific ones. We also show that there is little point in feeding machine learning models with long sequences of data points; predictions do not improve. Furthermore, we solve the technical challenge to design a lean predictor, which performs well on live data downloaded from crypto exchanges. A novel retraining method is defined and adopted towards this end. Finally, the trade-off between model accuracy and frequency of training is analyzed in the context of multi-label prediction. Overall, we demonstrate that promising results are possible for cryptocurrencies on live data, by achieving a consistent (Formula presented.) accuracy on the prediction of the mid-price movement on live exchange rate of Bitcoins vs. US dollars.
AB - The cryptocurrency market is amongst the fastest-growing of all the financial markets in the world. Unlike traditional markets, such as equities, foreign exchange and commodities, cryptocurrency market is considered to have larger volatility and illiquidity. This paper is inspired by the recent success of using machine learning for stock market prediction. In this work, we analyze and present the characteristics of the cryptocurrency market in a high-frequency setting. In particular, we applied a machine learning approach to predict the direction of the mid-price changes on the upcoming tick. We show that there are universal features amongst cryptocurrencies which lead to models outperforming asset-specific ones. We also show that there is little point in feeding machine learning models with long sequences of data points; predictions do not improve. Furthermore, we solve the technical challenge to design a lean predictor, which performs well on live data downloaded from crypto exchanges. A novel retraining method is defined and adopted towards this end. Finally, the trade-off between model accuracy and frequency of training is analyzed in the context of multi-label prediction. Overall, we demonstrate that promising results are possible for cryptocurrencies on live data, by achieving a consistent (Formula presented.) accuracy on the prediction of the mid-price movement on live exchange rate of Bitcoins vs. US dollars.
UR - http://www.scopus.com/inward/record.url?scp=85103583433&partnerID=8YFLogxK
U2 - 10.1080/1351847X.2021.1908390
DO - 10.1080/1351847X.2021.1908390
M3 - Article
SN - 1351-847X
JO - European Journal Of Finance
JF - European Journal Of Finance
ER -