Can investors' informed trading predict cryptocurrency returns? Evidence from machine learning

【Author】 Wang, Yaqi; Wang, Chunfeng; Sensoy, Ahmet; Yao, Shouyu; Cheng, Feiyang

【Source】RESEARCH IN INTERNATIONAL BUSINESS AND FINANCE

【影响因子】6.143

【Abstract】As an emerging asset, cryptocurrencies have attracted more and more attention from investors and researchers in recent years. With the gradual convergence of the investors in cryptocurrency and traditional financial markets, the research on investor trading behavior from the perspective of microstructure has become increasingly important in cryptocurrency market. In this paper, we study whether investors' informed trading behavior can significantly predict cryptocurrency returns. We use various machine learning algorithms to verify the contribution of informed trading to the predictability of cryptocurrency returns. The results show that informed trading plays a role in the prediction of some individual cryptocurrency returns, but it cannot significantly improve the prediction accuracy in an average sense of the whole market. The lack of market supervision of cryptocurrency market may be the main factor for relatively low efficiency of this market, and policymakers need to pay attention to it.

【Keywords】Cryptocurrency; Machine learning; Behavioral finance; Informed trading; Forecasting

【发表时间】2022 DEC

【收录时间】2022-07-10

【文献类型】实证性文章

【主题类别】

区块链治理-市场治理-市场分析