 Can investors’ informed trading predict cryptocurrency returns? Evidence from machine learningYaqi Wang, Chunfeng Wang, Ahmet Sensoy, Shouyu Yao and Feiyang Cheng Research in International Business and Finance, 2022, vol. 62, issue C 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 (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:riibaf:v:62:y:2022:i:c:s027553192200071x DOI: 10.1016/j.ribaf.2022.101683 Access Statistics for this article Research in International Business and Finance is currently edited by T. Lagoarde Segot More articles in Research in International Business and Finance from Elsevier |
Is your work missing from RePEc? Here is how to contribute.
Questions or problems? Check the EconPapers FAQ or send mail to .
EconPapers is hosted by the
School of Business at Örebro University.