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

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      Embargo Lift Date: 2025-05-24
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      Author(s)
      Wang, Y.
      Wang, C.
      Şensoy, Ahmet
      Yao, S.
      Cheng, F.
      Date
      2022-05-24
      Source Title
      Research in International Business and Finance
      Print ISSN
      0275-5319
      Electronic ISSN
      1878-3384
      Publisher
      Elsevier Inc.
      Volume
      62
      Pages
      101683- 1 - 101683- 22
      Language
      English
      Type
      Article
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      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
      Permalink
      http://hdl.handle.net/11693/111364
      Published Version (Please cite this version)
      https://doi.org/10.1016/j.ribaf.2022.101683
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