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Performance and learning in an ambiguous environment: A study of cryptocurrency traders

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  • Gemayel, Roland
  • Preda, Alex

Abstract

We investigate the performance and learning ability of traders in an environment governed by ambiguity, such as the cryptocurrency market. Using a profit decomposition methodology, we find significant cross-sectional and temporal heterogeneity in performance. Traders do not learn to progressively increase the magnitude of returns; however, they are able to improve on their ability to realise profits as a mechanism of adaptation to survive through ambiguity. This adaptation increases as traders progress through their career. Moreover, we find evidence in support of the gambler’s fallacy. We argue that learning in ambiguous environments has limitations, allowing traders primarily to survive.

Suggested Citation

  • Gemayel, Roland & Preda, Alex, 2021. "Performance and learning in an ambiguous environment: A study of cryptocurrency traders," International Review of Financial Analysis, Elsevier, vol. 77(C).
    • Handle: RePEc:eee:finana:v:77:y:2021:i:c:s1057521921001794
    DOI: 10.1016/j.irfa.2021.101847
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Yulin Liu & Luyao Zhang, 2022. "Cryptocurrency Valuation: An Explainable AI Approach," Papers 2201.12893, arXiv.org, revised Jul 2023.
    2. Almeida, José & Gonçalves, Tiago Cruz, 2023. "A systematic literature review of investor behavior in the cryptocurrency markets," Journal of Behavioral and Experimental Finance, Elsevier, vol. 37(C).
    3. Bowden, James & Gemayel, Roland, 2022. "Sentiment and trading decisions in an ambiguous environment: A study on cryptocurrency traders," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 80(C).
    4. Ajithakumari Vijayappan Nair Biju & Ann Susan Thomas, 2023. "Uncertainties and ambivalence in the crypto market: an urgent need for a regional crypto regulation," SN Business & Economics, Springer, vol. 3(8), pages 1-21, August.

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    More about this item

    Keywords

    Cryptocurrencies; Trading; Learning; Ambiguity; Performance appraisal; Skill;
    All these keywords.

    JEL classification:

    • C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General
    • G23 - Financial Economics - - Financial Institutions and Services - - - Non-bank Financial Institutions; Financial Instruments; Institutional Investors
    • G40 - Financial Economics - - Behavioral Finance - - - General
    • G41 - Financial Economics - - Behavioral Finance - - - Role and Effects of Psychological, Emotional, Social, and Cognitive Factors on Decision Making in Financial Markets
    • G50 - Financial Economics - - Household Finance - - - General

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