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

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  • Wang, Yaqi
  • Wang, Chunfeng
  • Sensoy, Ahmet
  • Yao, Shouyu
  • Cheng, Feiyang

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.

Suggested Citation

  • Wang, Yaqi & Wang, Chunfeng & Sensoy, Ahmet & Yao, Shouyu & Cheng, Feiyang, 2022. "Can investors’ informed trading predict cryptocurrency returns? Evidence from machine learning," Research in International Business and Finance, Elsevier, vol. 62(C).
    • Handle: RePEc:eee:riibaf:v:62:y:2022:i:c:s027553192200071x
    DOI: 10.1016/j.ribaf.2022.101683
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    References listed on IDEAS

    as
    1. J Christopher Westland, 2021. "Trade informativeness and liquidity in Bitcoin markets," PLOS ONE, Public Library of Science, vol. 16(8), pages 1-14, August.
    2. Chen, Zhenhua & Liu, Zhenya & Teka, Hanen & Zhang, Yifan, 2022. "Smart money in China's A-share market: Evidence from big data," Research in International Business and Finance, Elsevier, vol. 61(C).
    3. Lien, Donald & Hung, Pi-Hsia & Hung, I-Chun, 2019. "Order price clustering, size clustering, and stock price movements: Evidence from the Taiwan Stock Exchange," Journal of Empirical Finance, Elsevier, vol. 52(C), pages 149-177.
    4. Hung, Pi-Hsia & Lien, Donald, 2019. "Trading aggressiveness, order execution quality, and stock price movements: Evidence from the Taiwan stock exchange," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 60(C), pages 231-251.
    5. Laura Alessandretti & Abeer ElBahrawy & Luca Maria Aiello & Andrea Baronchelli, 2018. "Anticipating cryptocurrency prices using machine learning," Papers 1805.08550, arXiv.org, revised Nov 2018.
    6. Yao, Shouyu & Wang, Chunfeng & Cui, Xin & Fang, Zhenming, 2019. "Idiosyncratic skewness, gambling preference, and cross-section of stock returns: Evidence from China," Pacific-Basin Finance Journal, Elsevier, vol. 53(C), pages 464-483.
    7. Akhtaruzzaman, Md & Boubaker, Sabri & Nguyen, Duc Khuong & Rahman, Molla Ramizur, 2022. "Systemic risk-sharing framework of cryptocurrencies in the COVID–19 crisis," Finance Research Letters, Elsevier, vol. 47(PB).
    8. Jying-Nan Wang & Hung-Chun Liu & Shuang Zhang & Yuan-Teng Hsu, 2021. "How does the informed trading impact Bitcoin returns and volatility?," Applied Economics, Taylor & Francis Journals, vol. 53(28), pages 3223-3233, June.
    9. Amihud, Yakov, 2002. "Illiquidity and stock returns: cross-section and time-series effects," Journal of Financial Markets, Elsevier, vol. 5(1), pages 31-56, January.
    10. Makarov, Igor & Schoar, Antoinette, 2020. "Trading and arbitrage in cryptocurrency markets," LSE Research Online Documents on Economics 100409, London School of Economics and Political Science, LSE Library.
    11. Jia, Boxiang & Goodell, John W. & Shen, Dehua, 2022. "Momentum or reversal: Which is the appropriate third factor for cryptocurrencies?," Finance Research Letters, Elsevier, vol. 45(C).
    12. Cheng, Feiyang & Chiao, Chaoshin & Wang, Chunfeng & Fang, Zhenming & Yao, Shouyu, 2021. "Does retail investor attention improve stock liquidity? A dynamic perspective," Economic Modelling, Elsevier, vol. 94(C), pages 170-183.
    13. Erdinc Akyildirim & Ahmet Goncu & Ahmet Sensoy, 2021. "Prediction of cryptocurrency returns using machine learning," Annals of Operations Research, Springer, vol. 297(1), pages 3-36, February.
    14. Chang, Sanders S. & Chang, Lenisa V. & Wang, F. Albert, 2014. "A dynamic intraday measure of the probability of informed trading and firm-specific return variation," Journal of Empirical Finance, Elsevier, vol. 29(C), pages 80-94.
    15. Laura Alessandretti & Abeer ElBahrawy & Luca Maria Aiello & Andrea Baronchelli, 2018. "Anticipating Cryptocurrency Prices Using Machine Learning," Complexity, Hindawi, vol. 2018, pages 1-16, November.
    16. Makarov, Igor & Schoar, Antoinette, 2020. "Trading and arbitrage in cryptocurrency markets," Journal of Financial Economics, Elsevier, vol. 135(2), pages 293-319.
    17. Chu, Gang & Li, Xiao & Zhang, Yongjie, 2022. "Information demand and net selling around earnings announcement," Research in International Business and Finance, Elsevier, vol. 59(C).
    18. Ing, Julie, 2020. "Adverse selection, commitment and exhaustible resource taxation," Resource and Energy Economics, Elsevier, vol. 61(C).
    19. Nguyen, Thai Vu Hong & Nguyen, Binh Thanh & Nguyen, Kien Son & Pham, Huy, 2019. "Asymmetric monetary policy effects on cryptocurrency markets," Research in International Business and Finance, Elsevier, vol. 48(C), pages 335-339.
    20. Baur, Dirk G. & Dimpfl, Thomas, 2018. "Asymmetric volatility in cryptocurrencies," Economics Letters, Elsevier, vol. 173(C), pages 148-151.
    21. Helder Sebastião & Pedro Godinho, 2021. "Forecasting and trading cryptocurrencies with machine learning under changing market conditions," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 7(1), pages 1-30, December.
    22. Julie Ing, 2020. "Adverse selection, commitment and exhaustible resource taxation," Post-Print halshs-02885885, HAL.
    23. Feng, Wenjun & Wang, Yiming & Zhang, Zhengjun, 2018. "Informed trading in the Bitcoin market," Finance Research Letters, Elsevier, vol. 26(C), pages 63-70.
    24. Conlon, Thomas & Corbet, Shaen & McGee, Richard J., 2020. "Are cryptocurrencies a safe haven for equity markets? An international perspective from the COVID-19 pandemic," Research in International Business and Finance, Elsevier, vol. 54(C).
    25. Ding, Mingfa & Shen, Mi & Suardi, Sandy, 2022. "Blockholders, tradability and information asymmetry: Evidence from Chinese listed firms," Research in International Business and Finance, Elsevier, vol. 60(C).
    26. Yao, Shouyu & Wang, Chunfeng & Fang, Zhenming & Chiao, Chaoshin, 2021. "MAX is not the max under the interference of daily price limits: Evidence from China," International Review of Economics & Finance, Elsevier, vol. 73(C), pages 348-369.
    27. Goodell, John W. & Goutte, Stephane, 2021. "Diversifying equity with cryptocurrencies during COVID-19," International Review of Financial Analysis, Elsevier, vol. 76(C).
    28. C. Baek & M. Elbeck, 2015. "Bitcoins as an investment or speculative vehicle? A first look," Applied Economics Letters, Taylor & Francis Journals, vol. 22(1), pages 30-34, January.
    29. Thomas E. Koker & Dimitrios Koutmos, 2020. "Cryptocurrency Trading Using Machine Learning," JRFM, MDPI, vol. 13(8), pages 1-7, August.
    30. Easley, David, et al, 1996. "Liquidity, Information, and Infrequently Traded Stocks," Journal of Finance, American Finance Association, vol. 51(4), pages 1405-1436, September.
    31. Cheng, Feiyang & Wang, Chunfeng & Chiao, Chaoshin & Yao, Shouyu & Fang, Zhenming, 2021. "Retail attention, retail trades, and stock price crash risk," Emerging Markets Review, Elsevier, vol. 49(C).
    32. Yukun Liu & Aleh Tsyvinski, 2021. "Risks and Returns of Cryptocurrency," Review of Financial Studies, Society for Financial Studies, vol. 34(6), pages 2689-2727.
    33. Brandvold, Morten & Molnár, Peter & Vagstad, Kristian & Andreas Valstad, Ole Christian, 2015. "Price discovery on Bitcoin exchanges," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 36(C), pages 18-35.
    34. Dyhrberg, Anne H. & Foley, Sean & Svec, Jiri, 2018. "How investible is Bitcoin? Analyzing the liquidity and transaction costs of Bitcoin markets," Economics Letters, Elsevier, vol. 171(C), pages 140-143.
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    Cited by:

    1. Liu, Yujun & Li, Zhongfei & Nekhili, Ramzi & Sultan, Jahangir, 2023. "Forecasting cryptocurrency returns with machine learning," Research in International Business and Finance, Elsevier, vol. 64(C).

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    More about this item

    Keywords

    Cryptocurrency; Machine learning; Behavioral finance; Informed trading; Forecasting;
    All these keywords.

    JEL classification:

    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access
    • D82 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Asymmetric and Private Information; Mechanism Design

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