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Bitcoin: Speculative asset or innovative technology?

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  • Lee, Adrian D.
  • Li, Mengling
  • Zheng, Huanhuan

Abstract

We unite investment by speculators and tech-savvy investors with a heterogeneous agent model. While speculators seek to profit from extrapolating the price trends, tech-savvy investors trade based on the prospective value of Bitcoin, which is a function of factors that capture the market demand and technical supply of Bitcoin. Estimating the structural model, we find the coexistence of speculators and tech-savvy investors in the Bitcoin market. We further show that, regardless of the market states, tech-savvy investors consistently buy (sell) Bitcoin when its price goes below (above) the prospective value. However, speculators follow a momentum trading strategy in the high-market-volatility regime and switch to a contrarian strategy in the low-market-volatility regime. Our finding suggests that a significant fraction of tech-savvy investors value the potential of Bitcoin as an innovative technology. Incorporating heterogeneous investors yields better in-sample estimation efficiency and out-of-sample forecasting precision than models that consider only speculators or tech-savvy investors.

Suggested Citation

  • Lee, Adrian D. & Li, Mengling & Zheng, Huanhuan, 2020. "Bitcoin: Speculative asset or innovative technology?," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 67(C).
    • Handle: RePEc:eee:intfin:v:67:y:2020:i:c:s1042443120300937
    DOI: 10.1016/j.intfin.2020.101209
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    1. Bruno Biais & Christophe Bisière & Matthieu Bouvard & Catherine Casamatta & Albert J. Menkveld, 2023. "Equilibrium Bitcoin Pricing," Journal of Finance, American Finance Association, vol. 78(2), pages 967-1014, April.
    2. Weihong Huang & Huanhuan Zheng & Wai-Mun Chia, 2013. "Asymmetric returns, gradual bubbles and sudden crashes," The European Journal of Finance, Taylor & Francis Journals, vol. 19(5), pages 420-437, May.
    3. Selgin, George, 2015. "Synthetic commodity money," Journal of Financial Stability, Elsevier, vol. 17(C), pages 92-99.
    4. KiHoon Hong, 2017. "Bitcoin as an alternative investment vehicle," Information Technology and Management, Springer, vol. 18(4), pages 265-275, December.
    5. Levin, Eric & Abhyankar, Abhay & Ghosh, Dipak, 1994. "Does the Gold Market Reveal Real Interest Rates?," The Manchester School of Economic & Social Studies, University of Manchester, vol. 62(0), pages 93-103, Suppl..
    6. Frankel, Jeffrey A & Froot, Kenneth A, 1990. "Chartists, Fundamentalists, and Trading in the Foreign Exchange Market," American Economic Review, American Economic Association, vol. 80(2), pages 181-185, May.
    7. Boswijk, H. Peter & Hommes, Cars H. & Manzan, Sebastiano, 2007. "Behavioral heterogeneity in stock prices," Journal of Economic Dynamics and Control, Elsevier, vol. 31(6), pages 1938-1970, June.
    8. Gandal, Neil & Hamrick, JT & Moore, Tyler & Oberman, Tali, 2018. "Price manipulation in the Bitcoin ecosystem," Journal of Monetary Economics, Elsevier, vol. 95(C), pages 86-96.
    9. Chiarella, Carl & He, Xue-Zhong & Huang, Weihong & Zheng, Huanhuan, 2012. "Estimating behavioural heterogeneity under regime switching," Journal of Economic Behavior & Organization, Elsevier, vol. 83(3), pages 446-460.
    10. Veldkamp, Laura L., 2005. "Slow boom, sudden crash," Journal of Economic Theory, Elsevier, vol. 124(2), pages 230-257, October.
    11. Brock, William A. & Hommes, Cars H., 1998. "Heterogeneous beliefs and routes to chaos in a simple asset pricing model," Journal of Economic Dynamics and Control, Elsevier, vol. 22(8-9), pages 1235-1274, August.
    12. Jegadeesh, Narasimhan & Titman, Sheridan, 1993. "Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency," Journal of Finance, American Finance Association, vol. 48(1), pages 65-91, March.
    13. Day, Richard H. & Huang, Weihong, 1990. "Bulls, bears and market sheep," Journal of Economic Behavior & Organization, Elsevier, vol. 14(3), pages 299-329, December.
    14. Easley, David & O'Hara, Maureen & Basu, Soumya, 2019. "From mining to markets: The evolution of bitcoin transaction fees," Journal of Financial Economics, Elsevier, vol. 134(1), pages 91-109.
    15. O’Connor, Fergal A. & Lucey, Brian M. & Baur, Dirk G., 2016. "Do gold prices cause production costs? International evidence from country and company data," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 40(C), pages 186-196.
    16. Lux, Thomas, 1995. "Herd Behaviour, Bubbles and Crashes," Economic Journal, Royal Economic Society, vol. 105(431), pages 881-896, July.
    17. Baur, Dirk G. & Hong, KiHoon & Lee, Adrian D., 2018. "Bitcoin: Medium of exchange or speculative assets?," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 54(C), pages 177-189.
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    1. Lee, Kangsan & Jeong, Daeyoung, 2023. "Too much is too bad: The effect of media coverage on the price volatility of cryptocurrencies," Journal of International Money and Finance, Elsevier, vol. 133(C).
    2. Shimeng Shi, 2022. "Bitcoin futures risk premia," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 42(12), pages 2190-2217, December.
    3. Luo, Di & Mishra, Tapas & Yarovaya, Larisa & Zhang, Zhuang, 2021. "Investing during a Fintech Revolution: Ambiguity and return risk in cryptocurrencies," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 73(C).
    4. Cole, Benjamin M. & Dyhrberg, Anne H. & Foley, Sean & Svec, Jiri, 2022. "Can Bitcoin be Trusted? Quantifying the economic value of blockchain transactions," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 79(C).
    5. Panagiotidis, Theodore & Papapanagiotou, Georgios & Stengos, Thanasis, 2024. "A Bayesian approach for the determinants of bitcoin returns," International Review of Financial Analysis, Elsevier, vol. 91(C).
    6. Emmanouil M. L. Economou & Nikolaos A. Kyriazis, 2021. "Achieving Sustainable Financial Transactions under Regimes without a Central Bank—An Intertemporal Comparison," Sustainability, MDPI, vol. 13(3), pages 1-13, January.
    7. Jiri Kukacka & Ladislav Kristoufek, 2023. "Fundamental and speculative components of the cryptocurrency pricing dynamics," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 9(1), pages 1-23, December.
    8. Zdenek Smutny & Zdenek Sulc & Jan Lansky, 2021. "Motivations, Barriers and Risk-Taking When Investing in Cryptocurrencies," Mathematics, MDPI, vol. 9(14), pages 1-22, July.
    9. Ma, Chaoqun & Tian, Yonggang & Hsiao, Shisong & Deng, Liurui, 2022. "Monetary policy shocks and Bitcoin prices," Research in International Business and Finance, Elsevier, vol. 62(C).
    10. Kyriazis, Nikolaos & Papadamou, Stephanos & Tzeremes, Panayiotis & Corbet, Shaen, 2023. "The differential influence of social media sentiment on cryptocurrency returns and volatility during COVID-19," The Quarterly Review of Economics and Finance, Elsevier, vol. 89(C), pages 307-317.
    11. 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).
    12. Tang, Tao & Wang, Yanchen, 2022. "Liquidity Shocks, Price Volatilities, and Risk-managed Strategy: Evidence from Bitcoin and Beyond," Journal of Multinational Financial Management, Elsevier, vol. 64(C).
    13. Ao Shu & Feiyang Cheng & Jianlei Han & Zini Liang & Zheyao Pan, 2023. "Arbitrage across different Bitcoin exchange venues: Perspectives from investor base and market related events," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 63(5), pages 5183-5210, December.

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