IDEAS home Printed from https://ideas.repec.org/a/eee/finana/v69y2020ics1057521919306301.html
   My bibliography  Save this article

More heat than light: Investor attention and bitcoin price discovery

Author

Listed:
  • Ibikunle, Gbenga
  • McGroarty, Frank
  • Rzayev, Khaladdin

Abstract

We investigate how increased attention affects bitcoin's price discovery process. We first decompose bitcoin price into efficient and noise components and then show that the noise element of bitcoin pricing is driven by high levels of attention. This implies that high levels of attention are linked with an increase in uninformed trading activity in the market for bitcoin, while informed trading activity is driven by arbitrage rather than attention.

Suggested Citation

  • Ibikunle, Gbenga & McGroarty, Frank & Rzayev, Khaladdin, 2020. "More heat than light: Investor attention and bitcoin price discovery," International Review of Financial Analysis, Elsevier, vol. 69(C).
    • Handle: RePEc:eee:finana:v:69:y:2020:i:c:s1057521919306301
    DOI: 10.1016/j.irfa.2020.101459
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1057521919306301
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.irfa.2020.101459?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Hasbrouck, Joel, 1991. "Measuring the Information Content of Stock Trades," Journal of Finance, American Finance Association, vol. 46(1), pages 179-207, March.
    2. Shleifer, Andrei & Summers, Lawrence H, 1990. "The Noise Trader Approach to Finance," Journal of Economic Perspectives, American Economic Association, vol. 4(2), pages 19-33, Spring.
    3. Tiwari, Aviral Kumar & Jana, R.K. & Das, Debojyoti & Roubaud, David, 2018. "Informational efficiency of Bitcoin—An extension," Economics Letters, Elsevier, vol. 163(C), pages 106-109.
    4. Menkveld, Albert J. & Koopman, Siem Jan & Lucas, Andre, 2007. "Modeling Around-the-Clock Price Discovery for Cross-Listed Stocks Using State Space Methods," Journal of Business & Economic Statistics, American Statistical Association, vol. 25, pages 213-225, April.
    5. Urquhart, Andrew, 2018. "What causes the attention of Bitcoin?," Economics Letters, Elsevier, vol. 166(C), pages 40-44.
    6. Hirshleifer, David & Teoh, Siew Hong, 2003. "Limited attention, information disclosure, and financial reporting," Journal of Accounting and Economics, Elsevier, vol. 36(1-3), pages 337-386, December.
    7. Shen, Dehua & Urquhart, Andrew & Wang, Pengfei, 2019. "Does twitter predict Bitcoin?," Economics Letters, Elsevier, vol. 174(C), pages 118-122.
    8. Peng, Lin & Xiong, Wei, 2006. "Investor attention, overconfidence and category learning," Journal of Financial Economics, Elsevier, vol. 80(3), pages 563-602, June.
    9. Urquhart, Andrew, 2016. "The inefficiency of Bitcoin," Economics Letters, Elsevier, vol. 148(C), pages 80-82.
    10. 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.
    11. Bariviera, Aurelio F., 2017. "The inefficiency of Bitcoin revisited: A dynamic approach," Economics Letters, Elsevier, vol. 161(C), pages 1-4.
    12. Gianna Figá-Talamanca & Marco Patacca, 2019. "Does market attention affect Bitcoin returns and volatility?," Decisions in Economics and Finance, Springer;Associazione per la Matematica, vol. 42(1), pages 135-155, June.
    13. Bandi, Federico M. & Russell, Jeffrey R., 2006. "Separating microstructure noise from volatility," Journal of Financial Economics, Elsevier, vol. 79(3), pages 655-692, March.
    14. Pierre Collin‐Dufresne & Vyacheslav Fos, 2016. "Insider Trading, Stochastic Liquidity, and Equilibrium Prices," Econometrica, Econometric Society, vol. 84, pages 1441-1475, July.
    15. Haoxiang Zhu, 2014. "Do Dark Pools Harm Price Discovery?," The Review of Financial Studies, Society for Financial Studies, vol. 27(3), pages 747-789.
    16. Newey, Whitney & West, Kenneth, 2014. "A simple, positive semi-definite, heteroscedasticity and autocorrelation consistent covariance matrix," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 33(1), pages 125-132.
    17. Glosten, Lawrence R. & Harris, Lawrence E., 1988. "Estimating the components of the bid/ask spread," Journal of Financial Economics, Elsevier, vol. 21(1), pages 123-142, May.
    18. Yukun Liu & Aleh Tsyvinski, 2018. "Risks and Returns of Cryptocurrency," NBER Working Papers 24877, National Bureau of Economic Research, Inc.
    19. Rzayev, Khaladdin & Ibikunle, Gbenga, 2019. "A state-space modeling of the information content of trading volume," Journal of Financial Markets, Elsevier, vol. 46(C).
    20. Lixin Huang & Hong Liu, 2007. "Rational Inattention and Portfolio Selection," Journal of Finance, American Finance Association, vol. 62(4), pages 1999-2040, August.
    21. Nadarajah, Saralees & Chu, Jeffrey, 2017. "On the inefficiency of Bitcoin," Economics Letters, Elsevier, vol. 150(C), pages 6-9.
    22. Michael J. Barclay, 2003. "Price Discovery and Trading After Hours," The Review of Financial Studies, Society for Financial Studies, vol. 16(4), pages 1041-1073.
    23. Chan, Louis K C & Lakonishok, Josef, 1995. "The Behavior of Stock Prices around Institutional Trades," Journal of Finance, American Finance Association, vol. 50(4), pages 1147-1174, September.
    24. Brad M. Barber & Terrance Odean, 2008. "All That Glitters: The Effect of Attention and News on the Buying Behavior of Individual and Institutional Investors," The Review of Financial Studies, Society for Financial Studies, vol. 21(2), pages 785-818, April.
    25. Dastgir, Shabbir & Demir, Ender & Downing, Gareth & Gozgor, Giray & Lau, Chi Keung Marco, 2019. "The causal relationship between Bitcoin attention and Bitcoin returns: Evidence from the Copula-based Granger causality test," Finance Research Letters, Elsevier, vol. 28(C), pages 160-164.
    26. Jonathan Brogaard & Terrence Hendershott & Ryan Riordan, 2014. "High-Frequency Trading and Price Discovery," The Review of Financial Studies, Society for Financial Studies, vol. 27(8), pages 2267-2306.
    27. Chordia, Tarun & Roll, Richard & Subrahmanyam, Avanidhar, 2008. "Liquidity and market efficiency," Journal of Financial Economics, Elsevier, vol. 87(2), pages 249-268, February.
    28. Aalborg, Halvor Aarhus & Molnár, Peter & de Vries, Jon Erik, 2019. "What can explain the price, volatility and trading volume of Bitcoin?," Finance Research Letters, Elsevier, vol. 29(C), pages 255-265.
    29. David Easley & Marcos M. López de Prado & Maureen O'Hara, 2012. "Flow Toxicity and Liquidity in a High-frequency World," The Review of Financial Studies, Society for Financial Studies, vol. 25(5), pages 1457-1493.
    30. Bruno Biais & Pierre Hillion & Chester Spatt, 1999. "Price Discovery and Learning during the Preopening Period in the Paris Bourse," Journal of Political Economy, University of Chicago Press, vol. 107(6), pages 1218-1248, December.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Marmora, Paul, 2022. "Does monetary policy fuel bitcoin demand? Event-study evidence from emerging markets," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 77(C).
    2. ?ikolaos A. Kyriazis, 2021. "Impacts of Stock Indices, Oil, and Twitter Sentiment on Major Cryptocurrencies during the COVID-19 First Wave," Bulletin of Applied Economics, Risk Market Journals, vol. 8(2), pages 133-146.
    3. Hu, Yang & Hou, Yang (Greg) & Oxley, Les & Corbet, Shaen, 2021. "Does blockchain patent-development influence Bitcoin risk?," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 70(C).
    4. Ozdamar, Melisa & Sensoy, Ahmet & Akdeniz, Levent, 2022. "Retail vs institutional investor attention in the cryptocurrency market," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 81(C).
    5. Dunbar, Kwamie & Owusu-Amoako, Johnson, 2023. "Predictability of crypto returns: The impact of trading behavior," Journal of Behavioral and Experimental Finance, Elsevier, vol. 39(C).
    6. Li, Yue & Goodell, John W. & Shen, Dehua, 2021. "Comparing search-engine and social-media attentions in finance research: Evidence from cryptocurrencies," International Review of Economics & Finance, Elsevier, vol. 75(C), pages 723-746.
    7. Smales, L.A., 2022. "Investor attention in cryptocurrency markets," International Review of Financial Analysis, Elsevier, vol. 79(C).
    8. Al Guindy, Mohamed, 2021. "Cryptocurrency price volatility and investor attention," International Review of Economics & Finance, Elsevier, vol. 76(C), pages 556-570.
    9. Xun Zhang & Fengbin Lu & Rui Tao & Shouyang Wang, 2021. "The time-varying causal relationship between the Bitcoin market and internet attention," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 7(1), pages 1-19, December.
    10. Bouteska, Ahmed & Mefteh-Wali, Salma & Dang, Trung, 2022. "Predictive power of investor sentiment for Bitcoin returns: Evidence from COVID-19 pandemic," Technological Forecasting and Social Change, Elsevier, vol. 184(C).
    11. Su, Fei & Wang, Xinyi & Yuan, Yulin, 2022. "The intraday dynamics and intraday price discovery of bitcoin," Research in International Business and Finance, Elsevier, vol. 60(C).
    12. Yue, Yao & Li, Xuerong & Zhang, Dingxuan & Wang, Shouyang, 2021. "How cryptocurrency affects economy? A network analysis using bibliometric methods," International Review of Financial Analysis, Elsevier, vol. 77(C).
    13. Qingjie Zhou & Panpan Zhu & Yinpeng Zhang, 2023. "Contagion Spillover from Bitcoin to Carbon Futures Pricing: Perspective from Investor Attention," Energies, MDPI, vol. 16(2), pages 1-22, January.
    14. Mokni, Khaled & Bouteska, Ahmed & Nakhli, Mohamed Sahbi, 2022. "Investor sentiment and Bitcoin relationship: A quantile-based analysis," The North American Journal of Economics and Finance, Elsevier, vol. 60(C).
    15. Donglian Ma & Pengxiang Zhai, 2021. "The Accuracy of the Tick Rule in the Bitcoin Market," SAGE Open, , vol. 11(2), pages 21582440211, May.
    16. Manahov, Viktor & Urquhart, Andrew, 2021. "The efficiency of Bitcoin: A strongly typed genetic programming approach to smart electronic Bitcoin markets," International Review of Financial Analysis, Elsevier, vol. 73(C).
    17. 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.
    18. Goodell, John W. & Kumar, Satish & Li, Xiao & Pattnaik, Debidutta & Sharma, Anuj, 2022. "Foundations and research clusters in investor attention: Evidence from bibliometric and topic modelling analysis," International Review of Economics & Finance, Elsevier, vol. 82(C), pages 511-529.
    19. Wang, Chen & Shen, Dehua & Li, Youwei, 2022. "Aggregate Investor Attention and Bitcoin Return: The Long Short-term Memory Networks Perspective," Finance Research Letters, Elsevier, vol. 49(C).
    20. Sobti, Neharika & Sehgal, Sanjay & Ilango, Balakrishnan, 2021. "How do macroeconomic news surprises affect round-the-clock price discovery of gold?," International Review of Financial Analysis, Elsevier, vol. 78(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Aurelio F. Bariviera & Ignasi Merediz‐Solà, 2021. "Where Do We Stand In Cryptocurrencies Economic Research? A Survey Based On Hybrid Analysis," Journal of Economic Surveys, Wiley Blackwell, vol. 35(2), pages 377-407, April.
    2. Rzayev, Khaladdin & Ibikunle, Gbenga, 2019. "A state-space modeling of the information content of trading volume," Journal of Financial Markets, Elsevier, vol. 46(C).
    3. Xun Zhang & Fengbin Lu & Rui Tao & Shouyang Wang, 2021. "The time-varying causal relationship between the Bitcoin market and internet attention," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 7(1), pages 1-19, December.
    4. Flori, Andrea, 2019. "News and subjective beliefs: A Bayesian approach to Bitcoin investments," Research in International Business and Finance, Elsevier, vol. 50(C), pages 336-356.
    5. 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.
    6. Wu, Wanshan & Tiwari, Aviral Kumar & Gozgor, Giray & Leping, Huang, 2021. "Does economic policy uncertainty affect cryptocurrency markets? Evidence from Twitter-based uncertainty measures," Research in International Business and Finance, Elsevier, vol. 58(C).
    7. Goodell, John W. & Kumar, Satish & Li, Xiao & Pattnaik, Debidutta & Sharma, Anuj, 2022. "Foundations and research clusters in investor attention: Evidence from bibliometric and topic modelling analysis," International Review of Economics & Finance, Elsevier, vol. 82(C), pages 511-529.
    8. Ahmed, Walid M.A., 2022. "Robust drivers of Bitcoin price movements: An extreme bounds analysis," The North American Journal of Economics and Finance, Elsevier, vol. 62(C).
    9. Guégan, Dominique & Renault, Thomas, 2021. "Does investor sentiment on social media provide robust information for Bitcoin returns predictability?," Finance Research Letters, Elsevier, vol. 38(C).
    10. Parthajit Kayal & Purnima Rohilla, 2021. "Bitcoin in the economics and finance literature: a survey," SN Business & Economics, Springer, vol. 1(7), pages 1-21, July.
    11. Eross, Andrea & McGroarty, Frank & Urquhart, Andrew & Wolfe, Simon, 2019. "The intraday dynamics of bitcoin," Research in International Business and Finance, Elsevier, vol. 49(C), pages 71-81.
    12. Wang, Qiyu & Chong, Terence Tai-Leung, 2021. "Factor pricing of cryptocurrencies," The North American Journal of Economics and Finance, Elsevier, vol. 57(C).
    13. Böyükaslan, Adem & Ecer, Fatih, 2021. "Determination of drivers for investing in cryptocurrencies through a fuzzy full consistency method-Bonferroni (FUCOM-F’B) framework," Technology in Society, Elsevier, vol. 67(C).
    14. Pengfei Wang & Wei Zhang & Xiao Li & Dehua Shen, 2019. "Trading volume and return volatility of Bitcoin market: evidence for the sequential information arrival hypothesis," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 14(2), pages 377-418, June.
    15. ORĂȘTEAN Ramona & MĂRGINEAN Silvia Cristina & SAVA Raluca, 2019. "Bitcoin In The Scientific Literature – A Bibliometric Study," Studies in Business and Economics, Lucian Blaga University of Sibiu, Faculty of Economic Sciences, vol. 14(3), pages 160-174, December.
    16. Ibikunle, Gbenga & Aquilina, Matteo & Diaz-Rainey, Ivan & Sun, Yuxin, 2021. "City goes dark: Dark trading and adverse selection in aggregate markets," Journal of Empirical Finance, Elsevier, vol. 64(C), pages 1-22.
    17. Ozdamar, Melisa & Sensoy, Ahmet & Akdeniz, Levent, 2022. "Retail vs institutional investor attention in the cryptocurrency market," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 81(C).
    18. Ahmed H. Elsayed & Giray Gozgor & Chi Keung Marco Lau, 2022. "Causality and dynamic spillovers among cryptocurrencies and currency markets," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 27(2), pages 2026-2040, April.
    19. Wei Zhang & Pengfei Wang, 2020. "Investor attention and the pricing of cryptocurrency market," Evolutionary and Institutional Economics Review, Springer, vol. 17(2), pages 445-468, July.
    20. Duan, Kun & Li, Zeming & Urquhart, Andrew & Ye, Jinqiang, 2021. "Dynamic efficiency and arbitrage potential in Bitcoin: A long-memory approach," International Review of Financial Analysis, Elsevier, vol. 75(C).

    More about this item

    Keywords

    Investor attention; Price discovery; Noise trading;
    All these keywords.

    JEL classification:

    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:finana:v:69:y:2020:i:c:s1057521919306301. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/inca/620166 .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.