IDEAS home Printed from https://ideas.repec.org/a/kap/rqfnac/v60y2023i1d10.1007_s11156-022-01086-4.html
   My bibliography  Save this article

Investor sentiment and bitcoin prices

Author

Listed:
  • Dimitrios Koutmos

    (Texas A&M University - Corpus Christi)

Abstract

Using a rich data set of transaction-level buy and sell orders from the major digital currency exchange Coinbase, we formulate a measure for investor sentiment and shed new evidence on the sentiment-return relation for bitcoin. Using a bootstrapped quantile regression procedure we show a significant and robust relation between rising sentiment and price increases, and vice versa, across the distribution of bitcoin price changes. This relation is shown to be robust when controlling for a variety of exchange-specific and blockchain-wide variables. This relation is also robust when controlling for aggregate momentum across major cryptocurrencies. This finding is important as our data sample spans a period before and after the introduction of futures markets for bitcoin, which has arguably resulted in a regime shift in the time series behavior of its price. Taken together, our results show that bitcoin prices can undergo regime changes and that conventional regression-type models that focus on the center of the distribution of bitcoin price changes can yield misleading estimates.

Suggested Citation

  • Dimitrios Koutmos, 2023. "Investor sentiment and bitcoin prices," Review of Quantitative Finance and Accounting, Springer, vol. 60(1), pages 1-29, January.
    • Handle: RePEc:kap:rqfnac:v:60:y:2023:i:1:d:10.1007_s11156-022-01086-4
    DOI: 10.1007/s11156-022-01086-4
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11156-022-01086-4
    File Function: Abstract
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1007/s11156-022-01086-4?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. Galina Hale & Arvind Krishnamurthy & Marianna Kudlyak & Patrick Shultz, 2018. "How Futures Trading Changed Bitcoin Prices," FRBSF Economic Letter, Federal Reserve Bank of San Francisco.
    2. Dimitrios Koutmos, 2020. "Market risk and Bitcoin returns," Annals of Operations Research, Springer, vol. 294(1), pages 453-477, November.
    3. Koenker, Roger W & Bassett, Gilbert, Jr, 1978. "Regression Quantiles," Econometrica, Econometric Society, vol. 46(1), pages 33-50, January.
    4. Atsalakis, George S. & Atsalaki, Ioanna G. & Pasiouras, Fotios & Zopounidis, Constantin, 2019. "Bitcoin price forecasting with neuro-fuzzy techniques," European Journal of Operational Research, Elsevier, vol. 276(2), pages 770-780.
    5. Thomas Q. Pedersen, 2015. "Predictable Return Distributions," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 34(2), pages 114-132, March.
    6. Z. Eisler & J. Kertész, 2006. "Size matters: some stylized facts of the stock market revisited," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 51(1), pages 145-154, May.
    7. Catania, Leopoldo & Grassi, Stefano & Ravazzolo, Francesco, 2019. "Forecasting cryptocurrencies under model and parameter instability," International Journal of Forecasting, Elsevier, vol. 35(2), pages 485-501.
    8. Carr, Peter & Wu, Liuren, 2017. "Leverage Effect, Volatility Feedback, and Self-Exciting Market Disruptions," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 52(5), pages 2119-2156, October.
    9. Powell, James L., 1986. "Censored regression quantiles," Journal of Econometrics, Elsevier, vol. 32(1), pages 143-155, June.
    10. Guillermo Llorente & Roni Michaely & Gideon Saar & Jiang Wang, 2002. "Dynamic Volume-Return Relation of Individual Stocks," The Review of Financial Studies, Society for Financial Studies, vol. 15(4), pages 1005-1047.
    11. Julius Bonart & Martin D. Gould, 2017. "Latency and liquidity provision in a limit order book," Quantitative Finance, Taylor & Francis Journals, vol. 17(10), pages 1601-1616, October.
    12. Patricia Chelley‐Steeley & Neophytos Lambertides & Christos S. Savva, 2019. "Sentiment, order imbalance, and co‐movement: An examination of shocks to retail and institutional trading activity," European Financial Management, European Financial Management Association, vol. 25(1), pages 116-159, January.
    13. Timothy King & Dimitrios Koutmos, 2021. "Herding and feedback trading in cryptocurrency markets," Annals of Operations Research, Springer, vol. 300(1), pages 79-96, May.
    14. Buchinsky, Moshe, 1995. "Estimating the asymptotic covariance matrix for quantile regression models a Monte Carlo study," Journal of Econometrics, Elsevier, vol. 68(2), pages 303-338, August.
    15. Zoltan Eisler & Janos Kertesz, 2005. "Size matters: some stylized facts of the stock market revisited," Papers physics/0508156, arXiv.org, revised May 2006.
    16. Lahmiri, Salim & Bekiros, Stelios, 2019. "Cryptocurrency forecasting with deep learning chaotic neural networks," Chaos, Solitons & Fractals, Elsevier, vol. 118(C), pages 35-40.
    17. Feng Ma & Chao Liang & Yuanhui Ma & M.I.M. Wahab, 2020. "Cryptocurrency volatility forecasting: A Markov regime‐switching MIDAS approach," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(8), pages 1277-1290, December.
    18. Alok Kumar & Charles M.C. Lee, 2006. "Retail Investor Sentiment and Return Comovements," Journal of Finance, American Finance Association, vol. 61(5), pages 2451-2486, October.
    19. Andersen, Torben G, 1996. "Return Volatility and Trading Volume: An Information Flow Interpretation of Stochastic Volatility," Journal of Finance, American Finance Association, vol. 51(1), pages 169-204, March.
    20. Dimitrios Koutmos & James E. Payne, 2021. "Intertemporal asset pricing with bitcoin," Review of Quantitative Finance and Accounting, Springer, vol. 56(2), pages 619-645, February.
    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. Dimitrios Koutmos & Wang Chun Wei, 2023. "Nowcasting bitcoin’s crash risk with order imbalance," Review of Quantitative Finance and Accounting, Springer, vol. 61(1), pages 125-154, July.
    2. A. H. Nzokem, 2023. "Bitcoin versus S&P 500 Index: Return and Risk Analysis," Papers 2310.02436, arXiv.org.
    3. Shubham Singh & Mayur Bhat, 2024. "Transformer-based approach for Ethereum Price Prediction Using Crosscurrency correlation and Sentiment Analysis," Papers 2401.08077, arXiv.org.
    4. Arun Narayanasamy & Humnath Panta & Rohit Agarwal, 2023. "Relations among Bitcoin Futures, Bitcoin Spot, Investor Attention, and Sentiment," JRFM, MDPI, vol. 16(11), pages 1-24, November.

    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. Fan Fang & Carmine Ventre & Michail Basios & Leslie Kanthan & David Martinez-Rego & Fan Wu & Lingbo Li, 2022. "Cryptocurrency trading: a comprehensive survey," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-59, December.
    2. Abhinava Tripathi, 2021. "The Arrival of Information and Price Adjustment Across Extreme Quantiles: Global Evidence," IIM Kozhikode Society & Management Review, , vol. 10(1), pages 7-19, January.
    3. Parente, Paulo M.D.C. & Smith, Richard J., 2011. "Gel Methods For Nonsmooth Moment Indicators," Econometric Theory, Cambridge University Press, vol. 27(1), pages 74-113, February.
    4. 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).
    5. Halkos, George E., 2011. "Nonparametric modelling of biodiversity: Determinants of threatened species," Journal of Policy Modeling, Elsevier, vol. 33(4), pages 618-635, July.
    6. Li, Wei & Rhee, Ghon & Wang, Steven Shuye, 2017. "Differences in herding: Individual vs. institutional investors," Pacific-Basin Finance Journal, Elsevier, vol. 45(C), pages 174-185.
    7. Duc Huynh, Toan Luu & Burggraf, Tobias & Wang, Mei, 2020. "Gold, platinum, and expected Bitcoin returns," Journal of Multinational Financial Management, Elsevier, vol. 56(C).
    8. 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.
    9. Halkos, George, 2010. "Modelling biodiversity," MPRA Paper 39075, University Library of Munich, Germany.
    10. Helder Miguel Correia Virtuoso Sebastião & Paulo José Osório Rupino Da Cunha & Pedro Manuel Cortesão Godinho, 2021. "Cryptocurrencies and blockchain. Overview and future perspectives," International Journal of Economics and Business Research, Inderscience Enterprises Ltd, vol. 21(3), pages 305-342.
    11. Xiaofeng Lv & Gupeng Zhang & Xinkuo Xu & Qinghai Li, 2019. "Weighted quantile regression for censored data with application to export duration data," Statistical Papers, Springer, vol. 60(4), pages 1161-1192, August.
    12. Lahmiri, Salim & Bekiros, Stelios, 2020. "Intelligent forecasting with machine learning trading systems in chaotic intraday Bitcoin market," Chaos, Solitons & Fractals, Elsevier, vol. 133(C).
    13. Moshe Buchinsky & Jinyong Hahn, 1998. "An Alternative Estimator for the Censored Quantile Regression Model," Econometrica, Econometric Society, vol. 66(3), pages 653-672, May.
    14. White, Halbert & Kim, Tae-Hwan, 2002. "Estimation, Inference, and Specification Testing for Possibly Misspecified Quantile Regression," University of California at San Diego, Economics Working Paper Series qt1s38s0dn, Department of Economics, UC San Diego.
    15. Honore, Bo & Khan, Shakeeb & Powell, James L., 2002. "Quantile regression under random censoring," Journal of Econometrics, Elsevier, vol. 109(1), pages 67-105, July.
    16. Tae-Hwan Kim & Halbert White, 2003. "Estimation, Inference, And Specification Testing For Possibly Misspecified Quantile Regression," Advances in Econometrics, in: Maximum Likelihood Estimation of Misspecified Models: Twenty Years Later, pages 107-132, Emerald Group Publishing Limited.
    17. Erdinc Akyildirim & Oguzhan Cepni & Shaen Corbet & Gazi Salah Uddin, 2023. "Forecasting mid-price movement of Bitcoin futures using machine learning," Annals of Operations Research, Springer, vol. 330(1), pages 553-584, November.
    18. Machado, José A.F. & Santos Silva, J.M.C. & Wei, Kehai, 2016. "Quantiles, corners, and the extensive margin of trade," European Economic Review, Elsevier, vol. 89(C), pages 73-84.
    19. Bilias, Yannis & Chen, Songnian & Ying, Zhiliang, 2000. "Simple resampling methods for censored regression quantiles," Journal of Econometrics, Elsevier, vol. 99(2), pages 373-386, December.
    20. Gradojevic, Nikola & Kukolj, Dragan & Adcock, Robert & Djakovic, Vladimir, 2023. "Forecasting Bitcoin with technical analysis: A not-so-random forest?," International Journal of Forecasting, Elsevier, vol. 39(1), pages 1-17.

    More about this item

    Keywords

    Bitcoin; Bootstrap; Investor sentiment; Quantile regression; Robustness;
    All these keywords.

    JEL classification:

    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)
    • 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

    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:kap:rqfnac:v:60:y:2023:i:1:d:10.1007_s11156-022-01086-4. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://springer.com .

    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.