IDEAS home Printed from https://ideas.repec.org/p/arx/papers/1910.12281.html
   My bibliography  Save this paper

Deep convolutional autoencoder for cryptocurrency market analysis

Author

Listed:
  • Vladimir Puzyrev

Abstract

This study attempts to analyze patterns in cryptocurrency markets using a special type of deep neural networks, namely a convolutional autoencoder. The method extracts the dominant features of market behavior and classifies the 40 studied cryptocurrencies into several classes for twelve 6-month periods starting from 15th May 2013. Transitions from one class to another with time are related to the maturement of cryptocurrencies. In speculative cryptocurrency markets, these findings have potential implications for investment and trading strategies.

Suggested Citation

  • Vladimir Puzyrev, 2019. "Deep convolutional autoencoder for cryptocurrency market analysis," Papers 1910.12281, arXiv.org.
  • Handle: RePEc:arx:papers:1910.12281
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/1910.12281
    File Function: Latest version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Gkillas, Konstantinos & Katsiampa, Paraskevi, 2018. "An application of extreme value theory to cryptocurrencies," Economics Letters, Elsevier, vol. 164(C), pages 109-111.
    2. Urquhart, Andrew, 2016. "The inefficiency of Bitcoin," Economics Letters, Elsevier, vol. 148(C), pages 80-82.
    3. Baur, Dirk G. & Cahill, Daniel & Godfrey, Keith & (Frank) Liu, Zhangxin, 2019. "Bitcoin time-of-day, day-of-week and month-of-year effects in returns and trading volume," Finance Research Letters, Elsevier, vol. 31(C), pages 78-92.
    4. Shane A. Corwin & Paul Schultz, 2012. "A Simple Way to Estimate Bid‐Ask Spreads from Daily High and Low Prices," Journal of Finance, American Finance Association, vol. 67(2), pages 719-760, April.
    5. Bariviera, Aurelio F., 2017. "The inefficiency of Bitcoin revisited: A dynamic approach," Economics Letters, Elsevier, vol. 161(C), pages 1-4.
    6. 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.
    7. Demir, Ender & Gozgor, Giray & Lau, Chi Keung Marco & Vigne, Samuel A., 2018. "Does economic policy uncertainty predict the Bitcoin returns? An empirical investigation," Finance Research Letters, Elsevier, vol. 26(C), pages 145-149.
    8. Corbet, Shaen & Lucey, Brian & Yarovaya, Larisa, 2018. "Datestamping the Bitcoin and Ethereum bubbles," Finance Research Letters, Elsevier, vol. 26(C), pages 81-88.
    9. Balcilar, Mehmet & Bouri, Elie & Gupta, Rangan & Roubaud, David, 2017. "Can volume predict Bitcoin returns and volatility? A quantiles-based approach," Economic Modelling, Elsevier, vol. 64(C), pages 74-81.
    10. Wei Bao & Jun Yue & Yulei Rao, 2017. "A deep learning framework for financial time series using stacked autoencoders and long-short term memory," PLOS ONE, Public Library of Science, vol. 12(7), pages 1-24, July.
    11. Nadarajah, Saralees & Chu, Jeffrey, 2017. "On the inefficiency of Bitcoin," Economics Letters, Elsevier, vol. 150(C), pages 6-9.
    12. Vidal-Tomás, David & Ibañez, Ana, 2018. "Semi-strong efficiency of Bitcoin," Finance Research Letters, Elsevier, vol. 27(C), pages 259-265.
    13. Lahmiri, Salim & Bekiros, Stelios, 2019. "Cryptocurrency forecasting with deep learning chaotic neural networks," Chaos, Solitons & Fractals, Elsevier, vol. 118(C), pages 35-40.
    14. Dyhrberg, Anne Haubo, 2016. "Hedging capabilities of bitcoin. Is it the virtual gold?," Finance Research Letters, Elsevier, vol. 16(C), pages 139-144.
    15. Brauneis, Alexander & Mestel, Roland, 2018. "Price discovery of cryptocurrencies: Bitcoin and beyond," Economics Letters, Elsevier, vol. 165(C), pages 58-61.
    16. Guesmi, Khaled & Saadi, Samir & Abid, Ilyes & Ftiti, Zied, 2019. "Portfolio diversification with virtual currency: Evidence from bitcoin," International Review of Financial Analysis, Elsevier, vol. 63(C), pages 431-437.
    17. 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.
    18. Corbet, Shaen & Meegan, Andrew & Larkin, Charles & Lucey, Brian & Yarovaya, Larisa, 2018. "Exploring the dynamic relationships between cryptocurrencies and other financial assets," Economics Letters, Elsevier, vol. 165(C), pages 28-34.
    19. Wei, Wang Chun, 2018. "Liquidity and market efficiency in cryptocurrencies," Economics Letters, Elsevier, vol. 168(C), pages 21-24.
    20. Phillip, Andrew & Chan, Jennifer S.K. & Peiris, Shelton, 2018. "A new look at Cryptocurrencies," Economics Letters, Elsevier, vol. 163(C), pages 6-9.
    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. Kumar Yashaswi, 2021. "Deep Reinforcement Learning for Portfolio Optimization using Latent Feature State Space (LFSS) Module," Papers 2102.06233, arXiv.org.

    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. 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.
    3. Corbet, Shaen & Lucey, Brian & Urquhart, Andrew & Yarovaya, Larisa, 2019. "Cryptocurrencies as a financial asset: A systematic analysis," International Review of Financial Analysis, Elsevier, vol. 62(C), pages 182-199.
    4. Antonakakis, Nikolaos & Chatziantoniou, Ioannis & Gabauer, David, 2019. "Cryptocurrency market contagion: Market uncertainty, market complexity, and dynamic portfolios," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 61(C), pages 37-51.
    5. Andrea Flori, 2019. "Cryptocurrencies In Finance: Review And Applications," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 22(05), pages 1-22, August.
    6. Bedi, Prateek & Nashier, Tripti, 2020. "On the investment credentials of Bitcoin: A cross-currency perspective," Research in International Business and Finance, Elsevier, vol. 51(C).
    7. 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.
    8. 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.
    9. 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.
    10. Katsiampa, Paraskevi, 2019. "An empirical investigation of volatility dynamics in the cryptocurrency market," Research in International Business and Finance, Elsevier, vol. 50(C), pages 322-335.
    11. Cynthia Weiyi Cai & Rui Xue & Bi Zhou, 2023. "Cryptocurrency puzzles: a comprehensive review and re-introduction," Journal of Accounting Literature, Emerald Group Publishing Limited, vol. 46(1), pages 26-50, June.
    12. Aysan, Ahmet Faruk & Demir, Ender & Gozgor, Giray & Lau, Chi Keung Marco, 2019. "Effects of the geopolitical risks on Bitcoin returns and volatility," Research in International Business and Finance, Elsevier, vol. 47(C), pages 511-518.
    13. Pedro Bação & António Portugal Duarte & Helder Sebastião & Srdjan Redzepagic, 2018. "Information Transmission Between Cryptocurrencies: Does Bitcoin Rule the Cryptocurrency World?," Scientific Annals of Economics and Business (continues Analele Stiintifice), Alexandru Ioan Cuza University, Faculty of Economics and Business Administration, vol. 65(2), pages 97-117, June.
    14. Yaya, OlaOluwa S. & Ogbonna, Ahamuefula E. & Olubusoye, Olusanya E., 2019. "How persistent and dynamic inter-dependent are pricing of Bitcoin to other cryptocurrencies before and after 2017/18 crash?," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 531(C).
    15. Narayan, Paresh Kumar & Narayan, Seema & Eki Rahman, R. & Setiawan, Iwan, 2019. "Bitcoin price growth and Indonesia's monetary system," Emerging Markets Review, Elsevier, vol. 38(C), pages 364-376.
    16. Nikolaos A. Kyriazis, 2019. "A Survey on Efficiency and Profitable Trading Opportunities in Cryptocurrency Markets," JRFM, MDPI, vol. 12(2), pages 1-17, April.
    17. 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).
    18. Li, Xiao & Wu, Ruoxi & Wang, Chen, 2024. "Impacts of bitcoin on monetary system: Is China's bitcoin ban necessary?," Research in International Business and Finance, Elsevier, vol. 69(C).
    19. Wang, Pengfei & Zhang, Wei & Li, Xiao & Shen, Dehua, 2019. "Is cryptocurrency a hedge or a safe haven for international indices? A comprehensive and dynamic perspective," Finance Research Letters, Elsevier, vol. 31(C), pages 1-18.
    20. Pal, Debdatta & Mitra, Subrata K., 2019. "Hedging bitcoin with other financial assets," Finance Research Letters, Elsevier, vol. 30(C), pages 30-36.

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:arx:papers:1910.12281. 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: arXiv administrators (email available below). General contact details of provider: http://arxiv.org/ .

    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.