IDEAS home Printed from https://ideas.repec.org/p/arx/papers/2308.01013.html
   My bibliography  Save this paper

Bayesian framework for characterizing cryptocurrency market dynamics, structural dependency, and volatility using potential field

Author

Listed:
  • Anoop C V
  • Neeraj Negi
  • Anup Aprem

Abstract

Identifying the structural dependence between the cryptocurrencies and predicting market trend are fundamental for effective portfolio management in cryptocurrency trading. In this paper, we present a unified Bayesian framework based on potential field theory and Gaussian Process to characterize the structural dependency of various cryptocurrencies, using historic price information. The following are our significant contributions: (i) Proposed a novel model for cryptocurrency price movements as a trajectory of a dynamical system governed by a time-varying non-linear potential field. (ii) Validated the existence of the non-linear potential function in cryptocurrency market through Lyapunov stability analysis. (iii) Developed a Bayesian framework for inferring the non-linear potential function from observed cryptocurrency prices. (iv) Proposed that attractors and repellers inferred from the potential field are reliable cryptocurrency market indicators, surpassing existing attributes, such as, mean, open price or close price of an observation window, in the literature. (v) Analysis of cryptocurrency market during various Bitcoin crash durations from April 2017 to November 2021, shows that attractors captured the market trend, volatility, and correlation. In addition, attractors aids explainability and visualization. (vi) The structural dependence inferred by the proposed approach was found to be consistent with results obtained using the popular wavelet coherence approach. (vii) The proposed market indicators (attractors and repellers) can be used to improve the prediction performance of state-of-art deep learning price prediction models. As, an example, we show improvement in Litecoin price prediction up to a horizon of 12 days.

Suggested Citation

  • Anoop C V & Neeraj Negi & Anup Aprem, 2023. "Bayesian framework for characterizing cryptocurrency market dynamics, structural dependency, and volatility using potential field," Papers 2308.01013, arXiv.org.
    • Handle: RePEc:arx:papers:2308.01013
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/2308.01013
    File Function: Latest version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. Ze Shen & Qing Wan & David J. Leatham, 2021. "Bitcoin Return Volatility Forecasting: A Comparative Study between GARCH and RNN," JRFM, MDPI, vol. 14(7), pages 1-18, July.
    3. Kate Murray & Andrea Rossi & Diego Carraro & Andrea Visentin, 2023. "On Forecasting Cryptocurrency Prices: A Comparison of Machine Learning, Deep Learning, and Ensembles," Forecasting, MDPI, vol. 5(1), pages 1-14, January.
    4. Fruehwirt, Wolfgang & Hochfilzer, Leonhard & Weydemann, Leonard & Roberts, Stephen, 2021. "Cumulation, crash, coherency: A cryptocurrency bubble wavelet analysis," Finance Research Letters, Elsevier, vol. 40(C).
    5. Fan Fang & Carmine Ventre & Michail Basios & Leslie Kanthan & Lingbo Li & David Martinez-Regoband & Fan Wu, 2020. "Cryptocurrency Trading: A Comprehensive Survey," Papers 2003.11352, arXiv.org, revised Jan 2022.
    Full references (including those not matched with items on IDEAS)

    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. Soria, Jorge & Moya, Jorge & Mohazab, Amin, 2023. "Optimal mining in proof-of-work blockchain protocols," Finance Research Letters, Elsevier, vol. 53(C).
    2. Bouteska, Ahmed & Abedin, Mohammad Zoynul & Hajek, Petr & Yuan, Kunpeng, 2024. "Cryptocurrency price forecasting – A comparative analysis of ensemble learning and deep learning methods," International Review of Financial Analysis, Elsevier, vol. 92(C).
    3. Dante Miller & Jong-Min Kim, 2021. "Univariate and Multivariate Machine Learning Forecasting Models on the Price Returns of Cryptocurrencies," JRFM, MDPI, vol. 14(10), pages 1-10, October.
    4. Praveen Puram & Soumya Roy & Deepak Srivastav & Anand Gurumurthy, 2023. "Understanding the effect of contextual factors and decision making on team performance in Twenty20 cricket: an interpretable machine learning approach," Annals of Operations Research, Springer, vol. 325(1), pages 261-288, June.
    5. Giacomo di Tollo & Joseph Andria & Gianni Filograsso, 2023. "The Predictive Power of Social Media Sentiment: Evidence from Cryptocurrencies and Stock Markets Using NLP and Stochastic ANNs," Mathematics, MDPI, vol. 11(16), pages 1-18, August.
    6. Siu Hin Tang & Mathieu Rosenbaum & Chao Zhou, 2023. "Forecasting Volatility with Machine Learning and Rough Volatility: Example from the Crypto-Winter," Papers 2311.04727, arXiv.org, revised Feb 2024.
    7. Husam Rjoub & Tomiwa Sunday Adebayo & Dervis Kirikkaleli, 2023. "Blockchain technology-based FinTech banking sector involvement using adaptive neuro-fuzzy-based K-nearest neighbors algorithm," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 9(1), pages 1-23, December.
    8. Scindhiya Laxmi & S. K. Gupta & Sumit Kumar, 2024. "Intuitionistic fuzzy least square twin support vector machines for pattern classification," Annals of Operations Research, Springer, vol. 339(3), pages 1329-1378, August.
    9. Goodell, John W. & Ben Jabeur, Sami & Saâdaoui, Foued & Nasir, Muhammad Ali, 2023. "Explainable artificial intelligence modeling to forecast bitcoin prices," International Review of Financial Analysis, Elsevier, vol. 88(C).
    10. Marcin Wątorek & Jarosław Kwapień & Stanisław Drożdż, 2022. "Multifractal Cross-Correlations of Bitcoin and Ether Trading Characteristics in the Post-COVID-19 Time," Future Internet, MDPI, vol. 14(7), pages 1-15, July.
    11. Laurens Swinkels, 2023. "Empirical evidence on the ownership and liquidity of real estate tokens," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 9(1), pages 1-29, December.
    12. Luyao Zhang & Tianyu Wu & Saad Lahrichi & Carlos-Gustavo Salas-Flores & Jiayi Li, 2022. "A Data Science Pipeline for Algorithmic Trading: A Comparative Study of Applications for Finance and Cryptoeconomics," Papers 2206.14932, arXiv.org.
    13. Kirimhan, Destan, 2023. "Importance of anti-money laundering regulations among prosumers for a cybersecure decentralized finance," Journal of Business Research, Elsevier, vol. 157(C).
    14. Kate Murray & Andrea Rossi & Diego Carraro & Andrea Visentin, 2023. "On Forecasting Cryptocurrency Prices: A Comparison of Machine Learning, Deep Learning, and Ensembles," Forecasting, MDPI, vol. 5(1), pages 1-14, January.
    15. Marcin Wk{a}torek & Stanis{l}aw Dro.zd.z & Jaros{l}aw Kwapie'n & Ludovico Minati & Pawe{l} O'swik{e}cimka & Marek Stanuszek, 2020. "Multiscale characteristics of the emerging global cryptocurrency market," Papers 2010.15403, arXiv.org, revised Mar 2021.
    16. Rasoul Amirzadeh & Dhananjay Thiruvady & Asef Nazari & Mong Shan Ee, 2023. "Dynamic Bayesian Networks for Predicting Cryptocurrency Price Directions: Uncovering Causal Relationships," Papers 2306.08157, arXiv.org, revised Oct 2024.
    17. Hakan Pabuccu & Adrian Barbu, 2023. "Feature Selection with Annealing for Forecasting Financial Time Series," Papers 2303.02223, arXiv.org, revised Feb 2024.
    18. Zhang, Wei & Valencia, Andrea & Gu, Lixing & Zheng, Qipeng P. & Chang, Ni-Bin, 2020. "Integrating emerging and existing renewable energy technologies into a community-scale microgrid in an energy-water nexus for resilience improvement," Applied Energy, Elsevier, vol. 279(C).
    19. Nagula, Pavan Kumar & Alexakis, Christos, 2022. "A new hybrid machine learning model for predicting the bitcoin (BTC-USD) price," Journal of Behavioral and Experimental Finance, Elsevier, vol. 36(C).
    20. Luis Lorenzo & Javier Arroyo, 2023. "Online risk-based portfolio allocation on subsets of crypto assets applying a prototype-based clustering algorithm," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 9(1), pages 1-40, December.

    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:2308.01013. 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.