IDEAS home Printed from https://ideas.repec.org/a/eee/riibaf/v64y2023ics027553192200215x.html
   My bibliography  Save this article

A random forest-based model for crypto asset forecasts in futures markets with out-of-sample prediction

Author

Listed:
  • Orte, Francisco
  • Mira, José
  • Sánchez, María Jesús
  • Solana, Pablo

Abstract

In this study, a price prediction model for futures markets of crypto assets is presented. Random Forest was used to study three scenarios as a function of input variables: technical indicators, candlestick patterns and both simultaneously. In turn, the model parameters, the time intervals, and the most suitable investment horizons were studied. In addition to showing the results from the model, a one-year out-of-sample prediction was simulated. The entire year of 2020 was chosen because the three possible stock market scenarios occurred in this year: a sideways market, a bear market resulting from the global pandemic and an end-of-year bull market. Last, this out-of-sample simulation was analyzed as a real operation, that is, by retraining the model after each new collection of data, so that the model had the maximum information at all times. In conclusion, using candlestick patterns instead of technical indicators, improves the efficiency of the results.

Suggested Citation

  • Orte, Francisco & Mira, José & Sánchez, María Jesús & Solana, Pablo, 2023. "A random forest-based model for crypto asset forecasts in futures markets with out-of-sample prediction," Research in International Business and Finance, Elsevier, vol. 64(C).
    • Handle: RePEc:eee:riibaf:v:64:y:2023:i:c:s027553192200215x
    DOI: 10.1016/j.ribaf.2022.101829
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S027553192200215X
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ribaf.2022.101829?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. Panagiotidis, Theodore & Papapanagiotou, Georgios & Stengos, Thanasis, 2022. "On the volatility of cryptocurrencies," Research in International Business and Finance, Elsevier, vol. 62(C).
    2. Basak, Suryoday & Kar, Saibal & Saha, Snehanshu & Khaidem, Luckyson & Dey, Sudeepa Roy, 2019. "Predicting the direction of stock market prices using tree-based classifiers," The North American Journal of Economics and Finance, Elsevier, vol. 47(C), pages 552-567.
    3. 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.
    4. 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.
    5. TALWAR Shalini & SHAH Pranav & SHAH Utkarsh, 2019. "Picking Buy-Sell Signals: A Practitioner’S Perspective On Key Technical Indicators For Selected Indian Firms," Studies in Business and Economics, Lucian Blaga University of Sibiu, Faculty of Economic Sciences, vol. 14(3), pages 205-219, December.
    6. Ren, Yi-Shuai & Ma, Chao-Qun & Kong, Xiao-Lin & Baltas, Konstantinos & Zureigat, Qasim, 2022. "Past, present, and future of the application of machine learning in cryptocurrency research," Research in International Business and Finance, Elsevier, vol. 63(C).
    7. 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.
    8. Erdinc Akyildirim & Aurelio F. Bariviera & Duc Khuong Nguyen & Ahmet Sensoy, 2022. "Forecasting high-frequency stock returns: a comparison of alternative methods," Annals of Operations Research, Springer, vol. 313(2), pages 639-690, June.
    9. Ahmed M. Khedr & Ifra Arif & Pravija Raj P V & Magdi El‐Bannany & Saadat M. Alhashmi & Meenu Sreedharan, 2021. "Cryptocurrency price prediction using traditional statistical and machine‐learning techniques: A survey," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 28(1), pages 3-34, January.
    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. Xu, Yuhong & Zhao, Xinyao, 2024. "How does node centrality in a financial network affect asset price prediction?," The North American Journal of Economics and Finance, Elsevier, vol. 73(C).
    2. Feng, Lingbing & Qi, Jiajun & Lucey, Brian, 2024. "Enhancing cryptocurrency market volatility forecasting with daily dynamic tuning strategy," International Review of Financial Analysis, Elsevier, vol. 94(C).
    3. Li, Zhe & Liang, Shuguang & Pan, Xianyou & Pang, Meng, 2024. "Credit risk prediction based on loan profit: Evidence from Chinese SMEs," Research in International Business and Finance, Elsevier, vol. 67(PA).
    4. Liu, Yujun & Li, Zhongfei & Nekhili, Ramzi & Sultan, Jahangir, 2023. "Forecasting cryptocurrency returns with machine learning," Research in International Business and Finance, Elsevier, vol. 64(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. 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.
    2. Ştefan Cristian Gherghina & Liliana Nicoleta Simionescu, 2023. "Exploring the asymmetric effect of COVID-19 pandemic news on the cryptocurrency market: evidence from nonlinear autoregressive distributed lag approach and frequency domain causality," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 9(1), pages 1-58, December.
    3. Bernhard Haslhofer & Burkhard Raunig & Pietro Saggase & Esther Segalla & Michael Sigmund & Felix Zangerl, 2023. "Assessing the Solvency of Virtual Asset Service Providers: Are Current Standards Sufficient? (Pietro Saggese, Esther Segalla, Michael Sigmund, Burkhard Raunig, Felix Zangerl, Bernhard Haslhofer)," Working Papers 248, Oesterreichische Nationalbank (Austrian Central Bank).
    4. Pietro Saggese & Esther Segalla & Michael Sigmund & Burkhard Raunig & Felix Zangerl & Bernhard Haslhofer, 2023. "Assessing the Solvency of Virtual Asset Service Providers: Are Current Standards Sufficient?," Papers 2309.16408, arXiv.org, revised Apr 2024.
    5. Maira Andrade & Philip W. S. Newall, 2023. "Cryptocurrencies as Gamblified Financial Assets and Cryptocasinos: Novel Risks for a Public Health Approach to Gambling," Risks, MDPI, vol. 11(3), pages 1-14, February.
    6. 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.
    7. 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.
    8. 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.
    9. Kirimhan, Destan, 2023. "Importance of anti-money laundering regulations among prosumers for a cybersecure decentralized finance," Journal of Business Research, Elsevier, vol. 157(C).
    10. Wujun Lv & Tao Pang & Xiaobao Xia & Jingzhou Yan, 2023. "Dynamic portfolio choice with uncertain rare-events risk in stock and cryptocurrency markets," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 9(1), pages 1-28, December.
    11. Bennett, Donyetta & Mekelburg, Erik & Williams, T.H., 2023. "BeFi meets DeFi: A behavioral finance approach to decentralized finance asset pricing," Research in International Business and Finance, Elsevier, vol. 65(C).
    12. Walid Mensi & Mariya Gubareva & Hee-Un Ko & Xuan Vinh Vo & Sang Hoon Kang, 2023. "Tail spillover effects between cryptocurrencies and uncertainty in the gold, oil, and stock markets," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 9(1), pages 1-27, December.
    13. Riccardo Blasis & Luca Galati & Alexander Webb & Robert I. Webb, 2023. "Intelligent design: stablecoins (in)stability and collateral during market turbulence," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 9(1), pages 1-23, December.
    14. Marcin Wk{a}torek & Marcin Kr'olczyk & Jaros{l}aw Kwapie'n & Tomasz Stanisz & Stanis{l}aw Dro.zd.z, 2024. "Approaching multifractal complexity in decentralized cryptocurrency trading," Papers 2411.05951, arXiv.org.
    15. Waqas Hanif & Hee-Un Ko & Linh Pham & Sang Hoon Kang, 2023. "Dynamic connectedness and network in the high moments of cryptocurrency, stock, and commodity markets," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 9(1), pages 1-40, December.
    16. Danilo Bazzanella & Andrea Gangemi, 2023. "Bitcoin: a new proof-of-work system with reduced variance," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 9(1), pages 1-14, December.
    17. Kamyr Gomes Souza & Flavio Barboza & Daniel Vitor Tartari Garruti, 2024. "A Discourse Analysis of Tweets and Its Implications for Cryptocurrency Prices and Trade Volumes," Computational Economics, Springer;Society for Computational Economics, vol. 64(4), pages 2355-2383, October.
    18. Mingbo Zheng & Gen-Fu Feng & Xinxin Zhao & Chun-Ping Chang, 2023. "The transaction behavior of cryptocurrency and electricity consumption," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 9(1), pages 1-18, December.
    19. Tina Linden & Tina Shirazi, 2023. "Markets in crypto-assets regulation: Does it provide legal certainty and increase adoption of crypto-assets?," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 9(1), pages 1-30, December.
    20. Berend Jelmer Dirk Gort & Xiao-Yang Liu & Xinghang Sun & Jiechao Gao & Shuaiyu Chen & Christina Dan Wang, 2022. "Deep Reinforcement Learning for Cryptocurrency Trading: Practical Approach to Address Backtest Overfitting," Papers 2209.05559, arXiv.org, revised Jan 2023.

    More about this item

    Keywords

    Random Forest; Cryptocurrencies; Bitcoin; Technical indicators; Candlestick patterns;
    All these keywords.

    JEL classification:

    • G13 - Financial Economics - - General Financial Markets - - - Contingent Pricing; Futures Pricing
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques

    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:riibaf:v:64:y:2023:i:c:s027553192200215x. 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/ribaf .

    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.