IDEAS home Printed from https://ideas.repec.org/a/eee/finlet/v71y2025ics1544612324015162.html
   My bibliography  Save this article

Intelligent forecasting in bitcoin markets

Author

Listed:
  • Cohen, Gil
  • Aiche, Avishay

Abstract

This paper examines the effectiveness of Artificial Intelligence (AI) in predicting Bitcoin's price movements. To achieve this, we developed two distinct trading strategies and compared their performance against each other and the traditional Buy and Hold (B&H) strategy. Over the period from January 2018 to September 2023, we found that the strategy optimized by ChatGPT 01-Preview, which integrates multiple technical indicators and sentiment analysis into a weighted composite index, delivered an exceptional total return of 944.85 %. The second strategy, that is using Extreme Gradient Boosting (XGBoost) technique achieved a total return of 189.05 %. The AI strategy's excess return of 755.8 % over the XGBoost strategy highlights the significant advantage of AI particularly in utilizing diverse data sources, such as social media, to predict Bitcoin's price trends more effectively than relying solely on economic data. Both trading strategies significantly outperformed the traditional B&H strategy, which returned 73.08 % over the same period. Furthermore, we found that AI has an advantage during periods of high Bitcoin price volatility.

Suggested Citation

  • Cohen, Gil & Aiche, Avishay, 2025. "Intelligent forecasting in bitcoin markets," Finance Research Letters, Elsevier, vol. 71(C).
    • Handle: RePEc:eee:finlet:v:71:y:2025:i:c:s1544612324015162
    DOI: 10.1016/j.frl.2024.106487
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1544612324015162
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.frl.2024.106487?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. Mawuli Segnon & Stelios Bekiros, 2020. "Forecasting volatility in bitcoin market," Annals of Finance, Springer, vol. 16(3), pages 435-462, September.
    2. Shuyu Zhang & Xuanyu Zhou & Huifeng Pan & Junyi Jia, 2019. "Cryptocurrency, confirmatory bias and news readability – evidence from the largest Chinese cryptocurrency exchange," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 58(5), pages 1445-1468, March.
    3. Bazán-Palomino, Walter & Svogun, Daniel, 2023. "On the drivers of technical analysis profits in cryptocurrency markets: A Distributed Lag approach," International Review of Financial Analysis, Elsevier, vol. 86(C).
    4. 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.
    5. Panpan Zhu & Xing Zhang & You Wu & Hao Zheng & Yinpeng Zhang, 2021. "Investor attention and cryptocurrency: Evidence from the Bitcoin market," PLOS ONE, Public Library of Science, vol. 16(2), pages 1-28, February.
    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. Yousaf, Imran & Abrar, Afsheen & Ali, Shoaib & Goodell, John W., 2024. "Connectedness between energy cryptocurrencies and US equity markets: A quantile-based analysis," International Review of Financial Analysis, Elsevier, vol. 96(PB).
    2. Ardia, David & Bluteau, Keven, 2024. "Twitter and cryptocurrency pump-and-dumps," International Review of Financial Analysis, Elsevier, vol. 95(PB).
    3. Gunay, Samet & Sraieb, Mohamed M. & Muhammed, Shahnawaz, 2024. "Decrypting Metaverse crypto Market: A nonlinear analysis of investor sentiment," International Review of Financial Analysis, Elsevier, vol. 96(PB).
    4. Baeckström, Ylva & Jalan, Akanksha & Matkovskyy, Roman, 2024. "The role of promotion versus prevention-orientation to predict individual cryptocurrency participation," Finance Research Letters, Elsevier, vol. 67(PA).
    5. He, Xie & Hamori, Shigeyuki, 2024. "The higher the better? Hedging and investment strategies in cryptocurrency markets: Insights from higher moment spillovers," International Review of Financial Analysis, Elsevier, vol. 95(PA).
    6. Zhang, Xiangyu & Chen, Zhuming & Wang, Shengyu, 2024. "A study of the impact of cryptocurrency price volatility on the stock and gold markets," Finance Research Letters, Elsevier, vol. 69(PA).
    7. Shang, Dawei & Guo, Ziyu & Wang, Hui, 2024. "Enhancing digital cryptocurrency trading price prediction with an attention-based convolutional and recurrent neural network approach: The case of Ethereum," Finance Research Letters, Elsevier, vol. 67(PB).
    8. Wen, Danyan & Zhang, Zihao & Nie, Jing & Cao, Yang, 2024. "Investor attention and anomalies: Evidence from the Chinese stock market," International Review of Financial Analysis, Elsevier, vol. 96(PB).
    9. Azhar Mohamad & Sarveshwar Kumar Inani, 2023. "Price discovery in bitcoin spot or futures during the Covid-19 pandemic? Evidence from the time-varying parameter vector autoregressive model with stochastic volatility," Applied Economics Letters, Taylor & Francis Journals, vol. 30(19), pages 2749-2757, November.
    10. Assaf, Ata & Demir, Ender & Ersan, Oguz, 2024. "Detecting and date-stamping bubbles in fan tokens," International Review of Economics & Finance, Elsevier, vol. 92(C), pages 98-113.
    11. Wang, Yijun & Andreeva, Galina & Martin-Barragan, Belen, 2023. "Machine learning approaches to forecasting cryptocurrency volatility: Considering internal and external determinants," International Review of Financial Analysis, Elsevier, vol. 90(C).
    12. Efstathios Polyzos & Costas Siriopoulos, 2024. "Autoregressive Random Forests: Machine Learning and Lag Selection for Financial Research," Computational Economics, Springer;Society for Computational Economics, vol. 64(1), pages 225-262, July.
    13. Zhang, Yi & Liu, Tianxiang & Li, Weiping, 2024. "Corporate fraud detection based on linguistic readability vector: Application to financial companies in China," International Review of Financial Analysis, Elsevier, vol. 95(PB).
    14. Jalan, Akanksha & Matkovskyy, Roman & Urquhart, Andrew & Yarovaya, Larisa, 2023. "The role of interpersonal trust in cryptocurrency adoption," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 83(C).
    15. Hasanov, Akram Shavkatovich & Burkhanov, Aktam Usmanovich & Usmonov, Bunyod & Khajimuratov, Nizomjon Shukurullaevich & Khurramova, Madina Mansur qizi, 2024. "The role of sudden variance shifts in predicting volatility in bioenergy crop markets under structural breaks," Energy, Elsevier, vol. 293(C).
    16. Michael Cary, 2024. "Herding and investor sentiment after the cryptocurrency crash: evidence from Twitter and natural language processing," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 10(1), pages 1-23, December.
    17. Amaro, Raphael & Pinho, Carlos, 2022. "Energy commodities: A study on model selection for estimating Value-at-Risk," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 68, pages 5-27.
    18. Khakan Najaf & Philip Sinnadurai & K. S. Devi & Mohamed M. Dhiaf, 2024. "Does electronic economics matter to financial technology firms?," Electronic Commerce Research, Springer, vol. 24(1), pages 393-426, March.
    19. Zhang, Shuyu & Aerts, Walter & Lu, Liping & Pan, Huifeng, 2019. "Readability of token whitepaper and ICO first-day return," Economics Letters, Elsevier, vol. 180(C), pages 58-61.
    20. Qingchong Chen & Xiong Xiong & Ya Gao, 2021. "Is information really efficient for the market? Evidence of confirmatory bias in China," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 61(5), pages 5965-5997, December.

    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:finlet:v:71:y:2025:i:c:s1544612324015162. 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/frl .

    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.