Integrating Whale Transaction Flow Scoring with LSTM for Bitcoin Trend Forecasting
DOI:
https://doi.org/10.52436/1.jutif.2026.7.3.5436Keywords:
Bitcoin, Cryptocurrency, Forecasting, LSTM, Sentiment analysis, Whale transactionsAbstract
Bitcoin price prediction faces significant challenges due to high volatility and the influence of large holders, known as whales, whose transactions exceeding 500 BTC can affect market behavior. This study develops an LSTM model combining whale transaction sentiment scores with historical Bitcoin OHLC prices to forecast 7-day ahead price movements. The dataset comprises 2,069 whale transactions and 8,761 hourly price observations from April 20, 2024 to April 20, 2025. The scoring mechanism assigns +1 to exchange outflows, -1 to inflows, and 0 to neutral transfers, multiplied by logarithmically normalized transaction amounts. The LSTM architecture consists of two recurrent layers with 128 and 64 memory units, processing 720-hour input sequences to generate 168-hour OHLC forecasts. Training evaluation yielded R² of 0.9386, RMSE of 0.0686, and MAE of 0.0498. Test evaluation produced Mean Absolute Errors ranging from 871.72 USD to 3,482.27 USD across OHLC components. The model correctly predicted upward directional trends but systematically underestimated prices by 2,000-3,000 USD initially and failed to anticipate a 6,422 USD intraday surge on April 22, 2025. Results demonstrate that whale sentiment features enhance directional trend identification but do not enable precise multi-day price point prediction due to sudden market regime changes. These findings contribute empirical evidence that directional sentiment scoring of large-holder transactions provides complementary predictive value beyond conventional price-volume indicators, establishing a methodological foundation for integrating blockchain-native behavioral signals into cryptocurrency forecasting frameworks.
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References
S. Nakamoto, “Bitcoin: A Peer-to-Peer Electronic Cash System.” [Online]. Available: www.bitcoin.org
A. D. Gbadebo, A. O. Adekunle, W. Adedokun, A. O. A. Lukman, and J. Akande, “BTC price volatility: Fundamentals versus information,” Cogent Business and Management, vol. 8, no. 1, 2021, doi: 10.1080/23311975.2021.1984624.
J. Chen, “Analysis of Bitcoin Price Prediction Using Machine Learning,” Journal of Risk and Financial Management, vol. 16, no. 1, Jan. 2023, doi: 10.3390/jrfm16010051.
D. G. Baur and T. Dimpfl, “The volatility of Bitcoin and its role as a medium of exchange and a store of value,” Empir Econ, vol. 61, no. 5, pp. 2663–2683, Nov. 2021, doi: 10.1007/s00181-020-01990-5.
M. Nedved and L. Kristoufek, “Safe havens for Bitcoin,” Financ Res Lett, vol. 51, Jan. 2023, doi: 10.1016/j.frl.2022.103436.
A. Liu, H. Jahanshahloo, J. Chen, and A. Eshraghi, “Trading patterns in the bitcoin market,” European Journal of Finance, 2023, doi: 10.1080/1351847X.2023.2241883.
Z. Ye, Y. Wu, H. Chen, Y. Pan, and Q. Jiang, “A Stacking Ensemble Deep Learning Model for Bitcoin Price Prediction Using Twitter Comments on Bitcoin,” Mathematics, vol. 10, no. 8, Apr. 2022, doi: 10.3390/math10081307.
H. A. Aalborg, P. Molnár, and J. E. de Vries, “What can explain the price, volatility and trading volume of Bitcoin?,” Financ Res Lett, vol. 29, pp. 255–265, Jun. 2019, doi: 10.1016/J.FRL.2018.08.010.
A. Chernoff and J. Jagtiani, “Beneath the Crypto Currents: The Hidden Effect of Crypto ‘Whales,’” Federal Reserve Bank of Philadelphia, Philadelphia, PA, Jul. 2024. doi: 10.21799/frbp.wp.2024.14.
J. M. Griffin and A. Shams, “Is Bitcoin Really Untethered?,” Journal of Finance, vol. 75, no. 4, pp. 1913–1964, Aug. 2020, doi: 10.1111/jofi.12903.
V. Derbentsev, A. Matviychuk, and V. N. Soloviev, “Forecasting of Cryptocurrency Prices Using Machine Learning,” in Advanced Studies of Financial Technologies and Cryptocurrency Markets, Springer Singapore, 2020, pp. 211–231. doi: 10.1007/978-981-15-4498-9_12.
I. E. Livieris, E. Pintelas, S. Stavroyiannis, and P. Pintelas, “Ensemble Deep learning models for forecasting cryptocurrency time-series,” Algorithms, vol. 13, no. 5, May 2020, doi: 10.3390/A13050121.
P. S. T. P. Purnama, “Optimizing Bitcoin Price Prediction with LSTM: A Comprehensive Study on Feature Engineering and the April 2024 Halving Impact,” Elinvo (Electronics, Informatics, and Vocational Education), vol. 9, no. 1, pp. 165–177, Oct. 2024, doi: 10.21831/elinvo.v9i1.72518.
Y. Li and W. Dai, “Bitcoin price forecasting method based on CNN‐LSTM hybrid neural network model,” The Journal of Engineering, vol. 2020, no. 13, pp. 344–347, Jul. 2020, doi: 10.1049/joe.2019.1203.
L. Ante and I. Fiedler, “Market reaction to large transfers on the Bitcoin blockchain - Do size and motive matter?,” Financ Res Lett, vol. 39, p. 101619, Mar. 2021, doi: 10.1016/J.FRL.2020.101619.
V. Chutipat, R. Kasemrat, T. Kraiwanit, and I. Phaksipaeng, “SELECTION OF CRYPTOCURRENCY EXCHANGE PLATFORMS IN A DEVELOPING ECONOMY,” Corporate and Business Strategy Review, vol. 4, no. 2 Special Issue, pp. 344–350, 2023, doi: 10.22495/cbsrv4i2siart14.
F. Fang et al., “Cryptocurrency trading: a comprehensive survey,” Dec. 01, 2022, Springer Science and Business Media Deutschland GmbH. doi: 10.1186/s40854-021-00321-6.
M. Anas, S. J. H. Shahzad, and L. Yarovaya, “The use of high-frequency data in cryptocurrency research: a meta-review of literature with bibliometric analysis,” Dec. 01, 2024, Springer Science and Business Media Deutschland GmbH. doi: 10.1186/s40854-023-00595-y.
P. Jaquart, D. Dann, and C. Weinhardt, “Short-term bitcoin market prediction via machine learning,” Journal of Finance and Data Science, vol. 7, pp. 45–66, Nov. 2021, doi: 10.1016/j.jfds.2021.03.001.
I. E. Livieris, N. Kiriakidou, S. Stavroyiannis, and P. Pintelas, “An Advanced CNN-LSTM Model for Cryptocurrency Forecasting,” 2021, doi: 10.3390/electronics.
Z. Chen, C. Li, and W. Sun, “Bitcoin price prediction using machine learning: An approach to sample dimension engineering,” J Comput Appl Math, vol. 365, Feb. 2020, doi: 10.1016/j.cam.2019.112395.
L. Kristoufek, “Grandpa, Grandpa, Tell Me the One About Bitcoin Being a Safe Haven: New Evidence From the COVID-19 Pandemic,” Front Phys, vol. 8, Jul. 2020, doi: 10.3389/fphy.2020.00296.
H. Sebastião and P. Godinho, “Forecasting and trading cryptocurrencies with machine learning under changing market conditions,” Financial Innovation, vol. 7, no. 1, Dec. 2021, doi: 10.1186/s40854-020-00217-x.
Y. S. Kim, M. K. Kim, N. Fu, J. Liu, J. Wang, and J. Srebric, “Investigating the impact of data normalization methods on predicting electricity consumption in a building using different artificial neural network models,” Sustain Cities Soc, vol. 118, Jan. 2025, doi: 10.1016/j.scs.2024.105570.
Z. Li, H. Yu, J. Xu, J. Liu, and Y. Mo, “Stock Market Analysis and Prediction Using LSTM: A Case Study on Technology Stocks,” Innovations in Applied Engineering and Technology, pp. 1–6, Nov. 2023, doi: 10.62836/iaet.v2i1.162.
S. S. de Souza and J. E. Silva, “Application of LSTM recurrent neural networks for bitcoin price prediction,” REVISTA DELOS, vol. 17, no. 61, p. e2602, Nov. 2024, doi: 10.55905/rdelosv17.n61-062.
S. Ahmed, S. U. Hassan, N. R. Aljohani, and R. Nawaz, “FLF-LSTM: A novel prediction system using Forex Loss Function,” Applied Soft Computing Journal, vol. 97, Dec. 2020, doi: 10.1016/j.asoc.2020.106780.
D. Chicco, M. J. Warrens, and G. Jurman, “The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation,” PeerJ Comput Sci, vol. 7, pp. 1–24, 2021, doi: 10.7717/PEERJ-CS.623.
T. O. Hodson, “Root-mean-square error (RMSE) or mean absolute error (MAE): when to use them or not,” Jul. 19, 2022, Copernicus GmbH. doi: 10.5194/gmd-15-5481-2022.
H. Li, G. K. Rajbahadur, D. Lin, C.-P. Bezemer, Z. Ming, and Jiang, “Keeping Deep Learning Models in Check: A History-Based Approach to Mitigate Overfitting,” Jan. 2024, doi: 10.1109/ACCESS.2024.3402543.
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