@inproceedings{72d1b65a31ea40d38c2213a9da280221,
title = "LSTM-based Price Prediction and Dynamic Risk Management in Decentralized Blockchain Protocol",
abstract = "This study investigates Bitcoin price prediction and dynamic risk management strategies within decentralized finance (DeFi) protocols using Long Short-Term Memory (LSTM) neural network models. The research demonstrates that the LSTM model effectively captures Bitcoin's general price trends and short-term fluctuations under typical market conditions. However, during periods of extreme volatility, the prediction model exhibits notable lag and reduced amplitude in capturing abrupt price changes, highlighting its limitations when relying solely on historical price data. Furthermore, this paper proposes a dynamic collateral ratio adjustment mechanism based on predicted price deviations, aimed at mitigating liquidation risks in DeFi lending protocols. Dynamically adjusting collateral ratios has the potential to substantially improve protocol stability compared to traditional static collateral frameworks.",
keywords = "LSTM, Machine Learning, blockchain, stablecoin",
author = "Jinyan Song and Zhenfu Cao and Jiachen Shen",
note = "Publisher Copyright: {\textcopyright} 2025 IEEE.; 4th International Symposium on Computer Applications and Information Technology, ISCAIT 2025 ; Conference date: 21-03-2025 Through 23-03-2025",
year = "2025",
doi = "10.1109/ISCAIT64916.2025.11010385",
language = "英语",
series = "2025 4th International Symposium on Computer Applications and Information Technology, ISCAIT 2025",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "355--358",
booktitle = "2025 4th International Symposium on Computer Applications and Information Technology, ISCAIT 2025",
address = "美国",
}