Joint prediction of post-overreaction price movements across cryptocurrencies using multi-source and multi-output deep learning models

Abstract

Accurately forecasting cryptocurrency price movements following market overreactions is crucial for traders, investors, and risk managers operating in highly volatile environments. This study presents a novel multi-source, multi-output deep learning framework designed to predict the direction of price changes in four major cryptocurrencies — Bitcoin (BTC), Ethereum (ETH), Binance Coin (BNB), and Ripple (XRP) — immediately after overreaction events. By jointly modeling these assets, our approach captures their interconnected market dynamics, enhancing predictive accuracy. We compile an extensive dataset with over 656 features from diverse sources, including historical trading data, on-chain metrics, technical indicators, and social sentiment data from Google Trends, collected at both daily and intraday frequencies. To improve model interpretability and performance, we introduce two engineered features — price change magnitude and price variation speed — that effectively represent intraday volatility. Feature selection using a Random Forest approach reduces the feature set to 30 key variables, ensuring robustness and avoiding overfitting. Using three advanced deep learning architectures — LSTM, RNN, and CNN — we train models to classify the next-day price movement as upward or downward. Empirical results demonstrate that the multi-output LSTM achieves an F1-score of 73.42%, outperforming both single-asset models (62.95–68.25%) and alternative architectures. These findings highlight the benefits of joint modeling, leading to more reliable forecasts during turbulent market conditions. Our framework offers a practical tool for algorithmic trading, portfolio management, and risk mitigation in the dynamic cryptocurrency landscape.