Work

Multimodal Hybrid Deep Learning for Cryptocurrency Volatility Forecasting

Year: 2026

Type: software

Abstract: Benchmarking framework for evaluating multimodal deep learning against econometric baselines in predicting cryptocurrency realized volatility across multi-step horizons (1, 3, and 7 days). Fuses market price data, NLP news sentiment (FinBERT), and on-chain network metrics into a sequential 1D-CNN, Bidirectional LSTM, and Multi-Head Self-Attention architecture. Includes Diebold-Mariano significance testing, regime-aware stress testing (LUNA & FTX crashes), and Monte Carlo Dropout uncertainty quantification.

Source: Code Ocean

DOI: 10.24433/co.1492664.v1

Authors: Mohammed Alaa Ala'anzy, Yeskendir Kaliyev, Omar Alnaseri, Abdulmohsen Algarni

Institutions: Shandong University, University of Southern Denmark, Baden-Wuerttemberg Cooperative State University, King Khalid University

Language: English

Topic: Stock Market Forecasting Methods

Subfield: Management Science and Operations Research

Field: Decision Sciences

Domain: Social Sciences

Open Access status: green

OpenAlex ID: W7204604560

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