AL-Qadisiyah Journal For Administrative and Economic sciences

Deep Learning Based Forecasting of Cryptocurrency Markets By Using Bitcoin and Ethereum as Case Study for 2016–2024

Document Type : Research Paper

Authors

Mustafa Hussein Abd Al-Aali
Hussein Kalaf Aziz
Kawthar Kareem Abd Al-Razzaq

University of Wasit

https://doi.org/10.33916/qjae.2026.01224240
Abstract
The research aims to develop an accurate model for the prediction of Bitcoin and Ethereum values using deep learning methods. These are significant digital assets in today's financial markets. Daily data collected from 2016 to 2023 was analyzed by LSTM and CNN and the combined LSTM-CNN model in order to assess their performance in handling complex time patterns and price shifts, usually characterizing cryptocurrency markets. The statistical results revealed that the LSTM model performed best according to performance metrics such as RMSE and MAE and MAPE and R². Moreover, strong generalization with precision in predictions while avoiding overfitting was depicted. These findings give enhanced statistical and economic performance that involves enhanced prediction accuracy and therefore helps investment and risk management in an evolving financial scenario. It highlights the usage of AI in analyzing digital markets, giving better insight than previous studies dealing either with traditional models or less integrated models. The findings mark progress toward more reliable economic models capable of handling complex dynamic financial data.

Keywords

Cryptocurrencies
Deep Learning
LSTM Models
Bitcoin
and Ethereum
Crossmark
AL-Qadisiyah Journal For Administrative and Economic sciences
Volume 28, Issue 1
Spring 2026
Pages 224-240

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