Digital financial asset price fluctuation forecasting in digital economy era using blockchain information: A reconstructed dynamic-bound Levenberg–Marquardt neural-network approach

Abstract

Digital financial assets such as cryptocurrency are playing an increasingly crucial role in the digital economy era. Cryptocurrency is characterized by significant volatility and asset price fluctuations in the short term. Therefore, the development of an accurate and technologically reliable forecasting approach is important. For accurately predicting the closing price of the cryptocurrency, as a representative digital financial asset, we developed a reconstructed dynamic-bound Levenberg–Marquardt neural network (R-DB-LM-NN) architecture and a corresponding neural-network training algorithm with a moving-boundary mechanism for evaluating the correctness of each descent direction. We used a high-frequency blockchain information dataset for training and prediction. The dynamic bound was introduced to increase the step size so that the neural network could effectively cross the local minimum and to avoid interrupting the neural-network iteration process. Then, we built a high-frequency encrypted digital currency blockchain information dataset. Experiments confirmed that the proposed architecture and algorithm are superior to traditional neural-network machine-learning methods, such as artificial neural networks, and deep-learning methods, such as long short-term memory and convolutional neural networks, with regard to prediction performance. Finally, the implications of the study and limitations of the proposed approach are discussed, along with the extension of the approach to other time series research domains, for researchers and practitioners.