Identification of the Optimal Neural Network Architecture for Prediction of Bitcoin Return
Volume 36, Issue 1 (2025), pp. 175–196
Pub. online: 9 July 2024
Type: Research Article
Open Access
Open Access
Received
1 February 2024
1 February 2024
Accepted
1 June 2024
1 June 2024
Published
9 July 2024
9 July 2024
Abstract
Neural networks (NNs) are well established and widely used in time series forecasting due to their frequent dominance over other linear and nonlinear models. Thus, this paper does not question their appropriateness in forecasting cryptocurrency prices; rather, it compares the most commonly used NNs, i.e. feedforward neural networks (FFNNs), long short-term memory (LSTM) and convolutional neural networks (CNNs). This paper contributes to the existing literature by defining the appropriate NN structure comparable across different NN architectures, which yields the optimal NN model for Bitcoin return forecasting. Moreover, by incorporating turbulent events such as COVID and war, this paper emerges as a stress test for NNs. Finally, inputs are carefully selected, mostly covering macroeconomic and market variables, as well as different attractiveness measures, the importance of which in cryptocurrency forecasting is tested. The main results indicate that all NNs perform the best in an environment of bullish market, where CNNs stand out as the optimal models for continuous dataset, and LSTMs emerge as optimal in direction forecasting. In the downturn periods, CNNs stand out as the best models. Additionally, Tweets, as an attractiveness measure, enabled the models to attain superior performance.
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Biographies
Šestanović Tea
T. Šestanović obtained her PhD in economics in 2017. She is currently an assistant professor at University of Split, Faculty of Economics, Business and Tourism. She is teaching statistics and similar courses on all levels of studies, as well as business decision making. She is a president of Croatian Operational Research Society (CRORS) and an editor-in-chief of Croatian Operational Research Review (CRORR). Her main scientific interests are time series, neural networks, financial modelling and statistics.
Kalinić Milićević Tea
T. Kalinić Milićević is a teaching assistant at University of Split, Faculty of Economics, Business and Tourism (FEBT). She graduated in mathematics from University of Split, Faculty of Science, and she finished postgraduate specialist study program in business economics on FEBT. She is teaching mathematics, quantitative methods, financial modelling and actuarial analysis. She is a treasurer at Croatian Operational Research society (CRORS). Her main scientific interests are machine learning models, financial modelling, actuarial science and optimization.
