Artificial neural network analysis of the day of the week anomaly in cryptocurrencies

Financial Innovation

Research

Nuray Tosunoğlu¹  Hilal Abacı²  Gizem Ateş³  Neslihan Saygılı Akkaya⁴ 

[1] Faculty of Economics and Administrative Sciences, Ankara Hacı Bayram Veli University, Ankara, Turkey;Faculty of Economics and Administrative Sciences, Çankırı Karatekin University, Çankırı, Turkey;Faculty of Economics and Administrative Sciences, İnönü University, Malatya, Turkey;Institute of Graduate Studies, Ankara Hacı Bayram Veli University, Ankara, Turkey;

关键词: Cryptocurrency;    Bitcoin;    Ethereum;    Cardano;    Day-of-the-week anomaly;    Artificial neural network;   

DOI  :  10.1186/s40854-023-00499-x

 received in 2022-04-02, accepted in 2023-04-20,  发布年份 2023

来源: Springer

【 摘 要 】

Anomalies, which are incompatible with the efficient market hypothesis and mean a deviation from normality, have attracted the attention of both financial investors and researchers. A salient research topic is the existence of anomalies in cryptocurrencies, which have a different financial structure from that of traditional financial markets. This study expands the literature by focusing on artificial neural networks to compare different currencies of the cryptocurrency market, which is hard to predict. It aims to investigate the existence of the day-of-the-week anomaly in cryptocurrencies with feedforward artificial neural networks as an alternative to traditional methods. An artificial neural network is an effective approach that can model the nonlinear and complex behavior of cryptocurrencies. On October 6, 2021, Bitcoin (BTC), Ethereum (ETH), and Cardano (ADA), which are the top three cryptocurrencies in terms of market value, were selected for this study. The data for the analysis, consisting of the daily closing prices for BTC, ETH, and ADA, were obtained from the Coinmarket.com website from January 1, 2018 to May 31, 2022. The effectiveness of the established models was tested with mean squared error, root mean squared error, mean absolute error, and Theil’s U1, and ROOS2\documentclass[12pt]{minimal}\usepackage{amsmath}\usepackage{wasysym}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{amsbsy}\usepackage{mathrsfs}\usepackage{upgreek}\setlength{\oddsidemargin}{-69pt}\begin{document}$${R}_{OOS}^{2}$$\end{document} was used for out-of-sample. The Diebold–Mariano test was used to statistically reveal the difference between the out-of-sample prediction accuracies of the models. When the models created with feedforward artificial neural networks are examined, the existence of the day-of-the-week anomaly is established for BTC, but no day-of-the-week anomaly for ETH and ADA was found.

【 授权许可】

CC BY   
© The Author(s) 2023

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