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Abstract
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
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1 Ankara Hacı Bayram Veli University, Faculty of Economics and Administrative Sciences, Ankara, Turkey (GRID:grid.509259.2) (ISNI:0000 0004 7221 6011)
2 Çankırı Karatekin University, Faculty of Economics and Administrative Sciences, Çankırı, Turkey (GRID:grid.448653.8) (ISNI:0000 0004 0384 3548)
3 İnönü University, Faculty of Economics and Administrative Sciences, Malatya, Turkey (GRID:grid.411650.7) (ISNI:0000 0001 0024 1937)
4 Ankara Hacı Bayram Veli University, Institute of Graduate Studies, Ankara, Turkey (GRID:grid.509259.2) (ISNI:0000 0004 7221 6011)