Investigation of day of the week and January anomalies in crypto currency market

The fact that crypto asset markets exhibit a different structure and high volatility from traditional financial markets indicates that the operating mechanisms and price formation processes of these markets differ from traditional markets. This situation creates uncertainty about the validity of traditional models used in financial decision-making processes in crypto asset markets. Therefore, understanding and analyzing the dynamics of crypto asset markets is of great importance in terms of testing the applicability of financial theories on this new asset class. Such studies aim to contribute to investors' better understanding of the risk and return profiles of crypto assets, while also making a significant contribution to the literature by examining the validity of financial theories on different asset classes. Within the scope of the study, the return series of Binance Coin, Bitcoin, Ethereum and Ripple crypto assets between 01.01.2018-31.12.2023 were examined and the day of the week and January anomalies were analyzed. Considering their time series characteristics, the stationarity conditions of the series were first tested using the Augmented Dickey-Fuller (ADF) and Phillips Perron (PP) tests. After the stationarity conditions were provided, the analyses performed using the Ordinary Least Squares (OLS) method addressed heteroscedasticity and autocorrelation issues. In order to eliminate these issues, the analyses were conducted using the GARCH (1,1) model. As a result of the analysis, it was determined that Thursdays had a negative effect on the returns of all crypto assets and that the January anomaly was only valid for Ethereum. In addition, it was determined that Mondays were associated with positive returns in the Bitcoin return series and Wednesdays were associated with positive returns in the Ripple return series. In addition, it was determined that there were significant return differences in crypto asset returns according to different months of the year.