Another look at calendar anomalies

Chatzitzisi, Evanthia; Fountas, Stilianos; Panagiotidis, Theodore

doi:10.1016/j.qref.2019.04.001

Cited by 21 publications

(14 citation statements)

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“…The objective of this study is to investigate the day-of-the-week anomaly in the prediction of cryptocurrencies with ANN. The day-of-the-week anomaly refers to a phenomenon that implies that investors tend to earn more returns on a particular day-of-the-week than on other days (Chatzitzisi et al 2021 ). This study examines whether the predictions of crypto money values made using the values of the past period differ from those made by including the day-of-the-week dummy variables.…”

Section: Methodsmentioning

confidence: 99%

“…This study discusses the existence of a day-of-the-week anomaly in cryptocurrencies. The day-of-the-week anomaly indicates that investors in the markets receive more returns on a certain day-of-the-week than on other days (Chatzitzisi et al 2021 ). This study aims to detect the existence of the day-of-the-week anomaly in cryptocurrencies with feedforward artificial neural networks (ANNs).…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2023

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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 $${R}_{OOS}^{2}$$ R OOS 2 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.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

et al. 2023

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“…However, the majority of research papers appear to concentrate around USA and other developed markets (Guidi et al. , 2011; Chatzitzisi et al. , 2021; Khan and Rabbani, 2019; Plastun et al.…”

Section: Introductionmentioning

confidence: 99%

Day-of-the-week effect and volatility in stock returns: evidence from the Indian stock market

Aggarwal

Jha

2023

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PurposeThe purpose of this paper is to examine the existence of the day-of-the-week effect in the Indian stock market.Design/methodology/approachGeneralized Autoregressive Conditional Heteroskedasticity (GARCH) (1, 1), Exponential GARCH (EGARCH) (1, 1) and Threshold GARCH (TGARCH) (1, 1) models are employed to examine the day-of-the-week effect in the Indian stock market for the period of 28 years from 3rd July, 1990 to 31st March, 2022.FindingsThe empirical results derived from the GARCH models indicate the existence of day-of-the-week effects on stock returns and volatility of the Indian stock market. The study reveals that all the days of the week are positive and significant in National Stock Exchange (NSE)-Nifty market returns. The findings confirm the persistence of ARCH and GARCH effects in the daily return series. Moreover, the asymmetric GARCH models show that the daily stock returns exhibit significant asymmetric (leverage) effects.Practical implicationsThe results of this study established that the Indian stock market is not efficient and there exists an opportunity to the traders for predicting the future prices and earning abnormal profits in the Indian stock market. The findings of the study are important for traders, investors and portfolio managers to earn abnormal returns by cross-border diversification.Originality/valueFirst, to the best of the authors' knowledge, this paper is the first to study the day-of-the-week effect in Indian stock market considering the most recent and longer time period (1990–2022). Second, unlike previous research, this study used GARCH models (GARCH, EGARCH and TGARCH) to capture the volatility clustering in the data.

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“…There are multiple researches focused on the analysis of different anomalies using different methodologies and applied to a huge set of financial assets. Among the latest published about analysis of anomalies, we can point out, grouped by effect, the following: day of week effect (Jaisinghani et al, 2019;Xiong et al, 2019;Miss et al, 2020;Chatzitzisi et al, 2021;Chaouachi and Dhaou, 2020;Xiao and Maillebuau, 2020), month of year (Xiao and Maillebuau, 2020;Plastun et al, 2020aPlastun et al, , 2019aXiong et al, 2019;Harshita and Yadav, 2018;Hui and Chan, 2018;Sawitri and Astuty, 2018), turn of the month effect (Singh et al, 2020;Caporale and Plastun, 2017;Singh et al, 2020), turn of the year effect (Javed and Naveed, 2021;Plastun et al, 2019b), Halloween effect (Arendas et al, 2018;Kenourgios and Samios, 2021;Plastun et al, 2020b) or the presence of intraday pattern or time-of-day (Caporale et al, 2016;Inci, 2018;Shahzad et al, 2018;Yang et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

“…, 2019; Miss et al. , 2020; Chatzitzisi et al. , 2021; Chaouachi and Dhaou, 2020; Xiao and Maillebuau, 2020), month of year (Xiao and Maillebuau, 2020; Plastun et al.…”

Section: Introductionmentioning

confidence: 99%

Ramadan effect in the cryptocurrency markets

Martín

2022

RBF

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PurposeThis paper examines the effect of the holy month of Ramadan on the returns and conditional volatility of cryptocurrency markets.Design/methodology/approachThe closing prices of six cryptocurrencies have been considered. The study employs different classical tests for checking if the efficiency behaviour is similar during Ramadan celebration days and non-Ramadan days. Besides, dummy variable regression technique for assessing this anomaly on returns and volatilities has been applied.FindingsAlthough no significant effect on returns and volatility for Litecoin has been found, the results provide evidence about the existence of the Ramadan effects in cryptocurrency markets. The results of the mean equations show the existence of Ramadan effect for Ethereum, Ripple, Stellar and BinanceCoin for all considered models. Significant effect on Bitcoin returns is found with an autoregressive model of order 1. The results of conditional volatility show Ramadan effect on volatility is not detected.Originality/valueFirst, a new contribution in the incipient study of cryptocurrency analysis. Second, a comprehensive review of recently published empirical articles about Ramadan effect on traditional assets has been carried out. Third, unlike most of the papers focussed on the study of Bitcoin, this study has been extended to six cryptocurrencies. Ramadan effect have not been analysed in cryptomarkets yet. This study come to fill this gap and analyses Ramadan effect, previously documented for traditional assets, in particular, stock index from Muslim countries, but not yet analysed in the cryptocurrency markets.

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Another look at calendar anomalies

Cited by 21 publications

References 50 publications

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

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

Day-of-the-week effect and volatility in stock returns: evidence from the Indian stock market

Ramadan effect in the cryptocurrency markets

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