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The purpose of this paper is to determine the relative magnitude of calendar anomalies in international Real Estate Investment Trusts (REITs). The anomalies are the prior day effect, the Monday effect, the turn‐of‐the‐month effect and the January effect. The results are based on 14 countries. The corresponding stock index is used as the reference by which to gauge the anomalous behaviour of each REIT.

There are two primary dimensions to the statistical design. Between‐country differences, based on Gross Domestic Product (GDP) and a measure of shareholder protection, are examined using a panel model. Differences between the REITs and their stock index are examined using a repeated measures dependent variable design.

The presence of the four calendar anomalies is apparent in the REITs and the stock indices. There is not sufficient evidence to show that the magnitudes of the Monday, the turn‐of‐the‐month and the January anomalies differ between REITs and stock indices. However, there is evidence that the bad day effect is stronger for REITs compared to stocks.

In terms of market development, the sample of countries is unavoidably constrained. The sample represents developed economies. The degree that these results pertain to less developed economies has yet to be established.

Existing research into the influence of calendar anomalies on REITs is based on US data. This paper examines the influence in 14 countries, including the USA, using a robust and efficient statistical design.

The literature provides extensive evidence for seasonality in stock market returns, but is almost non-existent concerning the potential seasonality in American depository receipts (ADRs). To fill this gap, this paper aims to examine a number of seasonal effects in the market for ADRs.

The paper examines four ADRs for the period from April 1999 to March 2017 to look for signs of eight important seasonal anomalies. The authors follow the standard methodology of using dummy variables for the time period of interest to capture excess returns. For comparison, the same analysis on two US stock market indices is conducted.

The results show the presence of a highly significant pre-holiday effect in all return series, which does not seem to be justified by risk. Moreover, turn-of-the-month effects, monthly effects and day-of-the-week effects were detected in some of the ADRs. The seasonality patterns under analysis tended to be stronger in emerging market-based ADRs.

Overall, the results show that significant seasonal patterns were present in the price dynamics of ADRs. Moreover, the findings lend support to the idea that emerging markets are less efficient than developed stock markets.

This is the most comprehensive study to date for indication of seasonal anomalies in the market for ADRs. The authors use an extensive sample that includes recent significant financial events such as the 2007/2008 financial crisis and consider ADRs with different characteristics, which allows to draw comparisons between the differential price dynamics arising in developed market-based ADRs and in the ADRs whose underlying securities are traded in emerging markets.

This study aims to examine the market returns of the Ukrainian stock and bond markets to determine whether they exhibit calendar anomalies including the January effect, weekend effect, and turn‐of‐the‐month (TOM) effect. Ukraine provides an opportunity to examine the efficiency of an emerging market, adding to the extensive body of research on calendar anomalies.

Regression analysis is used to examine the relationship between January returns vs non‐January returns, Monday returns vs non‐Monday returns, and TOM returns vs non‐TOM returns. Non‐parametric t‐tests and Wilcoxon signed rank tests are also used to examine TOM returns vs the rest of the month returns.

There is no evidence of a January effect or a weekend effect in the Ukrainian stock and bond markets. However, our results support a TOM effect in the Ukrainian stock market. The mean daily TOM return is 0.35 vs 0.24 per cent for the rest of the month. Additionally, in 63 per cent of the months, the mean daily TOM return exceeds the return for the rest of the month.

The data are limited to five‐years of daily returns and two different Ukrainian indexes. Thus, the results could be biased by the time period analyzed. The results are important for portfolio managers and investors as they can benefit from the TOM effect, but not the January effect and weekend effect.

This is the first study to our knowledge that has extensively examined the calendar anomalies in the Ukrainian stock and bond markets.

This study examines the turn-of-the-month (TOM) effect in Bitcoin (BIT), Ethereum (ETH) and Litecoin (LIT) cryptocurrencies from August 2015 to August 2021.

Dummy regression model is used to examine the presence of the TOM effect and to test the efficiency of the cryptocurrency market. The characteristics of the returns during TOM days are compared with that of the non-non-TOM trading days. The authors also develop a trading strategy to earn abnormal returns using the TOM effect.

The authors show that TOM returns are positive and significantly higher than that of non-TOM returns. Interestingly, the authors empirically show that the TOM effect is not driven by the day-of-the-week (DOW) effect or the January effect. Based on the significant TOM effect, the authors formulate a trading strategy that annually outperforms the buy-and-hold strategy for BIT by 21.77% and for LIT by 47.10%. Finally, the results are robust to using a Generailzed Auto Regressive Conditional Heteroskedasticity (GARCH) (1,1) model and the January 2018 sell-off.

The results have important implications for both traders and investors. The findings suggest that the investors might be able to earn excess profits by timing their positions in BIT and LIT taking the advantage of the TOM effect.

First, the authors provide the only study to report the evidence of the TOM effect in three leading cryptocurrencies, viz., BIT, LIT and ETH. Second, the authors control for the DOW effect and the January effect while investigating the TOM effect in cryptocurrency market. Finally, this study develops a trading strategy based on which the investors can time the cryptocurrency markets as indicated by the pattern of the TOM effect during the studied time period.

The purpose of this paper is to examine the stock return impact of “lucky” numbered days in markets dominated by Chinese participants. The existence of such patterns might present arbitrage opportunities for investors who do not share a belief in the Chinese system of “lucky” numbers.

In univariate and multivariate analyses, the author examines the statistical significance of return differences between “lucky” numbered days and other days. The author examines samples which only consider single digit days and months, and the author also considers samples based on the last digit of the day or month. Based on the findings in these tests, the author designs and tests a trading strategy on the Shenzhen Exchange that produces significant risk-adjusted returns in excess of the buy-and-hold return on the Shenzhen Composite Index.

The author shows that “lucky” numbered dates impact stock returns in Chinese markets and demonstrate a “lucky” number date trading strategy for the Shenzhen market that produces risk-adjusted returns in excess of the market return.

Prior research on home address numbers and stock trading codes shows that, in markets dominated by Chinese participants, assets with identifiers containing numbers defined by Feng Shui as “lucky” sell at a premium and assets with identifiers containing “unlucky” numbers sell at a discount. In such markets, prices are more likely to end in a “lucky” number than an “unlucky” number. Chinese firms also tend to price their shares at IPO using “lucky” numbers and avoiding “unlucky” numbers. The author extends this literature to examine whether dates containing “lucky” and “unlucky” numbers experience stock returns significantly different than other days on Chinese stock exchanges.

The purpose of this paper is to test prominent calendar anomalies for Indian securities markets those are commonly reported for advanced markets.

The study considers closing values of 11 different indices of National Stock Exchange India, for the period 1994-2014. By using dummy variable regression technique, five different calendar anomalies namely day of the week effect, month of the year effect, mid-year effect, Halloween effect, and trading-month effect are tested. Also, the evidence of volatility clustering has been tested through the application of generalized autoregressive conditional heteroscedasticity (GARCH)-M models.

The results display weak evidence in support of a positive Wednesday effect. The results also display weak evidence in support of a positive April and December effect. The results show strong evidence in support of a positive September effect. The Halloween effect was not found significant. The test of mid-year effect provides evidence that the returns obtained on the second-half or the year are considerably higher than those obtained during the first half. The test of interactions effects showed possible presence of interactions among various effects. The GARCH-based tests display strong evidence in support of volatility clustering.

The results have several implications for investors, regulators, and researchers. For investors, the trading strategies based on results obtained have been discussed. Similarly, certain key implications for regulators have been described.

The originality of the paper lies in the long time frame and multiple indices covered. Also, the study analyses five different calendar anomalies and the interactions among these effects. These analyses provide useful insights regarding returns predictability for the Indian securities markets.

The purpose of this paper is to ascertain the monthly seasonality in the Indian stock market after taking into consideration the market features of leptokurtosis, volatility clustering and the leverage effect.

Augmented Dickey-Fuller, Phillips-Perron and Kwaitkowski-Phillips-Schmidt-Shin tests are deployed to check stationarity of the series. Autocorrelation function, partial autocorrelation function and Ljung-Box statistics are employed to check the applicability of volatility models. An exponential generalized auto regressive conditionally heteroskedastic model is deployed to test the seasonality, where the conditional mean equation is a switching model with dummy variables for each month of the year.

Though the financial year in India stretches from April to March, the stock market exhibits a November effect (returns in November are the highest). Cultural factors, misattribution bias and liquidity hypothesis seem to explain the phenomenon.

The paper endeavors to provide a review of possible explanations behind month-of-the-year effect documented in literature in the past four decades. Further, the unique evidence from the Indian stock market supports the argument in the literature that monthly seasonality, by nature, may not be a consistent/robust phenomenon. Therefore, it needs to be examined from time to time.

As the seasonality in the stock market and resultant anomalies are dynamic phenomena, the paper reports the current seasonality/anomalies prevalent in the Indian market. This would aid investors in designing short-term investment portfolios (based on anomalies present) in order to earn abnormal returns.

In the research of stock market efficiency, it is argued that the stock market moves randomly and absorbs all the available information. As a result, it is quite impossible to make predictions about the possible future movement by the investors. But literatures have detected certain calendar anomalies where a day(s) in a week or month(s) in a year or a particular event in a year becomes conducive for investors to earn more than the normal. Hence, the purpose of this study is to find out the month of the year effect in the Indian stock market.

In this study, daily time series data of Sensex and Nifty from 1996 to 2021 is used. The study uses month dummies to capture the effect. Different variants of generalised autoregressive conditional heteroskedasticity (GARCH) models, both symmetric and asymmetric, are used in the study to model the conditional volatility in the presence month effect.

This study found the September effect in the return series of both the stock market. Apart from that, asymmetric GARCH models are found to be the best fit model to estimate conditional volatility.

This study is an endeavour to study month of the year effect in the Indian context. This research will provide valuable insight for studying the different calendar anomalies.

The purpose of this paper is to analyze different seasonal anomalies for the Israeli securities markets for the pre- and post-global financial crisis periods.

The closing values of six indices of the Tel Aviv Stock Exchange (TASE) of Israel have been considered. The time frame ranges from 2000 to 2018. Further, the overall time frame has been segregated into pre- and post-financial crisis periods. The study employs dummy variable regression technique for assessing different calendar anomalies.

The results show evidence pertaining to different seasonal anomalies for the Israeli markets. The results specifically show that the anomalies change considerably across the pre- and post-financial crisis periods. The results are more apparent for three anomalies including the day of the week effect, the month of the year effect and the holiday effect. However, anomalies including the Halloween effect and the trading month effect are found to be insignificant across both pre- and post-financial crisis periods.

The study is first of its kind that analyzes different seasonal anomalies across pre- and post-financial crisis periods for the Israeli markets. The study provides newer insights about the overall return patterns observed in different indices of the TASE.

This paper examines the effect of the holy month of Ramadan on the returns and conditional volatility of cryptocurrency markets.

The 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.

Although 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.

First, 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.