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This paper aims to examine the month and the trading month effects under changing financial trends. The Greek stock market was chosen to implement the authors' assumptions because during the period 2002-2012, there were clear and long-term periods of financial growth and recession. Thus, the authors examine whether the financial trends influence not only the Greek stock market’s returns, but also its anomalies.

Daily financial data from the Athens Exchange General Index for the period 2002-2012 are used. The sample is separated into two sub-periods: the financial growth sub-period (2002-2007), and the financial recession sub-period (2008-2012). Several linear and non-linear models were applied to find which is the most appropriate, and the results suggested that the T-GARCH model better fits the sample.

The empirical results show that changing economic and financial conditions influence the calendar effects. The trading month effect, especially, completely changes in each fortnight following the financial trend. Regarding the January effect, which is the most popular month effect, the results confirm its existence during the growth period, but during the recession period, we find that it fades. Therefore, by examining the aforementioned calendar effects in different periods, different conclusions may be reached, perhaps because the financial trends’ influence is ignored.

The empirical results confirm the authors' assumption that a possible explanation for the controversial empirical findings regarding the calendar anomalies may be the different financial trends. However, these are some primary results that are confirmed only for the Greek case. Further empirical research for deeper stock markets and/or a group of countries may be useful to reach conclusions regarding the financial trends’ influence on the calendar anomalies patterns.

The findings are helpful to anyone who invests and deals with the Greek stock market. Moreover, they may pave the way for an alternative calendar anomalies research approach, proving useful for investors who take these anomalies into account when they plan their investment strategy.

This paper contributes to the literature by presenting an alternative methodological approach regarding the calendar anomalies study and a new explanation for the calendar effects existence/fade through time by examining the calendar anomalies patterns under a changing economic environment and financial trends.

The paper aims to investigate the monthly and trading month effects in the stock market returns of the ASE using daily data before and after the crisis of 1999‐2001. In addition, the study seeks to consider data from both periods of the ASE, before and after the upgrade of the market (May 2001).

This paper examines the calendar effects in the Greek stock market returns using an ordinary least squares (OLS) model. Daily closing prices of the General ASE Index, FTSE/ASE‐20 and FTSE/ASE Mid 40 are used to calculate daily returns. The time period includes data from 26 November 1996 to 12 July 2002 for General ASE Index, 23 September 1997‐30 August 2001 for FTSE/ASE‐20 and 8 December 1999‐30 August 2001 for FTSE/ASE Mid 40.

The results show that there is no January effect. In other words, daily returns are not higher in January than in any other month. Moreover, the results for the trading month effect show higher (but not significant) returns over the first fortnight of the month.

The results have important implications for both traders and investors. The findings are strongly recommended to financial managers dealing with Greek stock indices.

The contribution of this paper is to provide evidence using data before and after the financial crisis of 1999‐2001 in Greece.

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 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.

The aim of this paper is to examine the relationship between weather (temperature) and stock market returns using daily data from Portugal; also, to examine whether the temperature is driven by calendar‐related anomalies such as the January and trading month effects.

Daily financial and weather data from Lisbon Stock Exchange (PSI 20 index) and Lisbon capital for the period 1995‐2007 are considered. The paper employs an AR(1)‐TGARCH(1,1) model under several distributional assumptions (Normal, Student's‐t and GED) for the errors.

Empirical results show that temperature affects negatively the PSI20 stock returns in Portugal. Moreover, temperature is dependent of both January and trading month effects. Stock returns were found to be positive in January and higher over the first fortnight of the month. Lower temperature in January leads to higher stock returns due to investors' aggressive risk taking.

Further research should investigate the impact of other meteorological variables (humidity, amount of sunshine) and other calendar anomalies on the course and behaviour of major international stock indices using data before and after the recent crisis.

The findings are helpful to financial managers, investors and traders dealing with the Portuguese stock market.

The contribution of this paper is to provide evidence on the empirical linkages between temperature and stock market returns using GARCH models. To better understand the relationship between the temperature and stock market returns, the paper also examines whether the returns are higher in winter (January effect) and during the first or second fortnight of the month (trading month effect). To the best of the author's knowledge, this is the first empirical investigation on weather and stock market returns relationship for Portugal.

The purpose of this paper is to examine the presence of the turn-of-month effect in the Indian currency market for selected currency pairs: USD-INR, EUR-INR, GBP-INR and JPY-INR, from January 1999 to April 2014.

Ordinary least square regression analysis is used to examine the presence of the turn-of-month effect and to test the efficiency of the Indian currency market. The characteristics of the returns during the turn-of-month days are compared with that of the non-turn-of-month trading days. The sample period is later divided into two sub-periods, that is, pre- and post-2008 to capture the behavior of returns before and after the 2008 financial crisis.

The results indicate the existence of pricing patterns which are unique to individual currencies. For the entire sample period, USD and JPY exhibit turn-of-month effect and the returns in turn-of-month trading days are significantly lower than the returns during non-turn-of-month trading days. For the sub-period before 2008, all the currencies exhibit significant turn-of-month effects and the returns in the turn-of-month trading days are significantly lower than those in the non-turn-of-month trading days. However, post-2008; this effect vanishes for all the currencies except for USD.

The results have important implications for both traders and investors. The findings suggest that the investors might not be able to earn excess profits by timing their positions in some particular currencies taking the advantage of turn-of-month effect which in turn indicates that the currency markets have become more efficient with time. The results are in conformity with those reported for the developed markets.

To the best of the author’s knowledge, no study has yet examined these calendar anomalies in the currency markets using data which covers two important periods, pre-2008 and post-2008. Therefore, we provide a pioneer study in which we analyze the calendar anomalies in an emerging currency market (India) by segregating the data before and after 2008 financial crisis.

This study aims to examine the presence of the day-of-the-week (DOW), January and turn-of-month (TOM) effect in 20 currency pairs against the US dollar, from January, 1995 to December, 2014.

Ordinary least square with GARCH (1,1) framework is used to examine the presence of DOW, January and TOM effect to test the efficiency of the currency markets. The sample period is later divided into two sub-periods of equal length, that is, from 1995 to 2004 and 2005 to 2014, to explore the time-varying behavior of the calendar anomalies. Further, the authors also use the non-parametric technique, the Kruskal–Wallis test, to provide robustness check for the results.

For the DOW effect, the results indicate that the returns on Monday and Wednesday are negative and lower than the returns on Thursday and Friday which show positive and higher returns. The returns of all the currencies are higher (lower) in January (TOM trading days) and lower (higher) during rest of the year (non-TOM trading days). However, these calendar anomalies seem to have disappeared for almost all currencies during 2005 to 2014 and indicate that the markets have achieved a higher degree of efficiency in the later part of the sample.

The results have important implications for both traders and investors. The findings suggest that the investors might not be able to earn excess profits by timing their positions in some particular currencies taking the advantage of DOW, January or TOM effect, which in turn indicates that the currency markets have become more efficient with time. The results might be appealing to the practitioners as well in a way that they can consider the state of financial market for financial decision-making.

The findings of lower returns on Monday and Wednesday and high returns during Thursday and Friday for all the currencies indicate that the foreign investors can take the advantage by going short on Monday and Wednesday and long on Thursday and Friday. Similarly, the returns of all the currencies are higher (lower) in January (TOM trading days) and lower (higher) during rest of the year (non-TOM trading days). During this period, investors in the currency markets could benefit themselves by taking long (short) positions in January (TOM trading days) and short (long) positions during rest of the year (non-TOM trading days).

The author provides a pioneer study on the presence of calendar anomalies (DOW, TOM and the January effect) across a wide range of currencies using 20 years of data from January 1995 to December 2014. To the best of the author’s knowledge, no study has examined the presence of January effect in the currency market; therefore, the author provides the first study in which January effect in a number of currencies is investigated.

The purpose if this paper is to examine the turn-of-the-month effect in the equity market of three major emerging countries – Brazil, India and China – from January 2000 to December 2017.

Ordinary least square regression analysis is used to examine the presence of the turn-of-the-month effect and to test the efficiency of the emerging stock markets. The characteristics of the returns during the turn-of-the-month days are compared with that of the non-turn-of-the-month trading days.

The average returns during turn-of-the-month days for all the considered emerging market indices are significantly higher than the non-turn-of-the-month days for the full sample. For the subsample analysis, the average returns for Brazil and India for pre-GFC period are higher on the turn-of-the-month days than on the non-turn-of-the-month days. However, the effect disappears in China during the GFC period. During the crisis period, the results show that the turn-of-the-month effect disappears in Brazil and India, whereas for China, the effect is significant. For the post-GFC period, the-turn-of-the-month effect reappears for all the countries.

The results have important implications for both traders and investors. The authors’ results indicate that the market participants can time the stock markets of these countries by taking long positions especially during the times when the turn-of-the-month effect is highly significant.

To the best of the authors’ knowledge, this paper is the first to study the turn-of-the-month effect, in the key emerging countries such as Brazil, China and India. Second, the authors divide the sample into three subperiods based on the 2008 GFC such as pre-GFC, GFC and post-GFC to understand the dynamic behavior of turn-of-the-month effect over time. Most importantly, the authors control for the day-of-the-week effect while examining the turn-of-the-month effect.

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.