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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.

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 investigates whether cyclical turning points in the U.S. and U.K. stock markets are unevenly distributed over the year, that is, whether they are more likely to occur during certain months of the year. In examining this form of periodic seasonality, a Markov switching‐model is applied to U.S. and U.K. stock market chronologies of monthly peak and trough dates for the periods May 1835 through March 2000 and May 1836 through September 2000, respectively. In order to provide some evidence on robustness with respect to the sample data, results are obtained for the entire sample periods as well as for various sub‐. For both markets, the evidence indicates that while the probability of moving from an expansion to a contraction does not depend on the month of the year, the probability of switching from a contraction is greater for some months. Additionally, the durations of contractions, but not expansions, are dependent on the month of the year in which they begin.

The turn-of-the-month (TOM) effect is observed as one of the seasonalities in many markets. The author examines the TOM effect in the KOSDAQ market and finds that the effect is significant. The TOM effect in the KOSDAQ market is not due to size, turn-of-the-year, turn-of-the-quarter or index rebalancing effect. The author also finds that individual and institutional traders do not trade and buy more stocks at the TOM than on the rest days, not consistent with existing explanations of the increased liquidity by individual investors or institutional window-dressing activity. When the author investigated the net buying volume and net turnover of each investor, the net volume and turnover of individual investors at the TOM were significantly lower than those on the other days, rejecting the hypothesis of their increased demand. Interestingly, net foreign volumes at the TOM are significantly higher than on the other days. Finally, using panel regressions, the author finds that stocks with a higher net buying volume of foreigners for the TOM period tend to have higher returns, while stocks with a higher net buying volume of individual traders for the TOM period are likely to have lower returns. The results confirm that the TOM effect is not due to the increased demand of individual investors. Instead, higher net buying volume by foreigners may partially cause the TOM effect. Therefore, this study contributes to the literature by revealing the presence of the TOM effect in the KOSDAQ market and the foreign role in the anomaly in the market even mainly traded by retail investors.

This chapter simultaneously investigates the most important calendar anomalies in stock returns: day of the week, turn of the month, turn of the year and holiday periods, in four of the most important Latin American stock markets: Argentina, Brazil, Mexico and Chile. Previous evidence available for these countries is very limited. Our results indicate that the three markets show a rather similar pattern regarding return seasonality. A day of the week effect, consisting in negative returns on Mondays, is reported for all the stock markets but the Mexican. The turn of the year effect is observed only in Argentina, and moderate holiday and turn of the month effects are reported in the Brazilian and the Mexican markets, respectively. In addition, significant levels of first-order return autocorrelation are reported for the four stock markets. The contemporary financial crisis has dramatically affected the behaviour of stock prices worldwide, causing, among other effects, a huge increase in price volatility and probably changing the behaviour of participants in financial markets. We have also investigated to what extent our results have been affected by the current abnormal situation.

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.

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 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.

Market efficiency suggests that price of the security must reflect its intrinsic value by impounding all the available and accessible information. Asset pricing in capital markets has been an exceptionally dynamic area of scholarly research and is considered as a barometer for assessing market efficiency. This phenomenon was very well explained by several market pricing models and theories over the last few decades. However, several anomalies, which cannot be explained by the traditional asset pricing models due to seasonal and psychological factors, were observed historically. The same has been studied by several researchers over the years and is well captured in the literature pertaining to market asset pricing. The purpose of this paper is to revisit the research studies related to a few asset pricing anomalies, collectively referred to as “calendar anomalies”, such as – day-of-the-week, turn-of-the-month, turn-of-the-year and the holiday effects. In this pursuit, a thorough survey of literature in this area, published over the last 80 years (from 1934 to 2016) across 24 prominent journals, has been made and presented in a comprehensive, structured and chronologically arranged major findings and learnings. This literature survey reveals that the existing literature do provide a great depth of understanding around these calendar anomalies often with reference to specific markets, the size of the firm and investor type. The paper also highlights a few aspects where the existing literature is silent or provides little support leaving a gap that needs to be addressed with further research in this area.

The goal of the study requires a comprehensive review of the past literature related to calendar anomalies. As a consequence, to identify papers which sufficiently represent the area of study, the authors examined the full text of articles within EBSCOHost, Elsevier-Science direct, Emerald insight and JSTOR databases with calendar anomalies related keywords for articles published since inception. Further, each article was classified based on the anomaly discussed and the factors used to sub-categorize the anomaly. Once all the identified fields were populated, we passed through another article by constantly updating the master list till all the 99 articles were populated.

It is also important to understand at this juncture that most of the papers surveyed discuss the persistence of the asset pricing anomalies with reference to the developed markets with a very few offering evidences from emerging markets. Thus leaving a huge scope for further research to study the persistence of asset pricing anomalies, the degree and direction of the effect on asset pricing among emerging markets such as India, Russia, Brazil vis-a-vis the developed markets. Further, regardless of the markets with reference to which the study is conducted, the research so far appears to have laid focus only on the overall market returns derived from aggregate market indices to explain the asset pricing anomalies. Thus leaving enough scope for further research to study and understand the persistence of these anomalies with reference to various strategic, thematic and sectoral indices in various markets (developed, emerging and underdeveloped countries) across different time periods. It will be also interesting to understand how, some or all of, these established asset pricing anomalies behave over a certain time period when markets move across the efficiency maturity model (from weak form to semi-strong to strong form of efficiency).

The main purpose of the study entails a detailed review of all the past literature pertinent to the calendar anomalies. In order to explore the prior literature that sufficiently captures the research area, various renowned databases were examined with keywords related to the calendar anomalies under scope of current study. Furthermore, based on the finalized articles, a comprehensive summary table was populated and provided in the Appendix which gives a snapshot of all the articles under the current assessment. This helps the readers of the article to directly relate the findings of each article with its background information.

This study seeks to examine the market returns of five domestic real estate investment trust (REIT) indices to determine whether they exhibit a turn‐of‐the‐month (TOM) effect.

A test is carried out for the TOM effect by employing a battery of parametric and non‐parametric statistical tests that address the concerns of distributional assumption violations. An OLS regression model compares the TOM returns with the rest‐of‐the‐month (ROM) returns and an ANOVA model examines the TOM period while controlling for monthly seasonalities. A non‐parametric t‐test examines whether the TOM returns are greater than the ROM returns and a Wilcoxon signed rank test examines the matched‐pairs of TOM and ROM returns.

A TOM effect in all five domestic REIT indices is found: real estate 50 REIT, all‐REIT, equity REIT, hybrid REIT, and mortgage REIT. More specifically, the six‐day TOM period, on average, accounts for over 100 per cent of the monthly return for the three non‐mortgage REITs, while the ROM period generates a negative return. Additionally, the TOM returns are greater than the ROM returns in 75 per cent of the months.

The data are limited to five‐years of daily returns and five different indices. Thus, the results could be biased on the selected time period.

These results are important to REIT portfolio managers and investors. Domestic REIT markets experience a TOM effect from which investors and portfolio managers can benefit.

The daily returns of all five major domestic REIT indices are examined. Data are evaluated which include daily returns after the passage of the REIT Modernization Act of 1999 that resulted in numerous changes for REITs. Whether the TOM effect can be detected with both parametric and non‐parametric tests is examined.