Search results

The Revenue Reconciliation Act of 1993, implemented since 1 January 1994, facilitates the increase of institutional investment in the real estate investment trust (REIT) market. Utilizing this particular feature in the market, we examine the time‐series effect of institutional holdings to distinguish the tax‐loss‐selling hypothesis and the window‐dressing hypothesis for REITs. Consistent with the tax‐loss‐selling hypothesis, we have evidence that the January premiums decreased with the level of institutional involvement for REITs. Furthermore the January premiums declined significantly for equity REITs only that attracted more institutional investors than mortgage REITs. On the other hand, the January premiums did not decrease significantly for mortgage REITs. Overall the results suggest that trading strategies to profit the higher January returns may work only when institutional investors exit and leave the market.

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.

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.

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.

This study uses stochastic dominance (SD) theory to examine whether the traditional festival, such as the Spring Festival (often in February), affects the patterns of monthly anomaly for the Taiwan Stock Exchange (TWSE). The paper aims to discuss these issues.

The authors employ a new bootstrap-based test due to Linton, Maasoumi and Whang (hereafter LMW). The LMW test is well suited for financial time series data, such as monthly returns of various portfolios in this study, because it allows for general dependence among the prospects (distributions) and does not require the observations to be identically and independently distributed.

The particular findings of this study are that the February effect and the February-size effect indeed exist in the TWSE. Furthermore, allowing part of investors' assets is invested in the risky asset and the remaining part in a risk-free asset, first finding for monthly anomaly in the extant literature, is useful in distinguishing the performance among various size-month portfolios.

Instead of tax-loss and window dressing hypothesis, the Spring Festival money movement hypothesis can be used to well explain the findings.

This paper attempts to investigate the presence of seasonal effects in the Indian stock market.

The paper tests the efficiency of the Indian stock market through a number of hypotheses. Week day effects, day‐of‐the‐week, weekend, January and April effects are examined by applying a variety of statistical techniques.

The results are interesting and contradict some of the findings found elsewhere. The negative Monday effect and the positive January effects are not found in India. Instead the Monday returns are positive while Tuesday returns are negative.

The seasonal effects in the Indian market have been examined by the two major indices, the Bombay Stock Exchange Index and the National Stock Exchange Index. However, it must be remembered that the Indian economy became deregulated from 1991 and this may have had an impact on the markets.

This study indicates that the Indian stock market does not exhibit the usual seasonal anomalies such as Monday and January effect. The absence of Monday effect could be due to the settlement period in Indian market. That the tax year ends in March and December has no special significance may explain the non‐existence of January

Most of the studies on anomalies have dealt with the developed markets. The Indian market has its unique Badla financing, settlement period duration and trading regulations. The presence/absence of the anomalies may provide support to some of the hypotheses used to explain them.

The purpose of this paper is to focus on the weak-form efficiency of 24 emerging and nine industrial stock market indices around the world. It tests for the predictability and the presence of seasonal patterns in rates of return from January 2000 to December 2010.

It reports on the descriptive statistics for estimated monthly percentage returns. This is complemented by the use of both parametric and non-parametric techniques to test for abnormal return behaviour in stock markets.

The results show that: first, emerging economies which persisted with market-oriented reforms had higher returns relative to risk, indicating their attractiveness for risk diversification; second, successive changes in stock prices were interrelated with each other and therefore contained information for predicting future prices in two-thirds of the emerging markets compared to one-third of industrial economies; and third, the turn-of-the calendar year effect was present for half of the emerging markets vis-à-vis one-quarter of the developed countries. The authors found limited support for the tax-loss selling hypothesis for both the emerging and industrial economies.

The paper fails to specifically analyse the implications for security returns of changes in technology, institutions, volume of trading and regulations in the different stock markets.

The results should be particularly informative for foreign investors with regard to the risk diversification benefits of the various emerging and industrialised stock markets and the expected risk-return trade-offs.

The paper provides a more powerful explanation for the role of institutional arrangements, infrastructure, culture and other country-specific risk factors in asset pricing compared to disparate case studies.

This study is a pioneer in the academic literature to investigate the relationship between Valentine’s Day and stock market returns of major economies around the world.

Specific control variables for Valentine's Day are introduced to eliminate the potential influence of other effects.

The findings indicate that stock returns are higher on the days when Valentine's Day is approaching than on other days for most cases, showing “the Valentine Effect” in the stock market.

Unlike other holiday effects in the previous literature, the Valentine's Day effect cannot be explained by many conventional theories, such as tax-loss selling and the inventory adjustment hypothesis.

This study examines the durations of US stock market cycle expansions and contractions for the presence of seasonality. Specifically, it is determined whether the distributional characteristics (i.e., location and dispersion) of the durations of market expansions and contractions are dependent on the time of the year the market phase begins or ends. The duration data are obtained from a stock market chronology of monthly peak and trough dates for the period May 1835 through July 1998 and nonparametric rank‐based tests are used to test for the presence of seasonality. In order to provide some evidence on robustness with respect to the sample data, results are obtained for the entire sample period as well as for various sub‐periods. When the data are aggregated on a quarterly basis, the evidence suggests that seasonal structures are present in stock market cycle durations. These seasonals are related primarily to shifts in location over the course of the year and to when a market expansion or contraction begins. However, when the duration data are aggregated on a bi‐annual basis, support for seasonality is much more limited.

Daniel and Titman (1997) contend that the Fama‐French three‐factor model’s ability to explain cross‐sectional variation in expected returns is a result of characteristics that firms have in common rather than any risk‐based explanation. The primary aim of the current paper is to provide out‐of‐sample tests of the characteristics versus risk factor argument. The main focus of our tests is to examine the intercept terms in Fama‐French regressions, wherein test portfolios are formed by a three‐way sorting procedure on book‐to‐market, size and factor loadings. Our main test focuses on ‘characteristic‐balanced’ portfolio returns of high minus low factor loading portfolios, for different size and book‐to‐market groups. The Fama‐French model predicts that these regression intercepts should be zero while the characteristics model predicts that they should be negative. Generally, despite the short sample period employed, our findings support a risk‐factor interpretation as opposed to a characteristics interpretation. This is particularly so for the HML loading‐based test portfolios. More specifically, we find that: the majority of test portfolios tend to reveal higher returns for higher loadings (while controlling for book‐to‐market and size characteristics); the majority of the Fama‐French regression intercepts are statistically insignificant; for the characteristic‐balanced portfolios, very few of the Fama‐French regression intercepts are significant.