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The purpose of this paper is to identify drought conditions in Jordan in the period 1960‐2006 for two major basins – the Yarmouk and the Zarqa. This study aims to look at the long‐term drought conditions (hydrological droughts) and to investigate the possibility of changes in the monthly precipitation pattern for the period of 1997‐2006, as this period has suffered from the average slightly dry conditions for the two basins.

The Standard Precipitation Index (SPI) is used to assess drought conditions in the Yarmouk and Zarqa basins. The SPI is applied on an annual basis to assess the hydrological long‐term droughts. Average monthly precipitation is used as the input to the SPI procedure.

The SPI is applied on an annual basis (12 months) SPI‐12. Results of the SPI‐12 assessment show that the years 1978, 1995, and 1999 represented extremely dry conditions in both basins. The years 1989 and 1993 represented severely dry conditions in the Zarqa basin and extremely dry conditions in the Yarmouk basin. The statistical procedure for precipitation anomalies identifies precipitation anomalies for the years 2000, 2003, and 2006. However, due to the limited number of anomaly years this study does not conclude that there is a change in the pattern of monthly precipitation of the tested period 1997‐ 2006.

Using the SPI method this work can be extended to compare drought conditions in the remaining major basins in the country.

The paper provides important information about drought conditions in Jordan and ways to assess precipitation anomalies and pattern changes. This can be beneficial to the planning of water resources in Jordan and the associated developments (agricultural, industrial, tourism, and residential).

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.

The purpose of this paper is to explore the overlooked relationship between politics and the performance of anomaly-based investment strategies.

Monthly long-short portfolios are formed based on relative mispricing scores according to the Stambaugh et al. (2012, 2015) relative mispricing measure. Portfolio performance is examined throughout various presidential terms. The design also introduces economic policy uncertainty (EPU) as a possible explanatory variable for portfolio performance.

The analysis reveals that anomaly-based returns are higher under Republican administrations than they are under Democratic administrations. Moreover, the results show that the impact of EPU on the relationship between the political party affiliation of the president and future anomaly-based returns are driven by the election and post-election years.

The examination of returns on a long-short portfolio may be of particular value to investment companies, such as hedge funds, who regularly employ this type of strategy.

While the impact of presidential terms on raw equity returns has been well examined, the paper is the first to examine the impact of presidential terms on the return of an anomaly-based investment strategy. EPU is also introduced as an important contributing factor.

This chapter used empirical data from five developed markets and five emerging markets to perform an examination of anomalies using common financial economic approaches along with more innovative econometric models. Of the methodologies used to test for anomalies, the data-driven panel and quantile regressions were empirically found to be better suited over the traditionally common approaches to describe the non-linear, switching behavior of the anomalies. In the developed markets, the statistically significant small firms (size) had the highest average returns. In the developing markets, the lower price-to-earnings (P/E) ratios (value) had the highest average returns. In addition, the research found (1) a small country effect, (2) sales had a negative relationship with returns, and (3) a lower (higher) book-to-market (B/M) was associated with higher returns in the developed (developing) markets, indicating investors received a higher premium for growth (value) equities. The semi-strong form of the efficient market hypothesis was also found to be violated. The anomalies’ behavior varied between sorted portfolios, industries, and developed to emerging markets; though it was found to be consistent through time (not disrupted by bear or bull markets).

Deviations from efficiency widely documented for the case of developed capital markets include the presence of seasonal patterns. These anomalies, fairly well known by investors, could possibly lead to obtaining extraordinary gains. Although markets from the developing and transitional economies have grown significantly during the last decades, research concerning their seasonal behavior is limited. This paper examines the day-of-the week and month-of-the year effect for the seven Latin American stock exchanges: Argentina, Brazil, Chile, Colombia, Mexico, Peru, and Venezuela. Returns derived from the local nominal indexes, adjusted for inflation indexes, and dollar adjusted indexes are analyzed to identify the behavior of each exchange and draw some comparisons.

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.

The stock market anomalies have been studied across the globe with intermingled results for individual markets. The present study has investigated the financial year effect for Indian stock markets by testing month-of-the-year-effect anomalies.

The oldest stock exchange's index returns (Bombay Stock Exchange [BSE]) have been tested using ordinary least squares (OLS) and autoregressive conditional heteroskedasticity in mean (ARCH-M) models with Student's t and Student's t-fixed distributions for the period between 1991 and 2019. The Glosten, Jagannathan and Runkle-generalised autoregressive conditional heteroskedasticity (GJR-GARCH) model has been further used to find out existence of the leverage effect in returns.

The findings indicated no evidence for anomalies in the Indian stock market which may be used by investors for making unusual returns. However, the volatility in returns has shown weak but significant results due to the financial year impact. The leverage effect has not been found in the financial year cycle change over. The Indian market may be said to be moving towards a state of efficiency, leaving no scope for investors to gauge bizarre profits.

The study has incorporated the Indian context for testing anomalies during the start and end of the financial year cycle. The model may be extended further to developed and developing nations’ markets for testing efficiency in their stock markets during the same cycle.

The paper may be the first of its kind to test for the financial year effect on standalone basis for Indian markets. The paper also adds to the existing literature on testing events’ effect.

The study aims to test the existence of a meeting clustering effect in the Spanish Stock Exchange (SSE).

This paper studies the relationship between the clustering of annual general meetings and stock returns in the SSE. A multivariate analysis is carried out in order to analyse the relationship between monthly returns and the clustering of general meetings in the SSE.

The authors show that meeting clustering exists and that some months exhibit significant and positive additional returns related to the holding of ordinary or extraordinary general meetings.

The authors have explored some possible explanations for the meeting clustering effect, such as a potential link with the “Halloween” effect or the presence of higher-than-normal levels of volatility, trading volumes or investor attention. However, none of these can explain the meeting clustering effect that emerges as a new anomaly in the SSE.

The authors have documented significant and positive abnormal returns in some months that coincide with the holding of general meetings. Therefore, the holding of ordinary and/or extraordinary meetings in some months involves the release of relevant information for investors.

This study complements the financial literature because it is focused on the clustering of meetings and its effect on a stock market whose legal order is based on civil law. This fact allows us to shed new light on meeting clustering and its effect on other types of markets.

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.

Transient climate sensitivity relates total climate forcings from anthropogenic and other sources to surface temperature. Global transient climate sensitivity is well studied, as are the related concepts of equilibrium climate sensitivity (ECS) and transient climate response (TCR), but spatially disaggregated local climate sensitivity (LCS) is less so. An energy balance model (EBM) and an easily implemented semiparametric statistical approach are proposed to estimate LCS using the historical record and to assess its contribution to global transient climate sensitivity. Results suggest that areas dominated by ocean tend to import energy, they are relatively more sensitive to forcings, but they warm more slowly than areas dominated by land. Economic implications are discussed.