In this paper, we investigate the day of the week and the month of the year effects in African stock markets, both in the Gregorian and the Hijri calendars. Specifically, we investigate Monday effect, Friday effect, January effect and Ramadan effect, from January 2009 to December 2019, using OLS...

The study of stock market efficiency has been the objective of many researches across the globe since the last few decades. But the evidence is mixed on whether the stock market is efficient. While some studies conclude that the stock markets are efficient, other studies cast doubt on this...

In line with the Adaptive Market Hypothesis (AMH), the objective of this study is to investigate how the day-of-the-week (DOW) effect behaves under different bull and bear market conditions in African stock markets, and to examine the likelihood of being in a bull or bear regime for each market....

Easley / Kiefer / O'Hara / Paperman (1996) (EKOP) have proposed an empirical methodology that allows to estimate the probability of informed trading and that has subsequently been used to address a wide range of issues in market microstructure. The data needed for estimation is the number of...

Value-at-risk (VaR) is a useful risk measure broadly used by financial institutions all over the world. VaR has been extensively used to measure systematic risk exposure in developed markets like of the US, Europe and Asia. This paper analyzes the accuracy of VaR measure for Pakistan's emerging...

Given the process {X(t), t in T}, the definition of self-affinity is reformulated in terms of diameter of the space of the rescaled pdf's of X(t). Two necessary conditions are deduced which contribute to discriminate uniscaling processes. Furthermore, by properly choosing the distance, the...

Seasonal variation and calendar anomalies are known phenomena in equity markets worldwide. Many researchers have studied day-of-the-month, day-of-the-week, month-of-the-year, tax loss hypothesis and SAD cycle in equity markets across countries. There has been many evidences of calendar anomalies...

We apply machine learning techniques and use stock characteristics to predict the cross-section of stock returns in 33 international markets. We conduct a stringent out-of-sample test to examine concerns about overfitting: the models are trained with past U.S. data and used to predict...

We examine return predictability with machine learning in 46 international stock markets. We calculate 148 stock characteristics and use them to feed a repertoire of different models. The algorithms extract predictability mainly from simple, yet popular, factor types—such as momentum,...