scholarly journals Nonparametric analysis of the Shenzhen Stock Market: The day of the week effect

2012 ◽  
Vol 55 (3-4) ◽  
pp. 1186-1192 ◽  
Author(s):  
Dejian Lai ◽  
Anfen Bai ◽  
Kuang-Chao Chang ◽  
Heqing Wei ◽  
Liangqing Luo
Keyword(s):  
Stock Market ◽  
Nonparametric Analysis ◽  
Day Of The Week

Investigation of Timing Anomalies in the Russian Stock Market in the Post-Crisis Period

2019 ◽  
Vol 12 (3) ◽  
pp. 37-47
Author(s):  
I. Ya. Lukasevich
Keyword(s):  
Stock Market ◽  
Information Technologies ◽  
Real Data ◽  
Weak Form ◽  
Fair Value ◽  
Presidential Decree ◽  
Day Of The Week ◽  
Russian Stock Market ◽  
Economic Growth Rates ◽  
Global And Local

The implementation of the May presidential decree aimed at Russia’s joining the top five global economies and achieving economic growth rates above the world’s average while maintaining macroeconomic stability requires a highly developed and efficient stock market ensuring the accumulation of capital and its deployment in the most promising and productive sectors of the economy.The subject of the research is timing anomalies in the Russian stock market in 2012–2018. The relevance of the research is due to the information inefficiency of the Russian stock market and its imperfections leading to significant price deviations from the «fair» value of assets and depriving investors of the opportunity to form various strategies for deriving additional revenues not related to fundamental economic factors and objective processes occurring in the global and local economies and the economy of an individual business entity. Based on the trend analysis of the Broad Market USD Index (RUBMI), the paper demonstrates a methodology for simulating the analysis of price anomalies on large arrays of real data using statistical data processing methods and modern information technologies. The paper concludes that though the Russian stock market lacks even the weak form of efficiency, such well-known timing anomalies as the “day-of-the-week” effect and the “month” effect have not been observed in the recent years. Therefore, investors could not use these anomalies to derive regular revenues above the market average.

Does time-varying illiquidity matter for the Indian stock market? Evidence from high-frequency data

2021 ◽  
pp. 031289622110102
Author(s):  
Mousumi Bhattacharya ◽  
Sharad Nath Bhattacharya ◽  
Sumit Kumar Jha
Keyword(s):  
Stock Market ◽  
Stock Returns ◽  
High Frequency ◽  
Jel Classification ◽  
Exogenous Shock ◽  
Intraday Data ◽  
Day Of The Week ◽  
Holiday Effects ◽  
Indian Stock Market ◽  
The Impact

This article examines variations in illiquidity in the Indian stock market, using intraday data. Panel regression reveals prevalent day-of-the-week, month, and holiday effects in illiquidity across industries, especially during exogenous shock periods. Illiquidity fluctuations are higher during the second and third quarters. The ranking of most illiquid stocks varies, depending on whether illiquidity is measured using an adjusted or unadjusted Amihud measure. Using pooled quantile regression, we note that illiquidity plays an important asymmetric role in explaining stock returns under up- and down-market conditions in the presence of open interest and volatility. The impact of illiquidity is more severe during periods of extreme high and low returns. JEL Classification: G10, G12

Day Of The Week And Its Effect On Stock Market Volatility: Evidence From An Emerging Market

2013 ◽  
Vol 29 (6) ◽  
pp. 1727 ◽  
Author(s):  
Omar Farooq ◽  
Mohammed Bouaddi ◽  
Neveen Ahmed
Keyword(s):  
Stock Market ◽  
Stock Exchange ◽  
Emerging Market ◽  
International Markets ◽  
Conditional Variance ◽  
Market Volatility ◽  
Stock Market Volatility ◽  
Foreign Investors ◽  
High Volatility ◽  
Day Of The Week

This paper investigates the day of the week effect in the volatility of the Saudi Stock Exchange during the period between January 7, 2007 and April 1, 2013. Using a conditional variance framework, we find that the day of the week effect is present in the volatility. Our results show that the lowest volatility occurs on Saturdays and Sundays. We argue that due to the closure of international markets on Saturdays and Sundays, there is not enough activity in the Saudi Stock Exchange. As a result, the volatility is the lowest on these days. Our results also show that the highest volatility occurs on Wednesdays. We argue Wednesday, being the last trading day of the week, corresponds with the start of four non-trading days (Thursday through Sunday) for foreign investors. Fearing that they will be stuck up with stocks in case some unfavorable information enters the market, foreign investors tend to exit the market on Wednesdays. As a result of excessive trading, there is high volatility on Wednesdays.

Day Of The Week Effect: Empirical Evidence From Indian Stock Market

Paradigm ◽  
2004 ◽  
Vol 8 (2) ◽  
pp. 9-13 ◽  
Author(s):  
Mohit Gupta ◽  
Navdeep Aggarwal
Keyword(s):  
Stock Market ◽  
Empirical Evidence ◽  
Day Of The Week ◽  
Indian Stock Market

scholarly journals The Disappearing Calendar Anomalies in the Singapore Stock Market

2006 ◽  
Vol 11 (2) ◽  
pp. 123-139 ◽  
Author(s):  
Wing-Keung Wong ◽  
Aman Agarwal ◽  
Nee-Tat Wong
Keyword(s):  
Stock Market ◽  
Efficient Market Hypothesis ◽  
Stock Index ◽  
January Effect ◽  
Trading Behavior ◽  
Recent Period ◽  
Efficient Market ◽  
Day Of The Week ◽  
Calendar Anomalies ◽  
Stock Index Returns

This paper investigates the calendar anomalies in the Singapore stock market over the recent period from 1993-2005. Specifically, changes in stock index returns are examined surrounding January (the January effect), on different days of the week (the day-of-the-week effect), around the turn of the month (the turn-of-the-month effect) and before holidays (the pre-holiday effect). The findings reveal that these anomalies have largely disappeared from the Singapore stock market in recent years. The disappearance of these anomalies has important implications for the efficient market hypothesis and the trading behavior of investors.

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