Historical evolution of monthly anomalies in international stock markets

Plastun, Alex; Sibande, Xolani; Gupta, Rangan; Wohar, Mark E.

doi:10.1016/j.ribaf.2019.101127

Cited by 13 publications

(5 citation statements)

References 41 publications

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“…Generally before the financial market became more sophisticated, it was almost the major valid way to guide the investors. Using time series analysis approaches as reported by various studies, example of that (Arora & Hemavathi, 2015;Kannan et al, 2010;Urquhart & Hudson, 2013;Zapata et al, 2012;Angadi & Kulkarni, 2015;Plastun et al, 2020).…”

Section: Historical Datamentioning

confidence: 99%

The Reliability of Prediction Factors, for the World Stock Markets

Sabri¹

2021

TEL

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This paper is aimed to identify the predicted factors for the world stock market in the last six decades, and up to the first quarter of 2021 year. The historical data and analysis of trends for volumes, and prices besides the fundamentals data such as interest rates, currency exchange rates, inflation rates, trading volume, and annual returns for listed corporations, used to be the predicted factors available to forecast the financial stock markets prices and volume, for the listed companies, national stock index, and regional stock indexes. However, in the last six decades, other new major factors became more reliable keys for the prediction of future prices and volume trends, in the world stock markets besides the previous two factors. These include the options strike rates for underling shares, psychological price barriers, national, regional and international crises, irrational and noise trading, and finally the health crisis known as the COVID-19, as what happened in 2020. This paper discussed these predicted factors which now dominate in the world stock markets, and suggested stated ratios for the reliability of each presented measure, during different decades up to April of 2021, and how the reliability of theses indictors was changed from one decade to another.

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Section: Historical Datamentioning

confidence: 99%

The Reliability of Prediction Factors, for the World Stock Markets

Sabri¹

2021

TEL

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“…1. See Rossi (2015), Tadepalli and Jain (2018) and Plastun et al (2020) for comprehensive reviews of the principal studies on calendar effects.…”

Section: Notesmentioning

confidence: 99%

A new look at the meeting clustering effect

Pardo

Santandreu²

2021

RBF

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PurposeThe study aims to test the existence of a meeting clustering effect in the Spanish Stock Exchange (SSE).Design/methodology/approachThis 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.FindingsThe 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.Research limitations/implicationsThe 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.Practical implicationsThe 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.Originality/valueThis 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.

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“…There are multiple researches focused on the analysis of different anomalies using different methodologies and applied to a huge set of financial assets. Among the latest published about analysis of anomalies, we can point out, grouped by effect, the following: day of week effect (Jaisinghani et al, 2019;Xiong et al, 2019;Miss et al, 2020;Chatzitzisi et al, 2021;Chaouachi and Dhaou, 2020;Xiao and Maillebuau, 2020), month of year (Xiao and Maillebuau, 2020;Plastun et al, 2020aPlastun et al, , 2019aXiong et al, 2019;Harshita and Yadav, 2018;Hui and Chan, 2018;Sawitri and Astuty, 2018), turn of the month effect (Singh et al, 2020;Caporale and Plastun, 2017;Singh et al, 2020), turn of the year effect (Javed and Naveed, 2021;Plastun et al, 2019b), Halloween effect (Arendas et al, 2018;Kenourgios and Samios, 2021;Plastun et al, 2020b) or the presence of intraday pattern or time-of-day (Caporale et al, 2016;Inci, 2018;Shahzad et al, 2018;Yang et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

“…, 2020; Chatzitzisi et al. , 2021; Chaouachi and Dhaou, 2020; Xiao and Maillebuau, 2020), month of year (Xiao and Maillebuau, 2020; Plastun et al. , 2020a, 2019a; Xiong et al.…”

Section: Introductionmentioning

confidence: 99%

Ramadan effect in the cryptocurrency markets

Martín

2022

RBF

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PurposeThis paper examines the effect of the holy month of Ramadan on the returns and conditional volatility of cryptocurrency markets.Design/methodology/approachThe closing prices of six cryptocurrencies have been considered. The study employs different classical tests for checking if the efficiency behaviour is similar during Ramadan celebration days and non-Ramadan days. Besides, dummy variable regression technique for assessing this anomaly on returns and volatilities has been applied.FindingsAlthough no significant effect on returns and volatility for Litecoin has been found, the results provide evidence about the existence of the Ramadan effects in cryptocurrency markets. The results of the mean equations show the existence of Ramadan effect for Ethereum, Ripple, Stellar and BinanceCoin for all considered models. Significant effect on Bitcoin returns is found with an autoregressive model of order 1. The results of conditional volatility show Ramadan effect on volatility is not detected.Originality/valueFirst, a new contribution in the incipient study of cryptocurrency analysis. Second, a comprehensive review of recently published empirical articles about Ramadan effect on traditional assets has been carried out. Third, unlike most of the papers focussed on the study of Bitcoin, this study has been extended to six cryptocurrencies. Ramadan effect have not been analysed in cryptomarkets yet. This study come to fill this gap and analyses Ramadan effect, previously documented for traditional assets, in particular, stock index from Muslim countries, but not yet analysed in the cryptocurrency markets.

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Historical evolution of monthly anomalies in international stock markets

Cited by 13 publications

References 41 publications

The Reliability of Prediction Factors, for the World Stock Markets

The Reliability of Prediction Factors, for the World Stock Markets

A new look at the meeting clustering effect

Ramadan effect in the cryptocurrency markets

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