Testing the monthly anomaly with stochastic dominance

Ke, Mei-Chu; Chou, Jian‐Hsin; Hsieh, Chin-Shan; Chi, Tsung-Li; Chen, Cheng-Te; Liao, Tung Liang

doi:10.1108/mf-07-2013-0182

Cited by 8 publications

(5 citation statements)

References 59 publications

(32 reference statements)

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“…However, the study detected the September effect consistently throughout the study period in all the estimations of return series. This present study supports the study of Ciccone and Etebari (2008), Ke et al (2014) and Bajaj et al (2019). The September effect as detected in the study has several plausible explanations.…”

Section: Discussionsupporting

confidence: 93%

“…However, Floros (2008) reported the significance of June and October in the Greece stock market. Analysing for the period 1980–2009, Ke et al (2014) found the February effect in the Taiwan stock exchange. Similarly, Munir and Ching (2018) disclosed the presence of month effect on the share value of the selected banking and non-banking companies in Malaysia.…”

Section: Review Of Literaturementioning

confidence: 99%

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Capturing the month of the year effect in the Indian stock market using GARCH models

Acharya

Kaliyaperumal

Mahapatra

2022

XJM

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Purpose In the research of stock market efficiency, it is argued that the stock market moves randomly and absorbs all the available information. As a result, it is quite impossible to make predictions about the possible future movement by the investors. But literatures have detected certain calendar anomalies where a day(s) in a week or month(s) in a year or a particular event in a year becomes conducive for investors to earn more than the normal. Hence, the purpose of this study is to find out the month of the year effect in the Indian stock market. Design/methodology/approach In this study, daily time series data of Sensex and Nifty from 1996 to 2021 is used. The study uses month dummies to capture the effect. Different variants of generalised autoregressive conditional heteroskedasticity (GARCH) models, both symmetric and asymmetric, are used in the study to model the conditional volatility in the presence month effect. Findings This study found the September effect in the return series of both the stock market. Apart from that, asymmetric GARCH models are found to be the best fit model to estimate conditional volatility. Originality/value This study is an endeavour to study month of the year effect in the Indian context. This research will provide valuable insight for studying the different calendar anomalies.

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Section: Discussionsupporting

confidence: 93%

Section: Review Of Literaturementioning

confidence: 99%

Capturing the month of the year effect in the Indian stock market using GARCH models

Acharya

Kaliyaperumal

Mahapatra

2022

XJM

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“…Returns in the holy month of Ramadan (the festival of Muslims) 98 JAMR 15,1 being higher that the rest of the months has also been documented before by Al-Hajieh et al (2011) and Bialkowski et al (2013). Ke et al (2014) had documented the Spring Festival effect. On the similar lines, Sakakibara et al (2013) had reported "Dekansho-bushi" effect.…”

Section: Cultural Factors (Diwali Effect)mentioning

confidence: 92%

Calendar anomaly: unique evidence from the Indian stock market

Harshita

Singh

Yadav

2018

JAMR

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Purpose 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. Design/methodology/approach 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. Findings 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. Research limitations/implications 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. Originality/value 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.

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“…The method has been widely used in prior financial research on such topics as the IPO effect (Abhyankar et al 2006), the Monday effect (Cho et al 2007), the yen carry trades (Fong 2010), the monthly effect (Lee et al 2013;Ke et al 2014), the value premium (Hsu et al 2015), and the dim sum bond (Fung et al 2014). The stochastic dominance method offers several advantages (Linton et al 2005;Abhyankar et al 2006).…”

Section: Introductionmentioning

confidence: 98%

The performance of Taiwanese firms after a share repurchase announcement

Hsu

Fung

Chang

2015

Rev Quant Finan Acc

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This study uses stochastic dominance analysis to examine the financial performance of Taiwanese firms from 2000 to 2013 after their announcement of a share repurchase program. Our results show that the firms in the repurchase portfolio perform poorly prior to the announcement, but improve dramatically to outperform different benchmarks after the repurchase announcement. For firms in the repurchase portfolio, we find that (1) the firms with a high book-to-market ratio outperform firms with a low bookto-market ratio, (2) smaller firms outperform larger firms, and (3) there is no significant difference in performance between firms with different percentages of completing the repurchase programs.

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Testing the monthly anomaly with stochastic dominance

Cited by 8 publications

References 59 publications

Capturing the month of the year effect in the Indian stock market using GARCH models

Capturing the month of the year effect in the Indian stock market using GARCH models

Calendar anomaly: unique evidence from the Indian stock market

The performance of Taiwanese firms after a share repurchase announcement

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