2013
DOI: 10.1016/j.rfe.2013.08.003
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Resurrecting the size effect: Evidence from a panel nonlinear cointegration model for the G7 stock markets

Abstract: a b s t r a c t JEL classification: G10 C23 Keywords: Size effect Stock returns Panel threshold cointegration G7 stock marketsFirm size is known to be an important factor affecting stock returns. This study proposes a panel threshold cointegration model to investigate the impact of the size effect on stock returns for the panel of G7 countries: Canada, France, Germany, Italy, Japan, the U.K., and the U.S. over the period 1991:1-2012:12. The empirical analysis is based upon the nonlinear cointegration framework… Show more

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Cited by 10 publications
(6 citation statements)
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“…First, we describe the nonlinear auto-regressive distributed-lag (NARDL) model proposed by Shin, Yu, and Greenwood-Nimmo (2014), which allows for long-asymmetries, short-run asymmetries and hysteresis simultaneously. Secondly, the panel-NARDL model suggested by Apergis and Payne (2014) is introduced. Finally, the panel cointegration approach allowing for both structural breaks and cross-section dependence by Banerjee and Carrion-i-Silvestre (2015) is presented.…”
Section: Methodsmentioning
confidence: 99%
See 1 more Smart Citation
“…First, we describe the nonlinear auto-regressive distributed-lag (NARDL) model proposed by Shin, Yu, and Greenwood-Nimmo (2014), which allows for long-asymmetries, short-run asymmetries and hysteresis simultaneously. Secondly, the panel-NARDL model suggested by Apergis and Payne (2014) is introduced. Finally, the panel cointegration approach allowing for both structural breaks and cross-section dependence by Banerjee and Carrion-i-Silvestre (2015) is presented.…”
Section: Methodsmentioning
confidence: 99%
“…Instead, we have a very large cross-section at our disposal, including 79 Russian regions. Given this background, we also employ the panel-NARDL model suggested by Apergis and Payne (2014):…”
Section: The Panel-nardl Modelmentioning
confidence: 99%
“…Aharoni et al (2013) considered dividend discount model with comparative static valuation model and determined book to market ratio as a predictor of stock returns. Apergis and Payne (2014) studied the G-7 stock markets using the panel nonlinear co-integration model, and found that there is asymmetric effect between stock returns and size. Although some authors, such as Lo and MacKinnlay (1990), point out that the models for predicting the stock returns might be just data snooping, it is still widely believed that some financial and economic factors can explain much of the variation of the stock returns so as to have a great forecast power for stock return for example: the articles by Keim and Stambaugy (1986), Fama and French (1988), Campbell and Shiller (1988), Ferson and Harvey (1991, 1993), Whitelaw (1994, Pesaran and Timmermann (1995), Pointiff and Schall (1998) Bossaerts andHillion (1999) andMartijn Cremers (2002).…”
Section: Literature Reviewmentioning
confidence: 99%
“…Their results support the fact that negative rates of expenditures severely impact revenues. Apergis and Payne (2014) analyzed asset returns from the stock markets of the G7. They found long-lasting nonlinear dependencies in a significant portion of their dataset.…”
Section: Nonlinear Cointegrationmentioning
confidence: 99%