Global Factor Premiums

Baltussen, Guido; Swinkels, Laurens; Vliet, Pim van

doi:10.2139/ssrn.3325720

Cited by 22 publications

(29 citation statements)

References 97 publications

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“…A representative study of this type could be Geczy and Samonov (2017), who examine the momentum effect in the returns on major asset classes for the years 1800-2014. Baltussen et al (2019a) research several major anomalies for the years 1799-2016. Other studies researching similar long-run data sets include Ilmanen et al (2019), Hurst et al (2017), or Spierdijk et al (2012.…”

Section: Study Periodmentioning

confidence: 99%

“…Nearly all of country-level studies take a similar approach and utilize monthly returns (e.g., Richards 1997;Chan et al 2000;Blitz and van Vliet 2008). This refers, in particular, to the studies of early security data (e.g., Geczy and Samonov 2017;Baltussen et al 2019a), where the more frequent observations are hardly available.…”

Section: Return Measurement Periodsmentioning

confidence: 99%

“…Sometimes the local currency returns are also used in the studies of early security data to alleviate the problem of reliability of more than a century-old foreign exchange rate (Geczy and Samonov 2017). Nonetheless, even for the early asset prices studies the U.S. dollar is the very common choice (Baltussen et al 2019a).…”

Section: Currency Unitmentioning

confidence: 99%

“…Obviously, such long-run datasets are not free from different biases or omissions, and the index portfolios frequently contain very few securities, but certainly they provide a unique look into the past data. The GFD indices were employed by Geczy and Samonov (2017) and Baltussen et al (2019a), among others.…”

Section: Asset Universementioning

confidence: 99%

“…The detailed outline of the sample size in selected studies is presented in Table 1. Asness et al (2013) 18 1978-2011 MSCI indices Avramov et al (2012Avramov et al ( ) 75 1989Avramov et al ( -2009 MSCI and Datastream indices Bali and Cakici (2010) 37 1973 Datastream indices Baltussen et al (2019a) 14 1799-2016 Local country indices Balvers and Wu (2006) 18 1969-1999 MSCI indices Balvers et al (2000) 18 1969MSCI indices Berrada et al (2015 18 1975-2010 MSCI indices Bhojraj and Swaminathan (2006) 38 1975-1999 Datastream indices Cenedese et al (2016) 42 1983-2011 MSCI indices Chan et al (2000) 23 1980-1995 Local country indices Clare et al (2016) 40 1993-2011 MSCI indices Daniel and Moskowitz (2016) 18 1978-2013 Index futures Desrosiers et al (2007Desrosiers et al ( ) 19 1988Desrosiers et al ( -2005 MSCI indices Dobrynskaya (2015) 40 1983-2014 MSCI indices Ellahie et al (2019) 30 1993-2014 Aggregated stock-level data Erb et al (1995) 40 1980MSCI and IFC indices Estrada (2000 28 1988-1998 MSCI indices Ferson and Harvey (1994a) 18 1970-1989MSCI indices Fisher et al (2017 37 1990-2015 MSCI indices Geczy and Samonov (2017) 47 1800-2014 Global Financial Data and Bloomberg indices Guilmin (2015) 18 1975-2014 MSCI indices Hedegaard (2018) 25 1988-2018 MSCI indices Hurst et al (2017) 11 1880-2016 Index futures Ilmanen et al (2019) 23 1877-2018 Global Financial Data, Bloomberg Datast...…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

The Cross Section of Country Equity Returns: A Review of Empirical Literature

Zaremba

2019

JRFM

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The last three decades brought mounting evidence regarding the cross-sectional predictability of country equity returns. The studies not only documented country-level counterparts of well-established stock-level anomalies, such as size, value, or momentum, but also demonstrated some unique return-predicting signals such as fund flows or political regimes. Nonetheless, the different studies vary remarkably in terms of their dataset and methods employed. This study aims to provide a comprehensive review of the current literature on the cross-section of country equity returns. We focus on three particular aspects of the asset pricing literature. First, we study the choice of dataset and sample preparation methods. Second, we survey different aspects of the methodological approaches. Last but not least, we review the country-level equity anomalies discovered so far. The discussed cross-sectional return patterns not only provide new insights into international asset pricing but can also be potentially translated into effective country allocation strategies.

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