Rossi, Francesco (2011): U.K. cross-sectional equity data: do not trust the dataset! The case for robust investability filters.
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We propose a novel approach to cross-sectional equities sample selection, derived from best market practice in index construction and focused on investability. Using the U.K. market as a template, we first demonstrate how the popular Datastream dataset is plagued by data deficiencies that would surely invalidate statistical inferences, and that are not addressed by commonly used filters. We show the benefits and need for a supplementary data source. We then develop robust investability filters to ensure statistical results from cross-sectional analysis are economically meaningful, an issue overlooked by most studies on cross-sectional risk pricing
|Item Type:||MPRA Paper|
|Original Title:||U.K. cross-sectional equity data: do not trust the dataset! The case for robust investability filters|
|English Title:||U.K. cross-sectional equity data: do not trust the dataset! The case for robust investability filters|
|Keywords:||cross-sectional equities; idiosyncratic risk; U.K. equities; asset pricing; investability; Datastream; Bloomberg; sample selection; turnover; volume; equities; equity;|
|Subjects:||G - Financial Economics > G1 - General Financial Markets > G14 - Information and Market Efficiency ; Event Studies ; Insider Trading
G - Financial Economics > G1 - General Financial Markets > G12 - Asset Pricing ; Trading Volume ; Bond Interest Rates
G - Financial Economics > G1 - General Financial Markets > G11 - Portfolio Choice ; Investment Decisions
G - Financial Economics > G1 - General Financial Markets > G15 - International Financial Markets
G - Financial Economics > G1 - General Financial Markets > G10 - General
|Depositing User:||Francesco Rossi|
|Date Deposited:||23 Apr 2012 13:39|
|Last Modified:||05 Feb 2016 05:01|
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