Please use this identifier to cite or link to this item: https://scholarbank.nus.edu.sg/handle/10635/136099
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dc.titleEXTRACTING CONSUMER DEMAND: CREDIT CARD SPENDING AND POST-EARNINGS RETURNS
dc.contributor.authorZOU XIN
dc.date.accessioned2017-07-03T18:00:19Z
dc.date.available2017-07-03T18:00:19Z
dc.date.issued2017-03-03
dc.identifier.citationZOU XIN (2017-03-03). EXTRACTING CONSUMER DEMAND: CREDIT CARD SPENDING AND POST-EARNINGS RETURNS. ScholarBank@NUS Repository.
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/136099
dc.description.abstractUsing transaction-level credit card spending data from a large financial institution, this paper examines the return predictability implications of customer spending. After controlling for the quarterly earnings and sales surprises, one inter-quintile increase in the adjusted customer spending during a firm’s fiscal quarter leads to more than one percentage point increase in the 60-day post-earnings-announcement CAR. The predictive power arises from the information content in spending that captures sustainable customer demand: the effect is stronger in firms with more sales from high-spending-capacity consumers and with a more diversified consumer base, and adjusted customer spending is able to predict future firm earnings and sales surprises. Further analysis using public customer segment data for non consumer oriented firms corroborate our findings based on the proprietary spending data. Overall, our findings suggest that disaggregated spending patterns provide accurate and persistent signals of customer demand relevant to a firm’s growth potential and stock pricing.
dc.language.isoen
dc.subjectreturn predictability, earnings announcement, post-earnings returns, customer demand, credit cards, consumption
dc.typeThesis
dc.contributor.departmentFINANCE
dc.contributor.supervisorAGARWAL SUMIT
dc.contributor.supervisorQIAN WENLAN
dc.description.degreePh.D
dc.description.degreeconferredDOCTOR OF PHILOSOPHY
dc.identifier.isiutNOT_IN_WOS
Appears in Collections:Ph.D Theses (Open)

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