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Forecasting casino revenue by incorporating Google trends
Author(s) -
Kim WooHyuk,
Malek Kristin
Publication year - 2018
Publication title -
international journal of tourism research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.155
H-Index - 58
eISSN - 1522-1970
pISSN - 1099-2340
DOI - 10.1002/jtr.2193
Subject(s) - revenue , predictive power , revenue model , marketing , business , computer science , econometrics , economics , finance , philosophy , epistemology
This study investigates whether Google Trends has predictive power for improving casino‐revenue forecasting. More specifically, the present study aims to (a) examine the value of using readily available Google Trends data to improve predictive accuracy in the forecasting of casino revenue and (b) compare domestic casinos and foreigner‐only casinos given the significantly different spending patterns of such casinos' patrons. Accordingly, this research utilizes time‐series analyses that incorporate Google Trend data into five steps: the selection of keywords, the processing of search data, indexing construction, the prediction casino revenues, and evaluations of forecast accuracy. The evidence collected suggests that forecasting models using Google Trends data do significantly improve forecasting models for casino revenue. This study provides discussions of and implications for this type of methodology, for casino research, and for better industry practices.