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Name der Zeitschrift: 
East Asian economic review
Autor:innen: 
e-ISSN: 
2508-1667
Dokumentart: 
Article
Erscheinungsjahr: 
2017
Zusammenfassung: 
This study quantifies the dynamic interrelationship between the KOSPI index return and search query data derived from the Naver DataLab. The empirical estimation using a bivariate GARCH model reveals that negative contemporaneous correlations between the stock return and the search frequency prevail during the sample period. Meanwhile, the search frequency has a negative association with the one-week- ahead stock return but not vice versa. In addition to identifying dynamic correlations, the paper also aims to serve as a test bed in which the existence of profitable trading strategies based on big data is explored. Specifically, the strategy interpreting the heightened investor attention as a negative signal for future returns appears to have been superior to the benchmark strategy in terms of the expected utility over wealth. This paper also demonstrates that the big data-based option trading strategy might be able to beat the market under certain conditions. These results highlight the possibility of big data as a potential source-which has been left largely untapped-for establishing profitable trading strategies as well as developing insights on stock market dynamics.
Persistent Identifier der Erstveröffentlichung: 
Sprache: 
Englisch (eng)
Quellenangabe: 
Pyo, Dong-Jin (2017). Can big data help predict financial market dynamics? : evidence from the Korean stock market. In: East Asian economic review 21 (2), S. 147 - 165.
doi:10.11644/KIEP.EAER.2017.21.2.327.
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