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Granular Computing

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11.06.2019 | Original Paper

A deep learning approach for financial market prediction: utilization of Google trends and keywords

verfasst von: Min-Hsuan Fan, Mu-Yen Chen, En-Chih Liao

Erschienen in: Granular Computing | Ausgabe 1/2021

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Abstract

This study used the amount of Internet search on Google Trend and analyzed the correlation between the search volume on Google Trend and Taiwan Weighted Stock Index. The keyword search volume provided by Google Trend was used in the correlation test and the unit root test. Then, the keywords obtained were analyzed in two experiments—first, machine learning, and second, search trend. After empirical analysis, it was found that neural network in experiment one performed better than support vector machine and decision trees. Therefore, neural network was selected to compare with the search trend in the second experiment. Through comparative analysis of calculation of return values, it was found that the return value in search trend is higher than that of the neural network. Therefore, this paper revealed that there was a correlation between using company names of Taiwan 50 Index as search keywords and the rise and fall of TAIEX index.

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Titel: A deep learning approach for financial market prediction: utilization of Google trends and keywords
verfasst von: Min-Hsuan Fan
Mu-Yen Chen
En-Chih Liao
Publikationsdatum: 11.06.2019
Verlag: Springer International Publishing
Erschienen in: Granular Computing / Ausgabe 1/2021
Print ISSN: 2364-4966
Elektronische ISSN: 2364-4974
DOI: https://doi.org/10.1007/s41066-019-00181-7

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