A deep learning approach for financial market prediction: utilization of Google trends and keywords
| Autor: | Min Hsuan Fan, Mu Yen Chen, En Chih Liao |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
0209 industrial biotechnology
Index (economics) Artificial neural network business.industry Computer science Deep learning Return statement Decision tree 02 engineering and technology Stock market index Computer Science Applications Support vector machine 020901 industrial engineering & automation Artificial Intelligence Unit root test Statistics 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence business Information Systems |
| Zdroj: | Granular Computing. 6:207-216 |
| ISSN: | 2364-4974 2364-4966 |
| DOI: | 10.1007/s41066-019-00181-7 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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