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

机译:金融市场预测的深度学习方法:谷歌趋势的利用和关键词

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摘要

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.
机译:本研究利用谷歌趋势的互联网搜索量,并分析了谷歌趋势和台湾加权股指的搜索量之间的相关性。 Google趋势提供的关键字搜索卷在相关测试和单位根测试中使用。然后,在两个实验 - 第一,机器学习和第二,搜索趋势中分析所获得的关键字。经过经验分析,发现实验中的神经网络比支持向量机和决策树更好。因此,选择神经网络以比较第二实验中的搜索趋势。通过对返回值计算的比较分析,发现搜索趋势中的返回值高于神经网络的返回值。因此,本文透露,使用台湾50指数的公司名称与Taiex指数的上升和下降之间存在相关性。

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