| Popis: |
There are cases where news and its responses lead to debate and its shift explains socioeconomic changes.Tsuda（2015） introduced a method to:（1） numerically represent tendency of comments about chosen subject, and （2） extract differential features of news and its quotation in social media. In this paper, we propose an approach to:（1） predict stock price using the differential features,（2） keep predictive accuracy with reduced number of predictive models, and（3） adopt as simple method as possible for predictive models.So far many relevant studies have been carried out to predict stock price by statistical model utilizing publicly available text data such as those on internet, but while they have achieved predictive accuracy, they are not ready to use for real world purpose due primarily to lack of real-world applicability. In this paper we suggest an approach to apply likelihood ratio test to 2 periods of explanatory variables’ differential features to identify significant change between the 2 periods so as to limit frequency of rebuilding of predictive model, and the approach is applied to publicly available text data of news and its responses regarding Honda motors as well as its stock price data. The result turns out that while limiting number of models, predictive accuracy keeps as good as those reported in past relevant studies. It is also numerically represented that the more number of models the more accurate prediction becomes. |
