期刊名称：International Journal of Hybrid Information Technology
印刷版ISSN：1738-9968
出版年度：2016
卷号：9
期号：2
页码：57-66
DOI：10.14257/ijhit.2016.9.2.05
出版社：SERSC
摘要：The support vector machine (SVM) is a fast, and reliable machine learning mechanism. In this paper, we evaluated the stock price predictability of SVM, which is a kind of fundamental analysis that predicts the stock price from corporate intrinsic value. Corporate financial statements are used as input into SVM. Based on the results, we predicted the rise or drop of the stock. In addition, we evaluated how long a given financial statement can be used to predict a stock's price. Compared to the experts forecast, the results of SVM show good predictability. However, as times goes on, the predictability begins to drop. These predictions based on financial statements are excellent, but after a short period, the dissonance between financial statements and stock price can be offset by reasonable investors. These results support the efficient market hypothesis.
