Pyo Sujin, Lee Jaewook, Cha Mincheol, Jang Huisu
Department of Industrial Engineering, Seoul National University, Seoul, South Korea.
PLoS One. 2017 Nov 14;12(11):e0188107. doi: 10.1371/journal.pone.0188107. eCollection 2017.
The prediction of the trends of stocks and index prices is one of the important issues to market participants. Investors have set trading or fiscal strategies based on the trends, and considerable research in various academic fields has been studied to forecast financial markets. This study predicts the trends of the Korea Composite Stock Price Index 200 (KOSPI 200) prices using nonparametric machine learning models: artificial neural network, support vector machines with polynomial and radial basis function kernels. In addition, this study states controversial issues and tests hypotheses about the issues. Accordingly, our results are inconsistent with those of the precedent research, which are generally considered to have high prediction performance. Moreover, Google Trends proved that they are not effective factors in predicting the KOSPI 200 index prices in our frameworks. Furthermore, the ensemble methods did not improve the accuracy of the prediction.
股票和指数价格趋势的预测是市场参与者的重要问题之一。投资者基于这些趋势制定交易或财务策略，并且各个学术领域已经进行了大量研究来预测金融市场。本研究使用非参数机器学习模型预测韩国综合股价指数200（KOSPI 200）的价格趋势：人工神经网络、具有多项式和径向基函数核的支持向量机。此外，本研究阐述了有争议的问题并对这些问题进行了假设检验。因此，我们的结果与之前通常被认为具有高预测性能的研究结果不一致。此外，谷歌趋势证明在我们的框架中它们不是预测KOSPI 200指数价格的有效因素。此外，集成方法并没有提高预测的准确性。
