Predictability of machine learning techniques to forecast the trends of market index prices: Hypothesis testing for the Korean stock markets.

ABSTRACT: 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 th

SUBMITTER: Pyo S 

PROVIDER: S-EPMC5685607 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature