Before revision:   In this paper, we propose an empirical study on the Korean and Hong Kong stock market with an integrated machine learning framework that employs Principal Component Analysis (PCA) and Support Vector Machine (SVM).
After revision:  Many stock prediction studies focus on using macroeconomic indicators, such as CPI and GDP, to train the prediction model. However, daily data of the macroeconomic indicators are almost impossible to obtain. Thus, those methods are difficult to be employed in practice. In this paper, we propose an empirical study on the Korean and Hong Kong stock market with an integrated machine learning framework that employs Principal Component Analysis (PCA) and Support Vector Machine (SVM).