Indonesia Composite Index Prediction using Fuzzy Support Vector Regression with Fisher Score Feature Selection

Abstract

A precise forecast of stock price indexes may return a profit for investors. According to CNN Money, in the same month, as much as 93% of global investors have lost money for trading stock. One of the stock price indexes is the stock composite index. Exact predictions of the stock composite index can be critical for creating powerful market exchanging strategies. In this paper, a modified supervised learning method used to solve regression problems, Fuzzy Support Vector Regression (FSVR) is focused. As the complexity of many factors influences the movement of stock price prediction, the prediction results of Support Vector Regression (SVR) cannot always meet people with precision. Thus, this study implies Fuzzy Support Vector Regression (FSVR) stock prediction model, in which fuzzy membership with mapping function is employed to generate a precise price fluctuation of stock. To assure the use of features on model prediction, Fisher Score is used to find high-quality features that can enhance the accuracy. Indonesia Composite Index or Jakarta Composite Index (JKSE) is considered as input data and the result showed that Fisher Score could be applied as feature selection on Indonesia Composite Index prediction with the best model is eleven out of fifteen features with 80% of training data with 0.043529error.