Pendekatan Regresi Nonparametrik Kernel pada Data Indeks Harga Saham Gabungan

Abstract

Stock is one of the investment instruments that is very popular among investors. One indicator of stock price movements in Indonesia is the Jakarta Composite Index (JCI). JCI data is a time series data about joint stock prices which can be analyzed by time series analysis method. However, with this method there are assumptions that cannot be fulfilled. In this study, JCI data will be analyzed by a nonparametric method namely kernel regression with Nadaraya-Watson estimator. The weekly JCI closing price data from January 2015 to December 2015 is applied using various kernel functions that minimize the value of cross validation to get the optimal bandwidth. The results show that the biweight kernel regression with Mean Square Error = 9030,63 and bandwidth = 108,2 is the best model for predictions.

Keywords

• Bandwidth
• Jakarta Composite Index
• Kernel Regression
• Nadaraya-Watson Estimator
• Time Series Analysis