Forecasting on Closing Stock Price Data Using Fuzzy Time Series - Subanti, Sri; Rahmaningrum, Asti (Universitas Sebelas Maret, 2024)
The stock prices move up and down during trading time which is obtained from time series data. Investors need to estimate the fluctuation of stock prices in the future day to make the best investment decision. Fuzzy time series can be used as an alternative by investors in making stock price predictions. The advantage of this forecasting method compared to others is that it can formulate a problem based on expert knowledge or empirical data. This research aims to apply fuzzy time series in estimating the future value of closing stock price on the LQ45 Index. Three different methods will be applied to the data which are Chen, Lee, and Cheng. The data of the LQ45 Index will be obtained during the period of January, 4th until April 30th, 2021. The LQ45 index is chosen by many investors because it has high returns. All three model were applied and has a different rule in the calculation stage. The results show that all three models give different forecasting values and different performance of accuracy. The Lee method has the lowest values of accuracy, meanwhile the Cheng method has the highest value of accuracy. It can be concluded that Lee method is the best model indicated by the lowest value of RMSE, MAD, and MAPE for estimating the closing stock price of the LQ45 index.