Analysis Of Mutual Fund Performance In Indonesia Using Robust Regression - Doloksaribu, Arsella F; br Tarigan, Ernita Dewi; Syahmarani, Aghni; Darnius, Open; Hasibuan, Citra Dewi (MES: Journal of Mathematics Education and Science, 2024)
The Least Squares Method (MKT) is the best linear regression estimator if the classical assumption tests are met. However, if there are outliers in the data, this approach can provide inaccurate prediction results. Therefore, the purpose of this research is to overcome the inappropriate model due to outliers in variables that affect mutual fund performance in Indonesia. The variables in this study are risk level, inflation, fund size, turnover ratio, and cash flow. Robust regression is an approach designed to provide more accurate estimates without discarding observational data that indicates outliers. One of the estimation approaches is Least Trimmed Squares (LTS). This estimation minimizes the sum of squared residuals from h observations that are not considered outliers. The results showed that there were 10 outliers in the data, and the risk level variable had no effect on mutual fund performance, while the inflation, fund size, turnover ratio, and cash flow variables had a significant effect. So by comparing the  value and residual standard error of the two methods, it is found that the  value in the LTS method is greater than the MKT method, namely 0.589 0.273, and the residual standard error in the LTS method is smaller than the MKT method, namely 9.73 48.59. Therefore, it can be concluded that the Least Trimmed Squares method provides better estimation results and is more effective for handling outliers than the MKT method.