Perbandingan Ketepatan Model Regresi Robust Estimasi Method of Moment (MM) dan Estimasi Generalized-M (GM) dalam Memodelkan Harga Penutupan Saham Sektor Teknologi Tahun 2023

Abstract

Stock is the favorite investment instrument in capital market. It needs an approach to find some factors that affect the closing price of tech company's stock. Ordinary Least Square (OLS) estimator is often used to estimate a regression parameter, but it has susceptible to outliers. In the other side, we require an estimates that robust form outlier's influence. This research was conducted to find the best robust regression model and do BV, PBV, DER, ROE and NPM influence to closing price tech company's stock using MM estimator and GM estimator. The normality assumption was not satisfied and there are outliers in the data. The analyst continues using MM estimator and GM estimator then compute and compare and AIC to find the best model. This research concludes that GM estimator model was the best model and BV, PBV, DER, ROE and NPM influences closing price tech company's stock.