COMPARISON OF ROBUST REGRESSION RESULTS OF SCALE (S) ESTIMATION AND METHOD OF MOMENT (MM) ESTIMATION ON THE CLOSING PRICE OF ENERGY SECTOR STOCKS IN 2022 - Hilyatul Hilwy, Sarah; Susanti, Yuliana; Nirwana, Muhammad Bayu (International Conference on Humanity Education and Society (ICHES), 2024)
The development of the company is undoubtedly inseparable from financial factors. The company will issue shares that investors will purchase. Investors will consider the state of the company they invest in investment activities. Fundamental analysis can assess the company's condition by calculating company ratios. The existence of fundamental analysis can help investors make decisions. Capital market movements often experience fluctuations or extreme events in the stock market that cause outliers in stock price data. Outliers in the data can be overcome by using robust regression to reduce the impact of outliers on the data. This analysis uses S and MM estimations with Tukey Bisquare weights to estimate the model. Energy sector stock closing price data will be tested for classical assumptions, including normality, homoscedasticity, autocorrelation, and multicollinearity tests. If the energy sector stock closing price data does not meet normality, detect outliers and continue estimating data using S and MM estimations. The best model to estimate the data is the MM estimation with an adjusted R-Square value of 99.86%, fulfilling the parameter significance test, namely the t-test and F-test.