Kajian Model Empat Faktor Carhart dalam Ekstra Imbal Hasil Menggunakan Analisis Regresi Kuantil

Abstract

Quantile regression analysis is one of regression analysis that can be used for data with outliers. The objectives of this research are to see the impacts of explanatory variables in Carhart four-factors model to the excess return using quantile regression analysis and linear regression analysis in a few lags of momentum effect. Market factor has positive impact to excess return in every model. Firm size has positive impact in a low performing stock and negative impact in a high performing stock. Book-to-market ratio does not have significant impact to excess return. In a month lag, momentum effect gives negative impact. In three months lag, momentum effect has negative impact in low and mid performing stock and does not have significant impact in high performing stock. In six months lag, momentum effect has negative impact in low and high performing stock and does not have significant effect in mid performing stock. In nine months lag, momentum effect has negative impact in low performing stock and positive impact in mid and high performing stock. By the value of determinant coeficient, Carhart four-factors models is better to be used for explaining the diversity of the excess return in the high performing stock