Analysis of Federal Fund Rate and Bi Rate Announcement to Abnormal Return in Indonesia Stock Market (Published)
The purpose of this study is to determine the effect of Fed rate and BI rate announcement on abnormal return in Indonesian stock market. This study uses an event study methods of sectoral data from August 2016 to March 2017. The method to calculating abnormal return is using event study, with stages as determination of estimation period, which in this study using event window (-3, +3), (-5, +5), and (-7, +7) and the estimated period of 200 days, the next stage is the calculation of actual return and then followed the calculation of expected return by using ordinary least square (OLS). The results showed that sectoral indices in Indonesia stock exchange tend to have significant differences in the abnormal return is not consistent. This is because there are many other factors that influence abnormal return, such as the US presidential election in November 2016 and February 2017 governor election. The only sectors that responded consistent to the announcement were the transportation, infrastructure and utilization sectors. Meanwhile, agricultural sector did not respond to all the announcement of Fed rate dan BI rate. Investor must be carefully to invest in transportation, infrastructure and utilization sector. Because when fed rate increase and BI rate constant stock price company in transportation, infrastructure and utilization sector will volatility and give a negative abnormal return.
Keywords: Bi Rate, Event Etudy, Fed Rate, Ordinary Least Square, abnormal return
Robustness of Two-Part Fractional Regression Models in Modelling Fractional Outcomes (Published)
The bounded nature of the fractional dependent variables, for instance in corporate finance leverage ratio clustering with a substantial number of observations at unit interval raises some important issues in estimation and inference. Ordinary Least Square (OLS) regression with Gaussian distributional assumption has been the main choice to model fractional outcomes in many business problems. Nevertheless, it is conceptually flawed to assume Gaussian distribution for a response variable in the interval [0,1]. Tobit model which is a Single-component method for modelling proportional outcome also share properties with OLS. Two-part Fractional regression models have been shown as the most natural way of modelling bounded, proportional response variables. Beta regression method has been used to achieve the objective in this paper.
Keywords: Beta regression, Fractional outcomes, Gaussian distribution, Ordinary Least Square, Tobit model
