Portfolio Formation and Portfolio Return Modeling on Indonesian Capital Market


  • Renea Shinta Aminda Universitas Padjajaran Bandung


Diversification, Portfolio Formation, and Portfolio Return Modeling


Abstract. This study aims to find out how many n-size issuers and Portfolio-formation
components in a portfolio in the Indonesian capital market to achieve a minimum level of
risk with a certain rate of return; and to analyse Portfolio's behavior in Indonesia, related
with Portfolios that provide the lowest risk level and certain returns using Autoregression
AR(1), GARCH (p, q) and GARCH-M approaches.
The population in this study are all companies listed in Indonesia Stock Exchange during
observation period (January 2008 to December 2016) which are 540 emitents from 9
sectors and then 50 issuers are selected as sample based on a proportional combination of
336 issuers which has active transactions and each transaction completed by its price, using
purposive sampling technique. Analytical methods used starting from the formation of the
portfolio with 9 sector diversification, followed by modeling portfolio return with
Autoregression AR(1), GARCH (1,1) and GARCH-M.
The result shows that the optimal n-number of issuers in obtaining diversification benefit in
the portfolio in Indonesia is 12 securities in a portfolio where the risk value is lower than
the other n-number of issuers. Furthermore, the result of research indicates the combination
form of issuers that provide the lowest risk and become the best portfolio in Indonesia, not
only from certain sectors but also from a combination of sectors and in each portfolio
formation there are financial sector, transport sector, and trade sector within. There is
conditional mean and conditional variance in the portfolio return in Indonesia, where using
model AR(1) the portfolio-10 is the most significant, while using GARCH (1,1) and
GARCH-M model resulted in portfolio G as a good model according to SIA, AIC, and HQ
criteria, and model portfolio D has the best modeling prediction accuracy.

Keywords; Diversification, Portfolio Formation, and Portfolio Return Modeling