Open AccessAn investment decision-making model to predict the risk and return in stock market: An Application of ARIMA-GJR-GARCH
Author(s) -
Rizki Apriva Hidayana,
Herlipitupulu,
Sukono Sukono
Publication year - 2022
Publication title -
decision science letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.384
H-Index - 18
eISSN - 1929-5804
pISSN - 1929-5812
DOI - 10.5267/j.dsl.2022.3.003
Subject(s) - autoregressive integrated moving average , autoregressive conditional heteroskedasticity , econometrics , heteroscedasticity , value at risk , economics , stock (firearms) , market risk , financial economics , risk–return spectrum , volatility (finance) , moving average , actuarial science , time series , statistics , mathematics , risk management , finance , geography , portfolio , archaeology
In deciding to invest in stocks traded in the capital market, investors need to predict which stocks provide the prospect of return and the risks to be faced. This paper aims to predict the return and risk of stock asymmetry using a time series model approach. Predicting stock returns and risk is based on the Autoregressive Integrated Moving Average-Glosten Jagannatan Runkle-Generalized Autoregressive Conditional Heteroscedasticity (ARIMA-GJR-GARCH) model. In contrast, the largest risk potential measurement is performed using the Value-at-Risk (VaR) model. The data analyzed are the best ten stocks according to the criteria that apply on the IDX, the period between 17 December 2018 to 14 December 2021, which includes the names of stock BBCA, BBNI, BBRI, BMRI, ASII, ICBP, PGAS, PTBA, TLKM, and UNVR. The analysis results show that of the best ten stocks, based on the ratio between the predicted values of the average return and Value-at-Risk, those with relatively better performance are PTBA, TLKM, UNVR and BBCA stocks. Based on the results of this analysis, it can be used as a reference in making investment decisions for investors, specifically investing in the ten stocks analyzed.