Abstract
Modelling the stock closing price stock is useful so that the investors are expected to be able to understand the situation of the stock, in order to make the right decision when they want to buy or sell their stocks. This study uses the ARIMA and Family ARCH methods in modelling the volatility of four banking stocks that are in high demand by the public, which are Bank BRI (BBRI), Bank BNI (BBNI), Bank Mandiri (BMRI), and Bank BCA (BBCA) from January 1st 2017 until January 31st 2020. Stock returns are modelled by using the ARIMA model, then proceeded with the heteroscedasticity testing. Based on the test, we obtained the results of BBRI, BMRI, and BBCA are heteroscedastic. While BBNI are homoscedastic. The volatility models obtained from the test are BBNI has ARIMA models ([6,13], 1, [6,13]), BBRI has ARI models ([2,24,28), 1,0) -ARCH (1), BMRI has an ARIMA (2,1,4) -GARCH (1,1) model, and BBCA has ARI ([1,2], 1,0) -GARCH (1,1) model. Based on the rising value of the stock price, we suggest the best stock for the investors is BBRI because it has the largest increase of 10% followed by BBCA and BMRI
Highlights
Pemodelan data closing price saham berguna agar investor investor diharapkan mampu untuk mempelajari situasi saham, sehingga dapat mengambil keputusan yang tepat ketika melakukan pembelian maupun penjualan atas saham yang dimiliki
Based on the rising value of the stock price, we suggest the best stock for the investors is BBRI because it has the largest increase of 10% followed by BBCA and BMRI
Forecast: BNIF Actual: BNI Forecast sample: 1 794 Adjusted sample: 15 794 Included observations: 780 Root Mean Squared Error Mean Absolute Error Mean Abs. Percent Error Theil Inequality Coefficient
Summary
Pada zaman yang semakin melek teknologi, telah terjadi pergeseran pola pikir dalam mengelola keuangan. Investasi menggunakan reksa dana memiliki potensi risiko yang lebih rendah dari saham dan tentunya dengan potensi imbal hasil (return) yang lebih rendah pula. Metode tersebut digunakan karena tipe data harga saham memiliki volatilitas yang tinggi, sehingga paling sesuai untuk dimodelkan dengan menggunakan ARIMA dan Family ARCH. Sedangkan pada metode ARCH memiliki kelebihan untuk dapat mengolah data dengan karakteristik heteroskedastik, dimana hal ini tidak dapat dimodelkan dengan menggunakan model lain tanpa harus dilakukan transformasi terlebih dahulu. Ketika dilakukan transformasi data heteroskedastik menjadi homoskedastik akan terjadi hilangnya informasi, sehingga metode Family ARCH merupakan pilihan model yang tepat untuk digunakan saat model ARIMA tidak dapat mengakomodasi. Setelah mampu memodelkan data closing price saham, investor tidak memerlukan Manajer Investasi untuk mengelola saham, sehingga dapat meminimalkan resiko dan dapat menghasilkan nilai return yang tinggi.
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