Pendekatan artificial neural network dalam memprediksi harga saham sektor properti dan real estate menggunakan variabel-variabel makroekonomi (studi kasus saham Indonesia) = Artificial neural network approach in predicting price of property and real estate stock using macroeconomic variables (case study Indonesian stock)
Ludya Kesturi;
Rofikoh Rokhim, supervisor; Maria Ulpah, examiner; Lubis, Arief Wibisono, examiner
(Fakultas Ekonomi dan Bisnis Universitas Indonesia, 2016)
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Saham sektor properti dan real estate merupakan jalan bagi investor untuk berinvestasi di pasar properti dan real estate. Harga saham properti dan real esatate memiliki kecenderungan untuk mengalami pergerakkan yang fluktuatif. Untuk meningkatkan potensi perolehan capital gain serta untuk mengukur risiko investasi, harga saham dapat diprediksi menggunakan metode artificial neural network apabila faktor-faktor yang dapat mempengaruhinya diketahui. Variabel yang mempengaruhi harga saham properti dan real estate di Indonesia antara lain, Gross Domestic Product, inflasi, nilai Rupiah terhadap Dollar Amerika, uang beredar, harga minyak mentah, suku bunga jangka panjang, serta volume perdagangan saham.Hasil prediksi dan performa harga saham properti dan real estate Indonesia menggunakan artificial neural network kemudian dibandingkan dengan metode time series konvensional ARIMA dan regresi linier yang menunjukkan hasil berupa metode artificial neural network lebih unggul dibanding ARIMA dan regresi linier. Property and real estate stocks facilitates investors to invest their fund in property and real estate market. Property and real estate stock price has a tendency to move fluctuatively. The price can be predicted using artificial neural network, if the variables which affect the price of property and real estate stock could be identified. The variables which affecting the Indonesian poperty and real estate stock price are Gross Domestc Product, inflation, exchange rate of Rupiah to US Dollar, money aggregates, crude oil price, long-term interest rate, and stock trading volume.Predicticon results and the methods' performance then compared with the more conventional methods which are time series analysis ARIMA and linear regression. The result shows that performance of artificial neural network is better than ARIMA and linear regression. |
 T47045-Ludya Kesturi.pdf :: Unduh
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| No. Panggil : | T47045 |
| Entri utama-Nama orang : | |
| Entri tambahan-Nama orang : | |
| Subjek : | |
| Penerbitan : | Depok: Fakultas Ekonomi dan Bisnis Universitas Indonesia, 2016 |
| Program Studi : |
| Bahasa : | ind |
| Sumber Pengatalogan : | LibUI ind rda |
| Tipe Konten : | text |
| Tipe Media : | computer |
| Tipe Carrier : | online resource |
| Deskripsi Fisik : | xiii, 126 pages : illustration ; 28 cm + appendix |
| Naskah Ringkas : | |
| Lembaga Pemilik : | Universitas Indonesia |
| Lokasi : | Perpustakaan UI, Lantai 3 |
| No. Panggil | No. Barkod | Ketersediaan |
|---|---|---|
| T47045 | 15-18-609621257 | TERSEDIA |
| Ulasan: |
| Tidak ada ulasan pada koleksi ini: 20434088 |
