UI - Tesis Membership :: Kembali

UI - Tesis Membership :: Kembali

Pemodelan forecasting return saham menggunakan adaptive neural fuzzy inference system = Modelling forecasting of stock returns with adaptive neural fuzzy inference system / Adi Surya

Adi Surya; Zaafri Ananto Husodo, supervisor; Dony Abdul Chalid, examiner; Imo Gandakusuma, examiner ([Publisher not identified] , 2015)

 Abstrak

[ABSTRAK
Prediksi pasar saham adalah penting dan sangat menarik karena prediksi return
saham dapat menjanjikan keuntungan yang menarik. Dalam karya akhir ini,
penulis menyelidiki prediktabilitas return pasar saham dengan Adaptive
NetworkFuzzy Inference System (ANFIS). Tujuan dari penelitian ini adalah untuk
menentukan apakah suatu algoritma ANFIS mampu secara akurat memprediksi
return pasar saham dibandingkan dengan model Time Series ARIMA (Automatic
Regression Integrated Moving Average). Penulis mencoba untuk membuat model
dan memprediksi return saham ? saham dari Indeks LQ 45 di Bursa Efek
Indonesia (BEI) menggunaka metode ANFIS. Penulis menggunakan empat
variabel makroekonomi dan tiga indeks sebagai variabel input. Hasil eksperimen
karya akhir ini menunjukkan bahwa model forecasting return saham harian LQ 45
dengan ANFIS memiliki tingkat error lebih kecil bila dbandingkan dengan
metode ARIMA (Automatic Regression Integrated Moving Average). Metode
ANFIS ini diharapkan dapat menjadi pendekatan alternatif yang menjanjikan
untuk prediksi return saham. Sehingga ANFIS dapat menjadi alat yang berguna untuk ahli ekonomi dan praktisi yang berurusan dengan prediksi return dari saham.

ABSTRACT
Stock market prediction is important and of great interest because successful
prediction of stock return may promise attractive benefits. In this paper, we
investigate the predictability of stock market return with Adaptive Network-Based
Fuzzy Inference System (ANFIS). The objective of this study is to determine
whether an ANFIS algorithm is capable of accurately predicting stock market
return than Time Series Model Automatic Regression Integrated Moving Average
(ARIMA). We attempt to model and predict the return on stock of the LQ 45 Index
in Indonesia Stock Exchange (JSE) with ANFIS. We use four macroeconomic
variables and three indices as input variables. The experimental results reveal
that the model forecasts the daily return of LQ 45 stocks with ANFIS have less
error than Auto Regressive Integrated Moving Average Method. ANFIS provides
a promising alternative for stock market return prediction. ANFIS can be a useful
tool for economists and practitioners dealing with the forecasting of stock return, Stock market prediction is important and of great interest because successful
prediction of stock return may promise attractive benefits. In this paper, we
investigate the predictability of stock market return with Adaptive Network-Based
Fuzzy Inference System (ANFIS). The objective of this study is to determine
whether an ANFIS algorithm is capable of accurately predicting stock market
return than Time Series Model Automatic Regression Integrated Moving Average
(ARIMA). We attempt to model and predict the return on stock of the LQ 45 Index
in Indonesia Stock Exchange (JSE) with ANFIS. We use four macroeconomic
variables and three indices as input variables. The experimental results reveal
that the model forecasts the daily return of LQ 45 stocks with ANFIS have less
error than Auto Regressive Integrated Moving Average Method. ANFIS provides
a promising alternative for stock market return prediction. ANFIS can be a useful
tool for economists and practitioners dealing with the forecasting of stock return]

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 Metadata

Jenis Koleksi : UI - Tesis Membership
No. Panggil : T-Pdf
Entri utama-Nama orang :
Entri tambahan-Nama orang :
Entri tambahan-Nama badan :
Program Studi :
Subjek :
Penerbitan : [Place of publication not identified]: [Publisher not identified], 2015
Bahasa : ind
Sumber Pengatalogan : LibUI ind rda
Tipe Konten : text
Tipe Media : computer
Tipe Carrier : online resource
Deskripsi Fisik : xv, 131 pages : illustration ; 28 cm + appendix
Naskah Ringkas :
Lembaga Pemilik : Universitas Indonesia
Lokasi : Perpustakaan UI, Lantai 3
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No. Panggil No. Barkod Ketersediaan
T-Pdf 15-17-063587621 TERSEDIA
Ulasan:
Tidak ada ulasan pada koleksi ini: 20415570
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