Primaditya, Vincentius Iwan (2015) Pemodelan Box-Jenkins (Arima) Untuk Peramalan Indeks Harga Saham Gabungan. Masters thesis, Institut Teknologi Sepuluh Nopember.
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Abstract
Indeks Harga Saham Gabungan (IHSG) merupakan indikator utama yang
digunakan di Bursa Efek Indonesia (BEI) untuk mengukur kinerja pasar saham
secara keseluruhan. Peramalan yang akurat mengenai pergerakan indeks dapat
menghasilkan keuntungan bagi investor dan untuk mengembangkan strategi
perdagangan pasar saham yang efektif. Autoregressive Integrated Moving
Average (ARIMA) adalah model deret waktu yang berguna untuk peramalan
indeks harga saham dengan menggunakan autokorelasi dan variasi residual deret
waktu. Masing-masing tahap dalam pemodelan Box-Jenkins dilakukan dan
menghasilkan model ARIMA(3,1,2). Jumlah lag autoregresi yang dihasilkan
kemudian digunakan sebagai input ke dalam sistem Jaringan Syaraf Tiruan (JST)
untuk menghasilkan model prediksi Neural Network Autoregression (NAR).
Untuk mengetahui apakah pasar saham di BEI sesuai dengan hipotesis pasar
efisien bentuk lemah, maka peramalan dengan model Random Walk with Drift
juga dilakukan. Hasil evaluasi akurasi peramalan ketiga model pada dataset
testing menggunakan Mean Absolute Error (MAE), Root Mean Squared Error
(RMSE), dan Mean Absolute Percentage Error (MAPE) menunjukkan bahwa
NAR(3) menghasilkan kesalahan peramalan terkecil dengan akurasi yang berada
dalam kisaran 98%. Hasil penelitian keseluruhan memberikan indikasi bahwa
pasar saham di BEI tidak sesuai dengan hipotesis pasar efisien bentuk lemah.
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The Jakarta Composite Index (JCI) is the main indicator used in the
Indonesia Stock Exchange (IDX) to measure the overall performance of the stock
market. Accurate forecasting of the movement of the index would benefit
investors and to develop strategies for effective stock market trading.
Autoregressive Integrated Moving Average (ARIMA) time series model is useful
for forecasting stock price index by using autocorrelation and residual variation in
the time series. Each stage in the Box-Jenkins modeling were performed and
produced ARIMA (3,1,2). The order of lag of the autoregression generated is then
used as input to an Artificial Neural Network (ANN) system to generate a
predictive model Neural Network Autoregression (NAR). To determine whether
the IDX stock market is in accordance with the weak form of the efficient-market
hypothesis, the Random Walk with Drift forecasting model was also performed.
Forecasting accuracy evaluation of the three models on testing dataset using the
Mean Absolute Error (MAE), Root Mean Squared Error (RMSE) and Mean
Absolute Percentage Error (MAPE) indicates that the NAR (3) generates the
smallest forecasting error with accuracy within the range of 98%. Overall study
results provide an indication that the IDX is not in accordance with the weak form
of the efficient-market hypothesis.
| Item Type: | Thesis (Masters) |
|---|---|
| Additional Information: | RTMT 519.535 Pri p |
| Uncontrolled Keywords: | arima, autokorelasi, box-jenkins, indeks harga saham gabungan, peramalan, prediksi, random walk |
| Subjects: | H Social Sciences > HB Economic Theory > Economic forecasting--Mathematical models. |
| Divisions: | 61101-Magister Management Technology |
| Depositing User: | Mr. Tondo Indra Nyata |
| Date Deposited: | 02 Apr 2018 07:24 |
| Last Modified: | 24 Aug 2018 04:22 |
| URI: | http://repository.its.ac.id/id/eprint/51665 |
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