Juvialika, Fadhila (2021) Penerapan Metode Ensemble Kalman Filter Untuk Prediksi Harga Saham. Undergraduate thesis, Institut Teknologi Sepuluh Nopember.
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06111740000091-Undergraduate_Thesis.pdf - Accepted Version Restricted to Repository staff only Download (2MB) | Request a copy |
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06111740000091-Undergraduate_Thesis.pdf - Accepted Version Restricted to Repository staff only Download (2MB) | Request a copy |
Abstract
Investasi saham kemungkinan besar menjadi pilihan utama investor karena menawarkan keuntungan yang menarik. Dalam menentukan pilihan saham untuk investasi, para investor membutuhkan suatu cara untuk menilai harga saham yang akan dibeli sehingga dapat membantu mengoptimalkan keuntungannya. Metode analisis yang efektif untuk mengurangi risiko yang mungkin ditanggung investor salah satunya adalah dengan memprediksi atau mengestimasi harga saham dengan kategori close price sebagai bahan pertimbangan. Salah satu metode perhitungan untuk memprediksi harga saham dilakukan dengan metode Ensemble Kalman Filter (EnKF) dengan menentukan model dari harga saham dengan menggunakan model state space. Dalam penelitian ini dilakukan perbandingan estimasi dengan metode Ensemble Kalman Filter (EnKF) dan Kalman Filter. Untuk melakukan prediksi dengan metode Ensemble Kalman Filter (EnKF) dan Kalman Filter, pertama diidentifikasi model linear harga saham untuk dilakukan estimasi dengan implementasi algoritma Ensemble Kalman Filter (EnKF) dan algoritma Kalman Filter. Selanjutnya hasil prediksi dibandingkan dengan nilai yang sebenarnya untuk menentukan keakurasian hasil prediksi. Keakuratan hasil prediksi ditunjukan dengan menggunakan MAPE. Hasil prediksi harga saham PT Bank Central Asia Tbk (BBCA) oleh metode Ensemble Kalman Filter (EnKF) dengan membangkitkan 500 ensemble ditunjukkan dengan MAPE sebesar 0,000683118%, dan untuk hasil prediksi oleh metode Kalman Filter ditunjukkan dengan MAPE sebesar 0,000151502%. Berdasarkan hasil prediksi yang didapatkan, metode Kalman Filter lebih akurat dari metode Ensemble Kalman Filter (EnKF).
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Stock investing is most likely the top choice of investors because it offers interesting returns. In determining stock options for investment, investors need a way to analyze the stock prices that soon to be purchased so it can help to optimize their returns. The effective analyze method to reduce the risks that investors may bear is to predict or to estimate the stock prices with close price category as a consideration. One of the calculation methods to predict stock prices is done by Ensemble Kalman Filter (EnKF) method by determining the model of stock prices by using state space model. This research compares the estimation with Ensemble Kalman Filter (EnKF) method and Kalman Filter method. To do the predicting using Ensemble Kalman Filter (EnKF) and Kalman Filter, the first thing to do is to identify the linear stock price model that soon to be estimated by the Ensemble Kalman Filter (EnKF) algorithm and the Kalman Filter algorithm. Next, the prediction result is compared to the actual value to determine the accuracy of the prediction result. The accuracy of prediction result is shown by using MAPE. The result of PT Bank Central Asia Tbk (BBCA) stock price prediction using Ensemble Kalman Filter (EnKF) by using 500 ensemble is shown by MAPE with the value 0,000683118%, and the stock price prediction using Kalman Filter is shown by MAPE with the value 0,000151502%. According to the prediction result, it can be stated that the Kalman Filter is more accurate than Ensemble Kalman Filter (EnKF).
| Item Type: | Thesis (Undergraduate) |
|---|---|
| Uncontrolled Keywords: | Harga Saham, Close Price, Model State Space, Ensemble Kalman Filter, Kalman Filter, Stock Prices, State Space Model |
| Subjects: | H Social Sciences > HA Statistics > HA31.7 Estimation H Social Sciences > HG Finance > HG4012 Mathematical models H Social Sciences > HG Finance > HG4529 Investment analysis |
| Divisions: | Faculty of Science and Data Analytics (SCIENTICS) > Mathematics > 44201-(S1) Undergraduate Thesis |
| Depositing User: | Fadhila Juvialika |
| Date Deposited: | 26 Aug 2021 03:38 |
| Last Modified: | 26 Aug 2021 03:38 |
| URI: | http://repository.its.ac.id/id/eprint/89677 |
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