Agustina, Choiriyah Sapta (2022) Penerapan Stochastic Model Predictive Control (SMPC) Dengan Model yang Dipengaruhi oleh Ketidakpastian Parameter Dalam Optimalisasi Portofolio Saham. Masters thesis, Institut Teknologi Sepuluh Nopember.
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Abstract
Dalam penelitian Tesis ini, diajukan penerapan Stochastic Model Predictive Control (SMPC) untuk kelas sistem linier waktu diskrit dengan model yang dipengaruhi oleh ketidakpastian parameter. Ketidakpastian parameter dalam portofolio saham diasumsikan merupakan rangkaian deret waktu dari return aset yang saling berkorelasi. SMPC berperan untuk melakukan strategi kontrol yang terbaik dalam optimalisasi portofolio saham, sehingga investor dapat memperoleh imbal hasil dari investasi yang telah dilakukan. Pendekatan ini diuji pada data harga saham dari Indeks LQ45. Berdasarkan hasil simulasi, total seluruh aset dalam portofolio bergerak mendekati target yang diharapkan oleh investor. Hal ini disebabkan adanya pengendali SMPC yang bertindak sebagai pengambil keputusan terbaik berdasarkan estimasi Geometric Brownial Motion - Maximum Likelihood Estimation (GBM-MLE) untuk mendapatkan nilai return saham, sehingga optimalisasi portofolio saham berjalan sesuai dengan keinginan investor. Berdasarkan hasil simulasi, seluruh variabel kontrol berada dalam batasan yang telah ditentukan. Penerapan SMPC dalam mengoptimalisasi seluruh modal dalam portofolio berdasarkan data stokastik return saham, mampu memberikan hasil yang sangat memuaskan. Total modal yang diperoleh investor mengalami peningkatan sesuai dengan target yang diharapkan, mencapai hingga dua kali lipat dari modal awal.
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In this paper, we consider an application of Stochastic Model Predictive Control (SMPC) for a class of discrete-time linear systems with a model that is determined by stochastic parameters. The stochastic parameters in the stock portfolio are assumed to be a time series of correlated asset returns. The role of SMPC is to carry out the best control strategy in optimizing the stock portfolio, so that investors can get returns from the investments that have been made. This approach is tested on stock price data from the LQ45 Index. Based on the simulation results, the total assets in the portfolio move closer to the target expected by investors. This is due to the SMPC controller acting as the best decision maker based on the Geometric Brownial Motion - Maximum Likelihood Estimation (GBM-MLE) estimation to get the stock return value, so that the optimization of the stock portfolio moves according to the wishes of investors. Based on the results of the simulation, all control variables are within predetermined limits. The application of SMPC in optimizing all capital in the portfolio based on stochastic data (return stock), able to provide very satisfactory results and reach twice from the begining total asset.
| Item Type: | Thesis (Masters) |
|---|---|
| Additional Information: | RTMa 515.642 Agu p-1 2022 |
| Uncontrolled Keywords: | GBM-MLE, IndeksLQ45,KontrolOptimal,Ketidakpastian Parameter,PortofolioSaham,SMPC, LQ45Index,OptimalControl,Uncertain Parameter,StockPortfolio |
| Subjects: | Q Science > QA Mathematics > QA274.2 Stochastic analysis |
| Divisions: | Faculty of Science and Data Analytics (SCIENTICS) > Mathematics > 44101-(S2) Master Thesis |
| Depositing User: | - Davi Wah |
| Date Deposited: | 17 Apr 2024 08:13 |
| Last Modified: | 17 Apr 2024 08:13 |
| URI: | http://repository.its.ac.id/id/eprint/107873 |
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