Ardyana, Made Keysha Aimee Putri (2026) Optimasi Portofolio Black-Litterman Adaptif dengan Integrasi Input Menggunakan Markov Switching Autoregressive (MSAR) dan Penerapan Kendala Buy-In-Threshold pada Saham Indeks IDX30. Other thesis, Institut Teknologi Sepuluh Nopember.
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Abstract
Pertumbuhan signifikan pasar modal Indonesia menghadirkan tantangan fundamental bagi investor dalam menyusun portofolio yang optimal di tengah kondisi pasar yang dinamis. Model Black-Litterman (BLM) konvensional lebih unggul dari Mean-Variance Optimization, tetapi masih bergantung pada kondisi pasar yang statis dan pandangan investor yang bersifat subjektif, sehingga kurang adaptif terhadap kondisi pasar yang dinamis. Penelitian ini bertujuan untuk menyempurnakan Model Black-Litterman (BLM) yang konvensional dengan mengintegrasikan input yang adaptif dan kuantitatif dari Model Markov Switching Autoregressive (MSAR) untuk menghasilkan alokasi aset yang optimal pada saham indeks IDX30 dengan mempertimbangkan dinamika perubahan rezim pasar. Rezim yang digunakan dalam peneltian ini ada dua, yaitu bullish dan bearish. Model MSAR terbaik tiap saham berhasil dipilih berdasarkan nilai AIC terendah. Probabilitas rezim hasil MSAR digunakan untuk membentuk vektor pandangan investor (e_q) dan matriks ketidakpastian pandangan (Ω) pada proses Model Black-Litterman yang kemudian menghasilkan vektor return ekspektasi posterior (e_(B-L)). Proses optimasi portofolio selanjutnya menerapkan kendala Buy-in-Threshold (5%) menggunakan algoritma Differential Evolution (DEoptim) untuk memastikan bobot yang dihasilkan realistis dan layak diimplementasikan. Hasil utama menunjukkan bahwa portofolio optimal tersusun dari enam saham unggulan, yaitu BBCA (44,36%), INDF (18,87%), JPFA (12,25%), CPIN (11,76%), ASII (6,37%), dan ITMG (6,37%). Evaluasi kinerja dengan sharpe ratio membuktikan portofolio adaptif secara konsisten unggul dibandingkan Pasar (IHSG). Hal ini ditunjukkan oleh annualized sharpe ratio sebesar -0,00637 yang jauh lebih tinggi daripada annualized sharpe ratio IHSG sebesar -0,1362. Kesimpulan menunjukkan bahwa integrasi MSAR dan penerapan kendala Buy-in-Threshold berhasil menciptakan strategi alokasi aset yang efisien, menguntungkan, dan memberikan kinerja risiko yang superior terhadap tolok ukur pasar.
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The significant growth of the Indonesian capital market presents fundamental challenges for investors in constructing optimal portfolios amidst dynamic market conditions. The conventional Black-Litterman (BLM) model is superior to Mean-Variance Optimization, but it still relies on static market conditions and subjective investor views, making it less adaptive to dynamic market conditions. This study aims to refine the conventional Black-Litterman (BLM) model by integrating adaptive and quantitative inputs from the Markov Switching Autoregressive (MSAR) model to generate optimal asset allocation for IDX30 index stocks, taking into account the dynamics of market regime changes. Two regimes were used in this study: bullish and bearish. The best MSAR model for each stock was successfully selected based on the lowest AIC value. The MSAR regime probabilities are used to construct the investor view vector (e_q) and the view uncertainty matrix (Ω) in the Black-Litterman Model process, which then produces the posterior expected return vector (e_(B-L)). The portfolio optimization process then applies a Buy-in-Threshold constraint (5%) using the Differential Evolution (DEoptim) algorithm to ensure the resulting weights are realistic and feasible to implement. The main results show that the optimal portfolio is composed of six leading stocks, namely BBCA (44,36%), INDF (18,87%), JPFA (12,25%), CPIN (11,76%), ASII (6,37%), and ITMG (6,37%). Performance evaluation using the Sharpe ratio proves that the adaptive portfolio consistently outperforms the Market (IHSG). This is indicated by the annualized Sharpe ratio of -0,00637 which is much higher than the annualized Sharpe ratio of IHSG of -0,1362. The conclusion shows that the integration of MSAR and the application of the Buy-in-Threshold constraint successfully creates an efficient, profitable asset allocation strategy and provides superior risk performance compared to market benchmarks.
| Item Type: | Thesis (Other) |
|---|---|
| Uncontrolled Keywords: | Black-Litterman, Buy-in-Threshold, MSAR, Optimasi Portofolio, Saham Black-Litterman, Buy-in-Threshold, MSAR, Portfolio Optimization, Stocks |
| Subjects: | Q Science > QA Mathematics > QA274.7 Markov processes--Mathematical models. Q Science > QA Mathematics > QA371 Differential equations--Numerical solutions Q Science > QA Mathematics > QA372.B9 Differential equations--Numerical solutions. Runge-Kutta formulas--Data processing. Q Science > QA Mathematics > QA401 Mathematical models. |
| Divisions: | Faculty of Mathematics, Computation, and Data Science > Actuaria > 94203-(S1) Undergraduate Thesis |
| Depositing User: | Made Keysha Aimee Putri Ardyana |
| Date Deposited: | 12 Jan 2026 05:45 |
| Last Modified: | 12 Jan 2026 05:45 |
| URI: | http://repository.its.ac.id/id/eprint/129493 |
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