Optimalisasi Portofolio Saham Indeks IDX30 dengan Conditional Drawdown at Risk

Abstract

Optimalisasi portofolio memerlukan manajemen risiko yang mempertimbangkan berbagai karakteristik risiko. Risiko drawdown mengukur penurunan nilai portofolio berdasarkan nilai tertinggi yang pernah dicapai sebelumnya. Pendekatan minimum variance memiliki keterbatasan dalam mengelola risiko drawdown sebab tidak mempertimbangkan urutan kronologis return, sehingga kurang efektif dalam mengidentifikasi potensi akumulasi kerugian signifikan selama tren penurunan berkepanjangan. Penelitian ini bertujuan mengoptimalkan portofolio saham indeks IDX30 dengan pendekatan Conditional Drawdown at Risk (CDaR) yang dapat mengatasi keterbatasan tersebut. Data penelitian menggunakan saham indeks IDX30 periode 2020-2023 yang diseleksi berdasarkan kriteria konsistensi pencatatan, kemudian dipilih delapan saham dengan expected return tertinggi untuk pembentukan portofolio minimum variance. Evaluasi risiko drawdown dilakukan pada periode Januari 2024 hingga Maret 2025, diikuti optimalisasi portofolio dengan linear programming untuk meminimumkan nilai CDaR pada tingkat kepercayaan 90%, 95%, dan 99%. Hasil penelitian menunjukkan bahwa optimalisasi portofolio dengan pendekatan minimum CDaR efektif mengurangi risiko drawdown, dengan alokasi optimal yang cenderung terkonsentrasi pada saham-saham tertentu.

Portfolio optimization requires a risk management that considers various risk characteristics. Drawdown risk measures the decline in portfolio value based on the highest value previously achieved. The minimum variance approach has limitations in managing drawdown risk because it does not consider the chronological order of returns, making it less effective in identifying the potential for significant accumulated losses during prolonged downtrends. This study aims to optimize the IDX30 index stock portfolio using the Conditional Drawdown at Risk (CDaR) approach, which can overcome these limitations. The research data uses IDX30 index stocks from 2020 to 2023, selected based on consistency criteria, then eight stocks with the highest expected returns are selected to form a minimum variance portfolio. Drawdown risk evaluation was conducted from January 2024 to March 2025, followed by portfolio optimization using linear programming to minimize the CDaR value at confidence levels of 90%, 95%, and 99%. The study results indicate that portfolio optimization using the minimum CDaR approach effectively reduces drawdown risk, with optimal allocation tending to concentrate on certain stocks.