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PREDIKSI HARGA SAHAM SYARIAH DENGAN LSTM (LONG SHORT TERM MEMORY) DAN OPTIMISASI PORTOFOLIONYA MENGGUNAKAN METODE HIERARCHICAL RISK PARITY
The need of investors for optimal investment decision-making drives the utilization of prediction and portfolio optimization methods on sharia stocks included in the JII70 index. This study employs the Stacked Long Short-Term Memory (LSTM) model for time series forecasting and the Hierarchical Risk Parity (HRP) method for risk-based portfolio construction, with performance evaluation using the Sharpe Ratio and Omega Ratio. The data used consists of weekly adjusted closing prices of the JII70 index for the period December 2019–November 2024, obtained from Yahoo Finance. The research stages include data normalization, time series data splitting, LSTM model training with several parameter combinations, and stock selection based on positive returns from historical data and forecasting data as well as a Mean Absolute Percentage Error (MAPE) value of less than 10%. The results show that 10 stocks meet the criteria for portfolio construction. An investment simulation with an initial capital of IDR 100,000,000 produces relatively similar final values for both portfolios. However, the Sharpe Ratio of the portfolio combining historical and forecasting data at 0.2308 is higher than that of the historical data portfolio at 0.2253, while the Omega Ratio of the historical and forecasting data combination also yields a higher result of 1.0916 compared to 1.0895 for the historical data portfolio. This indicates that the addition of forecasting results is capable of improving portfolio performance, albeit not significantly. Thus, the LSTM model also plays an important role in filtering stocks that have better potential price movements going forward.
FAKULTAS SAINS DAN TEKNOLOGI, UIN SUNAN KALIJAGA YOGYAKARTA
PREDIKSI HARGA SAHAM SYARIAH DENGAN LSTM (LONG SHORT TERM MEMORY) DAN OPTIMISASI PORTOFOLIONYA MENGGUNAKAN METODE HIERARCHICAL RISK PARITY (Text)
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