<![CDATA[The Indonesian stock market in the banking sector is a popular investment instrument with high return potential but faces market volatility and global economic uncertainty that requires adaptive and data-driven portfolio management strategies. Traditional asset allocation strategies such as equal weighting or based on historical performance have limitations in dynamic market conditions, while the application of machine learning, especially Ridge Regression, in stock return prediction and portfolio optimization in the Indonesian market has not been widely explored. This study aims to build an integrated pipeline for portfolio prediction and optimization using Ridge Regression on Indonesian banking stocks. Methods: Daily closing price data of five major banking stocks (BBRI, BBCA, BMRI, BBNI, BBTN) for the period 2015-2025 are used with technical indicators of moving average and rolling standard deviation as input features. The Ridge Regression model is trained using TimeSeriesSplit cross-validation to predict daily returns, then the prediction results are integrated into the Mean-Variance optimization framework to maximize the Sharpe ratio. The Ridge Regression model shows excellent predictive performance with an average R² of 0.9986, MAE of 0.000466, and RMSE of 0.000720. The Ridge-based portfolio strategy achieves identical performance to the historical optimal strategy with an annualized return of 10.64% and a Sharpe ratio of 0.4705, significantly outperforming the equal-weight strategy (return of 6.63%, Sharpe ratio of 0.2562). A practical implementation simulation with IDR 100 million funds shows feasible execution with less than 1% deviation from the optimal weights. Ridge Regression is proven to be effective in capturing the return pattern of Indonesian banking stocks and enables superior portfolio performance when integrated with modern portfolio theory, providing investors with a robust and data-driven approach to portfolio optimization in emerging markets.]]>
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