This research introduces a novel hybrid architecture that combines deep learning, data-driven algorithms, and an affinity propagation-based approach to build robust investment portfolios. This study evaluates the efficacy of BiLSTM and BiGRU in constructing resilient portfolios of stocks from diverse sectors under varying market conditions. The results highlight the superior performance of BiGRU, particularly in dynamic and volatile market scenarios. The research emphasizes the importance of precise stock prediction and effective diversification for building resilient portfolios, leveraging advanced techniques from deep learning and data-driven optimization. Comparative analyses indicate similar performance between portfolios constructed with actual and predicted data using data-driven optimization. The findings offer valuable insights into constructing robust portfolios by employing advanced techniques, thereby enhancing decision-making in financial markets.
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