Hybrid Recommendation And Deep Learning Architectures For Investment Portfolio Optimization

The transition to artificial intelligence (AI) tools in the financial sector has transformed asset allocation methods, shifting from classical linear econometric models to complex architectures based on deep learning. In an era characterized by massive volumes of heterogeneous market data, traditional models often fail to capture hidden correlations and sudden market shifts. This theoretical paper proposes an innovative model for optimizing investment portfolios by integrating hybrid recommendation systems with deep neural networks. The hybrid methodology addresses the limitations of traditional robo-advisors by combining collaborative filtering for behavioral profiling of investor risk aversion with the automatic extraction of nonlinear features from financial time series. To achieve this, the architecture utilizes advanced neural layers to process sequential asset data alongside dense networks that map user-specific financial constraints. The research analyzes the calibration of composite cost functions and the application of regularization techniques to prevent overfitting historical capital market data. This statistical is imperative to minimize the probability of generating false trading signals, directly addressing the warnings issued by the European Securities and Markets Authority (ESMA) regarding the risks of unsupervised AI. Furthermore, the paper introduces elements of Explainable AI (XAI) to ensure that the deep learning output remains interpretable for financial auditors and regulatory bodies. The research findings demonstrate that hybrid architecture based on deep learning offers superior capabilities for modeling risk probabilities and market volatility. By optimizing risk-adjusted performance metrics, the system facilitates the construction of personalized and resilient investment portfolios. In conclusion, this paper establishes a transparent algorithmic framework that is essential for governance and the future decision-making infrastructure within the financial ecosystem.

Acknowledgement: This research was supported by grant from the Petroleum-Gas University of Ploiești, Romania, project number GO-GICS-14819/26.06.2026, within the Internal Grants for Scientific Research.