OPAL: An Ontology-Based Framework with LSTM and PPO for Return Forecasting and Portfolio Optimization - Battazza, Igor Felipe Carboni; Rodrigues, Cleyton Mário de Oliveira; Oliveira, João Fausto Lorenzato de (Anais do Encontro Nacional de Inteligência Artificial e Computacional (ENIAC); 2025: Anais do XXII Encontro Nacional de Inteligência Artificial e Computacional, 2025)
Traditional portfolio optimization struggles with market volatility and nonlinear dynamics. We propose OPAL, a hybrid framework integrating semantic reasoning and machine learning for financial decision-making. OPAL combines ontology-based asset selection, Markowitz diversification, LSTM return forecasting, and PPO-based allocation. Using S&P 500 data (2015–2023), OPAL achieves a 118.05% cumulative return, 2.58 Sharpe ratio, and 4.99 Sortino ratio, surpassing baselines. Its modular design ensures transparency and adaptability.