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Financial Decision Support System

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Publications

  • 2025
    Zakia Zouaghia, Zahra Kodia, Lamjed Ben Said

    Smapf-hnna: a novel Stock Market Analysis and Prediction Framework using Hybrid Neural Network Architectures Across Major US Indices

    International Journal of Data Science and Analytics (2025): 1-37., 2025

    Résumé

    Financial markets exhibit high volatility due to various external factors, making stock price prediction a complex yet crucial task for investors and financial institutions. Accurate forecasting not only enhances decision making but also mitigates financial risks. This paper introduces SMAPF-HNNA, an advanced framework that integrates multiple neural network (NN) architectures for robust time-series analysis and stock price forecasting. The proposed approach leverages Convolutional Neural Networks (CNNs) for automatic feature extraction, followed by the application of diverse NN models, including Simple Recurrent Neural Networks (SRNNs), Long Short-Term Memory (LSTM), Bidirectional LSTM (BiLSTM), Gated Recurrent Units (GRU), Bidirectional GRU (BiGRU), and Multilayer Perceptron (MLP) for precise stock price prediction. The framework is rigorously evaluated on multiple benchmark datasets, including NYSE, S&P 500, NASDAQ, and SSE, through extensive training and testing phases. Experimental results demonstrate that the hybrid CNN-MLP model outperforms other architectures across all datasets, achieving exceptionally low error rates with five key regression metrics. The model yields mean squared error (MSE) values between 0.000031 and 0.000004, root mean squared error (RMSE) between 0.0020 and 0.0056, mean absolute error (MAE) between 0.0018 and 0.0042, mean absolute percentage error (MAPE) between 0.12% and 0.32%, and R-squared (R) values ranging from 0.9995 to 0.9999, while maintaining low computational complexity across datasets. These results highlight the potential of SMAPF-HNNA as a highly accurate and computationally efficient solution for stock market prediction, addressing the limitations of previous methods. The proposed framework offers valuable insights for researchers and practitioners, paving the way for more reliable financial market forecasting models.

    Zakia Zouaghia, Zahra Kodia, Lamjed Ben Said

    A novel approach for dynamic portfolio management integrating K-means clustering, mean-variance optimization, and reinforcement learning

    Knowledge and Information Systems, 1-73., 2025

    Résumé

    Effective portfolio management is crucial in today’s fast-moving and unpredictable financial landscape. This paper introduces a powerful and adaptive investment framework that fuses classical portfolio theory with cutting-edge artificial intelligence (AI) to optimize portfolio performance during volatile market conditions. Our methodology seamlessly integrates K-means clustering to identify asset groupings based on correlation structures of technical indicators, mean-variance optimization (MVO) to achieve an ideal risk-return trade-off, and advanced Machine Learning (ML) and reinforcement learning (RL) techniques to dynamically adjust asset allocations and simulate market behavior. The proposed framework is rigorously evaluated on historical stock data from 60 prominent stocks listed on NASDAQ, NYSE, and S&P 500 indices between 2021 and 2024, a period marked by significant economic shocks, global uncertainty, and structural market shifts. Our experimental results show that our framework consistently outperforms traditional strategies and recent state of the art models, achieving superior metrics including Sharpe ratio, Sortino ratio, annual return, maximum drawdown, and Calmar ratio. We also assess the computational efficiency of the approach, ensuring its feasibility for real-world deployment. This work demonstrates the transformative potential of AI-driven portfolio optimization in empowering investors to make smarter, faster, and more resilient financial decisions amid uncertainty.

  • Zakia Zouaghia, Zahra Kodia, Lamjed Ben Said

    Pred-ifdss: An intelligent financial decision support system based on machine learning models

    2024 10th International Conference on Control, Decision and Information Technologies (CoDIT), Vallette, Malta, 2024, pp. 67-72, 2024

    Résumé

    Financial markets operate as dynamic systems susceptible to ongoing changes influenced by recent crises, such as geopolitical and health crises. Due to these factors, investor uncertainty has increased, making it challenging to identify trends in the stock markets. Predicting stock market prices enhances investors’ ability to make accurate investment decisions. This paper proposes an intelligent financial system named Pred-IFDSS, aiming to recommend the best model for accurate predictions of future stock market indexes. Pred-IFDSS includes seven machine learning models: (1) Linear Regression (LR), (2) Support Vector Regression (SVR), (3) eXtreme Gradient Boosting (XGBoost), (4) Simple Recurrent Neural Network (SRNN), (5) Gated Recurrent Unit (GRU), (6) Long Short-Term Memory (LSTM), and (7) Artificial Neural Network (ANN). Each model is tuned using the grid search strategy, trained, and evaluated. Experiments are conducted on three stock market indexes (NASDAQ, S&P 500, and NYSE). To measure the performance of these models, three standard strategic indicators are employed (MSE, RMSE, and MAE). The outcomes of the experiments demonstrate that the error rate in SRNN model is very low, and we recommend it to assist investors in foreseeing future trends in stock market prices and making the right investment decisions.