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Connexion

Financial market

Description

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Membres

Zakia Zouaghia Zakia Zouaghia

Publications

  • 2025
    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.

    Boutheina Drira, Haykel Hamdi, Ines Ben Jaafar

    Hybrid Deep Learning Ensemble Models for Enhanced Financial Volatility Forecasting

    Second Edition IEEE Afro-Mediterranean Conference on Artificial Intelligence 2025 IEEE AMCAI IEEE AMCAI 2025, 2025

    Résumé

    this paper presents a novel ensemble
    methodology that integrates deep learning models to enhance
    the accuracy and robustness of financial volatility forecasts. By
    combining Convolutional Neural Networks (CNNs) and GRU
    networks, the proposed approach captures both spatial and
    temporal patterns in financial time series data. Empirical results
    demonstrate the superiority of this ensemble model over
    traditional forecasting methods in various financial markets.
    Keywords: Volatility Forecasting, Deep Learning, Ensemble
    Modeling, CNN, GRU, Financial Time Series

  • Zakia Zouaghia, Zahra Kodia, Lamjed Ben Said

    A novel AutoCNN model for stock market index prediction

    Journal of Telecommunications and the Digital Economy, 12(1), 612-636., 2024

    Résumé

    Stock markets have an important impact on economic growth of countries. The prediction
    of stock market indexes has been a complex task for last years. Indeed, many researches and financial analysts are highly interested in the research area of stock market prediction. In this paper, we propose a novel framework, titled AutoCNN based on artificial intelligence techniques, to predict future stock market indexes. AutoCNN is composed mainly of three stages: (1) CNN for Automatic Feature Extraction, (2) The Halving Grid Search algorithm is combined to CNN model for stock indexes prediction and (3) Evaluation and recommendation. To validate our AutoCNN, we conduct experiments on two financial datasets that are extracted in the period between 2018 and 2023, which includes several events such as economic, health and geopolitical international crises. The performance of AutoCNN model is quantified using various metrics. It is benchmarked against different models and it proves strong prediction abilities. AutoCNN contributes to emerging technologies and innovation in the financial sector by automating decision-making, leveraging advanced pattern recognition, and enhancing the overall decision support system for investors in the digital economy.