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Financial market

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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.

  • Zahra Fathalli, Zahra Kodia, Lamjed Ben Said

    Stock market prediction of Nifty 50 index applying machine learning techniques

    Applied Artificial Intelligence, 36:1, 2111134, 2022

    Résumé

    The stock market is viewed as an unpredictable, volatile, and
    competitive market. The prediction of stock prices has been
    a challenging task for many years. In fact, many analysts are
    highly interested in the research area of stock price prediction.
    Various forecasting methods can be categorized into linear and
    non-linear algorithms. In this paper, we offer an overview of the
    use of deep learning networks for the Indian National Stock
    Exchange time series analysis and prediction. The networks
    used are Recurrent Neural Network, Long Short-Term Memory
    Network, and Convolutional Neural Network to predict future
    trends of NIFTY 50 stock prices. Comparative analysis is done
    using different evaluation metrics. These analysis led us to
    identify the impact of feature selection process and hyperparameter optimization on prediction quality and metrics used in the prediction of stock market performance and prices. The performance of the models was quantified using MSE metric.
    These errors in the LSTM model are found to be lower compared
    to RNN and CNN models.

  • Lamjed Ben Said, Zahra Kodia, Khaled Ghedira

    Design Of Cognitive Investor Making Decision For An Artificial Stock Market Simulation: A Behavior-based Approach

    Soft-Computing in Capital Market: Research and Methods of Computational Finance for Measuring Risk of Financial Instruments,2014, 41-56., 2014

    Résumé

    Computational Finance, an exciting new cross-disciplinary research area, depends extensively on the tools and techniques of computer science, statistics, information systems and financial economics for educating the next generation of financial researchers, analysts, risk managers, and financial information technology professionals. This new discipline, sometimes also referred to as "Financial Engineering" or "Quantitative Finance" needs professionals with extensive skills both in finance and mathematics along with specialization in computer science. Soft-Computing in Capital Market hopes to fulfill the need of applications of this offshoot of the technology by providing a diverse collection of cross-disciplinary research. This edited volume covers most of the recent, advanced research and practical areas in computational finance, starting from traditional fundamental analysis using algebraic and geometric tools to the logic of science to explore information from financial data without prejudice. Utilizing various methods, computational finance researchers aim to determine the financial risk with greater precision that certain financial instruments create. In this line of interest, twelve papers dealing with new techniques and/or novel applications related to computational intelligence, such as statistics, econometrics, neural- network, and various numerical algorithms are included in this volume.

  • Zahra Kodia, Lamjed Ben Said, Khaled Ghedira

    Simulation comportementale à base d’agents de la dynamique du marché boursier: Modèle cognitif de l’investisseur

    Revue d'intelligence artificielle, 25(1), 83-107., 2011

    Résumé

    This paper explores the dynamics of stock market from a behavioral perspective using
    a multi-agent simulation. The aim of this paper is to study the behavior of investors in the stock
    market to find a model as close as possible to reality. The main problem is to understand,
    through a novel model which includes behavioral and cognitive attitudes of the investor, the
    running of the market and determine the sources of his complexity. Simulation experiments are
    being performed to observe stylized facts of the financial time series. These experiments show
    that representing a behavioral model allows to observe emergent socio-economic phenomena.

  • Zahra Kodia, Lamjed Ben Said, Khaled Ghedira

    A Study of Stock Market Trading Behavior and Social Interactions through a Multi Agent Based Simulation

    Agent and Multi-Agent Systems: Technologies and Applications, 4th KES International Symposium, KES-AMSTA 2010, June 23-25, 2010, Proceedings. Part II pp. 302-311, Gdynia, Poland., 2010

    Résumé

    In this paper, we study the stock market trading behavior and the interactions between traders. We propose a novel model which includes behavioral and cognitive attitudes of the trader at the micro level and explains their effects on his decision making at the macro level. The proposed simulator is composed of heterogeneous Trader agents with a behavioral cognitive model and the CentralMarket agent matching buying and selling orders. Our artificial stock market is implemented using distributed artificial intelligence techniques. The resulting simulation system is a tool able to numerically simulate financial market operations in a realistic way. Experiments show that representing the micro level led us to validate some stylized facts related to stock market and to observe emergent socio-economic phenomena at the macro level.

    Zahra Kodia, Lamjed Ben Said, Khaled Ghedira

    Stylized facts study through a multi-agent based simulation of an artificial stock market

    Lecture Notes in Economics and Mathematical Systems, in: Marco Li Calzi & Lucia Milone & Paolo Pellizzari (ed.), Progress in Artificial Economics, pages 27-38, Springer., 2010

    Résumé

    This paper explores the dynamics of stock market from a psychological perspective using a multi-agent simulation model. We study the stock market trading behavior and the interactions between traders. We propose a novel model which includes behavioral and cognitive attitudes of the trader at the micro level and explains their effects on his decision making at the macro level. The proposed simulator is composed of heterogeneous Trader agents with a behavioral cognitive model and the CentralMarket agent matching buying and selling orders. Simulation experiments are being performed to observe stylized facts of the financial times series and to show that the psychological attitudes have many consequences on the stock market dynamics. These experiments show that the modelization of the micro level led us to observe emergent socio-economic phenomena at the macro level.

  • Zahra Kodia, Lamjed Ben Said

    Multi-agent Simulation of Investor Cognitive Behavior in Stock Market

    7th International Conference on PAAMS'09, AISC 55, pp.90-99, Salamanca, Spain. Springer Berlin / Heidelberg; ISSN: 1615-3871, 2009

    Résumé

    In this paper, we introduce a new model of Investor cognitive behavior in stock market. This model describes the behavioral and cognitive attitudes of the Investor at the micro level and explains their effects on his decision making. A theoretical framework is discussed in order to integrate a set of multidisciplinary concepts. A Multi-Agent Based Simulation (MABS) is used to: (1) validate our model, (2) build an artificial stock market: SiSMar and (3) study the emergence of certain phenomena relative to the stock market dynamics at the macro level. The proposed simulator is composed of heterogeneous Investor agents with a behavioral cognitive model, an Intermediary agent and the CentralMarket agent matching buying and selling orders. Our artificial stock market is implemented using distributed artificial intelligence techniques. The resulting simulator is a tool able to numerically simulate financial market operations in a realistic way. Preliminary results show that representing the micro level led us to build the stock market dynamics, and to observe emergent socio-economic phenomena at the macro level.

    Zahra Kodia, Lamjed Ben Said, Khaled Ghedira

    SiSMar: social multi-agent based simulation of stock market

    AAMAS '09: Proceedings of The 8th International Conference on Autonomous Agents and Multiagent Systems - Volume 2 Pages 1345 - 1346, 2009

    Résumé

    In this paper, we introduce a new model of the stock market. This model describes the behavioral and cognitive attitudes of the investor at the micro level and explains their effects on his decision making. A multi-agent based simulation is used to validate our model and to study the emergence of certain stock market phenomena at the macro level. The modelling and implementation details of our simulator will appear in the full version of the paper.