smart.lab.tunisia@gmail.com +216 71 588 443
Connexion

Zakia Zouaghia

Informations générales

Zakia Zouaghia
Grade

PES

Biographie courte

Zakia Zouaghia est enseignante de tronc commun depuis 2011, et exerce actuellement à l’Institut Supérieur d’Informatique (ISI) de l’Université de Tunis El Manar. Elle a obtenu sa maîtrise, son mastère et son doctorat en Informatique de Gestion à l’Institut Supérieur de Gestion (ISG-Tunis), Université de Tunis, respectivement en 2007, 2021 et 2025. Elle est membre du SMART Lab (Strategies for Modelling and ARtificial inTelligence Laboratory). Depuis 2021, ses recherches portent sur l’amélioration de la prise de décision sur les marchés financiers. Elle est également relectrice pour plusieurs revues et conférences internationales.

Axes de recherche

Artificial Intelligence
Computer Science
Deep Learning
Machine Learning
Multi Agent System
Simulation
Financial market
Stock market prediction
Portfolio management

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

    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.

    Zakia Zouaghia, Zahra Kodia, Lamjed Ben Said

    Predicting the stock market prices using a machine learning-based framework during crisis periods

    Multimed Tools Appl 84, 28873–28907., 2024

    Résumé

    Stock markets are highly volatile, complex, non-linear, and stochastic. Therefore, predicting stock market behavior is one of finance’s most complex challenges. Recently, political, health, and economic crises have profoundly impacted global stock prices, leading researchers to use machine learning to predict prices and analyze financial data. This article delves into two primary facets: firstly, examining stock price responses to the Russia-Ukraine war and the COVID-19 pandemic by assessing volatility and draw-downs from 2018 to 2023. Secondly, we introduce a framework named StockPredCris, designed to predict future stock indices employing two machine learning algorithms: Support Vector Regression (SVR) and eXtreme Gradient Boosting (XGBoost). Our experiments are conducted on four major stock market indices (NASDAQ, Russell 2000, DAX, and SSE) using a combination of historical stock index data and COVID-19 pandemic data. The performance of the implemented models is evaluated using five regression metrics along with the CPU time metric. The results of SVR demonstrates superior performance and signifcantly outperforms the other considered models for benchmarking. For instance, SVR achieved 0.0 MSE and 99.99% R for the four stock indices, with training times between 0.005 and 0.007 seconds. We recommend SVR for predicting future stock prices during crises. This study offers valuable insights for financial decision-makers, guiding them in making informed choices by understanding stock market reactions to major global crises, while addressing the uncertainty and fear of investors.

    Zakia Zouaghia, Zahra Kodia, Lamjed Ben Said

    A collective intelligence to predict stock market indices applying an optimized hybrid ensemble learning model

    In: Nguyen, N.T., et al. Computational Collective Intelligence. ICCCI 2024. Lecture Notes in Computer Science(), vol 14810. Springer, Cham., 2024

    Résumé

    The stock market which is a particular type of financial market, has attracted nowadays the attention of financial analysts and investors, as it is recognized one of the most challenging and unpredictable market. Recently, this kind of market is well known by its extreme volatility and instability due to the health (COVID-19), the geopolitical (the Russian, Ukraine, European, and American conflict), and the economic crises. This situation intensified the uncertainty and fear of investors; they need an intelligent and stable decision support system to assist them to foresee the future variations of stock prices. To address this issue, our paper proposes a hybrid ensemble-learning model that integrates different methods. (1) The Singular Spectrum Analysis (SSA) is used to eliminate the noise from financial data. (2) The Convolutional Neural Network (CNN) is applied to handle the task of automatic feature extraction. (3) Three machine-learning predictors (Random Forest (RF), Gradient Boosting (GB), and Extra Trees (ET)) are merged together and optimized using the Halving Grid Search (HGS) algorithm to obtain collective final predictions. To verify the validity of the proposed model, two major indices of Chinese and American stock markets, namely SSE and NASDAQ, were used. The proposed model is evaluated using RMSE, MAE, MAPE and CPU time metrics. Based on experiments, it is proven that the achieved results are better than other comparative prediction models used for benchmarking.

    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.

    Zakia Zouaghia, Zahra Kodia, Lamjed Ben Said

    A machine learning-based trading strategy integrating technical analysis and multi-agent simulation

    In: Mathieu, P., De la Prieta, F. (eds) Advances in Practical Applications of Agents, Multi-Agent Systems, and Digital Twins: The PAAMS Collection. PAAMS 2024. Lecture Notes in Computer Science(), vol 15157. Springer, Cham., 2024

    Résumé

    This paper introduces TradeStrat-ML, a novel framework for stock market trading. It integrates various techniques: technical analysis, hybrid machine learning models, multi-agent-based simulations (MABS), and financial modeling for stock market analysis and future predictions. The process involves using a Convolutional Neural Network (CNN) to extract features from preprocessed financial data. The output of this model is then combined with three machine learning models (Gated Recurrent Unit (GRU), Extreme Gradient Boosting (XGBoost), and Support Vector Regression (SVR)) to predict future stock price indices. Subsequently, the models are evaluated, compared, and the most accurate model is selected for stock market prediction. In the final stage, the selected model, along with the Simple Moving Average (SMA) indicator, is used to develop an optimized trading strategy. The TradeStrat-ML system is organized into four main layers and validated using MABS simulations. Comparative analysis and simulation experiments collectively indicate that this new combination prediction model is a potent and practical tool for informed investment decision-making.

  • Zakia Zouaghia, Zahra Kodia, Lamjed Ben Said

    Hybrid machine learning model for predicting NASDAQ composite index

    2023 International Symposium on Networks, Computers and Communications (ISNCC), Doha, Qatar, 2023, pp. 1-6, 2023

    Résumé

    Financial markets are dynamic and open systems. They are subject to the influence of environmental changes. For this reason, predicting stock market prices is a difficult task for investors due to the volatility of the financial stock markets nature. Stock market forecasting leads investors to make decisions with more confidence based on the prediction of stock market price behavior. Indeed, a lot of analysts are greatly focused in the research domain of stock market prediction. Generally, the stock market prediction tools are categorized into two types of algorithms: (1) linear models like Auto Regressive (AR), Moving Average (MA), Auto-Regressive Integrated Moving Average (ARIMA), and (2) non-linear models like Autoregressive Conditionally Heteroscedastic (ARCH), Generalized Autoregressive Conditional Heteroskedasticity (GARCH) and recently Neural Network (NN)). This paper aspires to crucially predict the stock index movement for National Association of Securities Dealers Automated Quotations (NASDAQ) based on deep learning networks. We propose a hybrid stock price prediction model using Convolutional Neural Network (CNN) for feature selection and Neural Network models to perform the task of prediction. To evaluate the performance of the proposed models, we use five regression evaluation metrics: Root Mean Square Error (RMSE), Mean Square Error (MSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE) and R-Square (R2), and the Execution Time (ET) metric to calculate the necessary time for running each hybrid model. The results reveal that error rates in the CNN-BGRU model are found to be lower compared to CNN-GRU, CNN-LSTM, CNN-BLSTM and the the existing hybrid models. This research work produces a practical experience for decision makers on financial time series data.

    Zakia Zouaghia, Zahra Kodia, Lamjed Ben Said

    Stock movement prediction based on technical indicators applying hybrid machine learning models

    2023 International Symposium on Networks, Computers and Communications (ISNCC), Doha, Qatar, 2023, pp. 1-4, 2023

    Résumé

    The prediction of stock price movements is one of the most challenging tasks in financial market field. Stock price trends depended on various external factors like investor's sentiments, health and political crises which can make stock prices more volatile and chaotic. Lately, two crises affected the variation of stock prices, COVID-19 pandemic and Russia-Ukraine conflict. Investors need a robust system to predict future stock trends in order to make successful investments and to face huge losses in uncertainty situations. Recently, various machine learning (ML) models have been proposed to make accurate stock movement predictions. In this paper, a framework including five ML classifiers (Gaussian Naive Bayes (GNB), Random Forest (RF), Gradient Boosting (GB), Support Vector Machine (SVM), and K-Nearest Neighbors (kNN))) is proposed to predict the closing price trends. Technical indicators are calculated and used with historical stock data as input. These classifiers are hybridized with Principal Component Analysis method (PCA) for feature selection and Grid Search (GS) Optimization Algorithm for hyper-parameters tuning. Experimental results are conducted on National Association of Securities Dealers Automated Quotations (NASDAQ) stock data covering the period from 2018 to 2023. The best result was found with the Random Forest classifier model which achieving the highest accuracy (61%).