Browsing by Author "Benatchba, Karima"

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    Artificial financial market. Risk analysis approach
    (CERIST, 2015) Hedjazi Dellal, Badiâa; Aknine, Samir; Benatchba, Karima
    Financial market is in constant confrontation with various financial risks. These contribute to market instabilities, financial crises and substantial losses for investors. To effectively manage these risks, we should understand the complexity of the market due to its evolution in an uncertain environment. This is possible through multi-agent modeling and simulation while taking into consideration risk indicators. We, propose, in this paper, to model a financial market simulation system using a multi-agent model, where agents represent the different market participants. The reasoning model of our agents is based on different risk indicators. We use the classifier systems as reasoning and learning model for the cognitive agents of our system. This system is a decision tool dedicated to managers or experts wanting to analyze and understand through the behaviour of the different participants, the evolution of the global dynamics of the market and the influence of the different risk factors on the market and on the various categories of market participants.
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    Artificial financial market. Risk analysis approach
    (CERIST, 2015) Hedjazi Dellal, Badiâa; Aknine, Samir; Benatchba, Karima
    Financial market is in constant confrontation with various financial risks. These contribute to market instabilities, financial crises and substantial losses for investors. To effectively manage these risks, we should understand the complexity of the market due to its evolution in an uncertain environment. This is possible through multi-agent modeling and simulation while taking into consideration risk indicators. We, propose, in this paper, to model a financial market simulation system using a multi-agent model, where agents represent the different market participants. The reasoning model of our agents is based on different risk indicators. We use the classifier systems as reasoning and learning model for the cognitive agents of our system. This system is a decision tool dedicated to managers or experts wanting to analyze and understand through the behaviour of the different participants, the evolution of the global dynamics of the market and the influence of the different risk factors on the market and on the various categories of market participants.
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    Game theory for Initial Public Offering (IPO): A multi-agent approach
    (CERIST, 2012) Hedjazi Dellal, Badiâa; Ahmed-Nacer, Mohamed; Aknine, Samir; Benatchba, Karima
    This work consists in simulating a real time interbank gross payment system (RTGS) through a multi-agent model, to analyze the evolution of the liquidity brought by the banks to the system. In this model, each bank chooses the amount of a daily liquidity on the basis of costs minimization (costs of liquidity and delaying) by taking into account the liquidity brought by the other banks. Banks agents’ strategies are based on a liquidity game during several payment days where each bank plays against the others. For their adaptability, we integrate into bank agents learning classifier systems. We carry out several simulations to follow the system total liquidity evolution as that of each bank agent with varying costs coefficients. The question to answer is: what are the cash amounts that banks must provide and under what costs of liquidity and delaying, the system avoids the lack of liquidity? We find that liquidity depends on costs coefficients.
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    Impact of Genetic Algorithms Operators on Association Rules Extraction
    (CERIST, 2016-10-02) Hamdad, Leila; Ournani, Zakaria; Benatchba, Karima; Bendjoudi, Ahcène
    In this paper, we study the impact of GAs’ components such as encoding, different crossover, mutation and replacement strategies on the number of extracted association rules and their quality. Moreover, we propose a strategy to manage the population. The later is organized in classes where each one encloses same size rules. Each class can be seen as a population on which a GA is applied. All tests are conducted on two types of benchmarks : synthetic and real ones of different sizes.
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    Impact of Genetic Algorithms Operators on Association Rules Extraction
    (2016-11-11) Hamdad, Leila; Ournani, Zakaria; Benatchba, Karima; Bendjoudi, Ahcène
    In this paper we study the impact of GAs’ components such as encoding, different crossover, mutation and replacement strategies on the number of extracted association rules and their quality. Moreover, we propose a strategy to manage the population. The later is organized in classes where each one encloses same size rules. Each class can be seen as a population on which a GA is applied. All tests are conducted on two types of benchmarks : synthetic and real ones of different sizes.
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    Multi-Agent Liquidity Risk Management in an Interbank Net Settlement System
    (Springer Berlin Heidelberg, 2012) Hedjazi Dellal, Badiâa; Ahmed-Nacer, Mohamed; Aknine, Samir; Benatchba, Karima
    A net settlement system is a payment system between banks, where a large number of transactions are accumulated, usually waiting until the end of each day to be settled through payment instruments like: wire transfers, direct debits, cheques, .... These systems also provide clearing functions to reduce interbank payments but are sometimes exposed to liquidity risks. Monitoring, and optimizing the interbank exchanges through suitable tools is useful for the proper functioning of these systems. The goal is to add to these systems an intelligent software layer integrated with the existing system for the improvement of transactions processing and consequently avoid deadlock situations, deficiencies and improve system efficiency. We model and develop by multi-agent an intelligent tracking system of the interbank exchanged transactions to optimize payments settlement and minimize liquidity risks.
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    Multi-agent liquidity risk management in an interbank net settlement system
    (CERIST, 2012-09) Hedjazi Dellal, Badiâa; Ahmed-Nacer, Mohamed; Aknine, Samir; Benatchba, Karima
    A net settlement system is a payment system between banks, where a large number of transactions between the banks are accumulated, usually waiting until the end of each day to be settled trough payment instruments like: wire transfers, direct debits, cheques, bank cards... These systems also provide clearing functions to reduce the number of interbank payments to achieve but are sometimes exposed to liquidity risks. Monitoring, controlling and optimizing the interbank exchanges through suitable tools is useful for the proper functioning of these systems. The goal is to add to these systems an intelligent software layer integrated with the existing system for the improvement and multilateral optimization of transactions and consequently avoid deadlock situations, bypass certain deficiencies and improve system efficiency. We model and develop by multi-agent an intelligent tracking system of the exchanged transactions through an interbank clearing system, to optimize payments settlement and minimize liquidity risks.
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    OVERHEARING IN FINANCIAL MARKETS: A Multi-agent Approach
    (SciTePress Science and Technology Publications, 2011) Hedjazi Dellal, Badiâa; Aknine, Samir; Ahmed-Nacer, Mohamed; Benatchba, Karima
    Open complex systems as financial markets evolve in a highly dynamic and uncertain environment. They are often subject to significant fluctuations due to unanticipated behaviours and information. Modelling and simulating these systems by means of agent systems, i.e., through artificial markets is a valuable approach. In this article, we present our model of asynchronous artificial market consisting of a set of adaptive and heterogeneous agents in interaction. These agents represent the various market participants (investors and institutions). Investor Agents have advanced mental models for ordinary investors which do not relay on fundamental or technical analysis methods. On one hand, these models are based on the risk tolerance and on the other hand on the information gathered by the agents. This information results from overhearing influential investors in the market or the order books. We model the system through investor agents using learning classifier systems as reasoning models. As a result, our artificial market allows the study of overhearing impacts on the market. We also present the experimental evaluation results of our model.
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    Overhearing in financial markets: a multi-agent approach markets: a multi-agent approach
    (CERIST, 2010-11) Hedjazi Dellal, Badiâa; Ahmed-Nacer, Mohamed; Aknine, Samir; Benatchba, Karima
    Open complex systems as financial markets evolve in a highly dynamic and uncertain environment. They are often subject to significant fluctuations due to unanticipated behaviours and information. Modelling and simulating these systems by means of agent systems, i.e., through artificial markets is a valuable approach. In this article, we present our model of asynchronous artificial market consisting of a set of adaptive and heterogeneous agents in interaction. These agents represent the various market participants (investors and institutions). Investor Agents have advanced mental models for ordinary investors which do not relay on fundamental or technical analysis methods. On one hand, these models are based on the risk tolerance and on the other hand on the information gathered by the agents. This information results from overhearing influential investors in the market or the order books. We model the system through investor agents using learning classifier systems as reasoning models. As a result, our artificial market allows the study of overhearing impacts on the market. We also present the experimental evaluation results of our model.
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    Parallel B&B Algorithm on Hybrid Multicore/GPU Architecture
    (IEEE, 2013-11-15) Bendjoudi, Ahcène; Chekini, Mehdi; Gharbi, Makhlouf; Mehdi, Malika; Benatchba, Karima; Sitayeb-Benbouzid, Fatima; Melab, Nouredine
    B&B algorithms are well known techniques for exact solving of combinatorial optimization problems (COP). They perform an implicit enumeration of the search space instead of exhaustive one. Based on a pruning technique, they reduce considerably the computation time required to explore the whole search space. Nevertheless, these algorithms remain inefficient when dealing with large combinatorial optimization instances. They are time-intensive and they require a huge computing power to be solved optimally. Nowadays, multi-core-based processors and GPU accelerators are often coupled together to achieve impressive performances. However, classical B&B algorithms must be rethought to deal with their two divergent architectures. In this paper, we propose a new B&B approach exploiting both the multi-core aspect of actual processors and GPU accelerators. The proposed approaches have been executed to solve FSP instances that are well-known combinatorial optimization benchmarks. Real experiments have been carried out on an Intel Xeon 64-bit quad-core processor E5520 coupled to an Nvidia Tesla C2075 GPU device. The results show that our hybrid B&B approach speeds up the execution time up to x123 over the sequential mono-core B&B algorithm.