1,085 research outputs found

    DEVELOPMENT OF INTELLIGENT DECISION MAKING MODEL FOR STOCK MARKETS

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    This paper is focused on the development of intelligent decision making model which is based on the application of artificial neural networks (ANN) and swarm intelligence technologies. The proposed model is used to generate one-step forward investment decisions. The ANN are used to make the analysis of historical stock returns and to calculate one day forward possible profit, which could be get while following the model proposed decisions, concerning the purchase of the stocks. Subsequently the Particle Swarm Optimization (PSO) algorithm is applied in order to select the „global best” ANNs for the future investment decisions and to adapt the weights of other networks towards the weights of the best network. The experimental investigations were made considering different number of neural networks, moving time intervals and commission fees. The experimental results presented in the paper show that the application of our proposed methodology lets to achieve better results than the average of the market

    Designing a Novel Model for Stock Price Prediction Using an Integrated Multi-Stage Structure: The Case of the Bombay Stock Exchange

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    Stock price prediction is considered a strategic and challenging issue in the stock markets. Considering the complexity of stock market data and price fluctuations, the improvement of effective approaches for stock price prediction is a crucial and essential task. Therefore, in this study, a new model based on “Adaptive Neuro-Fuzzy Inference System (ANFIS), Particle Swarm Optimization (PSO) and Genetic Algorithm (GA)” is employed to predict stock price accurately. ANFIS has been utilized to predict stock price trends more precisely. PSO executes towards developing the vector, and GA has been utilized to adjust the decision vectors employing genetic operators. The stock price data of top companies of the Bombay Stock Exchange (BSE) from 2010 to 2020 are employed to analyze the model functionality. Experimental outcomes demonstrated that the average functionality of our model (77.62%) was achieved noticeably better than other methods. The findings verified that the ANFIS-PSO-GA model is an efficient tool in stock price prediction which can be applied in the different financial markets, especially the stock market

    Network-centric Localization in MANETs Based on Particle Swarm Optimization

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    There exist several application scenarios of mobile ad hoc networks (MANET) in which the nodes need to locate a target or surround it. Severe resource constraints in MANETs call for energy efficient target localization and collaborative navigation. Centralized control of MANET nodes is not an attractive solution due to its high network utilization that can result in congestions and delays. In nature, many colonies of biological species (such as a flock of birds) can achieve effective collaborative navigation without any centralized control. Particle swarm optimization (PSO), a popular swarm intelligence approach that models social dynamics of a biological swarm is proposed in this paper for network-centric target localization in MANETs that are enhanced by mobile robots. Simulation study of two application scenarios is conducted. While one scenario focuses on quick target localization, the other aims at convergence of MANET nodes around the target. Reduction of swarm size during PSO search is proposed for accelerated convergence. The results of the study show that the proposed algorithm is effective in network-centric collaborative navigation. Emergence of converging behavior of MANET nodes is observed

    Multi-energy retail market simulation with autonomous intelligent agents

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    Tese de doutoramento. Engenharia Electrotécnica e de Computadores. 2005. Faculdade de Engenharia. Universidade do Port

    Efficient likelihood evaluation of state-space representations

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    We develop a numerical procedure that facilitates efficient likelihood evaluation in applications involving non-linear and non-Gaussian state-space models. The procedure approximates necessary integrals using continuous approximations of target densities. Construction is achieved via efficient importance sampling, and approximating densities are adapted to fully incorporate current information. We illustrate our procedure in applications to dynamic stochastic general equilibrium models. --particle filter,adaption,efficient importance sampling,kernel density approximation,dynamic stochastic general equilibrium model
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