Forecasting Iraq Stock Exchange index based on hybrid Genetic and partial swarm optimization

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Nahla Ibrahim Ismail, Asst Prof Dr Dahir Abbas Reda


An overall state of the economy of a country is reflected through the stock price market. Where high levels of index indicate an improved and growing economic state while low levels mean crisis and downturn. The prediction of stock price index through particle swarm optimization (PSO), artificial neural network and genetic algorithm (GA) can provide a great help. This paper attempts to identify the factors that affect the stock exchange index through the prediction and modeling Iraqi Stock Exchange Index on GAs with PSO approach. The paper aims of predicting the Iraq Stock Exchange Index and presents a general view of items that influence it, through implementing hybrid GA_PSO and compare with other regression model as GA,PSO, ANN. The simulation results the MSE of ANN, GA,PSO, hybrid GA-PSO 0.29332, 0.087423, 0.092345, 0.0077798 in order, that mean the GAPSO achieve the best result

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