PREDICTING STOCK MARKET PRICE BY USING ARTIFICIAL NEURAL NETWORK TRAINED WITH FOUR DIFFERENT ALGORITHM: A PILOT STUDY OF 15 BANGLADESHI COMPANY

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dc.contributor.authorMD FURKANUZZAMAN
dc.date.accessioned2025-11-23T05:44:55Z
dc.date.available2025-11-23T05:44:55Z
dc.date.issued2021-03-30
dc.description.abstractStock market price prediction is now a popular and important topic in financial and academic studies because stock market plays a vital rule in economy. Stock market price prediction is the act of trying to determine the future value of company stock. Stock market prices are actually time-series data and Artificial Neural Networks (ANNs) have the ability to find non-linear correlations between time-series data which makes it the best approach to predict stock market prices. Many researchers working on this topic and try to find the best algorithm with using stock market dataset which is suitable for predicting stock price. In this research historical data from Dhaka Stock Exchange is used to train and predict the price by using ANN. The Artificial Neural Network (ANN) is implemented by using multilayer Feedforward backpropagation model. In this paper fifteen companies six years data have been analyzed. To predict the specific result, the model has been trained in four different algorithm with change their parameter. Number of hidden layer, hidden neuron and percentage of training data have been change to get better output. After the training and testing process the predicted values are compared with the real data to find the accuracy. The trained network with the highest accuracy rate will able to predict the best possible price of the stock market.
dc.identifier.urihttps://dspace.nstu.ac.bd/handle/123456789/129
dc.titlePREDICTING STOCK MARKET PRICE BY USING ARTIFICIAL NEURAL NETWORK TRAINED WITH FOUR DIFFERENT ALGORITHM: A PILOT STUDY OF 15 BANGLADESHI COMPANY

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