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Stock market equity advisory tool using analytic hierarchy process and single-layer perceptron neural network

Lee, Woon Ming (2023) Stock market equity advisory tool using analytic hierarchy process and single-layer perceptron neural network. Final Year Project, UTAR.

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    These days, there are a few of application which can help the user to conducting stock comparison and stock rating. However, these applications cannot directly predict the stock trend for the user whether the stock is in an uptrend or downtrend. They also do not allow users to choose their preferred indicators during stock comparisons or predictions. Therefore, this project will deliver a mobile Android application with a dynamic stock prediction model which will be using Analytic Hierarchy Process (AHP) and Artificial Neural Networks (ANN) of Single-Layer Perceptron Neural Network as a combined technique to predict the stock trend. In this application, users can choose multiple or one of their preferred indicators from the fundamental indicator list and technical indicator list which have been provided in the application. The chosen indicators will be used as the input of the stock predictive model in the application and these chosen indicators will become the inputs of AHP and Single-Layer Perception Neural Network algorithm. Through this “Stock Market Equity Advisory Tool”, users can easily know advice on whether it is the right time for them to sell the stock or buy the stock from this tool according to their investment goals. Besides, this application will make the stock prediction process more user-friendly and accessible.

    Item Type: Final Year Project / Dissertation / Thesis (Final Year Project)
    Subjects: T Technology > T Technology (General)
    T Technology > TA Engineering (General). Civil engineering (General)
    Divisions: Faculty of Information and Communication Technology > Bachelor of Information Systems (Honours) Information Systems Engineering
    Depositing User: ML Main Library
    Date Deposited: 02 Jan 2024 23:30
    Last Modified: 02 Jan 2024 23:30
    URI: http://eprints.utar.edu.my/id/eprint/5992

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