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    New Zealand Stock Market prediction using sentiment analysis

    Bangar, Varinder Jot

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    MComp_2022_Varinder _Jot_Bangar +.pdf (10.39Mb)
    Date
    2022
    Citation:
    Bangar, V. J. (2022). New Zealand Stock Market prediction using sentiment analysis. (Unpublished document submitted in partial fulfilment of the requirements for the degree of Master of Computing). Unitec Institute of Technology, New Zealand. https://hdl.handle.net/10652/5917
    Permanent link to Research Bank record:
    https://hdl.handle.net/10652/5917
    Abstract
    In this research, I addressed this gap and tried to focus on the New Zealand Stock Exchange (NZX). For the proposed model, I used the financial news only, for the SA and for the stock data I took five of the biggest New Zealand organisations. The reason for choosing these organisations was that the textual data contains more mentions about these companies than others and hence, the sentiment scores can reflect the changes that take place. This research was conducted to provide a picture of how accurately NZX trends can be predicted using DL algorithms with and without taking into considerations the SA. Also, the purpose of this research was to draw a picture of how different NZX is from the rest of the bigger stock markets and if the same tools and techniques apply for the prediction of trends. The findings of this research adds to the knowledge that the similar tools and techniques that are used for bigger stock markets, can also be applied to predict the trends of NZX. It further adds that the length and quality of the textual data available for the sentiment analysis also plays an important role in the accuracy of the prediction model. This research also examines the impact of Covid-19 on the accuracy of the prediction model by including and excluding the timelines of data related to this global pandemic.
    Keywords:
    New Zealand Stock Exchange (NZX), stock markets, stock movement, prediction, deep-learning algorithms, computer modelling, algorithms, New Zealand, sentiment analysis (SA)
    ANZSRC Field of Research:
    350299 Banking, finance and investment not elsewhere classified, 461305 Data structures and algorithms, 461103 Deep learning
    Degree:
    Master of Computing, Unitec Institute of Technology
    Supervisors:
    Sharifzadeh, Hamid; Varastehpour, Soheil
    Copyright Holder:
    Author

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    This digital work is protected by copyright. It may be consulted by you, provided you comply with the provisions of the Act and the following conditions of use. These documents or images may be used for research or private study purposes. Whether they can be used for any other purpose depends upon the Copyright Notice above. You will recognise the author's and publishers rights and give due acknowledgement where appropriate.
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    • Computing Dissertations and Theses [91]

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