• Login
    View Item 
    •   Research Bank Home
    • Unitec Institute of Technology
    • Study Areas
    • Computing
    • Computing Dissertations and Theses
    • View Item
    •   Research Bank Home
    • Unitec Institute of Technology
    • Study Areas
    • Computing
    • Computing Dissertations and Theses
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Critical comparison of statistical and deep learning models applied to the New Zealand Stock Market Index

    Dassanayake, Wajira

    Thumbnail
    Share
    View fulltext online
    DComp_2022_Wajira Dassanayake +.pdf (7.220Mb)
    Date
    2022
    Citation:
    Dassanayake, W. (2022). Critical comparison of statistical and deep learning models applied to the New Zealand Stock Market Index. (Unpublished document submitted in partial fulfilment of the requirements for the degree of Doctor of Computing). Unitec Institute of Technology, New Zealand. https://hdl.handle.net/10652/5768
    Permanent link to Research Bank record:
    https://hdl.handle.net/10652/5768
    Abstract
    RESEARCH QUESTIONS 1. What are the critical fundamental determinants of the NZX 50 Index movements? 2. How can effective forecasting models based on HWES and ARIMA methodologies be devised and applied with high precision to the NZX 50 Index prediction? 3. How can an efficient univariate LSTM forecasting model and a multivariate LSTM forecasting model be formulated and applied to forecast the NZX 50 Index movement with a high degree of predictive efficacy? 4. Considering all the models tested in different sample periods and scrutiny processes, is it possible to identify a superior forecasting model? Is the recognised model consistently outperforming other tested models in all the testing procedures? Can the redeveloped models efficiently handle the impact of the COVID-19 pandemic? ABSTRACT Financial markets enable buyers and sellers to trade financial instruments (stocks, bonds, foreign currencies, and derivatives) and improve capital allocation. These markets play a pivotal role in facilitating the interactions between those who seek capital and those who are prepared for capital investments, allowing market participants to transfer risks and stimulate economic growth. Financial time series are inherently dynamic, interdependent, and highly sensitive to many factors. These time series contain deterministic and stochastic characteristics, and many interrelated factors influence them. Accurate predictions of financial time series benefit various market participants to generate wealth through the right trading strategies and other stakeholders to enhance funds. However, due to their inherent complexities, financial time series prediction is considered one of the most challenging problems in data mining. This thesis employs popular and efficient time series prediction models, reformulates them and implements them to analyse stock market index movements. This scientific exploration uses two widely used classical forecasting techniques [Auto-Regressive Integrated Moving Average (ARIMA) and Holt Winter's Exponential Smoothing (HWES)] and efficient deep learning (DL) [Long Short-Term Memory (LSTM)] network. The predictive precision of the reformulated models will be empirically tested using Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Root Mean Square Error (RMSE). Four research questions are meticulously examined to close the identified empirical research gaps in the time series prediction models applied to the New Zealand stock market. Once the redesigned models are sufficiently trained, they are implemented as prediction models on selected stock market indices. Several statistical and econometric tests are executed to substantiate my research findings.
    Keywords:
    New Zealand, S&P/NZX50 Index, stock price analysis, prediction, New Zealand Stock Exchange (NZX), computer modelling, deep-learning algorithms, algorithms, Long Short-Term Memory (LSTM), Holt-Winters Exponential Smoothing models (HWES), Auto-Regressive Integrated Moving Average (ARIMA)
    ANZSRC Field of Research:
    350299 Banking, finance and investment not elsewhere classified, 490503 Computational statistics
    Degree:
    Doctor of Computing (Computing), Unitec Institute of Technology
    Supervisors:
    Ardekani, Iman
    Copyright Holder:
    Author

    Copyright Notice:
    All rights reserved
    Rights:
    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.
    Metadata
    Show detailed record
    This item appears in
    • Computing Dissertations and Theses [90]

    Te Pūkenga

    Research Bank is part of Te Pūkenga - New Zealand Institute of Skills and Technology

    • About Te Pūkenga
    • Privacy Notice

    Copyright ©2022 Te Pūkenga

    Usage

    Downloads, last 12 months
    137
     
     

    Usage Statistics

    For this itemFor the Research Bank

    Share

    About

    About Research BankContact us

    Help for authors  

    How to add research

    Register for updates  

    LoginRegister

    Browse Research Bank  

    EverywhereInstitutionsStudy AreaAuthorDateSubjectTitleType of researchSupervisorCollaboratorThis CollectionStudy AreaAuthorDateSubjectTitleType of researchSupervisorCollaborator

    Te Pūkenga

    Research Bank is part of Te Pūkenga - New Zealand Institute of Skills and Technology

    • About Te Pūkenga
    • Privacy Notice

    Copyright ©2022 Te Pūkenga