Revealing predictive insights: Harnessing statistical and machine learning techniques for New Zealand Stock Index forecasting
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Supplementary material
Other Title
Authors
Anand, A.
Dassanayake, Wajira
Dassanayake, Wajira
Author ORCID Profiles (clickable)
Degree
Grantor
Date
2024
Supervisors
Type
Conference Contribution - Oral Presentation
Ngā Upoko Tukutuku (Māori subject headings)
Keyword
New Zealand Stock Exchange (NZX)
stock movement prediction
stock markets
Long Short-Term Memory (LSTM)
AutoRegressive Integrated Moving Average (ARIMA)
deep-learning algorithms
computer modelling
algorithms
New Zealand
stock movement prediction
stock markets
Long Short-Term Memory (LSTM)
AutoRegressive Integrated Moving Average (ARIMA)
deep-learning algorithms
computer modelling
algorithms
New Zealand
ANZSRC Field of Research Code (2020)
Citation
Anand, A., & Dassanayake, W. (2024, December 10). Revealing predictive insights: Harnessing statistical and machine learning techniques for New Zealand Stock Index forecasting [Paper presentation] Auckland Region Accounting Conference, Unitec Mt Albert Campus
https://hdl.handle.net/10652/6691
Abstract
The purpose of this presentation is to evaluate the forecasting effectiveness of the ARIMA model (a statistical approach) in comparison to the LSTM model (an advanced Machine Learning and Deep Learning technique)