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

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Title

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

Content partner

Unitec

Collection

Research Bank

Description

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 ...

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Format

Research paper

Research format

Thesis

Thesis level

Masters

Date created

2022

Creator

Dassanayake, Wajira

URL

https://hdl.handle.net/10652/5768

Related subjects

New Zealand / 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) / Commerce, management, tourism and services / Banking, finance and investment / Banking, finance and investment not elsewhere classified / Mathematical sciences / Statistics / Computational statistics

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