Forecasting New Zealand Stock Returns Through Intermarket Analysis Using Global Vector Autoregressive Model and Machine Learning Methods
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Dewabrata, Bisma
Wichitaksorn, Nuttanan
Otsubo, Yoichi
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MDPI AG
Abstract
This study applies intermarket analysis to forecast the New Zealand NZX 50 stock returns using a hybrid framework that combines econometric models with machine learning (ML) algorithms. Daily return data from 3 January 2001 to 31 December 2024 are employed to examine the interconnectedness between the New Zealand equity market and major global financial markets. This study follows a three-stage methodology: ML-based feature selection using LASSO, ridge, and elastic net regressions; econometric validation through a Global Vector Autoregressive (GVAR) model; and forecasting implementation using Support Vector Regression (SVR), Random Forest (RF), Long Short-Term Memory, and Artificial Neural Networks. Feature selection consistently identifies the Australian ASX 200, Japanese Nikkei 225, U.S. S&P 500, and U.S. 10-year Treasury yield as the most influential predictors, with Australia exerting the strongest impact. GVAR results reveal significant short-term spillover effects, but no long-term co-integrating relationships, indicating independent market trends. The U.S. market emerges as the dominant transmitter of shocks, while New Zealand acts as a net receiver. Forecasting results show RF and SVR outperform alternative models, with optimal performance achieved using a fifth-lag structure. The findings support short-term, spillover-based investment strategies and contribute to a transparent, replicable framework for forecasting equity markets in small open economies.
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35 Commerce, management, tourism and services, 38 Economics, global vector autoregression, intermarket analysis, NZX50, feature selection, machine learning, forecasting
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Journal of Risk and Financial Management, ISSN: 1911-8074 (Online), MDPI AG, 19(7), 517-517. doi: 10.3390/jrfm19070517
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CC-BY Creative Commons Attribution
© 2026 by the authors. Licensee MDPI, Basel, Switzerland. Open Access.
© 2026 by the authors. Licensee MDPI, Basel, Switzerland. Open Access.
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Except where otherwise noted, this item's license is described as CC-BY Creative Commons Attribution