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Managing finances through stock trading is essential, and it has a big impact on the economy and markets. Though research frequently overlooks smaller markets like the New Zealand Stock Exchange (NZX), accurate forecasts are essential for investors. The difficulty of forecasting NZX stock values which are greatly impacted by local news and public opinion is examined in this study. We suggest an approach that integrates models like LSTM, random forests, and SVM with sentiment analysis of historical market data and localized news. This method improves forecast accuracy for all NZX sectors, although it is still difficult to get high-quality localized data. The paper presents a novel trend-forecasting methodology and provides enhanced predictive skills for smaller markets such as NZX.

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Predicting New Zealand’s Stock Market Trends: Combining Sentiment Analysis and Deep Learning

  • 会议论文

  • 发表日期：

    2025年

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  • Mohita Trehan,
  • Soheil Varastehpour,
  • Masoud Shakiba,
  • Guillermo Ramirez Prado,
  • Bashar Barmada

摘要

Managing finances through stock trading is essential, and it has a big impact on the economy and markets. Though research frequently overlooks smaller markets like the New Zealand Stock Exchange (NZX), accurate forecasts are essential for investors. The difficulty of forecasting NZX stock values which are greatly impacted by local news and public opinion is examined in this study. We suggest an approach that integrates models like LSTM, random forests, and SVM with sentiment analysis of historical market data and localized news. This method improves forecast accuracy for all NZX sectors, although it is still difficult to get high-quality localized data. The paper presents a novel trend-forecasting methodology and provides enhanced predictive skills for smaller markets such as NZX.