GSPTSE Directional Forecasting via U.S. Market Signals and Technical Indicator

Beibo Jiang ()
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Beibo Jiang: The Ohio State University

A chapter in Proceedings of the 2025 International Conference on Hybrid Commerce, Human Capital, and Economic Dynamics (ICHCH 2025), 2026, pp 254-261 from Springer

Abstract: Abstract With the deepening of economic globalization and the international flow of financial capital and assets, the stock markets of different countries tend to be interconnected, and there will be correlations and co-movement among the stock markets globally or regionally. This study aims to predict the daily directional movement, whether upward movement or downward movement, of the Canadian major index S&P / TSX Composite Index (GSPTSE) by using the major stock market indices of the United States and technical indicators. The study used six major US market indices, the volatility index (VIX), the Relative Strength Index (RSI), Moving Average Convergence and Divergence (MACD), and MACD based on historical volatility (MACD-HVIX), and lagging returns for prediction. MACD-HVIX is an indicator based on dynamic volatility adjustment from MACD. Linear model Logistic Regression, and two nonlinear models, Random Forest and eXtreme Gradient Boosting (XGBoost) were applied. The research results showed that nonlinear models performed better than the linear model, and random forest is better than XGBoost. Moreover, the study found that the effect of XGBoost was much better than the other two models when using the MACD-HVIX indicator.

Keywords: Machine Learning; Cross-Market Forecasting; MACD-HVIX (search for similar items in EconPapers)
Date: 2026
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DOI: 10.2991/978-2-38476-585-0_30

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