Abstract
This study develops a signal-based trading strategy for the SPDR S&P 500 ETF Trust (SPY) using a multiple linear regression framework to analyze interrelationships between SPY and global equity indices across U.S., European, Asian, and Australian markets. By synthesizing historical pricing data from these major benchmarks, the model generates systematic trading signals through predicted price trajectories. In controlled training scenarios, the strategy achieved superior risk-adjusted returns compared to passive buy-and-hold approaches, demonstrating the value of cross-market signal integration. While the framework shows promise for algorithmic trading systems, the study acknowledges limitations in generalizing historical patterns to evolving market conditions. The findings highlight opportunities to enhance predictive accuracy through machine learning architectures capable of processing nonlinear market dynamics. These insights advance quantitative trading research by establishing methodologies for cross-market signal synthesis and proposing pathways to develop adaptive models for volatile capital markets.
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