Financial markets generate enormous amounts of data, but the effective signal is far smaller than raw counts suggest. Correlation across assets reduces independent information, regime changes limit how much historical data remains relevant, and noise further increases sample complexity. Together, these effects shrink billions of observations into a much smaller usable dataset. Understanding this constraint is critical for building effective models, emphasizing the importance of structure, denoising, and careful pooling across assets. Why massive financial datasets contain far less usable signal than they appear.
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