@theobaldnew

Regulatory event-driven trading requires converting text into signals. NLP models can score sentiment, assign weights based on source credibility, and estimate lag effects. Positive regulatory language with high confidence and broad coverage merits higher signal weight. Time lags, often 1–3 days, can be calibrated from past policy shocks. By assigning probability-weighted signals, traders avoid overreacting to noise. Event portfolios can be systematically built using these signals, ensuring that policy windows become actionable rather than anecdotal.

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