News Impact Scorer

Review the event calendar at the start of each week to see what's coming. For major events (Bank of England policy, the Budget), start preparing 2-3 days ahead. For regular events (economic data, results), day-before preparation is usually sufficient.

No, only reduce for events with high impact scores (6+) that affect your specific positions. Low impact events (score 1-4) usually don't require adjustments. Always consider whether the event actually affects your holdings.

Don't panic. First, assess if it affects your positions. If yes, check how the market has already reacted. Avoid chasing if the move has already happened - often waiting for settling is better than reacting late. Learn from it and set up better alerts.

For Bank of England policy, consensus expectations are published by analysts and news sources. For company results, analyst estimates are available on financial websites. For the Budget, market commentary indicates expectations. The surprise is when actual differs from these expectations.

For beginners, sitting out major events (like Budget) is a reasonable approach. As you gain experience, you can learn to trade around events with proper preparation. The key is not to be caught off-guard - either reduce exposure or have a specific event trading plan.

Wait for initial volatility to settle (15-30 minutes for normal events, longer for major ones). Assess the direction of the move and whether it's in line with historical patterns. If you see post-announcement drift opportunity, enter with defined risk. Don't rush - better to miss some move than to enter at the worst point.

Expiry week adds complexity. If a major event coincides with expiry (like a Thursday Bank of England decision near a FTSE 100 options expiry), expect even higher volatility due to options gamma effects. Reduce options positions, use wider strikes for protection, and expect exaggerated moves near key strikes.

Historical data provides context but shouldn't override current analysis. Market conditions change, and the same event can have different impacts in different environments. Use history to calibrate expectations, but adjust for current market sentiment, positioning, and any unique factors.

When multiple events cluster (e.g., the US Fed and Bank of England in the same week), consider cumulative impact. Position adjustments should account for total expected volatility. The interaction can amplify or dampen effects depending on outcomes. Be more conservative when events cluster.

Straddles or strangles profit from big moves regardless of direction, but high pre-event IV means the move must exceed IV-implied range. A better approach might be post-event: if you expect IV crush, sell premium after the event when IV is still elevated but direction is clearer.

Use walk-forward validation: train on historical events, test on out-of-sample future events. Calculate metrics like RMSE for magnitude predictions, accuracy for direction. Given limited samples, consider bootstrap methods. Compare to naive benchmarks (historical average). Track live performance over time.

Domain-specific models like FinBERT typically outperform general NLP models. They're pre-trained on financial text and understand domain-specific language. For real-time systems, balance accuracy with speed - simpler models may be faster. Ensemble approaches combining lexicon-based and ML can be robust.

Consider: liquidity dries up before major events (wider spreads, thinner books), first minutes after news are chaotic (poor execution), market makers may pull quotes. Use limit orders, accept partial fills, consider TWAP execution for larger orders. Factor in expected slippage when sizing positions.

Report confidence intervals, not just point estimates. Flag when current conditions differ significantly from training data. Maintain fallback rules (simple heuristics) when model confidence is low. Human oversight for critical decisions. Log all predictions for post-hoc analysis and model improvement.

Automate routine tasks: alerts, data collection, initial scoring. Keep humans in the loop for: critical decisions, unusual events, override authority. Full automation suits lower-impact routine events; high-impact or unusual events benefit from human judgment. Design systems with clear escalation paths.