News Impact Scorer

Review the event calendar at the start of each week to see what's coming. For major events (RBA decisions, reporting season, the Federal Budget), start preparing 2-3 days ahead. For regular events (economic data, individual 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 RBA decisions, consensus expectations are published by economists and the financial press, and ASX cash-rate futures imply the market-priced probability of a move. For company results, analyst estimates are available on financial websites. The surprise is when the actual outcome differs from these expectations.

For beginners, sitting out major events 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 a post-announcement drift opportunity, enter with defined risk. Don't rush - better to miss some of the move than to enter at the worst point.

ASX SPI 200 futures expire quarterly, on the third Thursday of March, June, September and December (trading in the expiring contract ceases at midday). If a major event coincides with quarterly expiry, expect amplified moves and reduced liquidity. Reduce derivative positions and allow for exaggerated swings near key levels.

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. a US Fed decision and an RBA decision in the same week), consider the 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 the IV-implied range, and Australian options can be illiquid. 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 and accuracy for direction. Given limited samples (the RBA meets only 8 times a year), 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 methods can be robust.

Consider: liquidity dries up before major events (wider spreads, thinner books), the first minutes after news are chaotic (poor execution), and market makers may pull quotes. This is more pronounced in Australian single-stock options. Use limit orders, accept partial fills, consider TWAP execution for larger orders, and 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. Use 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, and override authority. Full automation suits lower-impact routine events; high-impact or unusual events benefit from human judgment. Design systems with clear escalation paths.