FinSight
Earnings Intelligence
SHAP · LightGBM · Full Dataset

Feature Importance

SHapley Additive exPlanations on LightGBM trained across the full dataset. Shows which features actually drive predictions vs which are noise.

Top 20 Features by Mean |SHAP|
Colour: 🔵 RAG · 🟢 Mgmt FinBERT · 🟠 QA FinBERT
Importance by Group
Total SHAP contribution
Top 5 Feature Insights
#1
QA FinBERT
qa_neg_ratio
Analyst pushback proportion is the single strongest signal. When analysts push back hard, management is hiding something.
Mean |SHAP|0.0541
#2
Mgmt FinBERT
mgmt_sent_vol
Inconsistent management sentiment — oscillating between optimism and caution — precedes larger price moves in either direction.
Mean |SHAP|0.0476
#3
QA FinBERT
qa_n_sentences
Longer Q&A sessions signal more analyst scrutiny, correlating with higher uncertainty and larger subsequent price reactions.
Mean |SHAP|0.0453
#4
Mgmt FinBERT
mgmt_mean_neu
Deliberately neutral language can mask very good or very bad news — a hedging signal that markets react to.
Mean |SHAP|0.0445
#5
RAG
rag_guidance_specificity_relevance
Semantic relevance of guidance section to numerical targets — not just content — matters. Specific guidance = clearer market reaction.
Mean |SHAP|0.0420
🔑 Key Finding
Analyst Q&A features dominate management prepared remarks as predictive signals. qa_neg_ratio (SHAP=0.054) outperforms all management sentiment features combined. This is consistent with the hypothesis that management tone is endogenous and strategically managed, while analyst skepticism is a partially independent signal from sophisticated market participants. RAG features account for 34.6% of total SHAP importance despite comprising fewer features, validating the contribution of structured semantic retrieval.