The News Sentiment Model is Not About News Sentiment

Quick post to discuss some things...

First, what I am discovering after training this model daily, and running predictions, is that the news sentiment is NOT what is influencing this model. 

The news sentiment is based on Transformer models for financial news (Finbert). The news IS fresh, as we get these articles and and then predict over a 1-3 day period, before they're expired out to a training database where predicted returns are compared with actual returns.

News is NOT what this model is learning and training on. Instead, momentum and macro environment features are the aspects that is influencing the returns. Part of this could be the news being stale - the market already knows the news before the model is running. Also, we are only scoring headlines - not actual articles - due to processing constraints. So there's that.

From the training output, here's the feature importance ranked across all three horizons:

Macro features (dominating ~50% of importance):

• vix_0d — market fear index
• treasury_spread_0d — yield curve spread
• business_confidence_0d — FRED macro indicator
• consumer_sentiment_0d — FRED macro indicator

Momentum features (~40%):

• momentum_strength — short-term trend deceleration
• risk_adjusted_momentum — momentum relative to volatility
• trend_consistent — direction consistency

Sentiment features (~10%):

• tone_signed_resid — residualized tone score
• finbert_signed_resid — residualized FinBERT score
• tone_signed — raw tone
• finbert_signed — raw FinBERT

So the model is essentially saying: buy beaten-down stocks when the macro environment is calm. The news sentiment is contributing about 10% of the predictive signal, which is barely above noise given the R² of 0.002 on the residual models.

It's not really a news sentiment model at this point. It's a macro-regime mean-reversion model that happens to have sentiment features along for the ride.

Which raises the real question — is that actually a bad thing? Mean reversion in calm macro regimes is a legitimate strategy. The problem isn't the model's logic, it's that you only have 3 months of data so it hasn't seen enough regime diversity to be robust. And right now you're in a fear regime — elevated VIX, geopolitical uncertainty — which is exactly when this model historically underperforms.