Hello everybody, I’ve been engaged on a analysis prototype that goals to measure real-time monetary anxiousness from information and social narratives utilizing NLP method
The concept got here from a frustration with how most market volatility indicators are reactive—they let you know what occurred after it occurs. I questioned whether or not we might as an alternative measure how buyers really feel earlier than markets transfer, and whether or not that emotional sign might function an early indicator of stress.
So I developed the Monetary Sentiment Market Index (FSMI), a system that displays investor sentiment in actual time by analyzing each monetary information and Reddit discussions. The system makes use of FinBERT and RoBERTa to categorise sentence-level feelings, aggregates the outputs into standardized z-score indices, and tracks modifications in market-relevant emotional tone.
One in every of its core elements is the Monetary Nervousness Information Index (FANI), which isolates anxiety-related expressions particularly from monetary information articles. FANI gives a every day measure of hysteria depth by extracting, filtering, and quantifying linguistic markers of concern, concern, and uncertainty. It’s designed to function a forward-looking sign of stress, complementing conventional volatility measures just like the VIX.
The present model of the system analyzes knowledge from December 1, 2024 to April 9, 2025. Though the timeframe is comparatively brief, I selected it intentionally: it was a interval full of emotionally charged occasions—President Biden introduced he wouldn’t search reelection, Trump surged again because the Republican frontrunner, the AI bubble started to deflate following DeepSeek's collapse, and new tariff measures had been launched. These shocks created wealthy floor to look at how anxiousness in narratives builds and correlates with volatility.
To validate the system, I recognized ten dates the place FANI spiked above the ninetieth percentile. In all ten circumstances, the VIX (CBOE Volatility Index) elevated throughout the subsequent seven buying and selling days. Eight of these will increase had been statistically vital. These occasions had been usually linked to shock coverage strikes, central financial institution shifts, or sector-specific collapses.
Not like many sentiment instruments that depend on social media—which might be noisy and erratic—I targeted on structured monetary information to offer extra stability and interpretability. The system runs robotically twice a day, earlier than and after the U.S. market opens and closes. It additionally generates GPT-based every day summaries that cluster and clarify the emotional tone of market narratives.
I’m curious whether or not emotional alerts like this have worth in your individual work—particularly for anybody constructing fashions round volatility, market microstructure, or sentiment analytics. Suggestions is welcome, whether or not important or constructive.
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