Fear & Greed Index Explained
Understand VantMacro's operational sentiment composite, its configured inputs, and the limits of interpreting it.
What You'll Learn
- Understand what the operational sentiment composite measures
- Inspect its current normalization bounds and weights from source configuration
- Distinguish display labels from calibrated empirical thresholds
- Recognize the limitations of sentiment indicators
VantMacro's Fear & Greed display combines volatility, credit, and financial- conditions inputs into a bounded sentiment score. It is an operational summary, not a model of future returns.
The settings below are rendered from the same centralized configuration used by the application. Editors do not copy the weights, normalization bounds, or score bands into this article.
Computed inputs
| Input | Transformation | Weight |
|---|---|---|
| VIX | Latest finite observation ranked within its complete finite FRED history; higher raw stress maps to a lower score | 25% |
| High-yield spread | Latest finite observation ranked within its complete finite FRED history; higher raw stress maps to a lower score | 25% |
| NFCI | Latest finite observation ranked within its complete finite FRED history; higher raw stress maps to a lower score | 25% |
| STLFSI | Latest finite observation ranked within its complete finite FRED history; higher raw stress maps to a lower score | 25% |
Equal-width percentile display bands
| Score interval | Operational label |
|---|---|
| below 20 | Extreme Fear |
| 20 to below 40 | Fear |
| 40 to below 60 | Neutral |
| 60 to below 80 | Greed |
| 80 or above | Extreme Greed |
The score is the equal-weight mean of four current-vintage empirical low-stress percentiles. The five labels divide the 0–100 percentile scale into equal-width bands. This is descriptive; no predictive calibration or confidence interval is claimed.
Calculation
Each available input is normalized between its configured bounds. The stress components are combined using the configured weights, then inverted so higher stress maps to lower sentiment. Missing or malformed required data must remain unavailable rather than being replaced with a fabricated score.
The normalization bounds and weights are legacy product-policy settings. The repository currently contains no reproducible procedure showing that they are optimal, no uncertainty interval, and no validated mapping from a score to a future market return.
How to use the display
Use the score to inspect which stress inputs are unusually high or low under the declared normalization. Then look at the component observations themselves.
- A low score says the configured stress measures are high relative to their operational bounds.
- A high score says those measures are low relative to the same bounds.
- Neither condition is a buying or selling signal.
- Extreme labels can persist and do not identify a turning point.
Data sources
- VIX (
VIXCLS) from FRED/CBOE - High Yield OAS (
BAMLH0A0HYM2) from FRED/ICE BofA - Chicago Fed NFCI (
NFCI) - St. Louis Fed Financial Stress Index (
STLFSI4)
Provider observations have different frequencies and publication schedules. Alignment, freshness, and missing-data handling therefore matter as much as the formula.
Limitations
- The configured bounds and weights are not empirically calibrated.
- Current-vintage provider data can differ from what was available in real time.
- A composite can hide offsetting component movements.
- Sentiment and prices may respond to the same shock; association is not causation.
- No canonical predictive effect size or classifier accuracy is published.
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