Portfolio risk Lab 04

Factor Crowding

Where is a portfolio concentrated by factor, co-movement, and estimated crowded-unwind loss?

Saved example run

Generated using bundled data. These values illustrate the diagnostic and are not current market observations.

Portfolio weight by factor Bundled example · % of portfolio
  • AI Infrastructure56.0%
  • Rates Hedge13.0%
  • Defensive Value12.0%
  • Quality Growth11.0%
  • Cash8.0%

Values from the captured factor-exposure table, reproduced below.

↔ Wide tables scroll horizontally. Keyboard: focus a table, then use the arrow keys.

High crowding risk | score=94/100 | top_factor=AI Infrastructure (56.0%) | unwind_loss=-5.15%

Factor Crowding Radar Report

Executive summary

  • Risk level: High
  • Crowding score: 94/100
  • Top factor: AI Infrastructure (56.0%)
  • Average pairwise correlation: 1.00
  • Factor concentration HHI: 0.36
  • Unwind stress loss: -5.15%

Factor exposures

Factor Portfolio weight
AI Infrastructure 56.0%
Rates Hedge 13.0%
Defensive Value 12.0%
Quality Growth 11.0%
Cash 8.0%

Alerts

  • AI Infrastructure holds 56.0% of portfolio weight; single-factor exit liquidity may dominate risk.
  • Average pairwise correlation is 1.00; positions may de-risk together.
  • Correlation breadth is 83.0%; multiple sleeves share similar return paths.
  • Modeled unwind stress loss is -5.15%, large enough to merit desk-level review.

Methodology

  • Aggregates gross portfolio weight by declared factor bucket.
  • Computes normalized factor HHI to quantify concentration.
  • Measures position-return co-movement using exposure-weighted pairwise correlations.
  • Applies a basis-point unwind shock to the top factor sleeve with correlation amplification.
This is an offline research/risk-screening tool, not investment advice. Validate inputs and assumptions before using it in production.

The paper trail

Inspect. Reproduce. Extend.

Run this command from the finance-labs repository root to reproduce the bundled example.

Exact command
PYTHONPATH=labs/factor-crowding/src python3 -m factor_crowding_radar analyze labs/factor-crowding/examples/sample_portfolio.json --shock-bps 425 --format markdown

View original captured output
High crowding risk | score=94/100 | top_factor=AI Infrastructure (56.0%) | unwind_loss=-5.15%
# Factor Crowding Radar Report

## Executive summary

- **Risk level:** High
- **Crowding score:** 94/100
- **Top factor:** AI Infrastructure (56.0%)
- **Average pairwise correlation:** 1.00
- **Factor concentration HHI:** 0.36
- **Unwind stress loss:** -5.15%

## Factor exposures

| Factor | Portfolio weight |
| --- | ---: |
| AI Infrastructure | 56.0% |
| Rates Hedge | 13.0% |
| Defensive Value | 12.0% |
| Quality Growth | 11.0% |
| Cash | 8.0% |

## Alerts

- AI Infrastructure holds 56.0% of portfolio weight; single-factor exit liquidity may dominate risk.
- Average pairwise correlation is 1.00; positions may de-risk together.
- Correlation breadth is 83.0%; multiple sleeves share similar return paths.
- Modeled unwind stress loss is -5.15%, large enough to merit desk-level review.

## Methodology

- Aggregates gross portfolio weight by declared factor bucket.
- Computes normalized factor HHI to quantify concentration.
- Measures position-return co-movement using exposure-weighted pairwise correlations.
- Applies a basis-point unwind shock to the top factor sleeve with correlation amplification.

> This is an offline research/risk-screening tool, not investment advice. Validate inputs and assumptions before using it in production.

Capture execution time unavailable

Source revision was not recorded for this historical capture.

The source link points to the current repository; it may differ from this saved run.