# ｃｏｔｔａ (3359.T) — per-stock model accuracy

> Model predictability decomposition: measures how accurately the ml-factor-lgbm-9f-jp-growth model family predicts this stock's returns within the jp-listed universe. High hit_rate_long does NOT mean 'good stock to buy' -- it means 'the model historically predicted this stock well when selecting it LONG'. This is a property of model-stock fit, not intrinsic stock quality.

- **Name**: ｃｏｔｔａ
- **Sector**: 商社・卸売
- **Universe**: jp-listed
- **Decomposes**: `ml-factor-lgbm-9f-jp-growth` (strategy verdict: marginal) — https://qbuntu.ai/strategies/ml-factor-lgbm-9f-jp-growth
- **Computed asof**: 2026-10-01T02:33:09
- **HTML**: https://qbuntu.ai/stocks/3359.T
- **JSON**: https://qbuntu.ai/v1/stocks/3359.T
- **License**: CC-BY-4.0

## Accuracy (median across grid)

Median LONG hit rate is 0.3485 — below a coin flip.

| metric | median | p25 | p75 | n |
|---|---:|---:|---:|---:|
| hit_rate_long | 0.3485 | 0.2540 | 0.4257 | 12 |
| hit_rate_short | 0.9231 | 0.7496 | 1 | 8 |
| avg_return_long_bps | 73.7 | -3.7039 | 106.7 | 12 |
| avg_return_short_bps | 107.6 | 58.0 | 132.4 | 8 |
| selection_rate_long | 0.0041 | 0.0028 | 0.0063 | 12 |
| selection_rate_short | 0.0002 | 0 | 0.0011 | 12 |

Hit rate ≈ 0.5 means the model's picks on this stock were near coin-flip. Returns in bps/trade. Metrics with n = 0 are omitted; n < 5 is a thin sample.

## PnL contribution

- long: 6.0970
- short: 2.4708
- total: 8.5678
- appearances: LONG 835 / SHORT 191 of 182016

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Research, not investment advice. These metrics describe model–stock fit, not the stock's merit.
Cite as: qbuntu (2026). 3359.T per-stock accuracy. https://qbuntu.ai/stocks/3359.T