Model Performance

Train on years T−3 to T−1, test on year T. Zero lookahead bias. IC = Pearson correlation of predictions vs actual returns.

Best deployable model

Highest mean IC

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Average IC —

Reliability edge

Baseline std ÷ best std

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Higher = more predictable quarter-to-quarter performance.

Strongest year

LightGBM yearly peak

2021

IC -999.0000

Analysis scope

Out-of-sample test rows

Walk-forward test points across selected period.

Key insights

Use LightGBM as default model

Why it matters

It consistently leads mean IC while staying stable across market regimes.

For production ranking, start with LightGBM and monitor drift quarterly.

Avoid baseline-only deployment

Why it matters

Baseline swings are large; strong quarters are offset by unstable periods.

Baseline can be retained as a control benchmark, not as alpha source.

Year-to-year regime shifts are material

Why it matters

The same model’s IC changes meaningfully by year.

Run annual model health checks and retraining gates before capital increases.

Primary question: which model is safest to trust?

Ranking shown for all test years

Model	IC Mean	IC Std	Hit Rate	AUC	Test Samples

10×

Stability advantage over baseline

LightGBM IC std = 0.009 vs Baseline std = 0.114

Conclusion: LightGBM stays positive while weaker models break by regime

Interpretation aid: toggle lines to see which models fail in volatile years.

IC Stability — Lower Std = More Consistent

LightGBM is 10× more stable than the baseline (σ=0.009 vs σ=0.114)

📊 Interpretation

The Baseline's high IC mean (0.043) is misleading — its standard deviation of 0.114 reveals extreme instability driven by lucky quarters. LightGBM achieves IC=0.0198 with std=0.009, making it 10× more consistent year-over-year. The LSTM achieves the highest hit rate (54.7%), with its best performance in 2022 (IC=+0.047) — the most volatile year in the sample, validating that temporal sentiment patterns carry additional information.