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Quantile regression (sample ticker): sentiment vs next-day returns
Quantile regression on the sample ticker to see whether sentiment loads differently in tails vs median.
Updated: 2026-04-05
Study
Robustness • Status: LIVE
Updated: 2026-04-05
Sample ticker
AAPL
Quantiles
0.1, 0.5, 0.9
Horizon
t+1
Sample ticker
AAPL
Tickers (panel)
—
Obs (panel)
—
R² (TS)
—
R² (FE)
—
Key findings
- Quantile slopes can reveal whether the signal is stronger in downside tails or upside tails.
- If tail-loadings differ, linear mean regressions can hide economically relevant asymmetries.
Specification
- QuantReg_q: y_ret(t+1) ~ score_mean(t), q ∈ {0.1, 0.5, 0.9}.
Data
- Universe: S&P 500 tickers in your snapshot pipeline.
- Frequency: Daily.
- Returns: log(P_t) − log(P_{t−1}) from ticker JSON prices.
- Sentiment: score_mean from your sentiment pipeline (ticker JSON).
Limitations
- Timing: after-close articles can contaminate same-day results; predictive tests mitigate this but do not fully solve it.
- Causality: results are descriptive; omitted variables and measurement error remain.
- Trading: no transaction costs / slippage / capacity modeled.
Methodology
- Run QuantReg on the sample ticker: y_ret(t+1) ~ score_mean(t).
- This is descriptive (single-ticker) but useful to diagnose tail behavior.
Figures
Charts are from the sample ticker (not the full panel).
Next-day returns (sample)
log return (t+1)
Sentiment (sample)
score_mean
Models
This study is primarily descriptive (figures/tables). No regression model outputs were exported.
Appendix
Raw exported objects (reproducibility / debugging).
Raw exported JSON (collapsed)
{
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},
"quantiles_next_day": {
"0.1": {
"params": {
"const": -0.014639284146376243,
"score_mean": 0.018499086892309988
},
"tvalues": {
"const": -9.536107467030963,
"score_mean": 1.4062640060830622
},
"pvalues": {
"const": 5.790400332757187e-20,
"score_mean": 0.16024649322332785
}
},
"0.5": {
"params": {
"const": 1.1009284291229522e-7,
"score_mean": 6.456373148363479e-7
},
"tvalues": {
"const": 0.0005068918104483491,
"score_mean": 0.00047646025011890676
},
"pvalues": {
"const": 0.9995957543915466,
"score_mean": 0.999620023522366
}
},
"0.9": {
"params": {
"const": 0.01420356142381447,
"score_mean": -0.011752981283713014
},
"tvalues": {
"const": 9.883612369341295,
"score_mean": -1.33342967703772
},
"pvalues": {
"const": 3.2399144318742536e-21,
"score_mean": 0.1829780383008134
}
}
}
},
"methodology": [
"Run QuantReg on the sample ticker: y_ret(t+1) ~ score_mean(t).",
"This is descriptive (single-ticker) but useful to diagnose tail behavior."
],
"sections": [
{
"title": "Specification",
"bullets": [
"QuantReg_q: y_ret(t+1) ~ score_mean(t), q ∈ {0.1, 0.5, 0.9}."
]
},
{
"title": "Data",
"bullets": [
"Universe: S&P 500 tickers in your snapshot pipeline.",
"Frequency: Daily.",
"Returns: log(P_t) − log(P_{t−1}) from ticker JSON prices.",
"Sentiment: score_mean from your sentiment pipeline (ticker JSON)."
]
},
{
"title": "Limitations",
"bullets": [
"Timing: after-close articles can contaminate same-day results; predictive tests mitigate this but do not fully solve it.",
"Causality: results are descriptive; omitted variables and measurement error remain.",
"Trading: no transaction costs / slippage / capacity modeled."
]
}
],
"conclusions": [
"Quantile slopes can reveal whether the signal is stronger in downside tails or upside tails.",
"If tail-loadings differ, linear mean regressions can hide economically relevant asymmetries."
]
}