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Sentiment vs volatility proxy (abs returns)
abs_ret(t) ~ score_mean(t).
Updated: 2026-04-05
Study
Volatility & attention • Status: LIVE
Updated: 2026-04-05
β(score_mean) TS
-0.01706***
t-stat TS
-2.96
R² (TS)
0.0436
β(score_mean) FE
-0.0006334***
t-stat FE
-5.25
R² (FE)
0.000201
Sample ticker
AAPL
Tickers (panel)
507
Obs (panel)
261179
R² (TS)
0.0436
R² (FE)
2.009e-4
Key findings
- Sentiment vs volatility proxy (abs returns): score_mean is negative in TS (β=-0.0170556***, t=-2.959740109623123, R²=0.04357847269681936).
- Panel FE: score_mean is negative after removing ticker FE (β=-0.000633352***, t=-5.253686532703043, R²=0.00020094629995182078).
- Scale: +1.0 in score_mean ≈ -170.56 bps change in abs return (vol proxy) (log-return units).
- Interpretation: attention proxies (news volume) often correlate with volatility regardless of direction.
Specification
- |y_ret(t)| ~ n_total(t) + score_mean(t).
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.
References (minimal)
- Newey, W. K., & West, K. D. (1987). A simple, positive semi-definite, heteroskedasticity and autocorrelation consistent covariance matrix.
- Petersen, M. A. (2009). Estimating standard errors in finance panel data sets.
Methodology
- Dependent variable: abs_ret(t) = |y_ret(t)|.
- Regressors: n_total(t) (if available) and score_mean(t).
- Time-series: OLS with Newey–West HAC SE (maxlags=5).
- Panel: within estimator removing ticker FE; SE clustered by ticker.
Figures
Charts are from the sample ticker (not the full panel).
Next-day returns (sample)
log return (t+1)
Sentiment (sample)
score_mean
Models
Time-series (HAC)
SE: HAC(maxlags=5) • N: 520 • R²: 0.0436
| Variable | Coef | t | p | bps / 1.0 |
|---|---|---|---|---|
| const | 0.008521*** | 11.757 | 6.504e-32 | 85.21 |
| score_mean | -0.017056*** | -2.96 | 0.0031 | -170.56 |
Stars: *** p<0.01, ** p<0.05, * p<0.10.
Panel FE (clustered)
SE: cluster(ticker) • N: 261179 • R²: 2.009e-4
| Variable | Coef | t | p | bps / 1.0 |
|---|---|---|---|---|
| score_mean | -6.334e-4*** | -5.254 | 1.491e-7 | -6.33 |
Stars: *** p<0.01, ** p<0.05, * p<0.10.
Appendix
Raw exported objects (reproducibility / debugging).
Raw exported JSON (collapsed)
{
"results": {
"sample_ticker": "AAPL",
"n_tickers": 507,
"n_obs_panel": 261179,
"series": {
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},
"time_series": {
"params": {
"const": 0.008520585687541705,
"score_mean": -0.01705561280492048
},
"bse": {
"const": 0.0007247276185515265,
"score_mean": 0.005762537308416665
},
"tvalues": {
"const": 11.756949051522742,
"score_mean": -2.959740109623123
},
"pvalues": {
"const": 6.504235077140166e-32,
"score_mean": 0.0030789866348364457
},
"nobs": 520,
"rsquared": 0.04357847269681936,
"rsquared_adj": 0.04173209909198683,
"cov_type": "HAC(maxlags=5)"
},
"panel_fe": {
"params": {
"score_mean": -0.0006333516118287668
},
"bse": {
"score_mean": 0.00012055374980716728
},
"tvalues": {
"score_mean": -5.253686532703043
},
"pvalues": {
"score_mean": 1.4908453608317792e-7
},
"nobs": 261179,
"rsquared": 0.00020094629995182078,
"rsquared_adj": 0.0001971182629283419,
"cov_type": "cluster(ticker)"
}
},
"methodology": [
"Dependent variable: abs_ret(t) = |y_ret(t)|.",
"Regressors: n_total(t) (if available) and score_mean(t).",
"Time-series: OLS with Newey–West HAC SE (maxlags=5).",
"Panel: within estimator removing ticker FE; SE clustered by ticker."
],
"sections": [
{
"title": "Specification",
"bullets": [
"|y_ret(t)| ~ n_total(t) + score_mean(t)."
]
},
{
"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."
]
},
{
"title": "References (minimal)",
"bullets": [
"Newey, W. K., & West, K. D. (1987). A simple, positive semi-definite, heteroskedasticity and autocorrelation consistent covariance matrix.",
"Petersen, M. A. (2009). Estimating standard errors in finance panel data sets."
]
}
],
"conclusions": [
"Sentiment vs volatility proxy (abs returns): score_mean is negative in TS (β=-0.0170556***, t=-2.959740109623123, R²=0.04357847269681936).",
"Panel FE: score_mean is negative after removing ticker FE (β=-0.000633352***, t=-5.253686532703043, R²=0.00020094629995182078).",
"Scale: +1.0 in score_mean ≈ -170.56 bps change in abs return (vol proxy) (log-return units).",
"Interpretation: attention proxies (news volume) often correlate with volatility regardless of direction."
]
}