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Stocks Quarterly FactorSignals
Quarterly factor exposures and scores for US equities.
763,836 rows over 22,791 symbols, 35 columns, covering 1998-03-31 to 2026-06-30. Refreshed monthly.
Strategies Built on This Data
53 papers in the Papers With Backtest catalogue declare this dataset as an input. 52 of them have been coded and run over their own full history. The median replicated Sharpe ratio is +0.52, and 58% clear a t-statistic of 1.96 on their own sample, against 48% across all 4,837 replications in the library.
Some of the strongest results that lean on it:
| Strategy | Sharpe | t-stat | Tested over |
|---|---|---|---|
| Reversal Patterns in Risk-Adjusted Anomaly Returns | +0.71 | 3.8 | 28 years |
| Nonlinear Factor Attribution | +0.69 | 3.6 | 28 years |
A Sharpe ratio quoted without its t-statistic hides how much of the library cannot be distinguished from zero, which is why both are shown. The figures are in-sample over each strategy's own window and carry no transaction costs.
Why It Matters
This dataset accelerates factor investing research and portfolio design by:
- Systematic ranking: Pre-computed factor scores accelerate cross-sectional ranking for long/short books.
- Style tilts: Value, quality, growth, and volatility signals allow precise style targeting and risk budgeting.
- Attribution: Consistent factor definitions simplify performance attribution and portfolio diagnostics.
Load It
Installation/Upgrade:
pip install --upgrade pwb-toolbox
Load the Dataset:
from pwb_toolbox import datasets as pwb_ds
df = pwb_ds.load_dataset("Stocks-Quarterly-FactorSignals", symbols=["AAPL"])
print(df.iloc[0, :])
Example Output:
symbol AAPL
datetime 1998-03-31 00:00:00
profitability 50.0
value 10.0
solvency 53.0
cash_flow 61.0
illiquidity 12.0
momentum_long_term 22.0
momentum_medium_term 19.0
short_term_reversal 21.0
price_volatility 62.0
dividend_yield 62.0
earnings_consistency 89.0
small_size 1.0
low_growth 25.0
low_equity_issuance 100.0
bounce_dip 61.0
accrual_growth 59.0
low_depreciation_growth 35.0
current_liquidity 91.0
low_rnd 36.0
momentum 20.0
market_risk 32.0
business_risk 36.0
political_risk 56.0
inflation_fluctuation 55.0
inflation_persistence 48.0
returns 0.020202
rsquared NaN
rsquared_adj NaN
fvalue NaN
aic NaN
bic NaN
mse_resid NaN
mse_total NaN
Columns
| Column Name | Description |
|---|---|
| symbol | Stock ticker. |
| datetime | Quarter-end timestamp. |
| profitability | Profitability factor percentile. |
| value | Value factor percentile. |
| solvency | Solvency factor percentile. |
| cash_flow | Cash flow factor percentile. |
| illiquidity | Illiquidity factor percentile. |
| momentum_long_term | Long-term momentum percentile. |
| momentum_medium_term | Medium-term momentum percentile. |
| short_term_reversal | Short-term reversal percentile. |
| price_volatility | Price volatility percentile. |
| dividend_yield | Dividend yield percentile. |
| earnings_consistency | Earnings consistency percentile. |
| small_size | Small size factor percentile. |
| low_growth | Low growth factor percentile. |
| low_equity_issuance | Low equity issuance percentile. |
| bounce_dip | Bounce and dip factor percentile. |
| accrual_growth | Accrual growth factor percentile. |
| low_depreciation_growth | Low depreciation growth percentile. |
| current_liquidity | Current liquidity percentile. |
| low_rnd | Low R&D spending percentile. |
| momentum | Aggregate momentum percentile. |
| market_risk | Market risk percentile. |
| business_risk | Business risk percentile. |
| political_risk | Political risk percentile. |
| inflation_fluctuation | Inflation fluctuation percentile. |
| inflation_persistence | Inflation persistence percentile. |
| returns | Quarterly return used in factor regressions. |
| rsquared | Regression R-squared value. |
| rsquared_adj | Regression adjusted R-squared value. |
| fvalue | Regression F-statistic. |
| aic | Akaike information criterion. |
| bic | Bayesian information criterion. |
| mse_resid | Residual mean squared error. |
| mse_total | Total mean squared error. |
Access
Browsing the card and the schema is open to anyone. Downloading the files needs an approved request, tied to a subscription: what each plan includes. The same subscription covers the other datasets in this organisation.
Elsewhere
- Dataset page and coverage charts
- The strategy catalogue, 3,806 papers and 4,837 replicated strategies
pwb-toolbox, the loader used in the snippet aboveawesome-systematic-trading, the replicated strategies with their measured Sharpe- Every dataset in this organisation
Papers With Backtest publishes 32 datasets on the Hub and codes the papers that use them. Every strategy in the catalogue is run over its own full history before it is published, which is where the numbers above come from.
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