Returns Data Profiling AI Prompt

Profile this financial returns dataset and identify any data quality issues before analysis.

Asset class: {{asset_class}}
Frequency: {{frequency}} (daily, weekly, monthly)
Date range: {{date_range}}

1. Basic return statistics:
   - Count of observations and date range coverage
   - Mean, median, standard deviation, min, max
   - Annualized return: mean_daily × 252 (or ×52 weekly, ×12 monthly)
   - Annualized volatility: std_daily × sqrt(252)
   - Skewness and excess kurtosis — financial returns typically show negative skewness and excess kurtosis (fat tails)

2. Data quality checks specific to returns:
   - Zero returns: flag consecutive zero returns (>3 in a row often indicates a data freeze or illiquid asset, not a flat market)
   - Extreme returns: flag returns beyond ±10σ — likely data errors, corporate actions, or extreme events requiring investigation
   - Missing dates: check against the expected trading calendar. Missing dates should be explained (holidays, halts)
   - Stale prices: if using prices, identical consecutive closing prices for liquid assets signal a data problem
   - Survivorship bias check: is this a historical dataset? Were assets included only if they survived to the present?

3. Distribution analysis:
   - Plot return distribution vs normal distribution overlay
   - Jarque-Bera test for normality: JB = n/6 × (S² + K²/4) where S=skewness, K=excess kurtosis
   - Report: skewness (negative is left-skewed — bad tails), kurtosis (>3 indicates fat tails)
   - Quantile-Quantile plot: visual check for tail behavior relative to normal

4. Autocorrelation check:
   - Ljung-Box test for serial autocorrelation in returns (should be near zero for efficient markets)
   - Ljung-Box test on squared returns (should show autocorrelation — volatility clustering is expected)
   - Plot ACF and PACF for returns and squared returns

5. Corporate actions and outliers:
   - Flag dates with |return| > 3σ as requiring investigation
   - For each flagged date: check if the return aligns with a known event (earnings, index rebalance, dividend)
   - Adjust for dividends and splits if working with raw prices

Return: summary statistics table, data quality flag list, distribution plots, autocorrelation results, and a data quality verdict (suitable for analysis / needs adjustment / not suitable).