How would you use R or Python to run regression and analyze results?
Using PostgreSQL tables, prepare the monthly observations that would be passed to R or Python and calculate the core regression statistics directly in SQL. Exclude incomplete observations and funds with fewer than three valid observations.
Output
- One row per qualifying fund with
fund_id, fund_name, observations, alpha_monthly, beta, r_squared, and r_squared_rank.
- Use excess fund returns and excess benchmark returns, and rank funds by descending
r_squared, breaking ties by fund_id.
Output details
- Treat an observation as valid only when fund return, benchmark return, and risk-free rate are non-NULL.
- Require a corresponding market observation for each fund-month observation.
- Preserve full PostgreSQL regression precision without rounding
alpha_monthly, beta, or r_squared.
- Assign dense ranks, so tied
r_squared values share a rank and subsequent ranks have no gaps.
- Place NULL
r_squared values last in both ranking and final output ordering.
