Estimating Portfolio Beta and Alpha with a Short Monthly Sample

Summary

The document describes a systematic equity portfolio that selects the highest-scoring stocks and rebalances monthly against the MSCI World benchmark. Because holdings and weights may change substantially at each rebalance, the author wants to attribute each month’s return to market exposure and to a residual component. The proposed calculation is an ordinary least squares regression of daily portfolio returns on daily market returns, using the estimated beta with portfolio return, market return, and the risk-free rate to derive alpha.

The central question is whether approximately one month of daily observations provides a sound beta estimate for this purpose and what practitioners typically do. The document supplies the portfolio setup and proposed regression, but no regression output, uncertainty estimate, alternative method, or answer about market practice. Its key methodological concern is the short estimation window and the instability that may arise when the underlying portfolio changes frequently. It should be read as a question about return attribution design, not as evidence that a one-month beta or derived alpha is reliable.

Key ideas

• The portfolio selects stocks by score and rebalances monthly, potentially changing holdings substantially.
• The proposed attribution regresses daily portfolio returns on daily benchmark returns to estimate beta.
• The author plans to derive alpha using portfolio return, benchmark return, estimated beta, and the risk-free rate.
• The document raises the concern that roughly one month of daily data may be too short for a stable beta estimate.
• It asks about practitioner methods but provides no answer or empirical validation.

Tags

# Performance attribution and monthly rebalance: Is a month enough data to calculate Beta and Alpha?


# Performance attribution and monthly rebalance: Is a month enough data to calculate Beta and Alpha?












A portfolio is built systematically by calculating scores and rebalanced each month to invest only in the 80 best scores. Scores change frequently and therefore the portfolio changes each month, sometimes significantly. The benchmark is MSCI World.

At each month end I need to know what portion of the return is due to the market (Beta) and the idiosyncratic component not explained by the market (alpha).

I am thinking of running an OLS regression of daily portfolio returns vs daily market returns to calculate the Beta and deduce Alpha from the CAPM formula (if I have the actual portfolio return, the market return, the Beta, risk free rate, I can get alpha).

Is the fact that the Beta is calculated with OLS regression only 22 trading days (1 month between each rebalance) an issue? What is the market practice for this task?

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This summary was written by Stratmill's research agent from the original; it is not a copy of the source.