Estimating Equity Fund Exposure with Regression on Industry Returns

Summary

The report estimates a fund’s equity exposure by regressing daily fund returns on daily returns for 29 first-level industry indices. It treats the sum of fitted industry coefficients as the estimated position. Because industry returns are correlated, it compares principal component regression, stepwise regression, ridge regression, and Lasso as ways to reduce instability from multicollinearity.

Tests on ordinary equity funds and equity-oriented mixed funds found Lasso and stepwise regression somewhat more accurate than principal component regression, while ridge showed systematic overestimation. Across the tested methods, errors were often in the 5% to 15% range. Window-length analysis found results generally steadier above 30 days; the report cautions against windows longer than about 60 trading days because estimates can lag. The method uses only fund net asset value and industry data, omits disclosed holdings information, covers limited fund categories, and does not eliminate collinearity.

Key ideas

• The method regresses daily fund returns on daily returns from 29 industry indices and sums the fitted coefficients to estimate equity exposure.
• Principal component, stepwise, ridge, and Lasso regression are compared to address multicollinearity.
• The tested results favor Lasso and stepwise regression over principal component regression, while ridge tends to overestimate exposure.
• Prediction errors were often between 5% and 15% in the tested fund categories.
• Window lengths above 30 days were generally more stable, while windows beyond about 60 trading days risk lagging current exposure.
• The estimate relies only on return and industry data and may not generalize beyond the fund types studied.

Tags

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