Bond Fund Attribution with Campisi Factors for Manager Selection

Summary

This report proposes attributing bond fund returns from net asset value histories, addressing the limited holdings data available for many fixed income funds, especially private funds. Its Campisi-inspired regression model uses five risk factors representing interest-rate level and curve shape, credit spreads, defaults, and convertible bonds. The aim is to separate factor exposures from residual manager alpha, then rank private bond funds by estimated alpha and use the rankings to build a portfolio.

In tests on public bond funds, the report says the model’s full-sample fit exceeded 0.6, with rate-level, curve-shape, and credit-spread factors more consistently significant; default and convertible factors mattered more for mixed bond funds. It also reports that alpha-based selection was feasible in its private-fund sample. These findings support the proposed method, but the source warns that quantitative model specification may be biased. Net-value regressions can also leave omitted risks in the estimated alpha, so the results do not guarantee that manager skill will persist.

Key ideas

• The method estimates bond fund exposures and residual alpha using returns inferred from net asset values.
• Its five factors represent rate level, curve shape, credit spreads, defaults, and convertible bonds.
• The report finds stronger factor significance for rate and credit measures than for default and convertible measures in many funds.
• It uses estimated residual alpha to rank private bond funds and form a portfolio.
• Model specification may be biased, and unexplained risks can be mistaken for manager skill.

Tags

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