Fund Manager Rating

Core Question

The question we answer

Traditional due diligence asks: "Is this GP good?" QFT asks a sharper question: Is this manager's track record statistically distinguishable from what you would expect by chance, given the same strategy?

This is not a peer ranking. It does not depend on who else is in the database. It is a skill test against a bootstrapped benchmark of comparable transactions.

Assessment Framework

Three dimensions. Six KPIs.

Every fund manager is assessed across Return, Risk, and Liquidity. Each dimension uses two KPIs, computed at deal level and aggregated across the track record. The core metric is the Tailored PME (factor-adjusted Kaplan-Schoar Public Market Equivalent), making assessments independent from GP self-reported benchmarks.

Value Creation (conditional)

When deal-level operational data is available, a supplementary Value Creation sub-rating decomposes returns into three components: Leverage (capital structure decisions), Multiple Expansion (valuation timing), and Operational Improvement (revenue growth, margin development). This sub-rating does not change the headline composite but explains how performance was generated.

Benchmark Construction

Deal-level benchmarking. Apples to apples.

Most ratings compare fund-level returns against a peer group. The result depends entirely on who is in that group. QFT takes a fundamentally different approach:

Every deal in the manager's track record is matched against comparable transactions from our proprietary dataset of 20,000+ realized LBO transactions . Matching criteria: investment year, Fama-French 48 sector classification, region, and deal size.

The benchmark is built bottom-up, deal by deal. Not a generic peer group. Not dependent on data vendor coverage. True apples-to-apples comparison.

Dual benchmark architecture

Each manager is tested against two benchmarks simultaneously. The Broad Market benchmark uses all LBO exits from the same investment year, testing whether the GP outperforms random LBO exposure. The Tailored benchmark matches on sector, region, and deal size, isolating execution skill from strategy selection.

Statistical Test

Bootstrap: separating skill from luck

The grading is not based on arbitrary thresholds. It uses statistical bootstrapping : for each manager, we simulate thousands of counterfactual portfolios by randomly drawing comparable deals from the benchmark. The manager's actual KPIs are then ranked against this simulated distribution.

If a manager's track record falls in the 95th percentile, it means only 5% of random portfolios with the same strategic footprint would have performed better. That is evidence of skill, not luck.

This is the same methodology applied in the Journal of Finance to distinguish skill from luck, both in private equity (Cavagnaro et al., 2019) and in mutual funds (Fama and French, 2010).

Output

A+ to D: what the grades mean

The composite score aggregates the six KPI percentiles into a single grade. Return and Risk receive equal primary weight; Liquidity acts as a portfolio-implementation discipline.

A+ and A indicate statistically significant outperformance. B range indicates above-average execution. C is close to random expectation. D indicates persistent underperformance.

Ratings require a minimum number of realized deals. Below that threshold, only an indicative rating is provided.

Trend Arrow

Each rating includes a directional indicator: ▲ improving , ▶ stable , or ▼ declining . It compares recent exits against the historical track record using the same bootstrap logic. The question: is this manager getting better or worse?

Dataset

The data

All calculations use gross, deal-level returns (pre-fee, pre-carry) to measure investment skill directly. Net fund-level returns confound manager ability with fee structures.

The benchmarking dataset is QFT's proprietary database of 20,000+ realized LBO transactions . This is not the same as any academic dataset. It is commercially maintained, independently validated, and updated continuously.

Your data is uploaded under NDA and used exclusively for your rating. It is not redistributed, resold, or used as training data.

Research Basis

Academic foundations

The methodology builds on and extends research published in leading finance journals: