A quality of earnings report is the most important document produced during M&A financial due diligence. It translates a seller's reported EBITDA into a buyer's adjusted EBITDA by normalizing for non-recurring items, accounting policy differences, and add-back claims. A good QoE can shift a deal price by millions. A weak QoE can miss risks that surface post-close.
Most QoEs are prepared by Big Four or mid-tier accounting advisory firms and run 3–5 weeks for mid-market deals. They're expensive and thorough within their scope. The limitation isn't the quality of the analysts or the rigor of the methodology. It's that every QoE samples from the transaction population rather than analyzing it completely.
How QoE sampling works
A typical QoE process selects a representative sample of revenue transactions, expense categories, and adjustment items for detailed verification. The sample size is calibrated to the deal size and timeline — a $200M deal might involve detailed review of 300–500 transactions out of 15,000 total ledger entries. Extrapolation from the sample to the full population is standard practice.
Sampling works well for identifying systematic patterns: a consistent misclassification policy, an accrual reversal practice, a revenue recognition approach that doesn't match GAAP. It works less well for finding individual outliers. A single $3M transaction recorded in the wrong period doesn't appear in a 3% sample. A relationship with a related-party vendor that represents 8% of COGS might not be in the sample.
The standard QoE is designed to answer 'are the financial statements directionally accurate?' It is not designed to answer 'does every material transaction in the ledger have supporting documentation?' Those are different questions requiring different methodologies.
What falls through the QoE sampling gap
Isolated high-value discrepancies
A single invoice for $800,000 that doesn't match the corresponding ledger entry won't appear in a 3% sample unless sampling is stratified by transaction size. For most QoEs, stratified sampling is applied to revenue but not consistently to expenses. High-value expense transactions can fall through the gap — and a $800K discrepancy is often individually material.
Missing document coverage across the full population
QoE processes typically verify that the documents they review exist and support the sampled transactions. They don't produce a systematic map of which ledger entries have no supporting document in the data room. That map — a coverage matrix of the full ledger against all available documents — is different from the QoE output and provides a different kind of risk signal.
Add-back challenge documentation
Add-backs are the most contested part of any QoE. The seller proposes adjustments — 'one-time' expenses that should be excluded from normalized EBITDA. The buyer challenges them. The adjudication of these disputes requires document-level evidence: the invoice, the employment agreement, the one-time service contract. When add-back challenges rely on management representations rather than specific document citations, they're weaker in post-close disputes.
How document verification complements the QoE
Document-level reconciliation isn't a substitute for a QoE — it's a complement. The QoE provides the analytical framework: which items are recurring, which are adjustable, what the normalized EBITDA is. Document verification provides the evidential layer: for every material line item in that normalized EBITDA, here is the document that supports it.
The combination changes what's defensible. A QoE conclusion supported by a full reconciliation — where the auditing firm can point to specific document citations for every material adjustment — is a much stronger position than a QoE supported by a sample. It's also more useful for reps and warranties insurance underwriting, where completeness of review affects coverage.
The practical workflow for combining QoE and reconciliation
- —Run automated reconciliation at the start of the QoE period — before sampling decisions are made — to generate a full coverage map of which ledger entries have strong, weak, or no document support
- —Use the coverage map to inform stratified sampling: prioritize transactions with weak or no document support in the QoE sample
- —Use automated reconciliation output as the evidence layer for add-back challenges — specific document citations rather than 'per management'
- —Re-run reconciliation as the data room updates during the diligence period to catch new gaps introduced by document additions
This workflow doesn't require more time from the QoE team — it requires the reconciliation to run in parallel, automatically, so that its output is available when the QoE team needs it. That's exactly the kind of process change that makes a meaningful difference in deal quality without adding weeks to the timeline.