Methods & benchmarking
Multiple Year Data
Multiple year data refers to the practice of using several years of financial results, typically three, for both the tested party and its comparables in a transfer pricing benchmarking study. Averaging or comparing results across multiple years smooths out the effect of business cycles, one-off events and timing differences.
Relying on a single year of financial data can distort a transfer pricing conclusion because short term factors, such as a new product launch, a one-off cost or a temporary downturn, may not reflect the tested party's or comparables' typical performance. Using three years of data, often the current and two preceding years.
Multiple year data can be applied either by averaging each company's results over the period before ranking them, or by using each year's individual results within the data set, depending on local guidance and the chosen transfer pricing method. The OECD Guidelines support multiple year analysis as good practice, particularly.
In practice
What matters when applying multiple year data
- Uses several years, typically three, of financial results
- Smooths out cyclical and one-off distortions in the data
- Can be applied by averaging or using individual year results
- Particularly useful for margin-based methods like TNMM
- Supported as good practice under the OECD Guidelines
Frequently asked
Common questions
How many years of data should a transfer pricing study use?+
Three years is the most common practice, covering the tested year and the two preceding years, though some jurisdictions accept different periods depending on local rules or data availability. The goal is to capture a representative business cycle rather than relying on a potentially unusual single year.
Should multiple year data be averaged or used year by year?+
Both approaches are used in practice and the choice often depends on local tax authority guidance and the transfer pricing method applied. Averaging each comparable's results over the period can smooth volatility before ranking, while using individual year figures within a pooled data set preserves more granularity and allows testing of each year separately.
See how the tooling handles this in practice
Our transfer pricing tools calculate intercompany charges, benchmark financing and reconcile the intercompany ledger from your own data. Book a short walkthrough and we will show the workflow on a scenario that matches your group structure, rather than a generic demo dataset.