P&L attribution
Also known as P&L explained, the purpose of P&L attribution analysis and reporting is to provide users with a coherent breakdown of the drivers of P&L movements between two points in time with reference to a select number of easily understandable pricing factors.
But data is never as clean as you would like. That issue gets worse when data comes from different sources. But P&L Attribution requires a single, consolidated report that can handle all the data issues.
Let’s have a flexible, rapid and modular way of getting the information we need.
Acquire
Data is often in different places, sometimes even in a single asset class. Different file formats, different timings, different naming conventions. A flexible, light touch way of accessing the data without having to build a data warehouse is our first step.
Clean and join
When do data assumptions and specification documents survive the first contact with the data? A P&L attribution system will need data normalised before joining. A coherent process is required to standardise data. And the data can change over time: such a process needs to be flexible but with central control.
Calculate
Whether Valuation or Sensitivity based, the core process to determine the P&L explained versus unexplained.
Review and validate
The first step where the P&L Attribution process starts to add value: automated validation tests are carried out to help identify areas of focus; the joins to finance data are crucial to allow reconciliation to the firm’s source of truth and a sign off process needs to be followed and recorded.
Report
Excel is often the go-to tool but we need something better. Fixed reporting packs can be useful, but dynamic visualisation tools that offer drill down, historical views or changes put into context from just a few mouse clicks helps users meet the changing demands of the market.
How CP Analytics helps the P&L attribution process
Easily access disparate sources of data
Visual data model and processing
Whether it is legacy COBOL files, batch csv files, xml messages, big data repositories or databases, we can connect and acquire data without needing a data warehouse to be built. It’s light touch on IT as well as we clean the data ourselves.
It’s easy to see where data is coming from, where it is being cleaned and joined, even if the pathway is complex. The living data picture promotes understanding and decision making.
Modular Libraries
Standardisation of data requires standardised processes, we don’t want to duplicate work and need central control. Modular libraries allow us to repeat, adjust and deploy logic quickly and coherently.
Rapid and acurate
Our approach is highly iterative from the start: we get data, examine and validate which means we don’t have to get to the waterfall testing phase before find the issues. And the end product is robust and can be automated.