Evaluating Valuations

Is the value of your assets based on art rather than science- and how can you prove it?’ As sovereign debt crises continue to dominate the headlines, it’s a question worth asking, because like most fixed income assets, the value of government bonds is based on a combination of verifiable facts and informed assumptions. The more competent your people are, the more accurate the assumptions which underpin your pricing models will be. But with plenty of incentives to game the numbers and produce higher valuations, it’s not unknown for the tail to wag the dog when it comes to pricing fixed income assets, and for people to find ways of creating the price they want.

We’ve all seen the consequences of that, but today you need to justify those assumptions to regulators, auditors, investors and managers after the event. And that’s almost impossible if assumptions are recorded in various spreadsheets, random electronic files and post-it notes stuck to monitors. It’s also pretty hard if you have an impeccably controlled, technology-enabled environment in one department, and a complete free-for-all in another. The right data management solutions will give you the discipline and transparency into the art of valuation without cramping the style of those doing the valuing. It will enable consistency across the enterprise and enable every department to create, record, monitor and audit the valuations they need. No technology should stop you valuing any instrument or any asset in the ways you see fit – but at some point you will need to explain that decision. It’s time to think about a flexible, transparent, consistent, and repeatable approach that lets you do just that.

Beyond the golden copy

Far too many people in the data management industry think that there is a one-size-fits-all, static solution that will solve all data-related problems – and they spend a lot of their time promoting this idea. In reality there’s no such thing. This tired old fallacy has been hauled out for far too long, and unfortunately there are too many organizations that are just beginning to realize that what they’ve bought isn’t a solution – it’s just another problem.

So let’s go back to basics. Any data management infrastructure has to be appropriate for the size of the firm and the type of operation. The solution that is right for a 40-person hedge fund is very different from the solution needed by a global custodian with thousands of customers and tens of thousands of employees.

Most firms have multiple business units, product lines, and investment strategies – all of which require different data sets used in different ways. Accounting and risk management will need different data sets than the trading desk. Operations want data on actual holdings, so analysts can use it for modeling ‘what if’ scenarios. The idea that you can impose a monolithic, inflexible data management structure with a single data set and a single management tool, onto the modern business with all its complexities is manifestly false.

So let’s have a more realistic conversation about data management. And let’s start by calling out old-fashioned ideas about data management, and exposing them for the myths that they really are.