BI-SAM's B-One platform is a complex product. Not only is its performance and attribution functionality second to none, but its risk initiative is the best around this year, as evidenced by its dual win in the Buy-Side Technology Awards 2011. Developed in conjunction with a group of French asset managers, particularly Covéa Finance, it brings the ability to break down ex-ante and ex-post risk on a granular scale. David Cayet, business analyst at BI-SAM, tells Waters what sets B-One's risk modules apart from the crowd.
Can you tell us about the risk-related elements of B-One?
Basically, we have three parts of B-One that cover the risk area. The first one is the ex-ante risk decomposition module. This module allows the calculation of the absolute and the relative risk through a significant range of financial indicators. In addition to this, we also offer the ability to manage risk via the what-if module. This module helps a fund manager to assess the impact of potential decisions on the forecasted risk. That's the first part of our offering. Then, we have an ex-post risk attribution model. This module focuses on the actual risk, the objective is twofold. The first is to provide a decomposition of the main risk indicators such as volatility, tracking error and some risk-adjusted performance indicators. The second objective is to provide tracking error attribution, so we highlight the portion of relative risk that can be attributed to management decisions. In fact, from this module and from the equity attribution, we can depict the whole picture of the portfolio in terms of return and risk, therefore we can tell the true story of the portfolio. The last part we have is the ex-ante and ex-post risk reconciliation. The objective of this module is to perform ex-ante and ex-post attribution, and then explain where the differences come from. That's our current offer, to cover risk.
And this last module was developed in conjunction with Covéa Finance?
Exactly. The ex-ante and ex-post risk reconciliation module has been developed in partnership with Covéa Finance.
Why did you bring them on board?
This model, what we call the AMPERE [Asset Management, Performance and Reporting] model in French, has been developed by a group of French asset managers, the club AMPERE. In the group we have Amundi, Covéa Finance, BNP, State Street, AXA Investment Management, Société Générale Gestion, among others. Covéa Finance was really active in this group and confident in this model, and they wanted to be the first to propose this solution to their investors, because they have some strong requirements from their own clients. We also had a strong desire to propose this analysis, which is the one reason why we decided to work in a strong partnership with Covéa Finance to develop the module.
What sort of timeframe are we talking about for its development?
We split the project in three phases. A first phase where we have transposed the theory into practical through several examples. This phase has been useful to validate the model and highlight some deficiencies. We worked with Covea Finance's teams to find acceptable solutions and bring robustness to this model. Once we reached a complete agreement on the model, we made the development. Finally, during the third phase, we made the tests with Covéa Finance. All of these three phases took about nine months.
So what is it that makes the model stand out?
The industry proposes either the ex-ante risk analysis or the ex-post risk analysis, but there is no solution that attempts to explain where the difference between the expected risk and the actual risk comes from. That is the originality of the approach. By using this model, we can explain where the difference comes from via two effects: an environment effect and a management decision effect.
Bryan Cross, who heads UBS Asset Management's QED group, joins to discuss alternative data and AI.Subscribe to Weekly Wrap emails