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Risk Management

12:43 PM
Cristina McEachern
Cristina McEachern
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The Buy Side Picks Up the Risk-Management Pace

Fidelity works with Algorithmics to leverage risk management technology for its investment management needs.

Risk management has been a hot topic in the financial-services industry following Sept. 11, Enron's collapse and rogue-trading losses at Allfirst and Lehman Brothers. But while the sell side may have historically been faster to adopt new technologies and strategies to cover risk, the buy side is picking up speed, as asset managers are heeding the call for better risk-management techniques and technology. And to keep the momentum going, risk-management vendors are addressing specific buy-side needs more and more.

"It's a very hot area," says James Gerard, quantitative research analyst in the fixed-income division at Fidelity Investments, who adds that while the asset-management area has different needs, those needs are going to be an important area of vendor-technology attention moving forward. "People are finally realizing they can get tools that will help them manage groups of managers," he adds.

Asset managers are looking for platforms that analyze risk at the portfolio level, not only through shifts in market- and credit-risk factors, but with trading strategies and their impact as well. It is key for asset managers to view the risks associated with actively managed portfolios that change through time.

Taking one step back, from the perspective above the portfolio managers, this type of technology can help buy-side firms be more successful by gauging the quality of returns according to various manager strategies. This is accomplished through a risk-adjusted-return measure by comparing fund types with track records over long periods of time. "This could help the client who is a large state-pension fund, for example, and who has to hire a team of portfolio managers from different companies, who use different investment styles, to figure out how to manage them when they aren't managing the money themselves," Gerard explains.

For example, Gerard points out that while the investment styles employed on the buy and the sell side are obviously different, the point of producing information to manage risk is the same. For the buy side, this could include information to "compare an aggressive manager with one who is doing essentially a targeted-active management around an index." This could then be used to view whether the actual extra-risk increment that the firm's investors have been exposed to is worth the risk on average, when compared with the extra return that has presumably been generated, adds Gerard.

Working with Algorithmics
Gerard and his team at Fidelity Investments have been working closely with Algorithmics to leverage the technology work from both sides in order to work towards a comprehensive risk-management program. "Our first priority was more of a classic risk-management framework for our money-market funds, where Fidelity runs quite a lot of asset dollars in money funds," he adds.

In fact, Fidelity manages $250 billion in those money-market funds, which makes risk management a priority.

Gerard says that there was a risk concern in the money-fund area because, "It's interpreted by most of our clients, essentially as a guarantee, that we're going to maintain the constant value of these money-market funds." But the value of the assets in the funds moved around as the market moved. Fidelity wanted to find a way to assess how adverse changes had to be in order to pose a serious risk to the funds. In other words, how large would a market movement have to be to affect the value of the funds?

The technology that Fidelity was looking to implement was what Gerard calls "classic-risk management," or a risk-monitoring application. Fidelity turned to Algorithmics and its flagship RiskWatch product. "We generate part of the simulation work ourselves and we use RiskWatch for an increasing part of the whole task," he adds.

But the vendor/client relationship hasn't ended there; Fidelity is collaborating with Algorithmics to develop new modules to address certain simulations and scenarios that Gerard's money-market group wanted. "Part of the reason for bringing in the third party was so we could get some leverage," says Gerard. "Instead of having to build the risk-monitoring front end ourselves, as well as our own models, we can concentrate on our own models and interact with them for the central risk piece."

Fidelity is looking to leverage the Dynamic Trading Strategies module within the RiskWatch platform in order to analyze cash-flow risk within the money-market funds. "If you think of a fund running along in time and cash flows are coming in from, say, shareholders," he explains, "there's a sort of risk profile or risk scenario where yields in the fund are rising and cash is flowing out as people are redeeming their funds. But you can get into a situation where the average value of the securities held in the fund is going down, yet you're having to satisfy shareholder's demand for outflows." In other words, the fund is generating losses that are actually spread over a smaller number of remaining shares and there is risk in that, one that Fidelity would like to analyze.

"It's a risk scenario that we'd like to be able to analyze in more detail and, in order to do this, we have to tighten up some of the things within the DTS module," says Gerard. The functionality within the current module basically takes existing pools of cash and reinvests it in pre-defined areas as the simulation takes place. These simulations are done on a weekly basis at Fidelity, but the actual risk structure of the funds still changes during that time period as money comes into and out of the fund. Fidelity was therefore looking for similar scenario functionality to reinvest outside cash that was not in the portfolio when the scenario was started. "Once the cash is in the fund, somehow we want to be able to set up rules to deal with it," says Gerard.

"This whole idea of risk budgeting and risk control or the quantitative measure of risk control for funds, in our case for fixed-income funds, makes a lot of sense as a way of setting up how we actually manage the funds," says Gerard. "So we want to speak this language natively and be able to share results with those who may need to know how it is we produced the returns we did."

Gerard adds that the vendors seems to be listening up and delivering accordingly. "Certainly the Algorithmics, the Barras and the BlackRocks of the world are hearing the demand for this stuff and are beginning to tilt the way their software works toward that paradigm," he says. Fidelity may even look into extending some of the risk capabilities onto its retail-Web site in the future.

But first and foremost, Gerard explains that Fidelity is working to provide more fixed-income information and portfolio-planning tools for investors. "First off is getting investors to a level where they understand why bonds belong in a portfolio in the long term, and then we'll deal with the risk-mitigating aspects in the future," he says.


Algorithmics' DTS
Fidelity Investments is using Algorithmics' Dynamic Trading Strategies module to reinvest cash flows from bonds and other fixed-income instruments that settle during a four-week simulation for the money-market funds. The DTS module creates a view of how actively managed portfolios change over time. The module enables portfolio managers to set customized rules that will reinvest cash, buy and sell securities or modify the composition of portfolios during a specified time simulation.


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