Institutional portfolio management, risk analytics and portfolio construction for alternative and multi-asset portfolios.
Integrated investment data, stress testing, portfolio optimization, alternative investment analytics and shadow accounting within one platform.
Manage hedge funds, private equity and multi-asset portfolios with liquidity controls, custom classifications and flexible historical accounting.
Construct hedge fund-of-funds and multi-asset portfolios with stress-test, factor-exposure, liquidity and other user-defined constraints.
Use natural language to access investment research, risk analytics and portfolio workflows.
Screen managers by risk and return targets under user-defined macroeconomic scenarios.
Compare extreme-event outcomes with a relevant peer group.
Run FlexiRank™ to get ranking scores for each asset in a group.
FlexiRank™ workflow is a simple four-step process that can be applied to any groups of supported assets. Users can define thousands of ranking criteria based on their risk-return aversion and investment mandates.
Choose the investment universe to rank.
Set an importance level for each constituent statistic.
Run FlexiRank™ to get ranking scores for each asset in a group.
A FlexiRank™ statistic is a weighted combination of measures the user selects. Stress-event losses can be among them. A ranking rule can therefore include CVaR, annualized return and maximum drawdown, and also the estimated maximum loss in chosen historical events — the 2008 credit crisis, the Bear Stearns failure, the 2010 Greek crisis, or any other event in the library.
Those event figures are not realized returns from the crisis window. They are outputs of the regression factor model: exposures fitted on the available track record, applied to the factor shocks of the event. A fund with a three-year history still receives a value for an event that predates it. Because the stress-test database stores that estimate for the supported universe, the event loss is available as a constituent for every name in the group, on the same basis as CVaR or drawdown.
The weight on the event constituent is the user’s. A mandate that treats tail loss in a named scenario as decisive can put more importance on it than on annualized return. A mandate that treats it as a check can keep the weight low. The rank then reflects both the conventional profile and the estimated event loss, instead of leaving the stress result as a separate report after the ranking is done.
A composite score is useful for ordering a large investment universe, but the final ranking should not hide the statistics that produced it. Risk Shell therefore integrates FlexiRank™ with Multi-Statistic Peer Group Analysis. Users can review the underlying components of the synthetic metric and examine how each fund or manager compares with its peers across the individual statistics used in the model.
This combination provides both levels of analysis: FlexiRank™ produces the composite ranking, while Peer Group Analysis shows the structure behind that ranking. Investment teams can therefore move from a broad manager universe to a ranked shortlist and then examine the individual risk and performance measures responsible for each manager's relative position.
FlexiRank™ radar charts break the rank back into the underlying measures with multi-statistic peer group analysis. A fund at the top of the synthetic rank can be inspected on each axis: strong on the measures that carried the weight, or weak on one constituent and carried by the others. Used together, the rank orders the group and the peer chart shows whether that order is a balanced profile or an artefact of the weights.
A composite that weights every constituent equally is another single statistic. FlexiRank™ assigns an importance level to each measure, so the rank follows the allocation rule. A credit-sensitive book can put drawdown and estimated event loss above annualized return. A market-neutral book can put beta and correlation above absolute performance. The constituents can stay the same. The order will not. That is the operational difference from a screen or a peer chart. The screen drops names that fail a bound. The peer chart shows position on each axis. FlexiRank™ produces one order in which the axes have already been weighted. Changing a weight reruns that order on the same group. It does not require a new data extract or a second reconciliation of ten separate sorts.
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