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Rohtas Handa, MPI

MPI joins with BarclayHedge to produce dynamic hedge fund indices


Markov Processes International (MPI) has launched a hedge fund index business line that will aim to provide investors with improved benchmarks for measuring hedge fund performance.

MPI recently partnered with BarclayHedge to create the MPI Barclay Elite Systematic Traders Index (Bloomberg: MPBEST20), which seeks to capture the returns of the 20 largest systematic hedge funds reporting into BarclayHedge. The MPBEST20 is paired with the MPI BEST 20 Tracker Index (Bloomberg: MBEST20T). This index pair was launched on March 1.
 
 “We are delighted to be working with MPI to launch the MPBEST20 Index,” says Sol Waksman, President at BarclayHedge. “We’ve been approached in the past by firms looking to deliver on a similar promise. In those cases, and despite valiant efforts, index performance quality fell short of our standards. MPI, however, has delivered what we think will be a gamechanger.”
 
Rohtas Handa (pictured), EVP and Head of Institutional Solutions at MPI, with a background working with MSCI and FTSE, explains that MPI’s core business is providing institutional global investment management industry with tools to research and analyse various investment strategies.
 
“We are especially well known for our ability to analyse complex, or opaque, products such as hedge funds,” he says. “Using our Dynamic Style Analysis model, if we are given a set of returns, we can give you a clearer idea of what the drivers of performance are and how they change month to month.”
 
Within the long only world, how funds achieve returns doesn’t change so much month to month, but for hedge funds, which can adapt to a changing environment, the dynamic model enables MPI to keep up with where their returns are coming from.
 
Indexing hedge funds is a famously tricky subject. They have tended to have issues with data biases, over inclusion and over reliance on funds open to new investments, all of which has meant that, historically, benchmarks have struggled to provide stable and representative measure of hedge fund performance.
 
“Our goal was to see how we could create a better benchmark for gauging hedge fund performance more reliably. We also wanted to create an investable version of each index that is comprised of liquid investment products that mirror the returns of the underlying hedge fund index.”
 
And in order to do this, MPI uses ETFs. Each MPI hedge fund index is comprised of two components, one benchmark index that provides a measure of performance for a targeted set of premier hedge funds, and one tracker index that aims to deliver the performance of the benchmark using ETFs.
 
Handa says: “If you take 10 long-short equity funds investing in US equities, we could analyse the return streams and determine, for example, that they have exposure to seven sectors in the S&P500. We would then construct an index using vanilla ETFs that, when put together, reflect the returns of those funds.”

Given that the underlying hedge fund index is comprised of hedge funds, there is a month’s lag. To mitigate that, MPI selects larger hedge funds because they tend to be less active traders – their ability to get in and out of certain exposures being limited by their size.

MPI’s Hedge Fund Indices business launch follows the construction of the firm’s first hedge fund index, the Eurekahedge 50 (Bloomberg: EHFI400) and the MPI Eurekahedge 50 Tracker Index (Bloomberg: EHFI401).

This index, which was launched in 2014 in partnership with Eurekahedge, was developed to provide a measure of the world’s 50 most successful hedge funds, and a more representative benchmark for institutional portfolios of hedge funds that seek consistently attractive risk-adjusted returns.

All MPI tracker index data can be licensed to build investment products.

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