State Super, an A$38 billion ($25 billion) superannuation fund in Australia, is working with Hong Kong-based fintech Premialab to help it source data and make sense of its growing exposures to alternative risk premia.
“Premialab was introduced to cater primarily to our alternative risk premia strategy,” Alan Chan, senior investment manager, told DigFin (pictured).
“Over the last 24 months, we have collaborated with the quantitative investment solution groups from various investment banks to construct a suite of bespoke strategies that fit our investment criteria,” he said. “Taking into account State Super’s cash flow profile and the demand for liquidity, we will continue to deepen our understanding of this asset class.”
He declined to elaborate on the investment or business strategy.
State Super is the trustee of several superannuation funds for the New South Wales state government, including that of civil servants and police, with over 85,000 members. It also manages a pooled fund
Indexes and index funds
Pierre Trecourt, co-founder of Premialab, says institutional investors such as State Super are migrating more assets to systematic strategies.
These are the opposite of discretionary mandates to a fund manager, who charge higher fees to make calls on asset classes or individual securities based on a fundamental analysis. Systematic strategies use passive exposures such as indexes or index funds to focus on price trends within a given asset class, from commodities to small-cap equities. They will combine this to get a view on “factors” such as interest rates or the price of a commodity.
The point of this is to rely on predetermined rules around how to construct a portfolio, with the aim of allocating assets in a way that is low-fee, liquid (because index funds trade daily) and transparent.
The key to making systematic strategies works is data, such as security and fund prices, which is required to create indexes, set benchmarks, and measure the portfolio’s relative performance. The data resides among the trading desks of the 18 most prominent global banks.
These banks also have developed trading algorithms for rebalancing portfolios and capturing a certain risk premium. This type of trading includes programmatic trading, which is about executing around index benchmarks instead of single securities, and tends to be done electronically.
Investment bank infrastructure
“Bank algorithms are the building blocks of an asset allocation strategy, but the challenge for investors is getting the data, and then knowing how to use it to make decisions,” Trecourt said.
His colleague Marc Fisher, managing director at Premialab and head of its recently opened Sydney office, said, “With the adoption of systematic, we’re seeing a structural shift in how institutional investors allocate money. Out network of investment banks share their data with us because they consider Premialab a part of the quant industry infrastructure.”
Premialab’s technology sources data from banks. This may come in different reporting frameworks, so part of the fintech’s job is to make all this data comparable, and like-for-like. This allows users to use it to compare their own benchmarks to industry averages, giving them the means to track the performance of their portfolios, and understand what elements – factors, down to individual securities – are contributing alpha (or the reverse). Premialab’s analytics include data visualization tools to help investors make sense of the information.
“We don’t make strategies or advise on trading,” Trecourt said. “We provide the data and analytics to support the full investment lifecycle, and help investors’ risk departments understand their exposures across uncorrelated benchmarks.”
State Super’s Chan says using Premialab hasn’t involved a major integration. The superannuation group downloads the data via an API.
“Premialab demonstrated that they could add capability and efficiency to our processes,” he said.
