Many hedge funds aim to deliver truly uncorrelated and consistent returns to investors. A team based in Australia – partly motivated by the time zone gap with major markets – found that the most effective way to achieve this was by assembling a multi-strategy, cross-asset portfolio of systematic strategies. This includes a mix of bank quantitative investment strategies (QIS), customized QIS solutions, and proprietary models developed in-house.
In July 2022, Marco Barchmann – who previously worked at Deutsche Bank on QIS strategies out of Australia – and Jerome Yim – a former portfolio manager at Wellington in London who later returned to Australia, co-launched Spectrum Systematic Alpha at Challenger. This strategy, aimed at institutional investors, follows what Barchmann describes as “an unconstrained systematic approach with an objective of delivering positive returns of 5-7% above cash regardless of the market environment.” Instead of trying to time betas using a quantitative approach, “we build a balanced portfolio allocating to systematic strategies as core building blocks instead of asset classes.”
A broad and cross-asset strategy universe
Spectrum Systematic Alpha is a multi-strategy, cross-asset systematic investment strategy designed to deliver consistent absolute returns with low correlation to major asset classes. To achieve this objective, “our strategy universe is deliberately broad and cross-asset,” explains Yim. “We deploy systematic strategies across rates, FX, equities, commodities, credit, and volatility as an asset class. The addressable market is essentially everything and anything.”
“Our strategy universe is deliberately broad and cross-asset. The addressable market is essentially everything and anything.”
The range of strategies employed by Spectrum Systematic Alpha reflects its core objective of serving as a defensive alternative. “We aim to replace part of an investor’s equity allocation, which tends to be heavily exposed to risk-on/risk-off dynamics, with something that offers steadier, uncorrelated performance,” explains Yim. Unlike trend-following CTAs that seek to provide crisis alpha, Spectrum Systematic Alpha is designed to generate equity-like returns that are structurally uncorrelated to equities – making it a “defensive alternative” from a portfolio construction standpoint. “It’s about delivering highly uncorrelated absolute returns, not crisis alpha.”
QIS: A scalable and efficient infrastructure
Drawing on their deep QIS experience, Spectrum Systematic Alpha blends bank-provided QIS, customized QIS, and proprietary systematic strategies. “We don’t see QIS and proprietary strategies as fundamentally different,” says Yim. “A systematic strategy is a systematic strategy, whether it’s delivered via a bank platform or developed internally.” Leveraging bank QIS helps address the significant challenge of building a robust infrastructure capable of implementing sophisticated multi-asset strategies entirely in-house.
“We don’t see QIS and proprietary strategies as fundamentally different. A systematic strategy is a systematic strategy, whether it’s delivered via a bank platform or developed internally.”
“That means everything from strategy research and signal generation to order management, model deployment, trade execution, reconciling actual fills against model sizing, risk management, and ensuring we don’t miss any executions when signals trigger,” explains Yim. That’s where bank QIS offers an elegant solution, he explains. “QIS strategies provide a liquid, scalable, and operationally efficient way to implement systematic strategies, mitigating many of the infrastructure and execution risks.”
“These strategies have become increasingly sophisticated, competitive, and cost-effective, particularly in terms of pricing and transaction costs,” according to Barchmann. “We believe the trade-off is, in many cases, very attractive,” he emphasizes. Barchmann explains that relying more on bank strategies allows them to focus on the dynamic strategy selection whilst also benefiting from operational advantages. “Sometimes, banks offer additional advantages. For instance, if we managed everything internally, we’d have to post initial margin on futures across many different exchanges. Often, banks can handle that far more efficiently,” he notes.
“These strategies have become increasingly sophisticated, competitive, and cost-effective, particularly in terms of pricing and transaction costs.”
The duo has reviewed hundreds – if not thousands – of systematic strategies and conducted in-depth analysis on several hundred to construct a portfolio which currently consists of 23 distinct strategies, of which 20 are bank QIS. “The bank universe offers many commoditized strategies, but there remains a significant gap – there are effective strategies out there that simply aren’t available within the QIS universe,” points out Yim. This is precisely where their proprietary strategies come into play. “One gap is clear: the hedging strategies available in the QIS space often underperform, have a high cost of carry or lack consistency.” One proprietary “hedging” strategy was developed specifically to provide the needed exposure to complete the portfolio.
A dynamic, regime-aware framework
Rather than building a static portfolio of systematic strategies, Marco Barchmann and Jerome Yim apply a quantitative classification of strategies based on the prevailing market environment to guide their portfolio construction. “We didn’t want to take a static approach,” says Yim. “It’s tempting to just pick the ten strategies that performed best over the last decade and rely on those, but that’s a classic case of data-mined backtest bias.” What matters for the team was not what worked in the past 10 years, but what will work going forward. “Our approach is similar to how pod shops operate, where you can quickly turn strategies on and off as the opportunity set evolves.”
“Our approach is similar to how pod shops operate, where you can quickly turn strategies on and off as the opportunity set evolves.”
For example, the duo developed a model that categorizes the market environment based on volatility and identifies which strategies best fit each regime. “We divide volatility into three main regimes: high, medium, and low,” explains Yim. “For each regime, we calculate the conditional returns of all strategies in our universe, so we know ex-ante which strategies tend to perform well in low, medium, or high volatility environments.” However, “we don’t fully commit to any one regime. Instead, we tilt the portfolio accordingly while maintaining broad diversification,” Yim notes. “Our objective is to deliver consistent returns regardless of whether we accurately predict the market environment.”
Barchmann further emphasizes that while many adopt a classic risk parity approach –equally weighting the risk contribution of each strategy – that’s not their method. “We scale to the same strategy-level volatility simply to ensure a like-for-like comparison,” he explains. They also pay close attention to the tail risk properties of the strategies. “Some strategies exhibit skewness or heavy tails, meaning they might usually operate around 2 percent volatility but can spike to 10 percent during stress events,” says Barchmann. “In these cases, we apply a more conservative scaling factor, lower than what the simple ratio would suggest, to limit the portfolio’s exposure to extreme volatility in adverse conditions.”
“We adopt a middle-ground approach that provides greater flexibility to allocate to strategies based on the current market environment, while ensuring no single strategy dominates and the portfolio remains balanced overall.”
Another key element of the duo’s portfolio construction and risk management process involves stress testing. For example, they simulate scenarios where oil or equities drop by 20 percent and estimate the potential losses for each strategy. “We cap the maximum allocation to ensure no single strategy would lose more than around 3 percent in an extreme portfolio-wide stress event,” notes Barchmann. He highlights that while traditional approaches like risk parity or equal risk contribution each have merits and limitations, their method is different. “We adopt a middle-ground approach that provides greater flexibility to allocate to strategies based on the current market environment, while ensuring no single strategy dominates and the portfolio remains balanced overall.”
Practical experience and market intuition
“Our investment edge, in many ways, comes from not being pure quants,” Yim concludes. “We are market practitioners who have actively traded through multiple investment cycles, witnessing firsthand how strategies like FX carry unraveled during the financial crisis, the COVID crash, and other risk-off environments,” he adds. “This experience gives us a deep understanding – not just of the historical returns, but also of the actual positions these strategies hold and the risks embedded within them.”
“Our investment edge, in many ways, comes from not being pure quants. We are market practitioners who have actively traded through multiple investment cycles…”
A final advantage of their futures- and derivatives-based strategies is capital efficiency. Unlike other strategies that rely heavily on cash and must generate their entire return through trading, their approach captures both the underlying cash yield and the returns generated by systematic trading. In today’s environment, where interest rates are no longer near zero, this becomes especially significant. “We benchmark ourselves to cash and only charge performance fees on returns above the cash rate, not above zero,” explains the duo. This means investors don’t forego the cash yield when investing in their strategy.
This article features in the “Systematic Strategies and Quant Trading” publication below:
