Unlocking opportunities: How Quoniam’s science-based research fuels performance
What is research at Quoniam? Our research process is science-based and model-driven, setting us apart from traditional investment managers. In this interview, Dr Maximilian Stroh, CFA, Head of Research, explains how Quoniam’s approach leverages advanced quantitative models to consistently uncover information edges and drive investment performance.
Key takeaways
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Quoniam combines systematic models with data-driven research, integrates empirically proven performance drivers while avoiding subjective decisions.
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The interdisciplinary research team continuously develops and tests new factors to ensure that the investment process remains robust and adaptable in different market phases.
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A broad spectrum of alpha factors for equities and bonds results in diversified portfolios with stable excess returns.
Capital markets are more competitive than ever. What is Quoniam’s approach to generating attractive returns?
At Quoniam, we focus on exploiting numerous tiny edges of opportunity in equity and fixed income markets. We select assets from broad universes and take many positions in diversified portfolios. To cover these broad universes, we forecast returns and risks for the highest possible number of companies using statistical models, rather than relying on traditional analysts to cover individual stocks and sectors.
We combine academic research with data science to identify key performance drivers of stocks and bonds. Each driver, such as the explanatory power of a company’s patents, must pass rigorous testing before being incorporated into our forecasting model.
What are the main performance drivers in your models?
On the equity side, we currently incorporate over 100 alpha factors. Many of them can be categorised into value, quality, and sentiment, and form the core of our investment process. These drivers have emerged from years of research as statistically and economically significant indicators of future excess returns.
For corporate bonds, the main categories are value, carry, and momentum. Similar to our equity signals, we use a wide range of innovative performance drivers. Interestingly, the momentum driver is based on equity momentum, reflecting the lagging relationship between equities and corporate bonds, highlighting the cross-asset nature of some signals.
What do you mean by a “science-based” approach?
At Quoniam, a science-based approach means that every investment signal must both have an underlying fundamental idea and withstand a rigorous empirical evaluation.
- Clear hypothesis. We begin with an economically plausible rationale – for example, that equity markets react quicker to certain information than corporate bond markets, creating a lead-lag effect we can harvest.
- Reliable data. All raw data are quality checked, cleaned, and normalised before we use them in a research project.
- Robustness and feasible implementation. An effect should persist across different market segments such as regions or rating buckets, liquidity regimes, and after realistic transaction cost haircuts. Furthermore, it must be harvestable within feasible, risk-controlled portfolios.
- Peer challenge. Intermediate results are repeatedly presented to a cross-functional group of researchers and portfolio managers who challenge the results.
- Out-of-sample testing. We reserve part of the history as a holdout set and require that a signal improves risk-adjusted returns there, not just in-sample.
- Implementation and paper trading. Before a new iteration of our investment process is taken live, portfolio managers and traders evaluate it “on paper” for a few months to make sure the resulting portfolios are in line with our risk and liquidity objectives.
What do you see as the main benefits of science-based, model-driven investing?
Our approach boils down to three decisive advantages:
- Consistent, objective decisions. Systematic models apply the same rules every day, eliminating the emotional bias, fatigue, or style drift that can creep into discretionary processes.
- Deep, data-driven insights at scale. We process terabytes of fundamental, market and alternative data to detect subtle, persistent patterns that a human analyst would miss, allowing us to cover thousands of securities with equal discipline.
- Transparency and rigorous risk control. Our framework abstracts from the individual asset to its exposure to alpha factors, allowing us to dissect and explain investment results. At the same time, diversifying risk across a broad set of factors – and rebalancing systematically – means we can manage portfolio risks very effectively.
What questions do your researchers usually explore?
Our research agenda spans the full investment workflow: We continually scout for novel data sources that can enrich our models, determine which factors truly add alpha or diversification, study the optimal ways to blend those factors through time, refine portfolio construction techniques, and build the technology needed to implement and rigorously test each idea. We do not rush or follow the herd; instead, every prospective refinement undergoes deliberate, evidence-based scrutiny to keep our alpha engine effective and resilient.
What kind of people are behind your research?
Quoniam’s research bench is deliberately interdisciplinary: Doctorates and masters in mathematics, physics, engineering and computational linguistics work shoulder to shoulder with specialists in finance and business. Collaboration is built into our process – portfolio managers regularly challenge ideas and test prototypes, while our academic partnerships, such as the Quoniam Doctoral Programme and our regular Research Seminars, provide a two-way conduit for fresh theory and real-world insight. This lively exchange of views sharpens our models, sparks innovative thinking, and cultivates the next generation of quantitative talent.
