{"id":252845,"date":"2025-07-08T07:29:38","date_gmt":"2025-07-08T07:29:38","guid":{"rendered":"https:\/\/www.quoniam.com\/?p=252845"},"modified":"2026-03-25T08:16:28","modified_gmt":"2026-03-25T08:16:28","slug":"science-based-research","status":"publish","type":"post","link":"https:\/\/www.quoniam.com\/en\/interview\/science-based-research\/","title":{"rendered":"Unlocking opportunities: How Quoniam’s science-based research fuels performance"},"content":{"rendered":"\n
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Capital markets are more competitive than ever. What is Quoniam\u2019s approach to generating attractive returns?<\/strong><\/p>\n\n\n\n

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.<\/p>\n\n\n\n

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.<\/p>\n<\/div><\/div>\n\n\n\n

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What are the main performance drivers in your models?<\/strong><\/p>\n\n\n\n

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.<\/p>\n\n\n\n

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.<\/p>\n<\/div><\/div>\n\n\n\n

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What do you mean by a \u201cscience-based\u201d approach?<\/strong><\/p>\n\n\n\n

At Quoniam, a science-based approach means that every investment signal must both have an underlying fundamental idea and withstand a rigorous empirical evaluation.<\/p>\n\n\n\n

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  1. Clear hypothesis.<\/strong> We begin with an economically plausible rationale \u2013 for example, that equity markets react quicker to certain information than corporate bond markets, creating a lead-lag effect we can harvest. <\/li>\n\n\n\n
  2. Reliable data.<\/strong> All raw data are quality checked, cleaned, and normalised before we use them in a research project. <\/li>\n\n\n\n
  3. Robustness and feasible implementation.<\/strong>\u202fAn 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. <\/li>\n\n\n\n
  4. Peer challenge.<\/strong>\u202fIntermediate results are repeatedly presented to a cross-functional group of researchers and portfolio managers who challenge the results. <\/li>\n\n\n\n
  5. Out-of-sample testing.<\/strong> We reserve part of the history as a holdout set and require that a signal improves risk-adjusted returns there, not just in-sample. <\/li>\n\n\n\n
  6. Implementation and paper trading.<\/strong> 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. 
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    What do you see as the main benefits of science-based, model-driven investing?<\/strong><\/p>\n\n\n\n

    Our approach boils down to three decisive advantages:<\/p>\n\n\n\n