Managing risk has always been a priority for us. We have fostered a culture of risk management at Royce, rooted in the conviction that doing so is especially critical in the small-cap space, where volatility is typically higher and trading volumes thinner. Our portfolio managers see themselves as risk managers first and foremost, matching expected returns with their respective contributions to portfolio risk to achieve the best risk-adjusted expected return. And we integrate risk management principles and practices into both stock selection and portfolio construction.

Our more than five decades of managing small-cap portfolios have shown us that a collaborative approach to risk management works best. Several years ago, we saw that risk reviews independent of the portfolio manager could add significant value. Royce’s Chief Risk Officer Gunjan Banati works with our Risk Management Committee to review portfolio composition regularly. These meetings are designed to ensure that the risk profile conforms to each respective portfolio’s investment objectives and that there are no unintended risk concentrations or errors. By providing independent risk analysis to each investment team, the Risk Management Committee offers critical evaluation tools that can enhance the portfolio manager’s selection and construction activities while maintaining each manager’s autonomy in their respective investment processes. The goal is to improve decision-making by providing in-depth, actionable data.

Equally critical to our risk management efforts—and another important example of collaboration—is the important work done by our Quantitative Research and Analytics Group, which is helmed by Royce’s Director of Quantitative Strategies and Portfolio Manager George Necakov, who’s joined by Director of Portfolio Analytics and Assistant Portfolio Manager Michael Connors and Quantitative Analyst Fucheng Guo. George joins Gunjan and the rest of the Risk Management Committee in their meetings with portfolio managers.

Our Quant Group works on many data-driven activities and projects, nearly all of which touch on aspects of our risk management work. The Group builds portfolio models that utilize factor-based investment strategies—including value, quality, momentum, and market capitalization—to help us better understand relative performance advantages and disadvantages. Because a select group are designed to mirror our major Strategies, the models are particularly helpful in identifying unintentional portfolio tilts and portfolio scoring. By monitoring factor regimes and the timing of different investment strategies’ relative outperformance (and underperformance), the Group’s work allows our investment teams to stay informed about factor rotation and overall market dynamics.

Another key activity is the Group’s analysis of portfolio construction, where they examine position sizes, purchases and sales. This feedback often helps our investment teams make better decisions because they’re receiving customized data on their portfolios from an independent source—one that at the same time is intimately familiar with the current composition and long-term history of each portfolio. By implementing back-testing and portfolio simulation, the Group provides research that our investment teams can also use to uncover potentially successful investment strategies. In addition, they have developed bespoke risk management frameworks that utilize quantitative risk models and scenario analysis, which alert portfolio managers to market volatility dynamics that might otherwise go unnoticed. By also analyzing order flow, liquidity, and price impact, this work informs trading strategies, improves execution quality, and can enhance portfolio durability.

The Group has also initiated in-depth task automation and analysis with our proprietary reporting tool created in Tableau, which provides portfolio managers with extensive, real-time analytical and visualization capabilities for all portfolio metrics, transforming data into actionable insights beyond what is possible with static reports. Among other key metrics, Tableau offers performance attribution analysis that dissects portfolio returns, weighting them to sector allocation or stock selection decisions over time frames that range from one month to five years. Analyzing metrics over rolling 1-, 3-, and 5-year periods is especially useful for pattern identification. It allows portfolio managers to see both advantageous and disadvantageous elements that may have begun as seemingly inconsequential short-term issues before making a bigger impact. It also helps them recognize strengths and weaknesses they may not have been aware of.

To be sure, we think the vital role our Quant Group plays in enhancing risk management—and potentially boosting long-term performance—is a great example of how sophisticated analytical tools can help our investment teams make better investment decisions for the long-term benefit of our clients and shareholders.

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