A World in Flux: Managing Evolving Risks in Equity Markets

Risk management is being put to the test in 2025. How can equity portfolio teams cope with multiple hazards across equity markets this year?

It’s hard to keep up with this year’s fast-changing risk landscape. From artificial intelligence (AI) to tariffs to macroeconomic uncertainty, threats are mounting to businesses, equity holdings and portfolio returns. Bottom-up portfolio managers focused on company-specific outcomes could be particularly vulnerable to rapidly changing dynamics. This, in our view, is the most important overarching risk to manage in the current environment.

Put differently, our task is to ensure that portfolio returns are driven by long-term company outcomes and not the latest policy change. To this end, we recommend a “mosaic approach” to risk management, using different tools to address different risks. In the following case studies, we demonstrate how equity investors can apply these tools strategically to mitigate today’s evolving risks and to keep portfolios aligned with their long-term strategic objectives.

AI and Energy: Cluster Analysis Uncovers Correlation

Rapid advances in AI continue to create investor enthusiasm—and uncertainty. In 2024, investors began paying closer attention to AI’s energy demands. That storyline became more prominent in early 2025 after DeepSeek unveiled a more efficient AI model that could be run with lower energy intensity. As a result, in late January and February, AI-related technology stocks sold off along with energy stocks that were considered vulnerable to lower AI-driven demand, including nuclear power generators.

Traditional risk management techniques struggle to identify correlations between sectors or industries that don’t seem to have much in common. Portfolio managers (PMs) monitor various prevalent risks, including country and sector risks, to ensure balanced allocations. However, static labels like countries and sectors often fail to reflect firms’ evolving business models and regional shifts, necessitating more frequent updates. This is increasingly important as new trends, technologies and economic opportunities emerge. Cluster risk analysis helps PMs dynamically examine and manage risks by identifying links between stocks based on their movements.

Like most investors, we didn’t predict the market’s reaction to DeepSeek’s breakthrough. However, we have used cluster analysis to identify correlated performance patterns of AI-technology stocks and some energy stocks—two groups that wouldn’t intuitively seem related. Traditionally, energy stocks have tended to be more correlated with oil prices and underlying economic growth, while AI stocks have been the major force driving equity markets higher over the last two years.

Cluster analysis looks for correlated sources of risk that aren’t obvious to traditional quantitative risk models or fundamental analysis. It’s based on sophisticated machine-learning techniques that classify stocks into groups whose returns have been moving closely together over a defined period.

In this case, some energy and utility stocks were placed in the same cluster as AI-related stocks. This indicates that energy stocks are aligned with AI, reflecting the energy consumption of training models. Understanding this dynamic helps us assess more completely the level of a portfolio’s exposure to the AI theme.

Tariffs: Untangling a Web of Consequences

Since President Donald Trump took office in January, tariff talk has dominated headlines. Tariffs create cost and pricing uncertainties that affect companies in complex ways. For investment teams, tariffs are a big headache: how can you gauge the exposure of holdings to this risk and the potential impact on earnings and profitability, especially when we don’t know which tariffs will stick?

Analysts could study the supply chains of every stock covered to manually evaluate which of a product’s input components—often in the hundreds—are subject to tariffs. This would be painstaking and inefficient.

Instead, tools like broker baskets can improve our ability to quantify the tariff shock’s impact on an entire portfolio and identify highly exposed positions. Sector insights can help us pinpoint bigger potential winners and losers, enabling us to better calibrate risks and position sizes.

Broker baskets are groups of stocks that brokers believe are likely to rise or fall based on certain outcomes, such as higher tariffs (Display). However, representative broker baskets have blind spots since they include a limited number of names. So our portfolio teams start with broker baskets but extend the analysis to every stock in a relevant investment universe to understand how they covary with tariff risk.

This exercise helps surface potential winners and losers. For example, companies with more local production and domestic operations might benefit during a trade war. Conversely, major retailers that rely on imported goods would be more vulnerable to tariffs on their overseas suppliers.

Sector sensitivities are also instructive. Technology and materials are among the sectors that are more likely to suffer from tariffs, while utilities and financials might benefit (Display). Still, there’s a range of dispersion within each sector, reflecting differences in companies and business models.

Some of these observations might sound obvious for fundamental analysts who cover individual stocks. But in fast-moving markets, information on all holdings can’t always be updated in real time. The techniques above allow us to identify companies at risk and aggregate exposures to the portfolio level, providing a portfolio manager with a real-time window into an allocation’s susceptibility to tariff risks.

Identifying positions at risk is just a first step. The next step is to drill down into the holdings surfaced by the broker baskets and to follow up with fundamental research.

Research analysts with deep sector or industry expertise can confirm or reject the quantitative recommendations. What’s more, this analysis can help an investment firm with multiple portfolios check positioning across the platform, ensuring a balance of overweight/underweight positions in companies on both sides of the tariff-risk equation. Beyond tariffs, broker baskets can help manage other policy risks including healthcare, tax policy and defense.

Inflation: Monitoring Top-Down Risk in Bottom-Up Portfolios 

Macroeconomic risks are ever present in financial markets. Even for investment teams rooted in bottom-up stockpicking based on company fundamentals, the impact of top-down macro variables and the effects of the economic cycle on business results cannot be ignored.

Interest rates, yield curves, commodity prices, wages and inflation are among the well-known and widely observed macro risks. To aid fundamental analysts, we can monitor these risks from the top down. Drawing on daily measures of each macro variable, our quantitative experts can estimate how stocks move with them. Just as every stock has a beta to the market—which gauges its correlation to market moves—each stock also has measurable sensitivities to macro factors like inflation.

Stocks within an industry may have different sensitivities to inflation. For example, Constellation Energy exhibits positive correlation with inflation, possibly because of its nuclear energy business, which may become more attractive if inflation rises (Display). ExxonMobil’s slightly positive inflation beta could indicate its ability to pass on higher prices to consumers, in our view. In contrast, Vestas Wind Systems, a Danish company, suffers when inflation expectations rise, perhaps because rising prices in its supply chains would raise implementation costs for wind farms and erode profitability.

In the retail sector, companies such as Costco and Kroger in the US and Tesco in the UK show similar inflation betas, slightly above and below neutral. In our view, this implies that big brands benefiting from customer loyalty are more capable of passing on higher prices to consumers without taking a hit to earnings.

Inflation betas change over time and require interpretation by fundamental analysts. After identifying and interpreting the results, we aim to aggregate inflation sensitivities to the portfolio and review them weekly. Portfolio exposures can change when stocks with high or low sensitivities are bought or sold, or when companies make strategic adjustments to curb the effects of inflation.

Combining Quantitative Tools and Fundamental Expertise

What do the above risk scenarios have in common? As we see it, each issue and risk management approach requires a strategic combination of quantitative tools and fundamental research. Quantitative, data-driven tools provide portfolio teams with robust, real-time information that can surface risks across vast investment universes. Deep industry expertise is needed to interpret the data and reach meaningful fundamental conclusions about individual company exposures.

After using the tools described above to uncover portfolio risks, the investment team must then decide whether it has an edge in forecasting the outcome of that risk and size positions appropriately. This process can also unlock opportunities; when a stock is mistakenly associated with a risk, it might sell off more than it deserves and present an attractive buying opportunity.

Applying advanced risk management techniques is essential to investing with conviction in today’s uncertain environment, which helps clients stay invested through short-term volatility and remain focused on long-term investing plans. Combining quantitative data science and fundamental analysis is the best way, in our view, to ensure that long-term company outcomes remain at the heart of an investing process designed to meet clients’ strategic financial objectives.