Calculated Risk Management

Investors need a better grasp of risk-management tools to gauge a portfolio’s strategic resilience in a rapidly changing world. 

Danger lurks in every corner of the financial markets. From macroeconomic hazards to systemic volatility to business threats facing individual companies, countless forces can throw a stock or portfolio off course.

Getting risk management right is a timeless task that tests even the most seasoned portfolio manager and requires a mix of skill, experience and humility. While investors may not always understand the technicalities behind the tools, improving their knowledge of the principles can better equip them to evaluate a portfolio manager’s risk-management strategy.

A Mosaic Approach for Complex Markets

Global markets are immensely complex. That’s why we advocate for a “mosaic approach” to risk management, meaning different tools are used for different circumstances (Display). Using the right tools provides transparency on a portfolio’s exposure to various risks. It also enables portfolio teams to take calculated risks when they can develop conviction and insight that ultimately leads to alpha. And using multiple models helps reduce measurement error.

Traditional factor risk models are still the cornerstone of equity risk management. Standard risk models for equity portfolios focus on factors that affect performance, such as style, sector, country and currency. These models are designed to illuminate a portfolio’s exposures and help it avoid being too closely tied to particular areas of the market.

But traditional models have blind spots because they focus on narrowly defined risks with a narrow lens. As a result, we think equity portfolios must use several models that search for different angles on risk, leaning into artificial intelligence (AI)–driven technologies. Just as two art lovers might interpret the same painting differently, each risk model isn’t right or wrong; they provide alternative interpretations of risk for portfolio managers to consider when selecting stocks and constructing portfolios. In our view, three key types of risks have gained prominence.

Macro Menaces: Economic Threats Abound

Investors of all types have faced a wave of macroeconomic risks in recent years. As inflation surged and interest rates spiked, unfamiliar market conditions added new hazards to equity portfolios.

Portfolio managers must understand their exposures to macro risks such as inflation, interest rates and oil prices. The right tools provide essential intelligence on how individual stocks and a portfolio might perform in different macro circumstances.

Macro risks influence stock fundamentals, which is why they are important for bottom-up stock pickers. In some sectors, the connection between macro trends and fundamentals is self-evident. For example, interest rates affect bank businesses, and oil prices impact energy companies. Many technology companies, which have long-duration cash flows, also tend to get hurt by higher interest rates. In other industries, macro effects may be more nuanced, such as the impact of inflation on consumer spending and retailers. The challenge is to estimate how these sensitivities affect different assets; one way to do so is to develop macro betas. Analysts can create a time-series regression of each stock’s returns on the macro assets. Then, we can aggregate these up to the portfolio level to assess risks to the allocation. Once modeled, stress tests can be conducted to simulate how a portfolio would respond to a shock such as a jump in interest rates.

Scenario-Driven Risks: History Rhymes

Equity markets bear the battle scars of past crises. Scenarios that fuel market volatility don’t necessarily follow a historical script. But replaying history to understand how a portfolio would have reacted to a major crisis, such as the European sovereign debt crisis of 2010–2012 or the taper tantrum of 2013, can be insightful. Quantitative analysts can recreate historical scenarios using real data based on market returns and factor returns, and then subject a portfolio to those conditions.

What’s the benefit of hindsight? It allows us to examine how our contemporary portfolios would have performed during historical periods of stress and to assess how they would cope with a similar scenario in the future. Analysts must be careful not to over-interpret the results, since there could be significant differences in current and historical circumstances. History might not repeat itself—but it often rhymes and can unsettle a portfolio’s performance patterns.

Risks You Didn’t Know You Had: Cluster Analysis

History can’t always guide you to new risks. And in many cases, new risks are hard to spot and may emerge slowly, beneath the surface. Cluster analysis looks for correlated sources of risk that may not be obvious to traditional quantitative risk models or fundamental analysts.

This analysis is based on sophisticated machine-learning techniques that classify stocks into groups whose returns have been moving closely together over a defined period (Display). The idea is to identify newly forming risks in stocks that start to move together but might not be otherwise seen as similar securities.

Latent factors or clustering tools can help identify newly forming risks. For example, during the pandemic, we detected a “stay at home” cluster risk, in which stocks including internet retail companies, at-home entertainment names and other stocks that fit the theme were trading with similar patterns. Enthusiasm over AI could also create linkages between stocks that would typically not be seen as related. For example, our analysis has detected a cluster related to the AI theme, in which data centers and energy stocks are trading similarly. Two years ago, this cluster did not register since the related stocks had different performance patterns. Identifying the cluster has helped our portfolio managers understand their exposure to this new theme earlier in the cycle. It also enables the teams to manage risk relative to this cluster and not just via static industries as in traditional risk models. 

What’s Next? From Mega-Caps to Elections

Properly deploying these tools requires keeping an active eye on the risk horizon. Today, prominent risks on our radar include market concentration risk and the US election.

Since late 2022, enthusiasm over AI has fueled a surge in a group of seven mega-cap US stocks. The heavy concentration of benchmarks in the so-called Magnificent Seven (Mag 7) creates risks for investors who don’t load up on the entire group, as well as for portfolios that hold positions in the entire cohort, which may be vulnerable if sentiment toward the group sours.

From a risk-management perspective, we can use our mosaic to create a better understanding of the Mag 7. Start by understanding the factor exposures associated with this group. Then, check what scenario analysis and clustering can teach us about the cohort and other stocks that might be related to it. Finally, weigh the amount of risk budget being spent on the truly idiosyncratic part of positions in each stock and confirm whether it aligns with the portfolio’s conviction and philosophy.

Investors must pay special attention to the risks from underweighting stocks. Before the Mag 7’s dominance, the risk of not owning a large stock wouldn’t have been too worrisome. But now, not owning a company like NVIDIA adds elevated benchmark risk. Since the stock is such a huge component of the S&P 500, portfolios that avoid or underweight NVIDIA will persistently underperform the broad benchmark if its winning streak continues. 

What about the US election? A good starting point is to try and identify groups of stocks that could be affected in different ways. Broker baskets provide helpful clues. These are baskets of stocks that brokers believe are likely to rise or fall based on certain outcomes. Of course, a portfolio will only have exposure to some of these stocks. So we can fill in the gaps by taking a similar approach to the macro sensitivities and estimate election betas to these baskets, which can then be applied to all stocks.

To do this, we calculate a time series of returns for the broker baskets, similar to the macro sensitivity approach. Then, we calculate the rolling beta of all stocks versus the returns of these election baskets. This way, we can see how the performance patterns of all stocks relate to this basket, which can help investors detect adjacent stocks—such as customers or suppliers of companies in the baskets—that may be affected by elections but are not covered by brokers.

Culture Matters for Risk Management

Even the most cutting-edge risk tools won’t get the job done without the right organizational culture. That’s because these tools provide intelligence that requires thoughtful interpretation and application.

Quantitative analysts are typically responsible for developing and managing risk-management tools. In a large investment house with many equity portfolios, we think developing risk tools centrally can help spread best practices and scale innovation. But to be effective in fundamentally driven portfolios, we believe quants should be embedded in the investment team. This allows them to function as a second pair of eyes and raise questions about blind spots that might not be detected in fundamental research. Quantitative teams operating outside the core investment group might not enjoy enough trust for their recommendations to be taken seriously. Working within the team maximizes the benefit of multiple perspectives from quantitative and fundamental analysts to facilitate an integrated approach to risk analysis.  

At the same time, risk-management functions are also needed to evaluate looming dangers across the equity markets. This is especially important when looking at issues that are likely to affect all portfolios, such as election risks.

Clear communication is an essential ingredient for quantitative analysts to influence their peers who might not be versed in technical jargon. And for any of these tools to be effective, investment teams need humility—regarding what they know and what they don’t know. We believe these soft skills are just as important for effective risk management as having the right technical tools and should be emphasized in training programs.

AI can support clearer communication between quantitative analysts and the wider investment teams. For example, risk data often manifests in complex data tables that require deciphering. Using large language models (LLMs), an AI technology, it may be possible to create summaries of the data in written paragraphs that are more digestible to a portfolio team than a table of numbers. 

Questions to Ask Your Manager

With these issues in mind, investors can approach portfolio managers with a set of questions that sheds light on the efficacy of a firm’s risk-management process and culture.

What kinds of multidimensional tools are used? Which types of risks are targeted with each tool? How does the portfolio team create a risk-aware culture to thoroughly integrate assessments from various sources? Can they demonstrate actionable insights from a risk-management process? How are portfolio managers incentivized to create a risk-aware culture? Ask for an example of lessons learned from a big risk that was missed.

The answers to these questions can help investors determine whether an asset manager is strategically focused on risk management as an integral part of the investment process, or just treats it as an afterthought. Taking calculated risks will always be the cornerstone of alpha for actively managed portfolios. With a comprehensive risk-management structure, as well as the right tools and culture in place, clients can gain confidence that portfolio managers are dynamically attuned to the risks that can make—or break—even the best-laid investment strategy.