Trade War Turbulence Tests Limits Of Computer-Driven Hedge Funds

The escalating trade conflict between the United States and China has delivered a blow not just to global supply chains and equities, but also to some of the world’s most sophisticated hedge funds. Among the hardest hit are computer-driven, quantitative funds—strategies built on models designed to capture trends and forecast returns based on historical market patterns. As markets whipsawed in response to trade headlines, those models faltered.

Systematica Investments, led by renowned quant manager Leda Braga, is one of the firms now feeling the pressure. Once viewed as a prime example of algorithmic precision in financial markets, Systematica has suffered losses in the wake of a turbulent first quarter marked by rapid reversals and policy-driven unpredictability. The firm’s difficulties are emblematic of a broader reckoning within the quant hedge fund sector.

Trade War Disrupts Predictability

President Donald Trump’s renewed trade hostilities have created a market environment defined by uncertainty and volatility. Announcements on tariffs, retaliatory measures from Beijing, and shifting White House rhetoric have caused sudden movements across asset classes—currencies, equities, and bonds alike.

In early 2024, markets swung violently in response to a series of conflicting statements about the status of US-China negotiations. One week, optimism pushed risk assets upward; the next, a tweet from the president triggered a sharp sell-off. For quant strategies that rely on persistence in market direction or stable relationships between asset classes, such reversals have proved costly.

The issue isn’t volatility in itself—many quant funds can profit from turbulent conditions—but the unpredictability and erratic timing of political interventions. It is this inconsistency that has undermined the statistical assumptions underlying many algorithmic trading systems.

The Quant Model Breakdown

Quantitative hedge funds operate using algorithms trained on historical data. These models typically look for patterns in price movements, volatility clusters, or asset correlations and then position portfolios accordingly. Common strategies include trend-following, mean-reversion, and volatility targeting—each of which assumes that markets exhibit some level of behavioural consistency over time.

But when markets are no longer responding to economic fundamentals or technical signals—and are instead reacting to policy tweets and political statements—these models begin to struggle. The sudden reversals seen during the trade war have disrupted expected signals and upended carefully calibrated positions.

Systematica, whose strategies are largely trend-based, has seen a noticeable drop in performance. Investors have reportedly grown cautious, and internal risk teams have had to reassess model exposures to avoid further drawdowns.

Sector-Wide Difficulties

Systematica is not alone in facing these challenges. Other major quant funds, including AQR Capital Management and Two Sigma, have also experienced performance strain amid the shifting macroeconomic backdrop. While not all firms have disclosed figures publicly, analysts tracking fund performance note that quant trend-followers have broadly underperformed their discretionary peers in recent months.

One contributing factor is that many quant models are slow to adjust when correlations break down. For example, a traditional safe-haven response—such as a rally in government bonds during equity sell-offs—has not always held during recent trade-related market stress. This breakdown in correlation weakens the models' reliability and forces managers to either intervene manually or accept elevated losses.

The result is a credibility test for a sector that once claimed its ability to remove emotion and human bias from investment decisions. Now, it appears that inflexible automation may be just as vulnerable in the face of politically driven randomness.

Recalibration and Adaptation

In response, some quant firms are taking steps to adjust. This includes developing models that incorporate real-time news sentiment, headline parsing, or even elements of discretionary oversight to override or supplement algorithmic decisions. Others are recalibrating risk management systems to place less reliance on historical volatility patterns and more on adaptive drawdown controls.

There’s also a growing trend towards blending traditional quant methods with machine learning techniques that can process unstructured data, such as political speech and social media posts. These hybrid approaches aim to bridge the gap between hard data and the messiness of geopolitics.

Still, such changes take time—and carry no guarantee of success. Investors in the sector are watching closely, with some reallocating capital toward managers who employ more flexible or discretionary strategies.

A Sector Under Review

The troubles faced by Systematica and its peers reflect a broader shift in the investing environment. As geopolitical risks move to the forefront of market pricing, traditional quantitative models—grounded in historical consistency—are being put to the test.

While the long-term value of systematic investing remains intact, the recent period has exposed the limits of relying solely on past market behaviour to guide future trades. Until quant funds can account for the chaotic influence of political intervention, their performance may continue to lag during periods of heightened uncertainty.

As the trade war continues to cast a shadow over global markets, it is increasingly clear that adaptability—not just automation—will be key to survival in the next generation of hedge fund management.

Author: Brett Hurll