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US equities remain a cornerstone allocation for global investors, reflecting the market’s scale, liquidity and long-term upside potential. Yet scale does not always translate into evenly distributed exposure. In recent years, index returns have been driven by a relatively small number of large stocks, which can leave benchmark allocations more concentrated in certain companies and themes than investors might expect.
In the first seven months of 2026, 63% of the S&P 500 Total Return Index’s returns came from just 10 companies. This concentration underlines how benchmark outcomes can be disproportionately shaped by a small group of market leaders. When leadership rotates, or sentiment shifts, benchmark-return patterns and portfolio risk can change quickly.
Concentration is not just about weights
Concentration risk is not only about the weight of individual holdings. It can also emerge when exposures cluster around similar risk and return drivers. In periods when dominant players share comparable characteristics, benchmark allocations may be less diversified in practice than they appear. This matters because diversification is expressed by more than the number of holdings; it also involves spreading active risk across different portfolio positions and seeking to add value across the investment universe.
A systematic approach to investing can address concentration risks by evaluating the entire index – and often beyond – to quantify return and risk drivers. Rather than relying on a narrow set of market leaders, a quantitative strategy seeks to maximise exposure to attributes that are positive contributors to return while minimising unrewarded risk.
Why a systematic, data-driven approach
The case for a systematic, data-driven approach is strengthened by the scale of information generated by modern markets, which far exceeds what any individual can process consistently. Quant strategies can process and analyse data across an investment universe of thousands of stocks, converting large datasets into consistent signals and applying a model-driven framework to portfolio construction.
Many active quantitative equity strategies are built around a multi-factor framework. Factors are measurable features (such as value, quality, size, low volatility and momentum) that help explain the risks and returns of securities. Because factor performance can exhibit cyclicality, a multi-factor model does not rely on any single style or theme. Instead, it can adjust weights based on the assessment of factor strength and market signals, with the aim of enhancing outcomes across market cycles.
Market volatility and drawdowns are a reality of equity investing. Quant strategies can help navigate market shifts and uncertainty by combining disciplined portfolio construction with robust risk management.
Quant and active fundamental: Different tools, complementary strengths
Quant equity strategies are not a substitute for active fundamental strategies. They can complement fundamental approaches by providing a systematic way to research, implement and manage exposures across a broad investable universe.
Active fundamental strategies combine bottom-up company insights with top-down macro views and portfolio manager judgement. Quant strategies, on the other hand, use data and systematic models, often built around factor- or signal-based frameworks, to translate inputs into investment decisions, supported by predefined risk controls.
Model-driven does not mean there is no role for humans in the process. People remain central to quant investing, particularly in design and research, data integrity, risk control, implementation oversight and accountability.
HSBC Asset Management’s quant equity expertise
HSBC Asset Management has a dedicated quant equity team with more than 50 investment professionals across Europe and Asia, and over 20 years of experience. The team manages more than USD 50 billion in quant equity AUM across a range of funds and tailored solutions1. The platform is supported by an established quant infrastructure, including advanced proprietary quant techniques and customised risk models designed to support portfolio construction. The team’s approach combines data-driven research, disciplined portfolio construction and robust implementation, with the objective of delivering diversified portfolios and potentially better risk-adjusted outcomes over time.
With US equity market concentration elevated, investors may benefit from looking beyond headline benchmark exposure to understand how risk is distributed across return drivers. A systematic, multi-factor approach offers a disciplined way to broaden exposure and manage active risk, complementing fundamental insight as market leadership evolves.
To learn more about how quantitative investing works, please visit our website.
Note: 1. Source: HSBC Asset Management, data as of June 2026.
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This is a sponsored advertorial from HSBC Asset Management.