Understanding quant equity
Today's investors face two major challenges
Challenge 1
A handful of mega-cap companies now account for an increasing share of index returns. While this has supported market-cap-weighted investing in recent years, history shows market leadership changes over time. If market conditions reverse, concentrated portfolios may face greater risks.
Challenge 2
Markets generate more information than ever before. From company earnings and economic data to news, analyst research and investor sentiment, no individual can consistently process everything in real time.
Key takeaway
Active quant investing is designed to address both challenges.
Markets are becoming more concentrated, more complex and increasingly data-rich. Compelling stories, emotions and headlines can sometimes cloud judgement, leading investors to chase hype or back themes that don’t materialise. Systematic investing can support more consistent decision-making, leaving these approaches more relevant than ever.
Did you know?
Approximately 400 million terabytes of data are created each day1
1 IBM, https://www.ibm.com/case-studies/hsbc-usa
Source: HSBC Asset Management, June 2026.
What is active quant investing?
Active quant investing combines data, technology and investment expertise to analyse thousands of companies consistently, identify investment opportunities through systematic rules, and construct diversified portfolios with the aim of improving risk-adjusted returns. It often draws on academic and statistical research, with models and algorithms analysing large datasets to identify patterns or signals that may be associated with future returns.
What active quant investing may help with
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Processing more information – analyse far more companies and data than any individual investor. |
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Reducing concentration risk – identify opportunities beyond index constituents and avoid relying heavily on a small number of stocks. |
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Staying disciplined – apply investment decisions consistently through systematic rules, helping reduce emotional and behavioural biases. |
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Broader market coverage – the same approach can be applied consistently across a large investable universe. |
Key takeaway
Quant investing is becoming more popular
As markets become more complex, institutional investors are increasingly allocating to systematic investment strategies. Global quant funds are expected to expand to well over USD 2.5tn by the early 2030s2.
Did you know?
In the S&P 500 Index, the top 10 constituents account for nearly 40 per cent3 of the index — highlighting why concentration and diversification matter.
2 Verified Market Research 2026.
3 HSBC Asset Management, as of the end of December 2025.
3 approaches to equity investing
Active quant investing still looks at company fundamentals, such as profitability and valuation. The difference lies in how investment decisions are made. Rather than relying primarily on a manager's stock-picking judgement, quant strategies apply systematic rules consistently across a broad investment universe.
| Approach | How decisions are made | Main aim |
|---|---|---|
| Passive (index) |
Follows an index |
Match the index’s performance |
| Traditional active (fundamental) |
Manager judgement + research |
Seek alpha (excess return) through stock picking |
| Active quant |
Model rules + data analysis |
Seek alpha (excess return) using a scalable process |
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Cost efficient – systematic portfolio construction can often be delivered more cost efficiently than traditional active strategies. |
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Discover overlooked opportunities – look beyond the largest index constituents to access a wider range of investment opportunities. |
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Benchmark-aware flexibility – manage portfolio risk relative to an index, while allowing meaningful differences in holdings and returns. |
Did you know?
Quant investing is much older than AI. Its academic foundations date back to the 1930s, but advances in computing power over recent decades have enabled it to become a widely adopted investment approach today.
Factors: the building blocks behind quant decisions
Factors are measurable characteristics used to compare companies systematically. Companies can be scored on each factor and ranked, with higher scores indicating stronger exposure to the targeted characteristic. Some factors have historically been associated with excess returns and/or different risk outcomes, though their effectiveness may vary across market regimes.
Every factor asks a different investment question. Together, they provide a more complete picture of a company.
Commonly used factors include:
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Value |
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Quality |
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Size |
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Low Risk |
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Industry Momentum |
Key takeaway
Factors evolve over time
No single factor performs well in every market environment. By combining multiple factors, active quant investing aims to build more balanced portfolios across changing market conditions.
From data to portfolio: a simplified 3-step process
- Gather information
Data is collected from multiple sources, including company fundamentals, economic indicators, news, market information and investor sentiment. Mathematical models help transform unstructured information into usable investment inputs. - Model building
Quantitative models analyse the data, evaluate different factors and assign each company with factor scores. - Portfolio construction
Investment signals are combined with portfolio constraints and risk controls to construct an optimised portfolio aligned with the investment objective.
Main risks to be aware of
Model risk: results depend on how well the model is built and may differ from expectations if assumptions, calibration, data inputs or implementation aren’t sound.
Style (factor) risk: the strategy may favour certain styles (such as value, quality or momentum). These can fall out of favour and lead to periods of underperformance.
Regime sensitivity: Relationships observed historically may not persist across different macro environments, which can change the model’s effectiveness.
Portfolio tilts: even with limits in place, the portfolio can lean towards certain sectors or types of companies, which can increase ups and downs.
Market risk remains: markets can fall and drawdowns can occur even when the model behaves as intended.
Human oversight still matters
Even in quant systematic investing, people matter — particularly for design, data integrity checks, risk control and oversight.
Learn more about HSBC Quantitative Equity Capability
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