Fact checking quant equity claims

The quant strategy definition

The claim: Multi-factor quant investing is replacing active fundamental equity investing.

The verdict: Misleading.

What’s true: Multi-factor quant strategies are seeing clear, measurable demand, reflected in rising flows into systematic funds.¹ The appeal of rules-based, data driven equity allocations is structural: quant equity can be used to diversify in concentrated markets, minimise unintended macro/style biases, and apply a transparent, repeatable process in an environment with a growing abundance of data.

What’s misleading: The growth does not imply that multi-factor quant investing is replacing fundamental equity – both can coexist, and neither is necessarily a substitute for the other.

Active fundamental strategies typically rely more heavily on deep company research and manager skill to drive investment decisions. Multi-factor quant strategies use scalable, systematic data analysis (which is increasingly AI-enabled) to build potentially diversified, benchmark-aware portfolios using many smaller active bets.

The bottom line: Quant continues to complement traditional fundamental investing and is a different, systematic route to risk-adjusted returns.

The claim: Quant equity investing is equivalent to multi-factor equity investing.

The verdict: Misleading.

What’s true: Multi-factor equity strategies are just one type of quant equity investing. Quant investing is generally defined as a systematic, rules-based approach that uses data, mathematical models and statistical techniques to identify investment opportunities and build and manage portfolios. Within this realm are multi-factor strategies, which combine a set of factors that analyse and explain the returns and risks of securities. Five commonly cited equity factors include value, quality, size, low risk and momentum.

What’s misleading: Various types of strategies are labelled as quant equity other than multi-factor investing. Other quantitative approaches can use single factors, proprietary signals, statistical relationships, alternative data and other systematic methods.

The bottom line: A multi-factor approach is used by many long-only quant mutual funds, but it does not define the broader universe of quant equity strategies.

The claim: Multi-factor quant strategies all allocate equally across factors.

The verdict: False.

What’s true: Equal weighting of factors is one way to implement a multi-factor strategy.

What’s misleading: The claim is too narrow as multi-factor strategies are not all managed in the same way. While some use equal weights, others adjust factor exposures dynamically. Risk-weighted or optimised allocations are other types of implementation.

Yet, other approaches do not allocate capital to separate factor sleeves at all: they use a bottom-up portfolio construction process, which consists of selecting securities that rank highest in their combined factor scores. Often, multi-factor portfolios are calibrated with risk controls and constraints.

The bottom line: Multi-factor strategies do not always allocate to factor sleeves. In some, a bottom-up approach is used, where stock by stock factor signals drive the portfolio construction process.

The human element

The claim: Because quant strategies are model-driven, there is no need for humans in the process.

The verdict: False.

What’s true: A systematic, data-driven investment process requires people to design, maintain and oversee it. Quant researchers, portfolio managers, engineers and technology professionals are essential to systematic investing.

Investment teams determine, select, develop and refine models depending on the investment objective and portfolio restrictions. Importantly, portfolio managers monitor implementation of a strategy and evaluate whether the model continues to behave as intended. Ongoing data quality monitoring and controls are needed to detect issues early. If the rules or assumptions are flawed, results can disappoint. Independent challenge, validation, performance attribution, and stress testing help ensure models are used appropriately and risks are understood.

Models don’t build and improve themselves without human input. Research is needed for testing signals and factors across different periods and validating results against datasets. This is also of significance when there is an emergence of new data, technologies and market behaviours.

The bottom line: People remain central to quant investing, particularly in design, research, data integrity, risk control, implementation, oversight and accountability.

The claim: A multi-factor model does the decision making, so the quant portfolio manager just watches the model run.

The verdict: False.

What’s true: Models can automate some parts of the process including security selection and portfolio construction. Generally, long-only multi-factor quant strategies tend to be more cost effective than active fundamental equity strategies.

What’s misleading: Human oversight does not end when a model is in production. Ongoing responsibilities can include researching and validating signals, overseeing the process, monitoring model behaviour and performance, managing risk limits and constraints and refining the strategy as markets evolve (see claim #4).

The bottom line: A quant portfolio manager’s role does not become passive in managing a model-driven strategy.

The claim: The best quant strategies have no discretionary layer.

The verdict: Misleading.

What’s true: Some quant strategies deliberately do not have a discretionary portfolio overlay.

What’s misleading: Other quant strategies are designed with a defined discretionary overlay, allowing investment professionals to adjust model generated positions, which can be around implementation decisions rather than on overriding stock selection. Quant strategies that don’t build in a discretionary overlay still see humans involved in various parts of the process, from research and development to risk and oversight (see claim #4).

The bottom line: A case can be made for different approaches of running a strategy. Whether there’s a discretionary layer or not depends, but the investment process should be appropriate to its investment objective.

The hedge fund conflation

The claim: Mutual funds employing quant strategies and quant hedge funds are both forms of systematic investing, so their performance should behave in similar ways.

The verdict: Misleading.

What’s true: Both use model-driven processes involving algorithms, statistics and mathematics to make investment decisions.

What’s misleading: Quant hedge funds and quant mutual funds can have very different performance profiles. The opportunity set, portfolio construction, risk limits, liquidity requirements and return objectives are just some of the many differences between them.

Hedge funds generally have greater flexibility to use leverage, derivatives and shorting, while regulated mutual funds are typically more constrained in using these tools. Quant hedge funds tend to pursue more absolute return mandates, such as short-term momentum, statistical arbitrage, and other long/short strategies. By contrast, many quant equity mutual funds are long-only, benchmark aware, and use multi-factor strategies (though there are regulated funds that are long/short). These details can lead to different performance and risk outcomes, including during periods of heightened market volatility.

The bottom line: Just because two strategies are “quantitative” does not mean they will have similar performance and risk behaviour.

The claim: If quant hedge funds experience losses, it’s a sign that multi-factor investing is risky.

The verdict: False.

What’s true: Losses in prominent quant hedge funds can influence sentiment and potentially spill over into investor perception on “quant” more broadly. Periods of concentration, de-concentration, or rapid factor rotations can also create challenging conditions for multiple systematic styles at the same time.

What’s misleading: Quant hedge funds could at times be sensitive to crowding and forced deleveraging dynamics, particularly if they employ leverage, shorting and derivatives (see claim #7). This is not the situation that long-only multi-factor funds typically experience. Long-only quant equity can be a way to diversify, reduce unintended macro/style bets, and apply disciplined risk controls. The claim conflates different strategies, which may see materially different outcomes in volatile markets.

The bottom line: Quant hedge fund drawdowns don’t “validate” multi-factor risk; risk depends on the strategy design, constraints, and portfolio construction.

The claim: The same investor behaviour equally affects both quant hedge funds and quant mutual funds.

The verdict: False.

What’s true: General investor sentiment can influence flows across multiple segments of the fund industry.

What’s misleading: Hedge funds and mutual funds tend to attract different investor bases. Hedge fund investor bases are often skewed towards institutional and accredited investors, in line with hedge funds’ higher minimum subscriptions and investor eligibility requirements. On the other end, mutual funds commonly offer more frequent liquidity and operate under tighter regulatory constraints compared to hedge funds. Further, the investment objective, style, and risk profile can be markedly different, contributing to divergent investor bases. As a result, market events can drive different outcomes and investor responses.

The bottom line: Investor responses to market events do not necessarily play out in the same way across hedge funds and mutual funds, even when both are quantitative.

The model

The claim: Quant models can process millions of data points daily.

The verdict: True for some strategies.

What’s true: Systematic platforms can process enormous volumes of market, fundamental and alternative data. Modern data pipelines allow quant models to scale from data capture and cleaning through signal generation, portfolio construction, and optimisation.

What’s misleading: Just because quant models can ingest millions of data points does not mean the model can produce better decisions. More inputs can increase noise and make it easier to find patterns that look good on paper but don’t hold up. Models are not protected from bad data, such as missing values and errors, unless the right checks are built in. That’s why strong quant strategies focus on data quality and ongoing monitoring, often with human oversight.

The bottom line: Vast volumes of data sounds impressive, but the real question lies on the quality, robustness, and governance of the model.

The claim: Quant investing is a “black box”. Investors often do not understand how the models make investment decisions.

The verdict: Misleading.

What’s true: Some quant models can be complex and difficult for investors to understand.

What’s misleading: For certain strategies, the models can be explained to investors. In multi-factor strategies, for instance, fundamental factors can be transparent: value, quality, momentum, size and low volatility are factors that are based on observable characteristics. Regulated mutual funds often disclose the factors/attributes considered in their systematic investment process within offering documents.

Regulated funds also typically provide regular reporting on top holdings, allocation, and performance attribution, which provides insight into exposures and return drivers. Fund managers may also provide additional materials that illustrate model execution.

The bottom line: Though some quant models are complex, that does not make them black boxes. Certain strategies’ processes and models can be explained even when the underlying code is not disclosed.

The claim: The quant model stops working when the strategy sees large drawdowns.

The verdict: False.

What’s true: Large drawdowns can happen in any equity approach and should trigger enhanced monitoring. A drawdown may coincide with regime shifts (e.g., concentration changes, liquidity stress, fast factor rotations) and warrants review of attribution, exposures, data quality and implementation.

What’s misleading: A drawdown does not mean the model has stopped working. Systematic, multi-factor equity strategies are built to harvest multiple long-run return drivers (e.g., value, quality, momentum, low risk, size) with disciplined rebalancing and broad breadth (many small bets). Alpha is expected to accrue over full market cycles, even if individual factors or periods experience drawdowns, without implying model failure.

The bottom line: Drawdowns are not a failure signal on their own; the right test is whether behaviour remains consistent with design, risk controls and expected regimes over time (past performance is not predictive).

Learn more about HSBC Quantitative Equity Capability

Note 1: Funds domiciled in Europe and Hong Kong. Source is Broadridge, data as of December 2025.
Source: HSBC Asset Management, August 2026.
The views expressed above were held at the time of preparation and are subject to change without notice. The information provided does not constitute any investment recommendation or advice. Diversification does not ensure a profit or protect against loss. For illustrative purposes only.