In the height of summer 2026, the A-share market gave quantitative investors a vivid lesson in risk. Since July, as AI computing power, semiconductors, and other tech growth sectors that had led earlier gains quickly corrected, market risk appetite cooled in phases. Many quantitative private funds saw net value drawdowns of 15% or even over 20%, and the once-celebrated “excess return” myth began to crack. Third-party statistics show that in July, the average return of quantitative products was approximately -9.5%, with fewer than 20% achieving positive returns; year-to-date average returns narrowed to under 1%. With both beta and alpha returns weakening, more and more ordinary investors are re-examining how multi-factor stock selection—the core methodology of quantitative investing—actually works. Where do its returns come from, and why does it fail under certain market conditions?
1. The “Impossible Trinity” Behind the July Drawdown
To understand this round of drawdowns, one must first look at the industry’s growth curve. Public data shows that by the end of 2025, quantitative fund assets had climbed to 3.22 trillion yuan, accounting for over 43% of private securities investment funds; in the first half of 2026, quantitative equity long strategies reached 1.83 trillion yuan in scale, surging by more than 1.1 trillion yuan from approximately 720 billion yuan in 2025. Some mid-tier institutions saw their AUM double or triple within a year, and the “tens of billions” threshold quickly lost its scarcity.
However, as scale rose, excess returns failed to follow suit. A securities firm survey shows that during the 2020–2021 scale boom, when quantitative private fund AUM doubled, excess returns across the three major index-enhancement strategies all declined—CSI 1000 index-enhancement excess returns plunged from over 18% to 6.6%. By 2024, as scale approached 2 trillion yuan, CSI 300 index-enhancement excess returns turned negative for the first time. The industry thus summarized a simple rule: “Scale is the enemy of excess returns.”
Capacity, volatility, and returns form an “impossible trinity” under static conditions: capital inflows compress strategy capacity and excess return space; while scale expansion boosts management fee income, it also crowds the trading track. When more and more capital replicates similar signals and competes for the same executions, the former alpha naturally dulls. This is one of the macro backgrounds of the industry-wide drawdown, and the first lesson beginners must remember when understanding quantitative investing.
2. Deconstructing Multi-Factor Stock Selection: The “Sandwich” Structure of Quantitative Models
For students new to data-driven investing, multi-factor stock selection is the best entry point to understand quantitative investing. Its core idea is not mysterious: it breaks down the vague question “what kind of stocks are worth buying” into several quantifiable, backtestable, and verifiable dimensions—namely, “factors.”
Classic multi-factor models typically revolve around the following four categories of factors:
- Value factors: measure how cheap a stock is using P/E, P/B, dividend yield, etc.;
- Momentum and reversal factors: capture trends of price continuation or oversold rebounds;
- Quality factors: use ROE, gross margin, debt ratio, etc., to characterize corporate earnings quality and financial stability;
- Volatility and liquidity factors: identify premiums from low volatility, active trading, and other features.
When building a model, quantitative researchers go through a complete pipeline of “data cleaning—factor construction—factor testing—composite scoring—portfolio construction—backtest optimization.” Each factor does not operate alone; instead, factors are weighted and combined into a composite score, which is then used to rank stocks across the market and select the portfolio with the highest expected excess return. In other words, multi-factor stock selection is essentially a data-driven investment decision framework. Its advantage lies in diversified holdings and disciplined execution, avoiding the extreme risk of betting on a single track.
3. Factor Rotation and Style Equilibrium: The Underlying Logic of Excess Recovery
Since the model logic has not changed, why did multiple factors draw down simultaneously in July? Many quantitative managers attribute the cause to two points: first, in the first half of the year, the A-share market displayed a typical K-shaped trend, with capital highly concentrated in the AI and TMT tracks. High-momentum and high-growth factors continued to outperform, and the market structure tilted to reward participants who bet on a single track. Second, when style extremes reached a certain level, momentum and liquidity factors resonated in a reversal, causing previously effective factors to temporarily fail—this is not a model malfunction but an inevitability under a specific market structure.
From historical experience, however, factor failure tends to be cyclical. The latest strategy research points out that after this market adjustment, cross-sectional volatility among major broad-based index constituents has clearly expanded—the greater the divergence in individual stock pricing, the stronger the discriminative power of stock-selection factors. Meanwhile, market style is moving from extreme concentration toward equilibrium, and capital is shifting from a single hotspot to multi-directional competition. Quantitative strategies that rely on multi-factor models and diversified stock selection are expected to regain stable excess returns. For index-enhancement products, a market decline does not mean opportunity disappears; instead, it may provide a better allocation window: valuation pressure on the beta side is released, and factor dispersion on the alpha side returns, allowing the two to potentially form a positive resonance.
4. Four Entry-Level Suggestions for Beginners
Given the recent market volatility, the Data Investment Academy suggests that ordinary investors learning multi-factor stock selection can proceed along the following path:
- Solidify data foundations: master the use of financial databases and data quality checks, understand the scope and limitations of market, financial, and consensus expectation data, and guard against “dirty data” contaminating models;
- Start with a single factor: first study how a solo factor such as valuation or momentum performs in historical backtests, understand factor monotonicity and decay patterns, then gradually add multi-factor composites;
- Build a backtesting framework: use open-source tools or quantitative platforms to implement strategy backtests, with a focus on overfitting and look-ahead bias problems—never “drive by looking in the rearview mirror”;
- Cultivate risk awareness: keep in mind the impossible trinity of capacity, volatility, and returns, remain wary of any strategy claiming to “always work,” and elevate drawdown control to a first-class citizen in strategy design.
It is worth noting that leading quantitative institutions in mature markets have long operated with excess returns in the low single digits, yet they continue to thrive. This shows that the level of the excess return center and the survival of the industry are not the same issue. For individual investors, the value of multi-factor stock selection does not lie in chasing short-term rankings, but in building an investment system that is explainable, iterable, and capable of withstanding drawdowns. When market style shifts again, understanding the logic behind factors matters far more than memorizing a set of parameters—this is the true meaning of data-driven investment education.
