Welcome to this column on small-cap stocks, often labeled as "high volatility," "small market cap," and "hard to research." Under the lens of quantitative investing, however, they can be a treasure trove of "high alpha," "low correlation," and "high odds." In this column, we use data, logic, and strategies to guide you through the fog and show you the "other possibility" of small-cap stock investing. This time, we want to discuss how to use quantitative methods to make a contrarian investment in small-cap stocks.

What is the "reverse" in contrarian small-cap investing?

Simply put, contrarian investing means disciplined intervention when the majority becomes pessimistic or indifferent. In small-cap stocks, our quantitative model focuses on two main dimensions. First, the left side of volatility: when a small-cap stock has thin trading volume, narrowing volatility, and a dull price reaction to news, it often means short-term selling pressure has been fully released. Entering at this point offers controlled costs and potentially greater upside for price recovery. Second, the left side of attention: when a small-cap stock is not on hot lists, has no dense research reports, and is rarely discussed on social media, it is in an "information trough." The quantitative model does not rely on subjective judgment but captures signals of inflection points in attention from multiple dimensions, including price-volume, capital flow, and correlation. The core logic of contrarian investing is not to predict a stock will rise, but to act on an observable and verifiable judgment: the low-volatility state itself has a mean-reverting property, and a low-attention state is also difficult to maintain long-term. When both simultaneously rise from low levels, it creates a "double-hit" return.

What exactly does "double-hit return" mean?

We break down this source of return clearly. First, the return from volatility rebound: when volatility expands from a low level, it means improved intraday price spreads and restored liquidity. For quantitative strategies, trading space reopens. Second, the return from attention rebound: when attention rises from a low level, it is often accompanied by new capital inflows, higher turnover rates, and the re-pricing of the stock's discount. The combination of these two often has a synergistic effect, where one plus one is greater than two. What the quantitative model does is to position on the left side before the "double-hit" occurs and to systematically realize gains when the model issues a sell signal.

What market environment is it suitable for?

This strategy is not a universal solution. It is most comfortable in the following environments. First, a range-bound or weak market: chasing strategies often suffer repeated stop-losses, while the time value of left-side positioning becomes more apparent. Second, when liquidity is clearly stratified: small-cap stocks are systematically neglected, creating more opportunities for mispricing and undervaluation. Third, on the eve of a style rebalancing: when popular assets are too crowded, some funds begin to look for "places with fewer people." In a market that is fully euphoric, this type of strategy may underperform periodically. However, this is also the essence of an absolute return mindset—not pursuing the fastest returns every time, but striving for a more explainable and repeatable strategy in complex environments.

An easily overlooked point: the discipline and efficiency of quantitative methods

The advantage of quantitative contrarian investing lies not only in the stock selection logic but also in the discipline and efficiency of execution. Regarding discipline: buy points, sell points, and positions are all objectively defined by model rules, free from interference by market sentiment, short-term fluctuations, or personal judgment. The model says buy, so we buy; the model says sell, so we sell—no chasing highs due to greed, no missing opportunities due to fear, and no emotional attachment from holding too long. Regarding efficiency: small-cap stocks are numerous, individually have little information, and their prices change quickly, making manual coverage extremely difficult. The quantitative model can simultaneously process thousands of small-cap stocks across the entire market, tracking signals like volatility, attention, and price-volume divergence in real time, capturing left-side positioning opportunities promptly, and responding quickly when sell signals appear. Simply put: quantitative methods handle the breadth that humans cannot, and the model executes decisions that are easy for humans to waver on. This combination of discipline and efficiency may give contrarian investment strategies a repeatable, verifiable long-term vitality in small-cap stocks.

Our practical strategy

Strategic ideas must ultimately be implemented in products. The team led by Wang Ying at CITIC Prudential Fund manages three products: CITIC Prudential Anxin Return Bond, Multi-Strategy, and Jingqi Preferred. Although their positioning differs, past periodic reports show they all hold some small-cap stocks. First, looking at CITIC Prudential Anxin Return Bond, as a secondary bond fund, its quarterly and annual reports from 2024 to 2025 show equity positions below 10%, controlling volatility while enhancing returns through contrarian investing in small-cap stocks, with a very low long-term correlation to the secondary bond fund index. The holdings shown in periodic reports are only point-in-time data and do not represent current or future fund holdings. Next, looking at CITIC Prudential Multi-Strategy Hybrid (LOF) and CITIC Prudential Jingqi Preferred Hybrid, their quarterly and annual reports over the past two years show slightly different equity position averages. Data is as of June 30, 2026, from fund periodic reports, which only show point-in-time data and do not represent current or future fund holdings. It is worth noting that since Wang Ying took over management in September 2025, the three products have formed a product matrix with complementary characteristics, fully meeting the diversified allocation needs of different investors.

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