When Quantitative Investing Meets the Entire Market Stock Selection: An Excessive Exploration of Structural Market Trends

In recent years, quantitative funds have become an important tool for investors’ asset allocation, characterized by disciplined and systematic investment approaches. According to Dongfang Securities, by 2025, the number of domestic public quantitative fund products will exceed 700, with a total scale surpassing 400 billion yuan, an increase of over 100 billion yuan year-on-year. Performance analysis by China International Capital Corporation (CICC) research reports indicates that 2025 will be a “superior year” for quantitative investing, with excess returns improving compared to the past.

Against the backdrop of booming quantitative investment, starting May 11, GF Fund launched multi-long quantitative products through multiple channels including ICBC and GF Fund direct sales—namely GF Hui Zhi Quantitative Stock Selection Hybrid (Class A 026867; Class C 026868), with Yi Wei as the proposed fund manager. The product uses the CSI All Share Index as the main benchmark, centered on a multi-factor quantitative stock selection model, aiming to explore structural opportunities across the A-share market and strive for sustainable excess returns.

Using the CSI All Share Index as an anchor, capturing market opportunities

The fund prospectus shows that GF Hui Zhi Quantitative Stock Selection is an actively managed hybrid fund, with stock assets accounting for 60%-95% of the fund’s assets. Its performance benchmark is “CSI All Share Index return × 80% + ChinaBond-Total Price of Government Bonds (1-3 years) Index return × 15% + Bank Savings Deposit Rate × 5%.”

The CSI All Share Index is currently one of the most comprehensive broad-based indices covering A-shares, including stocks and depositary receipts from the Shanghai, Shenzhen, and Beijing stock exchanges that meet certain criteria. It covers all 31 first-level industries under Shenwan classification, making it highly representative of the market. With over 5,000 constituent stocks, it also provides a “broad net” for stock selection in quantitative models.

Choice data shows that as of March 31, the top five industries in the CSI All Share Index are Electronics, Electrical Equipment, Pharmaceuticals & Biologicals, Nonferrous Metals, and Banking, with a combined weight of 41.20%, reflecting diversified characteristics. In terms of market capitalization distribution, approximately 4% of companies have a market value exceeding 1 trillion yuan, while over 60% have a market value below 10 billion yuan. Such stocks are often undervalued, helping quantitative strategies better capture “mispriced” opportunities, and the wide market cap range also helps reduce the impact of style switching.

Figure 1: Industry composition of the CSI All Share Index (Shenwan first-level industries)

Data source: Choice, as of March 31, 2026

From historical performance, since its listing (December 31, 2004), the index has achieved a cumulative return of 484.23% as of March 31 this year, outperforming the CSI 300 Index’s return of 343.24% over the same period. Its annualized volatility is 25.42%, lower than major indices like CSI 500 and Shenzhen Component Index. This suggests that enhancing performance based on the CSI All Share Index may help improve risk-adjusted returns, providing a better holding experience.

Centering on multi-factor quantitative strategies, uncovering structural opportunities

Quantitative stock selection essentially involves processing vast amounts of information using big data and computing power, employing valuation, growth, momentum, sentiment, and other factors to select stocks across the entire market. Each factor represents a source of return, evaluating stocks from a single dimension, which can be influenced by specific market environments. Therefore, most quantitative funds adopt diversified multi-factor strategies to complement strengths and mitigate weaknesses, smoothing returns and enhancing robustness.

The fund prospectus states that GF Hui Zhi Quantitative Stock Selection will focus on a self-developed multi-factor quantitative stock selection model, integrating diverse and multi-dimensional information to build a factor library. It uses quantitative methods to optimize and adjust the weights of various factors, selecting high-investment-value listed companies to construct a stock portfolio, aiming for steady excess returns.

Regarding the accumulation of the factor library, Yi Wei’s quantitative investment team has designed a big data platform based on long-term investment research experience, collecting, cleaning, processing, and refining vast amounts of market data, forming a shared factor library. Currently, the library contains hundreds of effective factors, with a balanced contribution from fundamental and trading factors, providing solid data support for strategy development. Yi Wei states that the shared factor library offers strong support for multi-factor stock selection, allowing different products to reuse factor research results and significantly improve factor development and iteration efficiency.

Figure 2: Composition of GF Quantitative Investment Department’s Factor Library

As strategies evolve and optimize, machine learning has become one of the core driving forces in the current quantitative market. The fund prospectus indicates that GF Hui Zhi Quantitative Stock Selection will also incorporate machine learning models to explore factors and allocate weights. Yi Wei points out that machine learning models excel at uncovering nonlinear information structures and can complement linear multi-factor models, enhancing model adaptability and predictive power.

Since July 2023, Yi Wei has been managing GF Quantitative Multi-Factor (Class A 005225; Class C 025645), a long-short quantitative fund benchmarked against the China Securities 2000 Index. According to periodic reports and Galaxy Securities data, as of the end of the first quarter this year, GF Quantitative Multi-Factor returned 39.69% over the past year, with the performance benchmark rising 24.26%, ranking in the top 30% (124/472); over three years, it achieved a return of 71.18%, with the benchmark up 33.09%, ranking in the top 6% (26/470). [Note: The peer funds are flexible allocation funds (benchmark stock proportion 60%-100%) (Class A), with a performance benchmark of China Securities 2000 Index return × 95% + bank savings deposit rate (after tax) × 5%. Past performance does not predict future results; investment involves risks.]

Figure 3: Fund manager Yi Wei’s (proposed) managed products

Note: GF Growth Intelligence Selection Hybrid has been renamed from GF Dongcai Big Data Selective Flexible Allocation since November 6, 2025, and has been in operation for less than a year; relevant performance is not shown. Data as of March 31, 2026. Past performance does not predict future results; investment involves risks.

Currently, Yi Wei states that the market is at the beginning of technological innovation, and a moderate, ample liquidity environment is conducive to continuous incubation of innovations. From a very long-term perspective, real interest rates are trending downward, and moderate inflation allows for longer investment durations and higher valuation ceilings; mid-term, technological innovations like AI enable fields such as autonomous driving and embodied intelligence, relying on large-scale capital expenditure by upstream companies and matching market interest rates; short-term, inflation expectations may impact high-valuation sectors, but from a medium- to long-term view, this creates good opportunities for layout in the AI industry.

Regarding investment directions, he believes that the broad AI industry has vast development potential. The popularization of intelligent assistants is expected to bring more opportunities to A-shares assets rooted in the mid- and downstream of the industry chain. GF Hui Zhi Quantitative Stock Selection mainly benchmarks the CSI All Share Index, covering a wide range of industries, including upstream AIDC energy supply, midstream semiconductor manufacturing, and downstream consumer electronics iteration and service applications, all worth attention and exploration.

Supporting with an integrated platform, pursuing stable excess returns

With the Shanghai Composite Index surpassing 4,000 points and a continued structural rally in A-shares, pursuing stable excess returns has become the core goal for most investors. In this context, quantitative funds are increasingly favored by investors participating in equity markets. Whether they can continuously generate excess returns across different products and market styles has also become an important dimension for assessing the competitiveness of public fund managers.

GF Fund has been exploring quantitative business since 2011, establishing multiple professional, experienced teams managing portfolios with quantitative strategies. Among them, Yi Wei’s quantitative investment department was established earliest, led by Zhao Jie, with eight members—all graduates from renowned universities abroad and domestically, specializing in mathematics, computer science, financial engineering, and related fields, with experience spanning investment research, trading, and information technology.

The team’s core philosophy is the “multi-strategy quantitative investment system,” continuously exploring multi-factor and machine learning strategies, forming a pool of different styles and types of strategies. This allows the team to deploy appropriate tools under various market conditions and combine different strategies to build an all-encompassing excess return capability. Currently, the product line managed by the team covers broad-based indices such as CSI 300, CSI 500, CSI 800, and CSI A500, as well as small-cap quant, thematic quant, and full-market stock selection strategies.

Figure 4: GF Fund Quantitative Investment Department’s (proposed) product lines

Subjective and quantitative approaches complement each other and evolve continuously, which is the core support for the department’s long-term excellent performance. GF Fund adopts an “quantitative + active” integrated research and investment model, with solid fundamental research as the underlying driver of quantitative models. The company firmly believes that excess returns stem from a deep understanding of financial data, not from simply stacking models. This philosophy underpins the pursuit of stable, sustainable excess returns across different market environments, aiming to provide investors with reliable, replicable quantitative investment tools, especially valuable in structural market conditions.

Note 1: Performance data and benchmark return sources are from fund periodic reports; ranking data is from Galaxy Securities Fund Evaluation Center, as of March 31, 2026. Yi Wei manages three products; only Class A share performance data is listed below.

Note 2: GF CSI A500 Index Enhanced was established on January 17, 2025. Its performance benchmark is: CSI A500 Index return × 95% + bank savings deposit rate (after tax) × 5%. Past performance (benchmark): from 2025/01/17 to 2025/12/31, 20.97% (25.17%). Data from fund periodic reports. Past fund managers (dates of tenure): Yi Wei (from 2025/01/17 to present). Peer funds are enhanced scale index equity funds (Class A). Past data rankings and evaluations do not predict future performance; investment involves risks.

Note 3: GF Quantitative Multi-Factor Hybrid was established on March 21, 2018. Since May 29, 2024, its performance benchmark was adjusted from “CSI 300 Index return × 90% + bank savings deposit rate (after tax) × 10%” to “China Securities 2000 Index return × 95% + Hong Kong Stock Connect Index return (converted at valuation exchange rate) × 15% + ChinaBond-Total Price Index × 5% + bank savings deposit rate (after tax) × 10%.” Past performance (benchmark): 2021: -4.95% (-4.51%); 2022: -23.91% (-19.52%); 2023: -1.34% (-10.20%); 2024: 12.75% (17.50%); 2025: 52.72% (30.53%). Data from fund periodic reports. Past managers (dates): Wei Jun (2018/03/21–2019/03/29), Zhao Jie (2019/03/29–2020/05/06), Chen Yuting (2020/05/06–2023/07/06), Yi Wei (2023/07/06–present), Yu Xin Li (2023/10/23–present). Galaxy Securities data shows that as of March 31, the peer ranking over 1, 2, 3, 4, and 5 years was 124/472, 32/471, 26/470, 54/470, and 124/447 respectively. Peer funds are flexible allocation funds (benchmark stock proportion 60%-100%) (Class A). Past data and evaluations do not predict future results; investment involves risks.

Note 4: GF Growth Intelligence Selection Hybrid was established on December 11, 2017. Since November 6, 2025, it has been renamed from GF Dongcai Big Data Selective Flexible Allocation, with less than a year of operation; relevant performance is not shown. Data as of March 31, 2026. Past performance does not predict future results; investment involves risks.

Note 5: GF Hui Zhi Quantitative Stock Selection Hybrid sales fees are as follows: For Class A shares, subscription fees vary by amount: below 1 million yuan 0.80%, from 1 million to 5 million yuan 0.60%, and above 5 million yuan a flat 1,000 yuan per transaction. Redemption fees for Class A shares: less than 7 days held 1.50%, 7–30 days 1.00%, 30–180 days 0.50%, over 180 days 0%. Class C shares do not charge a subscription fee; redemption fees are the same as Class A, with an annual service fee of 0.40%. For details, please refer to the fund prospectus and legal documents.

Risk reminder: Past performance and ratings do not predict future results. The performance and ratings of other funds managed by the fund manager do not guarantee future performance. Investing in this fund involves risks, and investors should fully understand the product features and bear all associated risks. The fund invests in domestic and foreign markets, facing typical market risks, currency risks, and overseas market risks. Managed by GF Fund Management Co., Ltd., the distributor does not assume investment or redemption obligations. Please read the fund contract and prospectus carefully before investing. This is a hybrid fund with higher expected risks and returns than money market and bond funds but lower than equity funds, classified as a medium-high risk fund. The specific risk rating is based on the rating provided by the fund manager and sales institutions. Investors should choose products matching their risk tolerance and investment goals. All investments involve risks; please invest cautiously (CIS).

【Source: China Fund News】

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