The revival of quantitative active funds in the Chinese public offering sector has captured significant attention recentlySince the beginning of the year, there has been a noticeable uptick in the activity within the A-share market, providing a conducive environment for public quantitative funds to achieve excess returnsThe data from Wind shows that several funds, including Jin Xin Quantitative Selected, Hua Shang Computer Industry Quantitative, and Pu Yin An Sheng Hong Kong Stock Connect Quantitative Optimal, have delivered returns exceeding 10% year-to-dateAdditionally, there are funds like Bosera Intelligent Select Quantitative Multi-Factor and Tianfeng Quantitative Selection, which have recorded net values reaching all-time highs since their inception.

Previously, excess returns from public quantitative funds were largely derived from small and micro-cap stocksHowever, over the past few quarters, these funds have shifted to increase their allocation towards growth stocks, becoming one of the pivotal factors driving the ongoing performance improvements in public quantitative fundsThe upward trajectory in performance coincides with favorable national policies and a significant resurgence in daily trading volumes on the A-shares, maintaining levels above one trillion RMB, illustrating a robust recovery of investor confidence.

After a period of correction lasting several months, many public quantitative funds have managed to navigate past the severe dips caused by liquidity crises in the micro-cap sectorAs reported, funds like Tianfeng Quantitative Selection have steadily reclaimed their position, achieving positive returns of 15.27% for 2024 and over 6% this year, signifying three consecutive years of positive performance.

The reported success is not limited to larger funds; several smaller-sized funds have also shown aggressive performanceFor instance, Jin Xin Quantitative Selected has registered over 18% positive returns this year, while Hua Shang Computer Industry Quantitative, managed by Dr

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Ai Dingfei, has achieved positive returns exceeding 10% for the last two years consecutivelySimilarly, the Pu Yin An Sheng Hong Kong Stock Connect Quantitative Optimal, overseen by DrYu Jin, has enjoyed the same success over the same period.

Taking a closer look at the past year's trends, it becomes very evident that funds such as Hua Shang Computer Industry Quantitative achieved returns above 60%. Other funds like Guangfa Quantitative Multi-Factor and Shenyin Wanguo Intelligent Life Quantitative Stock Picking also performed strongly, delivering positive returns exceeding 50%.

A crucial observation from these developments is the significant contributions from the increased allocations in growth sectorsIn the tumultuous market of 2023, funds with a specific focus on micro and small caps initially shined, accumulating substantial excess returns amidst volatilityNevertheless, the onset of 2024 revealed that many of these strategies faltered, leading to major downturns for several funds heavily invested in these small-cap stocks, sparking controversy over their performance strategiesMany still struggle to recover from this downturn.

In contrast, numerous funds that recalibrated their strategies towards growth sectors have benefited significantlyA notable example comes from Liu Zhao, who maintained a high allocation to growth factors and over-allocated to innovative industriesHis strategy integrates a quantitative model for stock selection complemented by subjective research, thus amplifying specific stock weightings effectively.

Additionally, Wang Ping highlighted in the fund's quarterly report that the focus would remain on optimizing stock selection models based on fundamental factors and depth learning algorithms, which suggests a strategic pivot towards a more systematic approach in stock selection.

Risk management has also been underscored as a primary focusTan Jiajun mentioned the significance of diversifying investments to mitigate industry and style concentration risks while striving for stable returns across various market conditions

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