AI Empowers Asset Allocation as Public Fund-of-Funds Scale Surpasses 280 Billion Yuan in Q1

Public FOFs See Scale Leap, with AI Technology Playing a Crucial Role

As the disclosure of Q1 2026 public fund quarterly reports concluded, public FOFs (fund of funds) delivered an impressive scorecard. Data from Tianxiang Investment Consulting shows that by the end of Q1 this year, the market had a total of 593 public FOFs with assets under management reaching 284.701 billion yuan — an increase of 43 funds and 65.8 billion yuan respectively compared to the end of 2025, representing growth rates of 7.82% and 30.06%. Behind this growth, AI-driven intelligent asset allocation technology is playing an increasingly critical role.

Dual Growth in Scale and Quantity, with Standard FOFs Leading the Way

By category, standard FOFs showed particularly strong scale growth, surging from 156.145 billion yuan at the end of 2025 to 220.206 billion yuan by the end of Q1 this year — an increase exceeding 40%. Target-date retirement FOFs also maintained steady growth momentum. As professional fund selectors deeply focused on asset allocation, public FOFs have seen steadily rising popularity in recent years and have become an essential tool for investors seeking diversified portfolios.

Industry insiders point out that FOF products are essentially a process of secondary screening and portfolio optimization of underlying funds, with core competitiveness lying in asset allocation capability and fund selection ability. These two capabilities happen to be the areas where AI technology excels at providing empowerment.

AI Robo-Advisory Reshaping FOF Investment Logic

In recent years, multiple leading fund companies have introduced large language models and machine learning technologies into their FOF investment research workflows. In the fund screening process, AI systems can track holdings changes, style drift, and performance attribution of thousands of funds in real time, helping FOF managers precisely identify quality targets from a massive fund universe. In the asset allocation process, deep learning-based macroeconomic forecasting models and risk parity algorithms can dynamically adjust broad asset class proportions to effectively navigate market volatility.

A FOF investment director at a major public fund company stated that the introduction of AI tools has improved investment research efficiency by "several orders of magnitude." Fund evaluation work that previously required a team several weeks to complete can now be preliminarily screened with analysis reports generated by AI systems within hours, allowing research personnel to focus more energy on strategic judgment and risk management.

Intelligent Wave Drives Industry Ecosystem Upgrade

Beyond technological upgrades on the investment research side, AI is also playing an important role in investor services for FOF products. Multiple fund distribution platforms have launched AI robo-advisory features that automatically recommend suitable FOF product portfolios based on users' risk preferences, investment horizons, and financial goals. This personalized service model tailored to each individual effectively lowers the cognitive barrier for ordinary investors to participate in FOF investing, indirectly driving the rapid growth in FOF product scale.

Meanwhile, regulatory guidance on standardizing AI applications in the asset management sector is also gradually being refined. The industry expects that as relevant compliance frameworks become clearer, the application of AI technology in the FOF space will become more extensive and in-depth.

Outlook: Vast Room Remains for Deep Integration of AI and FOFs

Looking ahead, the development prospects for public FOFs remain promising. On one hand, household wealth management demand continues to be unleashed and the construction of the third pillar of the pension system advances steadily, providing a solid demand foundation for FOF products. On the other hand, the continuous iteration of AI large model capabilities will further enhance the refined management capabilities of FOF products in multi-asset, cross-market allocation.

Some analysts believe that the future integration of AI and FOFs will evolve from "tool-assisted" to "strategic collaboration," where AI is not merely an efficiency-enhancing tool but is poised to become a core engine for generating investment strategies and discovering alternative alpha. As technology matures and data accumulates, public FOFs are expected to enter a new growth cycle through their intelligent transformation.