During a recent brand launch event and investment strategy meeting marking the 20th anniversary of Zhong Ou Asset Management, General Manager Liu Jianping reflected on the company's journey. He described asset management as a "venture of growing time," where deep research, systematic frameworks, and trust are cultivated as the foundation for eventual results.
The event, themed "Seeing the Super Factory," showcased the results of the company's "specialization, industrialization, and digitalization" investment research system upgrade, which began in 2023. A roundtable discussion featuring Huang Hua, Chairman of the Multi-Asset Investment Committee, Qu Jing, Chairman of the Quantitative Investment Committee, and Ren Fei, Head of the Equity Research Department, detailed how various investment research production lines operate within the "Super Factory."
Liu Jianping noted that the company's 20-year milestone is both a significant landmark and a new starting line. He stated that Zhong Ou Fund has evolved through dual advancements in governance mechanisms and investment research systems, and will continue moving towards a more professional, stable, and trustworthy "super factory."
A 20-Year Foundation: From Governance Reform to System Upgrade
Liu Jianping revealed that as of the first half of 2026, Zhong Ou Fund managed assets totaling 923.7 billion yuan, serving over 98.83 million clients. Its active equity absolute returns ranked second among large fund companies over the past decade, while its fixed income absolute returns ranked third among medium-sized fund companies over the past seven years.
These achievements are tied to two key self-reforms. In 2014, the company pioneered governance mechanism reforms in the industry, laying the groundwork for subsequent institutional innovations. This breakthrough allowed for more flexible incentive structures, attracting top talent and cultivating internal leaders, which Liu Jianping identified as the primary driver of the company's rapid growth.
Since 2023, the company has pushed forward a three-pronged upgrade of its investment research system, enhancing core capabilities. The "Super Factory" concept is a tangible summary of the "specialization, industrialization, and digitalization" manufacturing system: specialization fosters superior investment insights, industrialization enables more efficient workflows, and digitalization provides advanced technological empowerment. Their synergy forms the core capability of this asset management super factory, building the infrastructure for consistent long-term performance.
How Different Investment Research Lines Operate in the Super Factory
In the context of rapid AI development, the roundtable discussion delved into the operational practices of the multi-asset, quantitative, and active equity lines.
Huang Hua shared the multi-asset team's industrialization transformation. The model where one person handled all asset classes has shifted to a specialized division of labor. Fund managers now act as assembly engineers, using standardized processes to combine specialized components into a complete product. Huang Hua revealed that five portfolios and five strategies now achieve high automation in strategy assembly. For example, traditional methods required 30-40 manual orders daily, but a proprietary system now automatically executes orders for hundreds of stocks and bonds based on strategy input, significantly improving efficiency.
Qu Jing used the quantitative team's daily workflow to illustrate the efficiency gains from "digitalization." Previously, quantitative researchers, developers, and IT staff worked separately. Now, under a new AI framework, researchers with strong coding skills can handle both strategy development and some development work, restructuring the workflow. Large language models bring new capabilities to strategy construction. Traditional quantitative investing relied on structured data for machine learning models, but now non-structured language-based strategies can be added, creating more angles for stock selection with lower correlations. Qu Jing emphasized that the core advantage of institutional investment lies in the strategy lifecycle management system, which relies on long-term accumulation of diverse strategies and systematic iteration based on effectiveness monitoring, embodying the "industrialization" of quantitative investing.
Ren Fei explained that AI has permeated the full workflow of active equity researchers, freeing them from tedious data collection without replacing final judgment. He noted that AI lacks the ability to make correct judgments based on facts and data, as it tends to follow the user's bias. The true moat of active research lies in the ability to make correct decisions based on information. Excess returns come from two sources: assessing a company's long-term value over 5-10 years, an area where AI lacks data and cannot replace humans, and grasping the era's main themes through proactive deduction of future social and economic trends to identify key industries for the next 2-3 years. While AI boosts efficiency, long-term thinking has become rarer and more valuable, making it the most irreplaceable aspect of professional institutions.
Wang Shen: Bond Market Pricing is Neutral and Reasonable, Still in a Favorable Environment
In the second half of the event, fund managers from the fixed income and equity teams shared their outlooks for asset allocation. Wang Shen, Head of the Fixed Income Research Group, analyzed the debt reduction progress of the household and corporate sectors. He estimated that the household sector's debt reduction cycle would near completion by the end of 2027 to the first half of 2028, suggesting the bottom of the real estate cycle may still be some time away. For the corporate sector, A-share listed companies experienced active capacity reduction from 2023 to the first half of 2025, with signs of profit stabilization emerging in the third quarter of 2025. New-economy sectors like AI and advanced manufacturing are seeing rapid growth, while traditional sectors face pressure.
Wang Shen reviewed the reasons for the bond market adjustment in the second half of 2025: an inverted yield between 10-year government bonds and bank liability costs created a pricing bubble, which has since corrected to a reasonable range after the overshoot earlier this year. He expects bank liability costs to continue falling by the end of this year, and if monetary policy remains accommodative, the bond market, if it avoids extreme pricing, can still offer stable coupon returns.
Wang Shen emphasized that during the debt reduction process, the structural divergence where traditional economies drive debt financing and new economies drive profit growth may persist, and fixed income investment must follow this macro trend. He concluded that the low-interest-rate environment is likely to continue, with overall bond market pricing at a neutral and reasonable level. As bank liability costs decrease further in the second half of the year, bonds can still provide stable returns, though absolute return levels may be lower than the first half.
Du Houliang: AI Supply-Demand Mismatch Unlikely to Resolve Soon, Application Scenarios Continue to Expand
The technology sector, after a significant rally in the first half of the year, has recently experienced increased volatility, drawing widespread attention. Du Houliang, a fund manager from Zhong Ou Fund's technology team, attributed the recent pullback to liquidation by overseas high-leverage funds, not a reversal of industry logic.
Du Houliang stated that AI is a long-cycle industry spanning over a decade. From a supply-demand perspective, supply is constrained by lower hardware and software costs and global capacity expansion, while a significant gap remains with rapidly growing demand. In terms of application scenarios, AI has already proven successful in coding, significantly reducing labor costs. In the medical field, some AI companies have acquired related businesses and secured large orders from top pharmaceutical companies for target discovery and protein structure prediction. Additionally, scenarios in cybersecurity, finance, and law are accelerating penetration, with AI moving from "replacing repetitive tasks" to "participating in scientific discovery." The continuous expansion of new AI application scenarios is expected to support future demand growth.
Du Houliang noted that after the recent adjustment, some companies in the AI supply chain have valuations in a relatively low range. This reflects market pessimism, but risks must be carefully considered, as low valuations alone do not guarantee a price increase. The current mismatch between compute power supply and token demand is unlikely to be fundamentally resolved in the short term, and the high growth phase still has fundamental support, but it requires consistent monitoring of order fulfillment and capacity release pace.
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