The financial services sector is evolving rapidly, as highlighted during the China Merchants Bank Wealth Partners Forum held in Guangzhou on August 11. Nearly 80 leading financial institutions, including insurance companies, fund managers, bank wealth management firms, private equity firms, and trusts, gathered to discuss the future of wealth management.
Zhong Ou Asset Management Chairman Dou Yuming presented at the forum, delivering a speech titled "From a Performance Assessment Perspective to Industrializing Investment Research." He noted that as China's wealth management industry enters a phase of rapid growth, the sector demands higher standards from upstream asset managers. The market now requires underlying investment tools that offer large-scale capacity, stable performance, consistent style adherence, and diversified product categories.
Dou Yuming explained that the asset management industry is moving away from traditional, workshop-style investment research methods. This shift towards a team-based, process-driven, and industrialized approach is now inevitable. At Zhong Ou Asset Management, the firm has set a target of an 80% "good product rate" for its investment research. This means that across its product lines, including public equity funds, quantitative strategies, multi-asset products, segregated accounts, and fixed income, more than 80% of products should outperform their benchmarks and the median of their peers over three- and five-year periods.
Why focus on just 10 ASX 200 shares?
According to Dou Yuming, the successful implementation of industrialized investment research relies on three core pillars: specialization, industrialization, and digital intelligence. Specialization is the primary prerequisite. Drawing a parallel to the division of labor in industrial society, he argued that refined specialization is key to improving productivity and ensuring quality stability in the fund industry. Zhong Ou Asset Management has built a team of over 80 investment research professionals, breaking them into specialized tracks for equity research, bond research, and quantitative research. The firm also categorizes its fund managers by distinct investment styles, such as value and growth, encouraging them to deepen their expertise in specific areas. Without this level of specialized division of labor, it is difficult for researchers to develop deep industry insights, making industrialization impossible.
Where to begin the transformation?
Building on specialization, the second step is industrialization. Dou Yuming pointed out that a single fund's performance is not the result of one fund manager's effort but the aggregate of multiple alpha contributions from the entire investment team. Relying solely on a fund manager's personal judgment cannot support the large-scale asset management needs required to serve a vast client base. The essence of industrialization in investment research is to unify the team's research methodology, break down communication barriers, and enable specialists from different areas to collaborate efficiently. He acknowledged that the standardization process for a fund company is, in essence, a process of standardizing the mindset of its people. It involves training each researcher and fund manager to adopt a more consistent investment philosophy and method, a process that can take five, ten, or even twenty years to complete.
Regarding digital intelligence, Dou Yuming noted that the asset management industry had already adopted many digital applications before the emergence of AI large models. He broke down the industry's digital intelligence into two dimensions: datafication and intelligentization. Datafication involves collecting and cleaning all types of data—external, internal, and historical—within the company. Intelligentization, on the other hand, is about modeling and making replicable the stock-picking logic and investment methods of top researchers. He further stated that AI large models are now reshaping the work patterns of investment research. Many of the newer generation of fund managers are already proficiently using AI agent tools to solidify their investment logic and handle standardized tasks like data organization. This frees up more of their time for core activities that AI cannot replace, such as on-site company visits and interviews with industry experts.