"Raising Lobsters" Trend Goes Viral in Fund Industry - Public Funds Experiencing a Profound Workflow Revolution

Securities Times Reporter Zhao Mengqiao and Pei Lirui

“We won’t be replaced by AI, but we will definitely be replaced by those who are skilled at using AI, especially in the field of public fund research and investment, where science and art intertwine, and rationality and emotion coexist,” said a public fund manager to Securities Times.

Recently, a “lobster farming” craze has swept from the tech industry into the financial sector. AI Agents, represented by OpenClaw, are gradually attracting the attention of public fund managers. Securities Times has learned that many fund companies are cautiously evaluating the application of this tool in fund research and investment. Some fund managers, especially quantitative fund managers, have already begun experimenting with OpenClaw for strategy development. AI is evolving from a super tool to an autonomous collaborator.

However, on the other side of the coin, the fund industry is also re-examining the impact of AI on traditional research and investment models. Whether it’s processing vast amounts of financial data, signal recognition in quantitative investing, or previously high-threshold research models, the public fund industry is experiencing a gentle yet profound workflow revolution. At the same time, new challenges such as human-machine replacement and data leaks are emerging.

“Lobster farming” heats up the fund circle

“My initial expectation was just for it to be an intern, helping us backtest scripts and handle data. But after using it for the past half month, I found it to be quite autonomous. It can independently extract good factors from raw data around the clock, broadening our sources of Alpha, and its accuracy is very high—like having a senior fund assistant by my side 24/7,” described a quantitative fund manager in Shanghai to Securities Times.

Recently, open-source AI Agent projects like OpenClaw have become very popular, sparking a nationwide “lobster farming” craze, especially in the complex and information-dense field of fund research and investment.

Cao Hongyuan, Chief Digital Officer of Bosera Fund, revealed that Bosera already has teams using OpenClaw on public clouds under compliant conditions, and is also exploring domestic software for internal security and compliance scenarios.

Additionally, E Fund has formed a dedicated team to conduct functional verification and technical exploration of OpenClaw in isolated network environments, but has not yet deployed it in production. According to insiders from E Fund’s fintech division, the main application scenarios focus on automated market information collection and analysis, corporate data governance, and related tasks.

“OpenClaw, as an open-source and highly customizable AI agent, ignites a new wave of enthusiasm for AI applications in the public fund industry. Its significance goes far beyond the tool itself,” Cao Hongyuan said. “OpenClaw is mainly aimed at individual users. It can greatly unleash each person’s innovative capacity. In application, individual initiative is key. Currently, the first movers are research personnel, as it provides them with a super digital assistant, helping to release individual creativity and productivity.”

Yimin Fund believes that OpenClaw is not just an enhancement of existing tools but is gradually triggering a gentle yet profound workflow revolution in fund research and investment.

“The core of traditional research tools is passive response—people input commands, tools output results. The breakthrough with AI Agents like OpenClaw is active execution, capable of autonomously completing the closed loop of ‘information gathering—data organization—preliminary analysis—result feedback’ based on preset goals. For example, previously, researchers spent 1-2 days organizing sentiment and financial report data for a certain industry. Now, with OpenClaw configured with relevant skill modules, it can automatically fetch, classify, and archive data 24/7. Researchers can then focus solely on data interpretation and logical validation. This is fundamentally a restructuring of the research workflow, not just a simple efficiency boost,” Yimin Fund explained.

Wang Ying, Deputy Director of Quantitative Investment at CITIC Prudential and a fund manager, said that their quantitative team has already integrated AI technology into daily research operations. Currently, about 30% of their strategies rely on quant factors trained via machine learning, mainly applied in price-volume trading strategies.

“We found that trading signals identified by AI models yield better returns when executed on the same day compared to next day,” she explained. “The logic behind this is that AI is good at capturing short-term pulses driven by liquidity expansion. Intervening at these moments not only seizes fleeting opportunities but also reduces trading costs due to ample liquidity. The entire process automates signal generation.”

Human-machine coexistence or replacement?

From large language models capable of acting as super brains to autonomous AI Agents that plan and execute independently, AI’s rapid evolution is directly impacting fundamental, repetitive tasks in traditional research—such as information collection, data organization, and report writing. For public funds, especially research practitioners, does this mean AI will lead to a new wave of industrial revolution, turning researchers into “weavers” of the past?

A fund manager from South China said, “I personally see AI as a ‘mature intern’ or ‘research newcomer.’ Tasks like data collection, cross-validation, and simple analysis are quite mature for AI. Once AI handles these basic tasks, research personnel can devote more time and energy to things AI can’t do yet.”

“AI and humans’ capabilities in research are actually non-overlapping and even complementary to some extent,” said Wang Yue, a fund manager at Minsheng JiaYin. “A good researcher should be someone who constantly asks good questions. Their goal isn’t to get a specific answer but to continually question the current situation, asking ‘why’ to identify the core variables of an industry or company. A good AI, on the other hand, is a tool that can provide good answers. While AI lacks strong reasoning and thinking abilities, it can give sharp and relatively accurate responses to researchers’ questions, improving research efficiency.”

Veyu, a fund manager at HSBC Jintrust, also believes that AI currently cannot replace fund managers and researchers. AI can assist in processing massive amounts of historical data, identifying key points, and summarizing patterns—more like a research assistant. Researchers and fund managers can then rely on their long-term experience and judgment to make more accurate industry and investment decisions.

“However, many tasks remain beyond AI’s reach. For example, on-site due diligence involves direct communication with company executives or management teams. An important goal is to perceive their work state—this may sound somewhat emotional, but it is reflected in company performance, often as early indicators. Also, uncovering non-public information—AI can only organize and analyze existing materials, but under compliance, non-public information can have high analytical value,” the South China fund manager added.

From breadth to depth in alpha sources

While AI has become a powerful tool for research, the professional barriers of fund managers and research teams remain clear and even more pronounced.

“My view is: AI replaces low-value tasks, not the entire position; it threatens those unwilling to adapt or with limited skills, not core research personnel,” said Yimin Fund.

Yimin Fund states that in the AI era, information gaps in the market will gradually narrow because AI can quickly gather and analyze vast amounts of information. Almost all research institutions can access the same basic data and information through AI. Therefore, future excess returns will no longer come from who can access information faster but from who can interpret it more deeply, judge trends more accurately, and control risks more effectively. Simply put, it’s not just about computing power but more about algorithms—this is the core source of individual alpha and research barriers for fund companies.

Wang Yue emphasized, “We value the depth of research thinking more than the breadth of information collection. Research personnel should ask the key questions, not just gather as much information as possible. Information is endless; finding the most critical variable and keenly capturing it is the true source of excess returns.”

Cao Hongyuan also said that AI, by automating multi-modal information processing, helps improve efficiency and pushes researchers to develop higher-level skills such as logical reasoning, industry insight, and cross-validation. This could lead to an evolution of the research system toward a human-AI collaborative network, changing the linear structure of ‘researcher pushes votes—fund manager makes decisions.’ Researchers and AI will work together on clue discovery, strategy development, and risk management.

“We won’t be replaced by AI, but we will definitely be replaced by those who are skilled at using AI; especially in research and investment, where science and art, rationality and emotion coexist,” said the South China fund manager. “Building research barriers at the company level still depends on creating an ecosystem suited to the company. To do that, good mechanisms, culture, talent, and tools are needed. In the future, these tools will definitely be related to AI. Ultimately, in this environment, everyone plays to their strengths, drawing nourishment from a unified platform, forming a coordinated ecosystem.”

Embracing efficiency while remaining vigilant of risks

AI’s assistance undoubtedly makes daily research work more efficient. However, many public funds have deeply realized that AI is a double-edged sword—it can greatly improve research efficiency but also harbors numerous risks. If these risks are not properly managed, they could lead to investment losses.

Yimin Fund’s Quantitative Finance Lab points out the need to be cautious of the “black box risk” of AI models, which is the most critical and concerning. Currently, most AI models (especially deep learning models) operate as “unexplainable boxes”—we only know the inputs and outputs but not how the model arrives at its results. This is the “black box problem.”

The lab believes this risk manifests mainly in two ways: first, in the pseudo-effectiveness of factor mining—AI may identify factors that seem significant but are actually results of historical fitting, unlikely to generate future returns and potentially causing losses; second, in misleading decision suggestions—AI might base recommendations on flawed logic or biased data, leading to incorrect investment decisions if blindly followed. For example, AI might recommend buying a stock with good historical data, ignoring deteriorating fundamentals, which could cause significant losses. Additionally, the “unexplainability” of AI models makes risk traceability difficult and problem root causes hard to identify.

Wang Ying also remains cautious about the broad application of AI. She notes that markets are “adaptive,” and trading behaviors in A-shares are a constantly evolving process. “The historical data used to train models already contains the behaviors of all past market participants. Once a model starts trading, its actions influence the market, creating a feedback loop. This is like a model affecting itself as it influences the data it learns from,” she explained.

This feedback loop causes the factors, especially price-volume factors, to show volatile excess returns. She cited examples of machine learning factors that performed well in 2023 but have shown huge performance swings recently, illustrating the “profit and loss are from the same source” phenomenon. “The biggest challenge is knowing when to stop,” Wang Ying admitted.

Wang Yue added that AI’s application in fund research must also be cautious about sensitive information leaks, which is a core hidden risk of current AI systems. “Therefore, we mainly use AI for scenario thinking, dialogue, and collecting public information, with strong privacy protection mechanisms and measures to prevent AI from accessing confidential company data,” she said.