Brokerages Hold Intensive Roadshows for OpenClaw; Sessions Pack the House—What Impact Will This Have on Financial Research and Investment?

Cailian Press, March 12 — (Reporter Wang Chen) The financial research and investment field is undergoing a major transformation, with AI-powered research tools centered around OpenClaw becoming a recent focus for securities firms’ research institutes.

OpenClaw’s research application roadshows and conference calls have been exceptionally popular. According to statistics, more than a dozen securities firms including CITIC Securities, Huatai Securities, Orient Securities, Guojin Securities, and Dongwu Securities have held intensive roadshows focused on OpenClaw’s research applications, explaining deployment methods, application techniques, and practical scenarios to institutional and individual investors.

This heightened attention is also reflected in visitor numbers. The average participation in OpenClaw-themed roadshows exceeds 100 people, with some popular sessions attracting over a thousand attendees.

Securities firms like CITIC, Huatai, and Orient focus on hands-on training, offering specialized courses to guide investors on local deployment, installation of financial skill packs, and other core operations, promoting the penetration of intelligent research tools from professional institutions to a broader investor base.

Since late February, Guojin Securities has been touring cities like Shanghai and Beijing with the “OpenClaw Empowering Intelligent Research” forum; during the spring strategy meetings, a dedicated “OpenClaw Empowering Research and Index Investment Forum” was held, covering industry trend discussions, practical applications of active/quantitative research, and hands-on training for building personal research assistants.

From the core content of these roadshows, it’s clear that OpenClaw’s application scenarios closely match the daily needs of research personnel. Keywords such as local deployment, financial skill pack installation, conditional stock selection, financial statement analysis, quantitative backtesting, report reproduction, and building personal research assistants are frequently mentioned. Its core value lies in enhancing the entire research process from Q&A to execution, differentiating itself from traditional AI tools that only provide text answers. OpenClaw can directly execute tasks, closing the loop on research needs.

Triple Revolution Reshaping Research Models

The reason OpenClaw has garnered collective attention in the financial research community is its fundamental change to traditional research workflows and execution modes, bringing about three revolutionary shifts in workflow, efficiency, and capability boundaries, freeing researchers from low-value tasks to focus on core analysis and decision-making.

First: Workflow Revolution. Traditional research requires manual switching between Wind, Excel, research report platforms, and browsers, designing each step manually. OpenClaw, with system-level permissions, can operate across software and systems independently, completing entire task chains automatically, achieving a “one-sentence command → automatic execution → delivery of results” closed loop.

In a demo at Guojin Securities, a researcher simply issued the command “Generate today’s A-share announcement summary and push,” and OpenClaw autonomously completed announcement scraping, key data extraction, Excel report and briefing generation, and scheduled delivery to mobile phones, all without human intervention.

Some securities firms have tested that in report writing scenarios, issuing commands like “Write analysis report on high-dividend leading stocks” can automatically query market data, build report frameworks, complete content writing, and export Word documents, compressing what used to take 1-2 days of manual work into hours.

In quantitative research, with access to financial data interfaces, OpenClaw can autonomously perform stock selection based on PB-ROE, strategy backtesting, factor mining, and other tasks, automating the entire quantitative research process and thoroughly restructuring traditional workflows.

Second: Efficiency Revolution. OpenClaw effectively addresses the pain points of research staff being consumed by low-value tasks such as data cleaning, announcement sorting, report formatting, and repetitive backtesting.

In information processing, handling massive daily market announcements, transitioning from manual extraction to OpenClaw’s automated structured summaries, can improve efficiency by over tenfold. In complex report reproduction tasks, OpenClaw excels at parsing report logic, pulling relevant data, and automatically coding for backtesting, reducing work from a full day to within an hour.

For quantitative teams, since OpenClaw can automatically perform factor screening, backtesting, and optimization, the entire strategy development cycle is significantly shortened, allowing teams to focus more on deep market logic.

Third: Capability Boundary Revolution. OpenClaw breaks through the traditional AI “only outputting text” limitation, possessing proactive execution, tool invocation, and skill extension capabilities, becoming a “super employee” capable of completing complex tasks independently.

Security is vital in financial research. Through local deployment and physical isolation from cloud servers, OpenClaw ensures data is stored locally, alleviating concerns over data security and privacy leaks. Its modular skill pack design allows users to install specialized modules such as financial analysis, report generation, or backtesting, creating highly personalized research assistants.

Cross-tool collaboration capabilities connect platforms like Wind, Tonghuashun, Mikuang, and Feishu, enabling seamless data, tool, and communication integration, greatly enhancing research collaboration.

Based on this foundation, OpenClaw’s full-process automation covers all research scenarios, such as continuously scanning news and social media for early signals, or analyzing annual reports within minutes to detect hidden financial anomalies. Even more impressive, it can automatically write and backtest quantitative strategy code, execute high-frequency arbitrage under set rules, and handle administrative tasks like emails and meetings.

Subjective and Quantitative Research Converge

The emergence of OpenClaw not only changes research workflows but also fundamentally reshapes the core logic of financial research. A leading securities firm summarized during a roadshow that intelligent research tools like OpenClaw will drive the evolution of investment research toward “more quantitative subjectivity and more subjective quantification.”

In subjective research, large models lower programming barriers, enabling verifiable and iterative subjective strategies, with research teams incorporating backtesting. In quantitative research, large models excel at processing unstructured text data, extracting short-term event logic from reports and conference calls (e.g., the impact of Event A on Company B), providing new factor sources for strategies.

Three Major Financial Data Providers Ready to Launch

As OpenClaw’s research empowerment matures, China’s top three financial data providers have also prepared their own distinctive products.

Wind launched WindClaw, deeply integrated with Wind’s real-time quotes, financial data, and industry information. Its core advantage is no coding required, one-click deployment, and support for fully local operation, ensuring research logic is isolated. Users can train their own investment agent matrices within WindClaw—some focus on fundamental analysis, others monitor capital flows, and some share insights from global peers—building a 24/7 AI research team.

Choice introduced the Super Data Query Agent, which seamlessly integrates four dedicated tools: financial, macroeconomic, conditional screening, and web search, enabling one-stop access to vast professional data. It also solves complex multi-hop reasoning, accurately identifying core targets with layered factors and cross-dimensional links, supporting native Excel formulas for rapid workflow takeover.

iFinD launched MCP financial data service, designed for AI agent interaction, integrating iFinD’s core database to enable “natural language queries” for research. It covers Alpha/Beta risk models for A-shares, ESG ratings, historical performance and holdings of public funds, global macroeconomic indicators, and semantic search summaries of announcements, fully meeting detailed data needs of mainstream research.

(Reporter Wang Chen, Cailian Press)