The gateway to capital market information has shifted from newspapers and trading floor screens to mobile devices and large language models. As information flows accelerate, individual investors now demand more than just market visibility—they need deeper self-awareness: announcements and sector movements must be contextualized within personal holdings, investment preferences, and trading plans. In response, financial AI has evolved to summarize announcements, explain indicators, and answer queries about price movements, pushing industry competition to the next level: prioritizing information, converting insights into disciplined observation, and continuously recalibrating with market shifts. Intelligent research, account diagnostics, and ongoing service delivery have become the new benchmarks of capability, while regulatory frameworks for algorithmic trading are driving improvements in data traceability, strategy transparency, and service standards—all of which form the backdrop for the latest upgrade of Gudao Zhihang.
On September 9, Shanghai Jiufang Yun Intelligent Technology Co., Ltd., a subsidiary of Jiufang Zhitou Holdings (9636.HK), launched Gudao Zhihang, an upgraded version of its predecessor Gudao Linghang. The new product integrates Jiufang Lingxi, AI Goldfinger PLUS, and Multi-Quant ETF Strategies, covering information comprehension, stock analysis, and portfolio allocation. To fully grasp the significance of this upgrade, it must be viewed within the competitive landscape of AI wealth management: when "providing answers" becomes a baseline capability, the new differentiator lies in how effectively those answers are delivered into specific accounts and integrated into continuous decision-making processes. At the launch event, industry experts, institutional representatives, and media professionals gathered to discuss the convergence of AI, quantitative methods, and securities advisory services.
Chen Wenbin, founder, chairman, and CEO of Jiufang Zhitou Holdings, contextualized this product upgrade within a longer capital cycle. He noted that over the past two decades, China's two most significant wealth waves originated from real estate and mobile internet, yet neither was fully captured by the A-share market. As more hard-tech companies enter the capital markets earlier in their lifecycle, the connection between capital markets and future industries is deepening, and the current AI anxiety harbors new growth opportunities.
Four Institutional Categories Competing in AI Wealth Management, Services Moving Toward Full-Process Integration
Financial AI for individual investors is primarily contested by four types of institutions: securities firms that connect accounts with research and trading; internet market platforms that excel in data, search, and information aggregation; fund sales and wealth platforms that focus on fund diagnosis, allocation, and companionship; and independent securities advisory firms that build value through vertical research and sustained delivery. Among publicly available products, Huatai Securities' AI Zhangle covers market monitoring, stock selection, account analysis, and post-market review; Guotai Haitong's Lingxi APP integrates dialogue, accounts, and trading into the full customer journey; Hithink RoyalFlush's iWencai emphasizes natural language stock screening and data queries; and Ant Fortune's Ma Xiaocai focuses on market interpretation, fund diagnostics, and investor education.
A comparison of competing AI advisory products based on publicly available information reveals that the competitive focus has shifted to how data enters accounts, how signals translate into actions, and how actions crystallize into review records. Jiufang's differentiation lies in its continuous advisory service foundation, organizing stocks, sectors, accounts, and ETF strategies within a unified scenario. Therefore, evaluating a product upgrade requires examining not only its feature set but also whether these features form a complete, seamless service chain.
From Feature Provision to Chain Completeness: Three High-Frequency Scenarios Restructured
Gudao Zhihang is built precisely along this service chain. The product upgrade from Gudao Linghang focuses on three high-frequency scenarios: intelligently prioritizing simultaneous announcements, research reports, and intraday market movements; interconnecting market data, research materials, accounts, and tasks; and distilling market perspectives into trackable, reviewable observation disciplines. Dispersed information is thus reorganized, giving the investment process clearer rhythm.
Once the chain is complete, the product must accommodate different decision-making styles. For the same sector movement, short-term traders focus on capital flow changes while long-term holders prioritize fundamental continuity. The value of personalization lies in aligning alert frequency and analysis depth with holding periods, investment preferences, and account characteristics. This divergence in user needs is also driving AI from a general-purpose tool toward a personal investment gateway. Research from Tsinghua PBC School of Finance and Ant Group Research Institute shows that over 40% of individual investors have already used AI tools.
Zhang Peihong, vice president of Jiufang Zhitou, stated that technological evolution is propelling securities advisory from information services and auxiliary tools toward an AI-native phase, with investor demands shifting from more content to directly accessible, professionally credible, personalized, and continuously evolving advanced capabilities. As he put it, "each generation of product advancement serves as the starting point for the next generation." Chen Wenbin further noted that in the AI era, financial services' core competencies will increasingly be built upon big data, large models, and intelligent agents. The focus of advisory research will extend beyond fundamentals and technicals to "studying people, studying products, and studying business models," enabling services to genuinely understand the relationships among investors, products, and markets.
When AI evolves from an information gateway to continuous service, products must establish more concrete connections. Zhang Peihong proposed that next-generation advisory products should make data and analysis verifiable, bring market information into specific accounts, and make quantitative capabilities accessible across professional thresholds. Jiufang's launch aligns with this vision: Jiufang Lingxi connects information with accounts, AI Goldfinger PLUS connects signals with discipline, and Multi-Quant ETF Strategies connects targets with portfolios, translating "professional credibility, callable skills, personalized service, iterative models, and democratized quantitative methods" into real-world scenarios.
How the Three Functions Deliver: From Account Understanding to Portfolio Allocation
As the on-stage presentation shifted to product demonstrations, the discussion moved from industry trends to practical problems investors face daily. What makes this progressive framework viable is a unified research and technology foundation. Zhang Peihong summarized the underlying logic of Gudao Zhihang as "dual-end connection": one end represents nine years of accumulated professional content, research methods, market data, and service experience from Gudao Linghang, while the other end represents sustained investment in AI and quantitative technology. "Professionalism as the root, AI and quantitative methods as the wings" means all three functions share this research foundation and update synchronously with market data and model versions.
Built upon this unified foundation, the first function is Jiufang Lingxi Black Gold Membership. It connects intelligent dialogue, a skills plaza, account analysis, and a task center, enabling users to query market conditions, financials, capital flows, and news while setting up market monitoring, stock diagnosis, and custom tasks—consolidating previously fragmented research workflows into callable, continuously running skills. Function integration alone doesn't guarantee continuous service; it depends on the product's long-term understanding of users. Wang Bing, head of Jiufang Zhitou's AI Center, explained that Jiufang Lingxi builds individual investor profiles through an independent user sandbox that captures holdings, areas of focus, investment preferences, and usage habits. Nearly 100 research methods are packaged as directly callable skills, and users can create automated tasks with a single sentence. In her view, "understanding you better, being more professional, and knowing how to work" means AI can both respond to immediate needs and deliver professional methods at the precise moment they're needed.
This long-term understanding is first evident in account management. Take the Account Analysis Master as an example: the system first performs a full-account attribution review to identify structural optimization opportunities, then analyzes each position's profit and loss, and conducts pre-market information organization, intraday anomaly interpretation, and post-market review around holdings. "From you seeking AI to AI working for you," significant account-related changes are proactively identified and delivered in a timely manner. As Wang Bing put it, "professional capabilities should follow wherever users are."
Once information and accounts are connected, the next step is converting observations into rules. Qiu Yijun, director of Jiufang Zhitou's Financial Engineering Department, then unveiled AI Goldfinger PLUS: the product integrates market conditions, price-volume data, capital flows, sectors, and information, transforming hundreds of market characteristics into daily K-line entry, exit, neutral, and flat signals, star ratings, intraday signals, and capital game matrices, making complex quantitative calculations intuitive scales for ordinary investors. These market signals are ultimately placed within a unified analytical interface. Users can follow the sequence of "what happened—why—what to observe next," with the intraday module aggregating nearly 300 price-volume and capital features with per-minute rolling calculations.
To understand signals alone isn't enough; investors need executable, reviewable observation methods. To this end, the product embeds signals into trading rules: post-market signal pools narrow the scope, intraday observation focuses on price and capital, and daily K-line, intraday, and sector information provide cross-validation. Extending outward from individual stock rules, the third function, Multi-Quant ETF Strategies, elevates the perspective to portfolio level. According to the Shanghai Stock Exchange's "ETF Industry Development Report (2026)," as of the end of 2025, mainland-listed ETFs reached 1,381 with a total scale of 6.02 trillion yuan, a 61.4% year-on-year increase. This market expansion has created demand for more professional screening and portfolio management. At the portfolio level, the product incorporates factor libraries, machine learning, position adjustment, and dynamic weighting into a unified framework, forming three sub-strategies: balanced offense-defense, multi-asset, and industry aggressive, while displaying simulated returns, maximum drawdowns, and historical rebalancing.
These three strategies convert single-point judgments into ongoing portfolio management. Goldfinger addresses trading-oriented needs while Multi-Quant ETF Strategies serves allocation-oriented needs; together they complement each other, respectively supporting timing observation and portfolio management. "Quantitative democratization" means delivering more computational, factor research, and portfolio optimization methods—traditionally reserved for professional institutions—to ordinary investors through productization. Sustaining trading signals and portfolio strategies requires a stable engineering infrastructure. The core competitiveness of quantitative ETFs lies in "continuous evolution capability": supercomputing infrastructure, professional teams, algorithm models, and user experience collectively form the research system, with traditional multi-factor frameworks and AI, multi-agent collaboration accelerating factor mining and portfolio optimization. Backtesting, market tracking, and market data form a feedback loop for model updates, moving the product from opportunity identification toward more stable operations.
From Three Functions to Unified Service: Gudao Zhihang Opens a New Advisory Paradigm
If a product launch demonstrates functional boundaries, then what determines whether these capabilities can withstand market changes is the organizational and engineering foundation built through long-term investment. Financial AI requires continuous data absorption, model validation, and strategy updates, converting complex research judgments into stable, user-perceivable services. "Continuous evolution" is therefore not a single product iteration but the result of long-term collaboration among R&D teams, computing infrastructure, and professional research. Jiufang Zhitou Holdings' interim report for 2026 shows R&D investment of approximately 167 million yuan in the first half of the year, up 13.7% year-on-year, with 657 R&D personnel. Intelligent advisory AI Q&A token consumption reached approximately 254.3 billion, eight times the year-earlier period. Zhang Peihong noted that Jiufang has established a complete foundation of professional advisory, AI, and quantitative engineering—"one team makes AI better understand securities investment, and another team makes quantitative methods better understand market changes." In his view, "behind every capability leap is steadfast, long-term investment."
From a strategic perspective, Chen Wenbin stated that while Jiufang previously emphasized dual-driven development of research and technology, the accelerated deployment of vertical models and intelligent agents will fundamentally change technology's influence on products. Technology, he believes, does not simply replace traditional research but rather packages professional capabilities into products at greater scale and frequency, expanding service boundaries previously constrained by human resources. How these resources are perceived by users ultimately returns to product presentation. Jiufang incorporates financial data sources, analytical bases, strategy versions, and applicable environments into its products, then through continuous computation, dynamic validation, and model iteration, forms an intelligent research system characterized by clear evidence, traceable processes, and market-responsive updates. AI advisory thus progresses from "being able to answer" to "being credible, usable, and sustainable," transforming technological investment into continuously deliverable professional services.
Viewing this launch in retrospect, its significance lies in unifying research, AI agents, stock quantification, and ETF portfolios under a single entry point. For Jiufang, this represents a systematic upgrade of nine years of accumulated expertise into callable digital services; for the industry, it pushes competition toward AI-native full-process services; for investors, previously fragmented, complex, and high-threshold professional tools are now entering daily investing in more intuitive, proactive, and account-aligned ways. After the launch event concludes, the market will open again at 9:30 AM the next day as usual. Announcements, anomalies, and capital signals will continue to pour in, and investors will still need to determine within limited time which changes are relevant to their accounts. What Gudao Zhihang accomplishes is identifying, filtering, and recontextualizing this information within the account context. The industry proposition posed at the outset of this article thus finds a more concrete answer: as information gateways multiply, what becomes truly scarce is the efficiency with which information reaches individual decisions. It is in this sense that the one-character change from "Gudao Linghang" to "Gudao Zhihang" marks a renewal of service approach. Jiufang integrates nine years of research content, stock quantification, and portfolio management capabilities into queries, alerts, diagnostics, and reviews, channeling three new functions into a continuously operating service. This launch ultimately settles into an everyday advisory approach: Gudao Zhihang enhances retrieval efficiency during information-dense periods, provides timely alerts during account changes, and offers observation frameworks during waiting periods. Thus, Gudao Zhihang becomes a clear milestone in Jiufang's journey toward AI-native advisory services.