Uweb AI Investment Research Training Camp Holds Multiple Courses, Incubation Projects Showcase in Roadshow

Uweb AI Investment Research Courses Assist Industry Research, Comprehensive AIization from Research Judgments to Decision-Making Tools

Focusing on the capacity building of investment research talents in the AI era, Hong Kong Uweb (University of Web3) Business School is advancing offline training and online incubation simultaneously, connecting concentrated teaching, industry research, and ongoing project practices, to establish a training pathway covering cognitive enhancement, method training, and outcome incubation.

Since the launch of courses in April 2026, the offline AI investment research training camp at Hong Kong Uweb Business School has held four sessions, enrolling over 250 participants; the online AI investment research incubation camp conducts selections aimed at applicants with research and practical foundations, attracting over 800 registrations, from which 50 participants were ultimately selected, further deepening professional training through eight weeks of courses, mentorship, and project co-creation.

The new course focuses on the application of AI in investment research, covering information acquisition, industry analysis, value judgment, and research result expression. Uweb combines professional investment research methods with AI tool practices, guiding participants to use AI to organize market information, analyze corporate and industry changes, and verify data sources and conclusions, gradually establishing an AI investment research workflow applicable to their own research fields.

Dr. Yu Jianing, President of Hong Kong Uweb Business School, rotating chair of HKCDAA Academic Committee, and co-chair of China Communications Industry Association Blockchain Committee, stated, “AI is simultaneously changing both the objects and methods of investment research. On one hand, technological evolution constantly generates new business models, requiring researchers to comprehend industry logic, corporate management, and value judgment; on the other hand, intelligent tools are deeply engaging in information processing and research analysis, necessitating professionals to possess stronger skills in problem definition, evidence identification, and human-machine collaboration.”

“AI investment research education should help learners establish research systems that can continuously update with technological and market changes, allowing tool efficiency and professional judgment to mutually reinforce,” Dr. Yu Jianing pointed out that Uweb hopes to leverage its accumulation in digital assets and cutting-edge industry research to integrate industry cognition, research methods, and practical project experience into the talent cultivation process, assisting participants to transform their understanding of technological trends into evidence-based research outcomes and professional capabilities applicable in practical work.

Incorporating Technological Evolution into Investment Research Analysis, Deepening AI Industry Cognition

In this session of Uweb AI Investment Research Training Camp, Wesley, the manager of "Wesley's Investment Research Notes", discussed the relationship between corporate profits, valuation changes, and industry cycles by combining historical innovation cycles with macroeconomic analysis.

Wesley also shared six key insights from the AI wave, specifically: 1. Each wave requires significant upfront capital deepening; early waves focused on physical capital and infrastructure, while later waves focus on computers, peripherals, and software. 2. Labor substitution is transitional, not permanent; past concerns about large-scale technological unemployment have not materialized. Job compositions change, some positions disappear while others emerge, but employment persists; 3. Productivity growth is the ultimate sign of these waves. Early diffusion is often slow and uneven, but once supporting investments and organizational changes are in place, per capita output accelerates; 4. Innovation waves repeatedly include at least one major boom and - recession, each wave exhibits a similar pattern of enthusiastic capital formation, speculative finance, and rising leverage, ultimately leading to contraction; 5. Technological changes enhance overall productivity while also redistributing income among different skill groups and industries; 6. Policies are crucial; antitrust, social insurance, and human capital investments shaped the outcomes of past waves. Institutional adaptability determines whether returns are widely shared.

Regarding AI industry analysis and investment research methodology, tech investor Bill Qian outlined the industry chain from levels of energy, chips, infrastructure, models, and applications, discussing updates to research frameworks using corporate case studies. He pointed out, “Navigating through cycles, the unchanging principle is that returns follow a power law rather than a normal distribution; Alpha rotates among sectors, diversification is especially critical; volatility is a necessary cost of long-term returns.”

In the course "Grasping the Eight Major Recognitions of AI Compounding," Lester Li, founder of Zhifujie, linked industry cycles, business models, and personal capability development.

Lester stated, “Post-AGI, only talent surpassing AI will remain; AI will eliminate all capability gaps sustained through hard work. Closing the cognitive gap has a practical executable method, which is to let AI identify what you do not understand and then help you catch up. After identifying your capabilities, the next step is to amplify them. The way to amplify is to shift from being a user to a manager, treating AI as a position to allocate rather than just a tool to use.”

Receiving Academic Support from Hong Kong Polytechnic University's Innovation Center, Conducting Eight Weeks of Elite Incubation

Beyond offline training, Uweb collaborates with Techub News to co-host the AI Investment Research Elite Incubation Program, with academic guidance provided by the Digital Stablecoin and Real-World Asset Innovation Center of the School of Business at Hong Kong Polytechnic University. The incubation program launched in August 2026, setting an eight-week training cycle, freely open to selected participants with research foundations and AI application experience. Currently, a number of student projects have entered the roadshow presentation and evaluation stage, covering investment research decision-making, evidence verification, asset allocation, and market theme identification.

The incubation program offers courses focused on financial investment research methodology, practical AI tools, and cultivating professional influence, organizing over ten mentors from industry research, AI applications, and content fields to participate in the training. Core mentors include Dr. Yu Jianing, President of Hong Kong Uweb Business School and Co-Director of the TGG Stablecoin and RWA Innovation Center at Hong Kong Polytechnic University; Fang Jun, Uweb technology partner and author of "Becoming a Question Engineer"; Dr. Deng Huacheng, founder of the Innovation Center; Dr. Cao Ling, an AI trainer who previously held executive positions at Huawei and Dell; Lester, founder of Zhifujie; Todd, CDAA Level 2 certificate holder and senior buy-side analyst; BTCDayu, a Web3 researcher; and Anonymous Doctor, co-founder of W+, among others. Mentors assist students in refining research frameworks, application plans, and result presentations through case breakdowns and作品点评 (work critiques).

Uweb integrates students’ research questions, analytical methods, and tool solutions into ongoing evaluations through periodic tasks and project roadshows. According to the announced selection rights, students can obtain Uweb Investment Research Skill and Token, API usage support, and present results through research reports, graphical works, and tool demonstrations.

According to the ranking released by the organizers of the third project roadshow, Crazyox's "Modular AI Investment Decision-Making Closed Loop," zkBernard's "Investment Evidence Discounter," and Elina's "All-Weather Asset Allocation Decision Table" ranked in the top three. The three projects revolve around the verifiability of investment research judgments, the credibility of research evidence, and the asset allocation process, transforming research methods from the classroom into projects available for showcasing and discussion.

Other projects entering the rankings include Jack's "US Stock Market Mainline Identification System," the AI token and crypto token parity index proposed by HuiDAO, and Barry Jiang's "Verifiable Personal AI Investment Research System." Other students' research tasks involve strategy experiment archiving, backtesting result verification, financial report analysis, and multi-source data integration, addressing specific issues such as information fragmentation in investment research work, the difficulty of verifying research conclusions, and the challenge of accumulating strategy experiments.

According to the rules, outstanding results from the incubation program may be selected for the university case library and achievement manual, and exceptional students may also participate in subsequent AI teaching assistant, AI lecturer, and content co-creation work, connecting project practice with subsequent professional collaborations.

Linking Research Methods with Project Practice, Extending Elite Training Pathways

Hong Kong Uweb Business School is a professional high-end education institution for digital assets, rooted in Hong Kong and influencing globally, with the mission of “creating a top global Web3 institution that empowers a billion people to embrace digital assets,” inheriting eight years of knowledge accumulation, possessing an industry-leading alumni network and highly acclaimed celebrity teaching resources.

Uweb regularly offers Web3 digital asset analysis courses, covering the tokenization of real-world assets (RWA), stablecoin economies, and digital asset investment research allocation, integrating fundamental judgment, on-chain data analysis, and macroeconomic linkage into commercial teaching. Since the beginning of this year, Uweb has further expanded AI investment research courses, inviting researchers, quantitative traders, and technical developers to participate in discussions, centered on multi-asset research, agent applications, and professional talent cultivation, exploring the specific pathways for AI to enter financial services.

As AI continues to penetrate various aspects of investment research, researchers are required to enhance information processing efficiency while bearing professional responsibilities for problem definition, value judgment, and conclusion verification. Uweb aims to incorporate industry cognition, investment research methods, and project practice into an ongoing cultivation process through offline training and online incubation, helping participants transform classroom learning into evidence-based research outcomes, and continually refine and improve their methods through practical feedback.

With mentorship guidance, result presentations, and subsequent co-creation gradually connecting, Uweb hopes to provide investment research talents with a growth path from learning to practice, from research to professional collaboration, enabling accumulated knowledge and experience to be utilized in teaching, research, and industry communication, thus cultivating professionals with independent judgment and practical capabilities for financial research and business innovation in the AI era.

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