We should review the 2021 "chicken dinner shrinking" market trend. Initially, everyone thought there would be a rapid recovery after the pandemic, anticipating a surge in overall market performance. However, as economic data was released in the second quarter, investors began to realize that there might not be a sustained overall market rally. This gradually evolved into a market focused on structural changes rather than overall market performance, leading to the "chicken dinner shrinking" trend. New energy (lithium batteries, photovoltaics, etc.) became the absolute main driver of capital inflows, with the "Ningbo Index" significantly outperforming the market and some leading stocks experiencing explosive growth. Meanwhile, traditional consumer blue chips (the Kweichow Moutai Index) and most small-cap/non-core sectors continued to suffer losses. The index remained relatively stable, but individual stocks generally declined, with trading highly concentrated in a few "group-buying" stocks. Trading became extremely congested, preventing the "group-buying" trend from spreading. When external conditions worsened, risk appetite decreased, and funds became even more concentrated in these stocks, accelerating the cycle of "the worse the market, the more funds are held, and the more funds are held, the worse the market becomes." Then, in early 2022, the collapse of these "group-buying" stocks was triggered by tightening external liquidity, a valuation correction in US stocks, underperformance of the "group-buying" stocks, falsified fundamentals, negative feedback from mutual fund redemptions, and further losses in small-cap stocks, creating a spiral cycle.

A useful but little-known Gemini API feature 🔬 Research across multiple sources and compile a report. Gemini's agent can handle that. Gemini's "Deep Research" is an autonomous research agent that plans, gathers information, analyzes, and generates reports. It also supports MCP integration. 📌 Title: Deep Research (agent, preview) 🔗 URL: 🧩 Overview Research tasks like "investigate the latest trends in this technology and summarize" require exploring multiple sources, organizing information, and producing a coherent analysis. Deep Research does this autonomously: it creates a research plan, gathers information from multiple sources, analyzes and structures the findings, and generates a report. 🛠 How to use it Describe your research objective and scope in natural language. The agent creates an investigation plan, uses web search and tools to collect information, shows progress as it works, and outputs a structured report. Connect MCP-compatible tools to let it pull from your own data sources as well. 🏗 Building it into production ・Automated market research: generate periodic reports on competitive landscape and technology trends. ・Due diligence support: comprehensively gather and analyze publicly available information on investment or partnership targets. ・Technical investigation: auto-run comparative studies of new libraries and frameworks. ・Academic research assistance: semi-automate paper surveys on specific topics with organized summaries. 💡 Use cases 📊 Periodic competitive and market trend reports 💼 Due diligence and corporate research 🔧 Comparative studies for technology selection 📚 Academic paper surveys and organization ⚠️ Watch out As a preview feature, accuracy and comprehensiveness aren't perfect. Always have humans review agent output and fact-check before important decisions. Research depth trades off against time and cost, so set an appropriate scope. ✨ Automate the entire "research, organize, report" flow. Start with a routine research task and see what Deep Research can do. #Gemini# #LLM#