Stock market live streaming has become an important channel for investors to access market information, but such content is often emotionally charged and loosely structured, potentially affecting viewers' emotions and trading decisions, thereby indirectly disturbing market stability. To address the need for automated analysis of massive volumes of live streaming text, this study introduces Large Language Models (LLMs) for sentiment analysis and information mining. We collected 7,912 text segments from 20 live sessions on the Tiantian Fund platform and manually annotated financial entities, state descriptions, and sentiment tendencies. The Qwen2.5 model was fine‑tuned with LoRA via the LLaMA‑Factory framework. Results show that on entity‑containing content, the fine‑tuned model achieves significantly higher text similarity and ROUGE scores; however, on noisy text without entities, performance declines, indicating that automated processing still faces challenges such as overfitting and semantic drift. Overall, LLMs show the potential to process unstructured financial live streaming text in batch, offering new technical references for precise market regulation and investor education.

Under the flow of digital transformation, Finance-Operations Integration became a key factor of effectiveness improvement in manufacturing enterprises. In the conventional model, the Finance department and Operations department are unconnected, which may form a data silo and make it difficult to support real-time decision-making and cost control. However, most of the current studies focus on AI technology itself and lack focus on the mechanism of Finance-Operations Integration. The study takes Siemens as a single case to explore the specific process of Finance-Operations Integration with "questions →motivation of transformation → AI application→ effectiveness → key factors" analysis framework. The results find that because of the digital transformation, such as ERP-MindSphere integration, Digital Twin (DT) system and OT/IT data integration, the enterprises decrease their defect rate, increase their customer stickiness, decrease their inventory and improve their cash flow. This study finds the successful factors, risks and challenges of the enterprise, which may provide a practical reference for digital transformation in Finance-Operations Integration in manufacturing enterprises, such as real-time cost control. Because this single-case study is limited, there should be more multiple-case studies in the future.

There have been significant developments of the Chinese coffee market during recent years. The main factors behind the growth are changing consumer behavior in the coffee market and digitization. Even after experiencing growth, the Chinese coffee market still has great potential. Competition and pricing among other issues of the market have raised concerns regarding the profits that can be earned by the companies operating there. The purpose of the current work is to identify key trends in order to discover possible opportunities and risks associated with RTD coffee to Luckin coffee. In addition, the comparison between RTD and other more conventional approaches will be made. Finally, possible benefits associated with different methods of delivery in connection with Luckin Coffee will be discussed as well. According to research findings, entering the field of ready-to-drink (RTD) coffee is a wise decision on the part of Luckin Coffee. It offers a reliable means to expand the scope of profit sources.

This paper tests the von Neumann‑Gale (VNG) production‑based asset pricing model, which predicts that production efficiency should be a priced factor in stock returns. Using total factor productivity (TFP) growth as a proxy for efficiency, this paper examines four U.S. industries (Retail, Wholesale, Manufacturing, and Transport) from 1990 to 2018, a period covering two business cycles and the 2008 financial crisis. Full sample results indicate that only the Retail industry shows a significant positive relationship between TFP growth and positive excess return (referred as VNG prediction). However, the 2008 financial crisis changes this particular relationship: Retail no longer continues to positively contribute whereas Wholesale post-crisis demonstrates a significant negative TFP-return relationship (i.e. a low productivity premium). Manufacturing and Transport exhibit no significant relationship in all evaluated years. The results of this paper claim have VNG model industry-level evidence, document crisis-induced pricing of productivity to reverse sign and define the importance of sector-specific investments during financial crisis.

With VC/PE generally regarded as a prerequisite for IPO, how companies with low VC financing can gain recognition from the capital market and obtain reasonable valuations has become an important issue. This paper takes Cofoe Medical Technology Co., Ltd.(hereinafter Cofoe Medical) as an example and uses a combination of case analysis, literature analysis and financial analysis to study its financing structure, financial fundamentals, core capabilities, and merger and acquisition expansion process. This paper finds that Cofoe Medical is a typical company where the founder holds control and external capital financing accounts for a small proportion. Its valuation does not rely on the certification effect of VC/PE, but is supported by its sound financial condition and growth potential, its continuously developing level of commercialization, and industry dividends. Capital backing is not the sole determinant of IPO valuation; both companies and investors should conduct a comprehensive assessment across multiple dimensions to gain a more holistic understanding of the underlying logic behind the valuation.