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.

This paper examines the dynamics of foreign direct investment (FDI) at the city-industry level. We ask two questions. Does past investment make future investment in the same place more likely? And does it arrive faster as more projects accumulate? The first is a persistence effect—more investment attracts more investment. The second is an acceleration effect — the gaps between projects get shorter over time. To test these, we use panel count models for persistence. For acceleration, we turn to survival models — log-interval regression and Cox/AFT models. The results hold up across both sets of tests, even after we account for macroeconomic conditions, city-industry fixed effects, and clustered standard errors. The findings point to clear micro-level mechanisms behind FDI clustering. Together, they give local governments a clearer picture of when and provide evidence to judge whether in designing investment promotion strategies or deciding how to allocate resources across industrial parks.

The central theme of this paper is to analyze the strategic transformations and diversifications of the top Chinese internet companies in the face of the blistering AI advancement, and how their core competencies affect AI-based strategies. The existing research has been predominantly concerned with the way AI transforms business models and competitive dynamics whereas little attention has been paid to how real-life firms are different in their strategic responses, in specific patterns of diversification, and the role of core competencies in determining these strategies. According to the case study of Alibaba, Tencent, and ByteDance, all three companies position AI at the center of their strategic change, but they have different diversification strategies based on their competitive advantages. Alibaba is more oriented towards cloud infrastructure and large model services, Tencent is more oriented towards ecosystem integration and user connection, and ByteDance is more oriented towards algorithm optimization and scenario-based application. This research also reveals that the AI technological positioning and market expansion of firms are much aligned with their core competencies using patent data and search trend data. Based on these results, AI-based diversification can become a capability-based process, and the combination of existing and new AI capabilities is an ability that companies should consider as a valuable approach to transitioning the company and supporting existing competitive advantages.

In the development of the Internet industry, mergers and acquisitions(M&A) of enterprises have become a strategic means to quickly maximize value. Expected operating synergy after M&A is one of the main motivations for corporate M&A. The value of the merged or acquired company and the evaluation of the synergy it produces will directly determine the implementation of the M&A and the transaction price. Therefore, how to correctly identify and quantify the operating synergy of enterprise is crucial for the value analysis of M&A. This article will take the Internet giant Microsoft's acquisition of LinkedIn, a workplace social platform, as a research case, focus on the realization path of operating synergy, and use a combination of case analysis and financial analysis to evaluate the impact of operating synergy on the valuation of M&A. It aims to explore how large Internet enterprises can create value through M&A, and provide reference for M&A for the science and technology industry.

During global capital market variability and China's registration-based Initial Public Offering (IPO) reform, the macroeconomic environment's impact on IPO pricing and private equity exits is becoming increasingly critical. This article intends to explore the transmission mechanism by which multidimensional macroeconomic fluctuations determine the first-day returns of A-share IPOs. The study selected 1,139 A-share IPO companies from 2015 to 2023 as samples and conducted empirical tests by constructing a multivariate robust regression model that controls for micro-characteristics and fixed effects. Research has shown that the macroeconomic sentiment index and Purchasing Managers' Index (PMI) has a significant positive effect on the first-day return. However, the inflation rate consumer price index (CPI) and the term spread of government bonds have a significant negative inhibitory effect. Concurrently, heterogeneity analysis indicates that this macroeconomic sensitivity is particularly strong in the Science and Technology Innovation Board and the ChiNext. Further comprehensive mechanism testing shows that the impact of macroeconomic factors mainly takes effect through the liquidity channel rather than through the price-to-earnings ratio of issuance. This study expands the macro perspective on IPO pricing and provides a future-oriented basis for institutions to boost their exit strategies.

This article will discuss how digital transformation affects the comprehensive development of enterprises. Digitalization is a novel enterprise development model for contemporary enterprises, and it is of great significance in economic and social contexts. Contemporary enterprises, driven by advances in emerging technologies, have found that their traditional corporate structures are no longer well-adapted to the fierce competition in modern business and to social demands for environmental protection. Therefore, enterprises need to adopt a digital transformation policy to enhance their profits, ecological protection, and innovation capabilities. The enterprises mainly involved and studied in this article are primarily from China. This is because, as China has become one of the major economies in contemporary times, enterprises have made significant contributions to the economy. This article argues that digital transformation influences three areas: financial performance, that is, the ability of enterprises to generate profits and raise funds; and environmental protection capacity, that is, the ability of an enterprise to reduce pollution emissions and other ecological impacts during operations.