The research utilizes case analysis techniques to examine the driving forces behind, and the effectiveness of, the international corporate mergers and acquisitions (M&A) in China Resources Pharmaceutical within the RCEP structure. Research indicates that international mergers and acquisitions markedly improved the firm’s expertise in market expansion, technology acquisition, resource integration, and synergy effects. Such initiatives have stimulated an increase in revenue and profits, streamlined the cost framework, and further enhanced the firm’s brand prominence, innovative prowess, and competitive edge in the market. Enacting RCEP presents not only prospects for China Resources Pharmaceutical but also introduces obstacles including adherence to policies, cultural assimilation, and the merging of operations. The research suggests related strategic approaches to tackle these issues and boost the firm’s global competitive edge and market impact.

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This study uses value chain analysis to examine the basic and supporting activities in the homestay industry, explore the challenges present in the value chain activities, and offer recommendations. The study points out that homestay businesses should focus on core competencies and implement a series of measures to achieve value creation and cost control. Additionally, optimizing the policy environment and applying sustainable development are crucial for the transformation and upgrading of the homestay industry. The results provide practical guidance for the industry to achieve sustainable development in an increasingly competitive market.

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As US Federal Reverse Systems has continued to raise federal funds in the past few years in order to curb inflation, this has had a huge impact on chinese capital market. Investors and financial institude are actively engaged in predicting stock returns to maximize their investment income. This study constructs a model to analyze and predict the financial factor of stock income based on supervised machine learning methods and evaluate the results. The datasample is extracted from wind, for the period half a year before and after the recent federal funds rate hike. Utilizing machine learning packages in R, this paper construct models based on linear discriminant analysis, decision tree and random forest models. After constructing estimation models, this paper visualizes the prediction result by utilizing drawing package in R. It has been found that the random forest algorithm predicting model generates very successful results for the financial factor prediction as the model accuracy reaches 99%. According to the analysis, relevant model to predict financial factors and non-financial factors based on non-linear supervised machine learning algorithm can be built by investors or hedge fund agency to estimate stock performance in the future. This study also investigates the economic result of the Federal Funds Rate hikes, which is instructive for investors to make well-informed decisions.

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With the rapid development of China's economy, the status and role of the financial industry in the national economy have become increasingly prominent. The healthy development of the financial industry is of great significance for promoting the development of the real economy, adjusting the economic structure, and upgrading the economy. However, there are still some problems with financial services supporting the real economy in China, such as the misallocation of financial resources, high financing costs, and rising financial risks. These issues limit the support financial services can provide to the real economy. This paper analyzes the main problems currently existing in financial services supporting the real economy and proposes countermeasures from optimizing the allocation of financial resources, reducing financing costs for the real economy, preventing and resolving financial risks, and deepening the structural reform of the financial supply side. The aim is to provide references for relevant personnel.

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