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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In today’s digital era, the evolution of internet supply chain finance presents trans-formative opportunities and challenges for enterprises worldwide. This research explores the development and influence of Internet supply chain finance on enterprises, examining its impact on financial management, operational efficiency, and strategic competitiveness within supply chain networks. The study delves into the growing adoption of Internet supply chain finance solutions by enterprises seeking to optimize working capital, streamline transaction processes, and enhance collaboration with suppliers and partners. As digital technologies reshape traditional finance practices, understanding the implication of Internet supply chain finance becomes imperative for businesses navigating complex global supply chains. A comprehensive literature review synthesizes existing knowledge on internet supply chain finance. Preliminary findings suggest that Internet SCF offers significant advantages to enterprises, including improved liquidity management, reduced transaction costs, and enhanced supply chain resilience. However, challenges such as personal security risks and integration complexities underscore the need for strategic planning and technological readiness. By elucidating the development and influence of internet SCF on enterprises for practitioners, policymakers, and scholars in the field of supply chain finance and digital transformation.

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Since May 2023, when Heytea established a co-branding alliance with Fendi, its sales and popularity have significantly increased. Several retailers faced a shortage of the product within the first week following its launch. This study sought to examine the elements that have contributed to the successful cross-border co-branding cooperation between Heytea and Fendi. This study employs the marketing mix theory of the 4Vs to analyze the situation of Heytea from three angles: value strategy, variation strategy, and vibration strategy. Heytea had success by using three crucial factors: augmenting the perceived worth of their product, providing a wide array of products to cater to a broader consumer demographic, and producing enthusiasm to captivate public attention. Similarly, the three suggestions are to improve the caliber of products, provide customized service, and engage influencers. This study offers an extensive investigation into cross-border co-branding in China, with significant implications for other companies and industries.

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