Influencers refer to people who spread information through the internet and become popular due to their own attention from netizens. This idea is expanded by the term "Internet celebrity brand," which refers to a brand that has become popular with consumers through online marketing. The aim of this study was to explore how Internet celebrity brands could improve their brand image and foster consumer goodwill. This paper further developed Marketing Mix Theory by exploring the effectiveness of dimensions of Marketing Mix Theory and brand image. Besides, the research also exploited the SWOT method to analyze the product, price, place, and promotion of the Chicecream. This research concluded that Chicecream' s high pricing can generate negative perceptions among consumers, and inappropriate positioning strategies can lead to fragmentation of the brand image and detract from the effectiveness of brand marketing. Through the above summary and analysis, the article provided ideas on how to cultivate a good brand image for online celebrity brands from the perspectives of price, product, place, and promotion.

Starting in July 2021, a far-reaching event occurred in the education industry: the Double Reduction Policy (DRP) was fully implemented in China. This policy has a great impact on the vast majority of families with children, as well as on the operation of public schools and private education institutions. In this paper, New Oriental Education & Technology Group (New Oriental) was used as a case study to conduct SWOT research and analyze the long-term impact of this behavior on its brand image (BI) and brand loyalty (BL) after the group entered the live-commerce domain under the DRP. Through the detailed investigation of the case, it was proven that the strategic decision of entering the live-commerce business can bring financial advantages to New Oriental in the short term, but it had a negative impact on its BI and BL in the long term. Through reviewing the literature and analyzing this specific case, this paper believed that entering a new business field, especially when selling products or services at lower prices, will have a negative impact on the BI and then affect the BL.

In early stages of the COVID-19 outbreak, Chinese stock market experienced a sharp decline as investors became increasingly concerned about the economic consequences of the pandemic. As the pandemic continued to spread globally, the Chinese government implemented strict measures to control its spread, which included lockdowns, travel restrictions, and other forms of social distancing. Although these measures worked, economy was drastically affected with many stores closing down and consumer expenditures significantly declining. This, meanwhile, led to a decrease in corporate profits and a reduction in investor confidence. However, on December 8, 2022, the Chinese government issued a pandemic deregulation policy identifying people would return to their normal life. In this paper, prices of the Shanghai Stock Exchange Composite (SSEC) index and Shenzhen Securities Component (SZSE) index were retrieved and ARIMA method was adopted to predict the stock prices for a period after the pandemic. The author compared forecast prices with the actual stock prices and then analyzed the implications of the deregulation policy on the stock market. These two indexes are only a snapshot of the Chinese economy, and certain informative feedback can be obtained through this study, which is helpful to relevant investors and policy makers.

In recent years, with the continuous development of customer relationship management, the two indicators of consumer satisfaction and consumer loyalty are often regarded as the criteria for measuring the success of customer relationship management, but at the same time whether these two indicators are applicable to the market competition environment is often controversial, this article will use the questionnaire survey method to explore the relationship between consumer satisfaction and consumer loyalty in the catering industry in the Macao Special Administrative Region, and use the case summary method to explore the other three industries in different market environments. It also summarizes the factors that contribute to this situation and also tries to provide more suggestions for improvement for practitioners.

In security markets, how to achieve optimal allocation of asset has become the focus of investors. The mean-variance model is a method to achieve revenue maximization and risk minimization on investing stock portfolio. This paper will research on investment problem in Shanghai Stock Exchange. It involves research result about mean variance model since 1952. To take the experiment, 10 stocks are chosen from Shanghai Stock Exchange. Organizing rates of return for 360 days and using the average daily rate of return as the expected rate of return. Applying mean variance model, selecting data, the curve of frontier portfolio is obtained by experiment. Based on the result, the investment advice is given. It shows that higher expected rate of return accompanies by higher risk. To achieve higher returns, investor could analyse the variation of each asset along with the rise of the expected rate of change. When negative weight appears, there exists short selling. If short selling is not allowed, investor could shrink the interval of the expected rate of return.

Combined with the 47th Statistical Report on the Development Status of the Internet in China issued by the China Internet Information Center, it can be found that by the end of December 2020, the number of Internet users in China has exceeded 989 million, and there are 986 million mobile phone Internet users, and the mobile phone Internet access rate of the nationals has reached 99.7%. The demand for mobile internet applications has led to the development of a large number of internet industries. In the financial sector, for better financial business, many customers use financial trading platforms, but illegal criminals use blockchain technology in the name of virtual currency to carry out illegal fund raising, pyramid selling, money laundering and other criminal activities, involving a large amount of money and a relatively wide audience, which seriously affects China's Internet financial security. Therefore, the only way to ensure the safety of Internet finance is to strengthen the governance of such crimes. In this paper, based on the existing research results on blockchain technology and the governance of virtual currency money laundering crimes in Internet finance crimes, we use interdisciplinary research methods to determine the ways of governance that combine the pre-existing, ex-post and post-existing governance, taking into account the current status of legislation, existing cases, the dilemma of governance, and the existing governance achievements.

People are paying more attention to body skin care as the economy and society develop, and the demand for body care products is increasing. At the same time, with the continuous progress of Internet technology, China ushered in the "traffic era". Various social platforms play an important role in users' use of the Internet, so the traffic-guided marketing strategy has huge value in the market. This paper applied Marketing Mix theory and SWOT analysis, combined with the actual situation of the L'Occitane brand, through the analysis of the brand's marketing status and existing problems from four aspects: product strategy, price strategy, channel strategy, and promotion strategy, to analyze the shortcomings of L 'Occitane traditional marketing model. The new marketing strategy of L'Occitane and its similar skin care products in the age of traffic is proposed. This paper put forward specific measures for L'Occitane and similar skin care products to cope with the new marketing model in the traffic era from the aspects of paying attention to the traffic promotion on social media platforms and expanding the scope of skin care products publicity.

Machine learning has been increasingly used in stock price prediction with outstanding success. Decision tree regression models and linear regression models are both important models for predicting stock prices. The paper use decision tree regression and linear regression models to predict the opening price, closing price, high price and low price of Apple's stock price data respectively. The prediction effects of the two models are evaluated by the indicators of goodness of fit, mean square error, root mean square error and mean absolute error, and the prediction effects of the two models are compared. This experimental concludes that the decision tree regression model has better and more advantageous prediction results compared to the linear regression model. This study has guiding significance for machine learning in predicting stock prices when choosing a basic model or a combination of models for prediction.

The stock market is changing daily and people are paying more and more attention to stocks. After the establishment of the stock market, the research on stocks has become more and more influential. The core of researching stock is the stock future price trend, bullish or bearish. In order to predict stock information in simple and efficient ways, this paper aims to predict stock rise or fall by using classification model with better performance index and strong operability. Firstly, the paper acquires Maotai Corporation’s daily stock data from tushare package. To define the label “up” and “down”, the paper compares the daily closing price with its yesterday price. If it is positive, it is recorded as up; if it is negative, it is recorded as down. The random forest, logistic regression and SVM models are established respectively. The best model was selected by comparing three models’ evaluation scores. The results show that logistic regression is better than the other two models in predicting the rise and fall of stocks. This study can promote the cross integration of financial field and technical level and provide new ideas for future stock investment.

The purpose of this paper is to investigate the impact of Federal Reserve interest rate hikes on the entertainment industry, with a focus on the American film company Disney. The study utilizes a VAR model for impulse response analysis and an ARMA-GARCH model for assessing stock returns and conditional variances. The findings indicate that a rise in the USD index leads to a decrease in Disney's earnings from foreign markets, and a Fed rate hike could have a negative impact on its profitability. However, higher interest rates can encourage funds to flow into the stock market and increase the demand for stocks, leading to a rise in stock prices. Negative effect dominates at the beginning, followed by positive effect, and then changes back to negative effect, gradually net effect tends to 0 over time. The paper suggests that investors diversify their portfolio, and that enterprises develop targeted risk management and seek policy support. The study provides insights into the multifaceted effects of a Fed rate hike on the entertainment industry, and contributes to the existing literature on exchange rate risk and capital structure in multinational corporations.