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.

With the fast growth of mobile Internet technology, people are spending more and more time watching short videos on their phones. In response to the fragmentation of users' use of mobile terminals, product placement in short videos is also gradually gaining favor. This study aimed to explore the impact of sports-related short video product placement on consumers’ purchase intentions. In this paper, the SOR model was used, different influencing factors were added to the model, and SPSS 26 was used for significant impact analysis. The conclusion of this study was that consumers pay more attention to the attitude and relevance of product placement and that good advertising attitude and highly relevant advertising can have a positive impact on their purchase intention. Therefore, video creators should pay more attention to advertising content, improve advertising quality, and increase highlights, which will have a certain production direction for video creators or advertisers, and strive to produce excellent videos.

Electric vehicles are now a common mode of transportation due to the aggressive promotion of new energy. Tesla currently has the largest market share among all brands, as well as its techniques and patents may ensure the benefits of future development. And also, Elon Musk is a powerful and ambitious businessman who can steer a technological company toward a brighter future. Yet, due to some uncontrollable factors like Covid-19, disaster incidents, technical staff resignations, etc., might influence the prospects of Tesla stock. So, in this study, machine learning techniques will be primarily employed to forecast Tesla stock prices' trajectory over the next 30 days. The two primary methods used to forecast and evaluate accuracy to determine which model is more appropriate are linear regression and random forest. Before the model is trained, all of the stock data is divided into a training side and a test side. According to the research, linear regression model performs better in predicting the direction of Tesla stock than a random forest model. Based on the search results, it can be said that machine learning methods are likely to unearth patterns and insights that humans haven’t seen before and can be used to make accurate and unmistakable stock predictions.

Stock forecasting has historically been a popular and lucrative field of study. It has been demonstrated that machine learning applications improve accuracy and return in the area of finance forecasting and prediction. This study chose data from the Yahoo Finance database that represented Apple's (AAPL) close price for research. This study categorized articles using a series of machine learning models, encompassing Linear Regression, Random Forest and so on. This paper also examines each article's dataset, variable, model, and findings. The survey in use showcases the findings using the most popular performance metrics. Recent models that combine LSTM with other techniques, For instance, RF has received a lot of study. Deep learning techniques like reinforcement learning and others produced excellent results. In conclusion, the use of deep learning-based techniques for financial modeling has become growing in popularity over the past few years.

Fission marketing is a special kind of marketing that is based on the old ways of marketing and is used to get more customers by splitting the terminal market. Luckin Coffee is one of the successful users of the fission marketing model. This study aimed to explore the advantages and disadvantages of Luckin’ s coffee marketing model as well as its impact on consumers. This paper further develops the factors in the marketing mix theory, uses the SWOT analysis method in the model analysis, and comprehensively discusses the advantages and disadvantages of the Luckin Coffee marketing model. This study concluded that Luckin Coffee has obtained a large number of customers through relationship marketing, price promotion, and other means, but customer loyalty is insufficient, brand understanding is inadequate, and the capital flow has many drawbacks. The overall marketing model has advantages and disadvantages, which is one of the typical cases of fission marketing. More opportunities and threats to Luckin’ s marketing model will also be discussed in the following section.

The influence of daily news on the economy, especially stock market, cannot be underestimated with big data gaining its momentum. In this article, the author uses two years of daily news headlines to predict the stock market movements of the Dow Jones Industrial Average. The first treatments on the dataset are collecting news from Kaggle.com and stock data from Yahoo Finance. The two datasets are then combined into one CSV file and split into training and test sets. Two machine learning models, Logistic Regression and Long Short-Term Memory, are built to fit the combined dataset, and the test index is the accuracy of prediction. The test accuracy is 0.58 with the three-word phrase by Logistic Regression and 0.65 after ten times training with the LSTM model. The final result demonstrates that the two models are feasible and effective for seeking the relationship between daily news and stock market movements and, thus, valuable for stock prediction. The attempts to set parameters give reference to further study, especially the word count of phrases and the number of training circulation.

In recent years, the price of diamonds has fluctuated significantly. This article mainly studies the factors that affect the price. This article mainly uses linear regression and random forest to calculate the coefficients between prices and influencing factors, and determines the degree of impact between them by comparing their sizes. In addition, the histogram analyzes whether there is a relationship between cutting clarity color and price. However, the relationship between cutting and diamond prices is not so strong. Finally, it is concluded that the weight of the price table, as well as the length and width of the table have a significant impact on the diamond price. When purchasing diamonds, the conclusions of this article can be used to determine whether the characteristics of various aspects of the current purchase price are directly proportional to the price they sell to ensure that consumers do not enter this diamond scam.

Stock price prediction has always been a problem that investors are very concerned about. This paper studies the use of financial information of listed companies to predict whether the stock price will rise after the release of its financial report. This paper extracts the financial information of listed companies from the Guotaian database, obtains their stock price data from Yahoo Finance, and calculates the corresponding technical indicators. And exploring the effect of these indicators on the stock price prediction. The study found that there is a small gap between forecasting with financial information alone and forecasting with technical indicators alone. The combined model performs slightly better than the single model. This study demonstrates that financial information can effectively aid in predicting stock prices and overcome the limitations of certain technical indicators. By incorporating both financial data and stock price information, investors can make more accurate prediction regarding fluctuations in the stock market.

Affected by the COVID-19 epidemic, economic development is under downward pressure. revitalizing the economy plays a unique role in easing unemployment and stabilizing society. The article first focuses on the reasons and current situation of the Chinese stall economy, then discusses the problems and solutions in the long-term development with the proposal that more specific time and space management, as well as digital technology, should be added to street vending to adapt to the future urban construction and social development. This helps to find a sustainable developing way for street business.

This paper aims to describe how to use machine learning models to predict the situation changing of stocks. This paper will use linear regression and random forest model to predict the Uber stocks stock's future closing price and probability of rise and fall. This paper firstly collected stock related information from Kaggle. The data of Uber stocks are from May 10, 2019 to March 24, 2022. The closing price and the future closing price are divided by taking 80% as the training set and 20% as the proportion of the test set. Then setting some technical indicators to analyze the accuracy and deviation of the prediction, such as root mean square error (RMSE), mean deviation error (MBE) and R-square. In future research, these methods could be used to apply machine learning models in stock forecasting, as well as other more accurate methods such as radio frequency technology and neural networks.

With the continuous development of society, big data, cloud computing, and other financial technologies have been applied to financial credit. Some “algorithmic black box" inherent in big data and other problems have been brought by those technologies to the governance of personal credit systems. At the same time, due to the imperfection of China's current legislation and evaluation standards in the personal credit field, it is difficult to protect the personal credit of citizens. Compare with China, some Western countries, such as the USA have achieved better results in the field of personal credit. Therefore, through drawing lessons from overseas’ successful experiences, and connecting with Chinese real conditions, this paper tries to propose a sound personal credit system by applying the principle of "minimum necessary", clarifying the boundary between personal privacy and credit information collection, and establishing and improving the remedial and supervisory system of the personal credit system.