The COVID-19 pandemic has had far-reaching effects on global industries. The pandemic's impact on financial performance varied widely across sectors. This report aims to provide a preliminary understanding of the impact of the pandemic on companies in different industries and with different business models. This report selects four other companies, Microsoft, Apple, Pfizer, and Merck, from two different sectors, pharmaceuticals and technology. By comparing companies from these diverse industries, the report gains insights into the different business models reflected in their financial data. The financial statistics examined include annual stock returns, EBIT margin, and EPS. This analysis of technology and pharmaceutical companies' financial data highlights differences in performance and trends. The findings emphasize the significance of operating margin and EPS as key indicators of financial performance across industries. The differing financial performances of these four companies before and after the pandemic are primarily influenced by the characteristics of their respective industries, the nature of their business operations, and their financial strategies.

This paper summarizes and describes the impact of the COVID-19 on the consumption structure of China. The study conducts analysis on consumption from the perspectives of the total domestic consumption scale, the consumption structure and green consumption during the pandemic, and then summarizes the positive impact of boosting consumption and promoting economic recovery. It also explains how consumption began to recover as COVID-19 was effectively controlled. The epidemic had a huge impact on the domestic and international economy, including the impact on foreign trade and GDP growth, as well as the obvious negative impact of the COVID-19 on the consumption structure of the population. As the epidemic has been effectively controlled, the economy is slowly recovering. The contribution of this paper is to study the impact on the consumption status of Chinese residents after the pandemic, the extent of the impact caused is of great significance, and it serves as a revelation for the recovery of the economy in the future.

While numerous papers investigate on the co-movement of global commodities and stock indices, this paper extends to previous papers by focusing on the individual stock price of palm oil companies namely Sime Darby Plantation, Wilmar International, and Astra Agri Lestari. Weekly data spanning from January 2018 to September 2023 was selected and Autoregressive Distributed Lags (ARDL) model employed to demonstrate the possible co-movement and long-run relationship. Empirical results provide sufficient evidence of co-movement between global commodities with Sime Darby Plantation and Astra Agri Lestari in the long-run. This will allow fund managers, individual investors, and stock traders to make investment decisions, trading strategies, and portfolio management based on the global commodity markets.

This paper explores the variations in Return on Equity (ROE) and Price-to-Sales Ratio among publicly traded companies across different time periods and market capitalizations. The analysis employs Linear Regression and Correlation Coefficients to examine the relationships between ROE, Price-to-Sales Ratio, Market Capitalization, and economic conditions during these periods. The findings reveal that Price-to-Sales Ratio serves as a reliable indicator of a company's market capitalization. Furthermore, companies within the same industry exhibit substantial disparities in Market Capitalization, ROE, Price-to-Sales Ratio, Price-to-Earnings (P/E) Ratio, and PEG Index due to differing corporate strategies and backgrounds. For instance, in the new energy sector, both BYD and NIO display a negative correlation between market capitalization, while Tesla contradicts this trend. This discrepancy is attributed to internal factors such as asset composition, liabilities, and shareholder structure, which significantly influence a company's financial performance. The Market Capitalization of these new energy companies is predicted to rise due to global interest in AI technology, with technology companies being at the forefront of AI development. In the technology sector, companies like Google have solidified their positions by advancing AI concepts. New entrants, like Apple, combining Artificial Intelligence and Robotics, are gaining consumer support, indicating potential Market Cap. growth. The fashion industry also demonstrates distinctive strategies, with companies like NIKE, Adidas, and Anta focusing on sustainability and carbon neutrality. Therefore, future company evaluations should consider both financial indices and a company's long-term vision and sustainability practices to predict their future value effectively.

Since 2016, blind boxes have been loved by consumers, and the blind box economy has exploded. However, the blind box economy is closely linked with mathematical models. The mathematical models could be research predictions of profit and loss in blind boxes. Such functions as the loss function can assess the purchase probability of the blind box as well as how to avoid massive losses. Neural networks and Linear regression can predict the price changes of the blind box. Besides, we can predict and analyze the psychological and behavioral motivations of consumers. Therefore, the passage through literature reading and specific data analysis methods focuses on the mathematical models such as Loss function , Neural network and Linear regression that are applied in the blind box now. And the passage also describes the predictional model of profit and loss controdiction to avoid consumers consuming excessively ‘a pig in a poke’. At present, Linear regression can analysis and calculate investment with the blind box, but it should consider a lot of factors that can cause errors. Neural network can predict price changes, and it needs a large amount of data to support the model. Loss function can be used to predict the profit and loss of a blind box, but it also needs a large amount of data to prove its accuracy.

This study delves into the intricate relationship between the Capital Asset Pricing Model (CAPM) and Environmental, Social, and Governance (ESG) factors in the investment landscape. Through an extensive review of existing literature and comparative analyses, the paper uncovers the historical development of CAPM, the rise of ESG considerations, and their convergence in modern financial theory. The research aims to shed light on the ongoing debates surrounding integrating ESG factors into the CAPM framework, revealing both supportive and critical viewpoints. Through a critical evaluation of empirical studies, the paper identifies gaps in the current body of knowledge, emphasizing the need for further research to understand the interplay between ESG factors and CAPM. The study concludes that while ESG considerations have increasingly influenced investment decisions, their precise relationship with traditional financial models, such as CAPM, remains an area ripe for exploration.

Trademark anti-dilution protection is a legal measure aimed at protecting the rights and interests of trademarks and guaranteeing fair competition in the market, but whether it should be canceled has been controversial. The development of the theory of anti-dilution protection of well-known trademarks is a relatively popular research topic, while the Paris Convention and the Agreement on Trade-Related Aspects of Intellectual Property Rights (TRIPS) have provisions on anti-dilution of trademarks. Therefore, through the research method of literature review and theoretical analysis, this paper will discuss trademark anti-dilution protection in terms of its definition, implementation effect, and problems. In addition, the importance of trademark anti-dilution protection is elaborated from the perspectives of consumer protection, brand value, and innovation incentives, and the viewpoint of maintaining the existence of trademark anti-dilution protection is confirmed and put forward.

Because of some new policies in China, the government has begun to lower house prices and encourage people to buy more houses, at least in Anhui. However, the reduced housing prices have not led to an increase in residential housing sales. So the author analyzes the reasons for the current insufficient purchasing power of Chinese residents' housing based on existing literature and data. The result shows that, in the context of contemporary society, personal income levels have significantly decreased. This decline is influenced by various factors, including tax policy reforms and the profound impact of the COVID-19 pandemic on the global economy, including China. At the same time, the hedging ability of the real estate industry has also decreased, which hinders the improvement of personal purchasing power. Although the real estate market has shown strong risk resistance and has gradually recovered from the initial impact of the epidemic, the lingering impact and uncertainty still affect individual purchasing power.

In recent years, with the widespread popularization of outdoor sports culture and sports awareness in China, outdoor activities have evolved from a niche interest to widespread sports and relaxation in society. Especially against the backdrop of the pandemic and the reduction of social distancing, low-intensity and recreationally lightweight outdoor activities have gained popularity among urban youth. Consequently, the popularization of lightweight outdoor activities has led to the flourishing development of the market for lightweight outdoor activity brands. This study takes the Beneunder brand as a case study, utilizing the SWOT analysis to explore the brand's marketing strategies in the Chinese market systematically. A detailed analysis and summary of the strengths and weaknesses of its marketing strategies will be conducted, and corresponding strategic and optimal suggestions will be proposed. Beneunder, with its co-branding efforts, accurate market positioning, and mature sales channels, has successfully entered the Chinese market for lightweight outdoor activities. However, challenges arise due to weak offline sales capabilities and insufficient investment in scientific research, hindering the brand's development. Faced with competitors from specialized outdoor brands and the impact of negative public sentiment, Beneunder must seize market demands and enhance product design and quality to further achieve brand upgrading.

In a post-COVID-19 era of digital transformation and a gradually recovering tourism economy, the travel industry must continually seek innovative ways to enhance social media marketing strategies and better understand consumer behaviour. This paper provides insights into how social media marketing strategy optimisation can shape consumer travel preferences. Using Airbnb as a case study, this paper analyses the social media comments of Airbnb users to reveal consumers' overall positive attitudes towards Airbnb's services through sentiment analysis and explore how methods such as predictive analytics can be used to tailor social media marketing campaigns to provide a more personalised and effective service to potential travellers. The results show that consumers are very concerned about whether the service experience differs from what is advertised on social media, such as quality of accommodation, customer service and pricing strategies. Therefore, Airbnb should prioritise consumer feedback, improve social media content quality, and use predictive analytics to identify its target audience. Future research should incorporate diverse data sources and advanced analytics techniques to understand consumer needs better, establish stronger customer connections, and influence travel choices.

The main popular videos are short videos these years, Bilibili is still popular among young people and earns a lot of revenue even though they are focused on long videos of their unique products. In this paper, an introduction about the general background of video websites and the background of Bilibili is given, and other’s progress in this topic is also presented. Moreover, the situations of other video websites and Bilibili are analyzed in the second part. This part talks about the situations and reasons why consumers choose them in this era. Then the analysis of sources of profits online and offline is conducted. This paper found that in both online and offline sections, mobile games occupy the largest amount of revenue but the proportion of mobile games occupied in the general revenue is declining.

With the rapid development of science and technology, electronics continue to emerge, and the market competition is increasingly fierce. Apple, with successful marketing strategies, and strong technical support, emerged among many electronics products, becoming the industry giant. Since Apple launched the App Store, the online application market has been developing rapidly, which not only promotes mobile Internet. The development of the network has changed the revenue structure of mobile value-added services and promoted the re-integration and improvement of the mobile network industry chain. In this stage, how to enter and seize the market better and faster has become the primary problem of Apple. This paper discusses the current market situation of Apple, analyzes the target market selection, and focuses on the main competitive advantages of Apple. Then, by introducing a marketing information management system, a series of marketing channels are organized for Apple and marketing countermeasures. The management system enables to effective implementation of the marketing and promotion plan, as well as evaluation, monitoring, and update of the implementation results in a reasonable and timely manner, laying a good foundation for successful marketing. The work provided valuable advice. Through an in-depth analysis of Apple, people can deeply understand its core competitiveness and enterprise development of the layout of ideas. The paper also provides examples and references for enterprises to optimize their business models.

Ford Motor Company is an American multinational automaker founded in 1903. It is one of the largest automobile manufacturers in the world. This research delves into the financial condition of Ford Motor Company, an automotive industry leader, to provide a comprehensive analysis of its financial performance and prospects. Against the backdrop of an evolving automotive landscape and increasing consumer demand for sustainable and technologically advanced vehicles, this study is timely and relevant. The primary objective of this paper is to scrutinize Ford's financial health and prospects in-depth. The research combines quantitative data analysis of Ford's financial statements, historical performance, and market trends with a qualitative assessment of its strategic positioning and competitive environment. The findings indicate that while Ford faces certain financial challenges, it possesses the potential for growth and innovation. Recommendations include leveraging 5G technology to enhance product quality and cautiously exploring opportunities in the electric vehicle sector.

In the age of big data, business intelligence technology is pivotal in enhancing user experiences and driving innovation across industries. This paper focuses on Alibaba Group, a trailblazer in e-commerce, to examine the transformative role of business intelligence. This paper investigates Alibaba’s cutting-edge application of business intelligence technology, focusing on intelligent recommendation systems, personalized marketing strategies, and efficient supply chain management. The recommendation system harnesses data analysis to provide tailored product suggestions, boosting user satisfaction and sales. Data-driven marketing strategies enable Alibaba to create personalized promotions and coupons, enhancing user experiences and building loyalty. Intelligent supply chain management employs real-time monitoring, optimized transportation, and data-driven decisions to ensure timely deliveries and cost efficiency. A case study of Alibaba's "Singles’ Day Global Shopping Festival" illustrates how business intelligence technology creates a dynamic, data-powered shopping event. Every decision during this event is informed by real-time analysis and AI insights, enabling swift responses to evolving consumer needs. In summary, business intelligence, a driving force in the age of big data, is at the heart of Alibaba’s success. Alibaba has tapped the potential of business intelligence by enhancing the user experience and facilitating data-driven decision-making.

With the widespread popularization of digital technology and its continuous penetration into family life, the impact of digital technology on the national economy and residents' lives has gradually emerged. Based on the CFPS database, this article uses the A-F model to measure the household digital penetration index, explores the popularity and application of digital technology in households, and uses an empirical model to analyze the impact of the household digital penetration index on household income, consumption and income gaps. Research results show that digital technology has increasingly penetrated households from 2010 to 2020. The household digital penetration index can effectively increase household income and stimulate consumption, but it also widens the household income gap to a certain extent. At the same time, the impact of household digital penetration index on residential households shows obvious spatial heterogeneity.

In the quickly expanding landscape of contemporary financial markets, the paramount significance lies in comprehending and effectively employing novel technologies. Trading, a practice that bears resemblances to the use of sticks, has become a prominent option for structuring investment portfolios owing to its inherent diversification properties. This concept has the potential to enhance individuals' comprehension and decision-making abilities inside the intricate and densely populated realm of contemporary finance. This research aims to explore the correlation between Exchange-Traded Funds (ETFs) and the utilization of sophisticated machine learning methodologies, with a particular focus on XGBoost (eXtreme Gradient Boosting). The study additionally offers an extensive overview of the significance of artificial intelligence (AI) in the field of finance. It employs the concept of Xgboost to address the challenge of handling the substantial volume of datasets in the stock market. This approach is based on the utilization of decision trees, which are a robust machine learning algorithm. The objective is to examine and investigate the profound impact of AI in the realm of finance.

ESG, as an increasingly important evaluation indicator for today's enterprises, is being widely used. This article aims to study the relationship between ESG performance in the energy industry and stock value. This article takes the data of energy companies listed in the Shanghai and Shenzhen A-share markets from 2015 to 2022 as samples, controls for two variables: energy type and industry profitability, and uses the P/E ratio estimation method for analysis. Research has found that energy companies with environmental friendliness, high social reputation, and good management practices have higher stock prices, making them more likely to be favored by investors. The relevant research in this article can effectively help investors optimize their decision-making by analyzing the ESG situation when choosing the energy industry. At the same time, this study can provide some development insights for new and traditional energy enterprises.

In the past century, the international monetary system has undergone obvious changes, from the domination of the British pound to the domination of the US dollar, and then to the emergence and strengthening of "de-dollarization", which is also in line with the trend of The Times of economic multi-polarization, and the multi-polarization of the monetary system has become the trend of history. However, whether in foreign exchange reserves, international trade pricing and settlement, or due to the strong comprehensive national strength of the United States, it can be said that the US dollar will remain stable for a long time, and the international monetary system will remain relatively stable. How to maintain the stability and development of the national economy in emerging economies under the transformation of the old and new monetary systems, and how to develop the international monetary system will be an important proposition of the times.

In February 2023, China promulgated the full implementation of the registration system in the entire stock market, replacing the original verification system. Taking the main board and GEM companies listed from January 2019 to January 2023 as research samples, this paper uses PSM-DID Model to investigate the impact of the registration system on the IPO underpricing rate. Meanwhile, OLS model is used to analyze the impact of investor sentiment and the interaction of the registration system on the IPO underpricing rate. The implementation of the registration system significantly improves the IPO underpricing rate on the basis of 1%. Parallel trend test and placebo test further verify the robustness of the conclusion. the interaction between investors' speculation and the registration system also improves the IPO underpricing rate.