Proceedings of the 7th International Conference on Economic Management and Green Development
Canh Thien Dang, King's College London
The purpose of this study is to compare the differences in disclosure quality between Chinese and American companies listed in the United States. Despite the fact that Chinese companies listed on a U.S. stock exchange are required to adhere to the same disclosure and financial reporting regulations as U.S. companies listed on that exchange, variations between the two persist. Consequently, this study seeks to explore and compare the specific disparities in disclosure quality between Chinese companies listed in the U.S. and American companies. The "use of proceeds" section of the initial IPO prospectus for both American and Chinese companies will be employed to assess the specificity of disclosure quality. Statistical data sampling and analysis will be conducted to compare their specificity of disclosure. Finally, a T-test will be employed to compare and contrast the results. Based on our research findings, it can be concluded that Chinese companies listed in the U.S. exhibit a significantly higher overall quality of information disclosure compared to domestically listed U.S. companies.
In order to obtain a higher long-term average return from a portfolio, investors need to increase the level of risks that cannot be dispersed by diversification in the portfolio, and the asset pricing model can help investors to judge how much risk is reasonable to take. This paper will introduce the development process of several asset pricing models, and integrate and overview the applicability of the above several asset pricing models in China’s capital market by combining the relevant research of domestic and foreign scholars over the years based on existing literature and data analysis results. Finally, it can be found that due to its strict prerequisites, Capital Asset Pricing Model (CAPM) has the lowest applicability in the Chinese stock market, while the Five-Factor Model and the pricing model based on beta decomposition both have good applicability, but it is hard to say which one is the best one to adapt to the Chinese stock market at present.
As AI technology is at the heart of technology and driving the world's economy in different forms, NVIDIA was once again in the industry spotlight and where the world's attention was gathered at the end of May this year. This paper provides a financial analysis of this company's key figures and a comparison and commentary with its peers, using NVIDIA's fiscal year 2023 as the primary data base. It contributes recommendations for NVIDIA, the leading company, and provides ideas for the development of the related technology industry. Through the comparison of cross-sectional data, NVIDIA presents its internal data in an objective and clear manner, with significant changes in data that are reasonably and comprehensively articulated and well documented. In comparing the company's financials, NVIDIA performs well and has strong and core competitive capabilities. Using the formulas to estimate the stock price and enterprise value, NVIDIA is greatly overvalued. The reasons for this and the potential risks of the company are presented at the end of this paper.
The study focuses on the phenomenon of initial public offering (IPO) underpricing in China's Science and Technology Innovation Board (STAR market). The study employs the stochastic frontier model to analyze IPO underpricing and decomposes it into two components: primary market underpricing and secondary market premium. Additionally, the research investigates the impact of research and development (R&D) investment on IPO underpricing. The empirical analysis is based on a sample of 480 companies listed on the STAR market between July 2019 and January 2023. The results reveal that IPO underpricing on the STAR market is substantial, with an average underpricing rate of 131.81%. The primary market underpricing component contributes 4.24% to the overall underpricing rate, while the secondary market premium accounts for the remaining 95.76%. Furthermore, the study explores various factors that influence the secondary market premium using multiple linear regression analysis. The findings indicate that variables such as issuance volume, underwriting fee, turnover rate, and R&D investment significantly impact the secondary market premium.
In order to have a brief insight into the process of business data analysis for the big mart’s product and through which to find out the inner logic about data analysis. This research did a brief research based on the big mart sales dataset from Kaggle. The data are collected in 2013 for 1559 products across 10 stores in different cities. This research aims to build a predictive model and forecast the sales of each product at the specific stores and then try to understand the properties of products and outlets which play a key role in increasing sales. After using some basic analysis methods based on python, the author gets the distribution outcome of a big mart’s product and creates five simple models to predict the final outlet-sales and find out the most performed model using MAE criteria. The outcome shows that finally the XGB Regressor model performed best and for the real business, it is the most suitable selection.
Bank telemarketing campaigns play a pivotal in fostering customer relationships and promoting financial products. However, the factors that contribute to the success of these campaigns are multifaceted and often elusive. This study utilizes a range of machine learning techniques to analyze an extensive dataset of telemarketing campaigns from a Portuguese banking institution, shedding lights on critical determinants of its success. The data underwent the application of several machine learning algorithms, including Decision Trees, Random Forest, Logistic Regression, Gradient Boosting, and Naïve Bayes, facilitating the discovery of notable patterns and correlations. Findings revealed that variables such as age, occupation, seasonality, and the number of phone calls exert significant influence on campaign outcomes. By leveraging these insights, banking institutions and marketing strategists can craft more effective, data-driven telemarketing strategies. This in turn stands to enhance marketing efficacy, customer acquisition, and retention, translating into improved business performance.
This article aims to analyze the overall performance of Nike Inc. over the past three years and make predictions about its future. Firstly, the paper will discuss the three most significant accounting policies in Nike's 2022 annual report. This will help determine the company's asset management and utilization, profitability, and long-term development trends. Furthermore, the article will evaluate the company's performance in the previous year by comparing various financial ratios with its competitors in the Nike industry. This assessment will enable an evaluation of the company's financial position, and operational achievements, and showcase Nike's position within the industry. Lastly, based on Nike's overall performance in 2022, the paper will provide forecasts for the next two years regarding Nike's market value and performance. As a leading brand in the world of sports goods, Nike has proven through practice to be one of the most successful companies to date. It has adopted a strategically significant product portfolio strategy, resulting in significant market advancements. The conclusion of this article will contribute to other sports goods brands learning from Nike's relevant marketing strategies, thereby driving the overall development of the industry.
Scale effect in the stock market refers to the negative correlation between company size and expected return, which is a common anomaly in the financial market. Investor sentiment is the opinion that investors create based on their expectations of the potential future cash flow from their assets and the inherent dangers of making investments. The initial goals of this paper were to develop an investor sentiment index system and investigate how it relates to the market scale impact. In this paper, principal component analysis and the CICSI index, which can better measure Chinese investor sentiment, are combined to construct investors More than 3000 sample stocks are grouped by size, the average return rate of each group is calculated, and the scale effect is tested. Through the verification of the scale effect under the optimistic, neutral, and pessimistic sentiment index, the applicability of generated sentiment index to the scale effect is confirmed.
With the rapid development of economics, customers tend to favour clothes that are fashionable, light and comfortable, so the competition in the clothing industry is fierce. In order to gain steady growth and an advantageous position, companies have to transform to adapt to the changing environment. This paper concentrates on Bosideng, which is in the leading position in the clothing industry and has transformed successfully, so it can be a great example. This article uses case study: firstly, the article introduces the background of its transformation, using Porter’s five forces model and SWOT analysis to explain why Bosideng needs to transfer. Then, citing the 4P model to analyze how Bosideng could transform successfully. At last, through financial analysis, Bosideng has achieved better financial performance after the transformation, which means that the transformation is effective. From the case study, it is clear that when diversification could not sustain the company’s development, going back to the main business might be a wise choice. This article’s analysis might provide some experience for other companies.
The development of China's securities market has been more than 30 years, among which China's A-share market has become one of the largest stock markets in the world, and has a good development trend. Since the new era, the digital economy has become an indispensable part of people's lives. Cloud computing related conceptual stocks have received increasing attention from the capital market since 2019. Therefore, this paper classifies the cloud computing stocks and gives some suggestions to investors. 47 cloud computing stocks were randomly selected and their basic data were downloaded from the RESSET database. Through sorting and constructing, two categories of market indicators and fundamental indicators are selected, with a total of 17 variables. In order to reduce the dimensionality of variables, five common factors were first extracted using Factor Analysis and named as price factor, transaction factor, debt paying factor, growth factor, and profit factor according to their characteristics. Using these five factors to carry out K-means clustering, the selected stocks are divided into mature stocks and ordinary stocks.
The study examines the impact of the rise of the Federal Funds Rate (FFR) on the stock price returns and volatility of the United States of America. The study extracted the stock price from January 3, 2022, to June 16, 2023, from the Investing finance terminal, including the Standard and Poor’s (S&P 500), US Dollar Index, and the National Association of Securities Dealers Automated Quotations (NASDAQ) Index. This study uses the Vector Autoregression (VAR) model and the ARMA-GARCH model to evaluate the stock returns and daily return volatility, respectively. The two models inspect the effect of the US Dollar index on both the NASDAQ and S&P 500. The result of this study suggests that the rise in the Federal Funds Rate would cause the stock market price to decline for a relatively long period. However, the volatility of the daily return remains stable. By studying the effect of the rise in the Federal Funds Rate, policymakers can change the stock prices by adjusting the Federal Funds Rate based on the market’s needs.
Since the Industrial Revolution, global development and progress have caused serious environmental damage and resource consumption. It is not in line with the concept of sustainable development to measure the economic level solely by GDP. Therefore, GGDP was introduced as an alternative. To evaluate the global impact of GGDP replacing GDP, we have constructed a GGDP multiple linear regression model. This model takes into account GGDP in different countries at different times and incorporates multiple influencing factors. Additionally, we introduce the grey prediction model to demonstrate that GGDP can serve as a suitable measure of economic development level. This supports the idea that promoting GGDP is applicable on a global scale. Furthermore, we introduce five natural resource indicators and conduct a Spearman correlation analysis between GGDP, GDP, and these five natural indicators. To illustrate this, we take the United States, a representative developed country, as an example.
The 2007-2009 financial crisis, considered the worst crisis in human history since the Great Depression of the 1930s was not expected to end the period of the Great Moderation when financial and economic stability lasted from the mid-1980s to 2007. Although more than a decade has passed since the crisis, the analysis of the causes and consequences of the 2007-2009 financial crisis remains relevant for today's global economy, which is affected by both COVID-19 and the war in Ukraine. The crisis came as a surprise to almost everyone, but its roots were already in the U.S. financial system a decade ago. The explosion of the housing market in the United States at the beginning of the 21st century attracted a large number of banks and financial institutions to invest. The immediate cause of the financial crisis was the Federal Reserve's monetary policy, inflation in subprime mortgages, and the abuse of credit default swaps. The financial crisis also triggered a large number of banks to go bankrupt due to insolvency or default, and after the collapse of Lehman Brothers, the US government had to use a large number of emergency funds to bail out the market. These bailouts largely saved these too-big-to-fail companies and had no significant effect on the years of high unemployment.
This essay gives a general overview of how fintech has impacted the financial services sector, highlighting the significance of technologies like blockchain, Robo-Advisors, online payment, and P2P lending. The essay examines how blockchain technology affects financial transactions, including its potential to replace conventional middlemen and improve security and transparency. The rise of Robo-counselors as a disruptive technology that gives investors a cheap and practical substitute for traditional financial counselors is also covered. The article emphasizes the significance of striking a balance between technology and human competence by contrasting the benefits and drawbacks of robot advisors with those of actual advisors. Furthermore, the essay covers the expansion of online payment options, such as digital wallets and mobile payment applications, and how these may change how customers make purchases. Finally, the article explores the potential and hazards raised by the emergence of peer-to-peer lending platforms, which give borrowers an alternative to conventional bank loans. Overall, the article underlines how fintech has the potential to revolutionize the financial services sector, but it also stresses how crucial it is to strike a balance between innovation, financial stability, and regulatory compliance.
With the introduction of Web 2.0, internet users have become targets of information dissemination. Advertising based on user-generated content (UGC) has gained popularity. The purpose of this study was to investigate the benefits of the Little Red Book. Using Little Red Book as a case study, relevant data on UGC and the Little Red Book platform over the past five years, along with the 4Ps factors of marketing mix theory, were applied to analyze the three primary reasons why Little Red Book successfully occupied a large share of the UGC market and to provide three recommendations. First, Little Red Book's user verticality is high, accurately positioning the female market and the middle-to-upper-class demographic. Suggest increasing the number of users, penetrating a "shrinking market," and developing the economy. Second, Little Red Book UGC advertising is driven by content quality, which is advantageous for brand image formation. Propose establishing an online e-commerce business and a commercial closed-loop. Lastly, the UGC advertising investment cost for Little Red Book is low, and the return rate is high. It is advised to standardize the advertising pricing mechanism, increase the KOL threshold, and establish a brand cooperation system in collaboration with MCN.
With the sudden rise of various sports events in recent years, the sports industry has gradually come into the limelight, and how to grasp the economic benefits as well as the external impacts brought by sports events will become a problem that must be faced. Based on the impact of large-scale sports events on the local economy and the stock market, this paper studies the various impacts of sports events on society through data collection and comparative analysis and focuses on the negative impacts that are easy to be ignored. Among them, in the stock market, through collecting the stock market fluctuations during the recent Olympic Games, it is estimated that the Olympic Games will disperse the stock market, and further lead to the stock market downturn, and weak rebound. In terms of the local economy, sports events can promote development, whether in terms of social infrastructure construction or nominal GDP. However, the effect of sports on urban economic development is not as great as that of paper data, because the money spent on sports in people's leisure time is nothing more than the replacement of other entertainment, which cannot bring about additional consumption. The conclusion shows that the development of the sports industry is accompanied by certain losses in other aspects, so it is necessary to balance the development plan reasonably in order to maximize the social benefits.
With the continuous advancement of technology and productivity in China, most companies are gradually expanding. However, due to the emergence of New Crown Pneumonia in 2019, the profitability of most enterprises is affected by the New Crown Pneumonia epidemic. Therefore, enterprises are facing greater challenges, and the only way to survive in such an environment is to continuously improve the profitability of enterprises based on legal and reasonable compliance in order to adapt to the progress and development of the times. Facing the new economic environment and policies, enterprises need to analyze the complex economic environment they are currently facing from multiple perspectives. The main object of this paper is to analyze the corporate profitability of Ningxia Baofeng Energy and the impact of various indicators on profitability, identify key indicators that have a significant impact on corporate profitability, and find ways to improve corporate profitability through discussion and analysis. The main methods used in this paper are literature analysis and empirical research, and the analysis of corporate profitability is further developed and discussed by analyzing the content of the literature and studying the specific situation of Ningxia Baofeng Energy. This paper came to the following conclusions about the study of corporate profitability: companies should reduce operating costs through cost control and focus on increasing sales margins to ensure sustainable profitability.
In accordance with the double carbon target proposed in 2020: to achieve "carbon peak" by 2030 and "carbon neutrality" by 2060. The background of this research is how to optimize the industrial structure and guide low-carbon consumption through energy production and consumption restructuring. In the post-epidemic era, a series of changes in people's consumption concepts and behaviour have taken place, which made positive or negative impacts on low-carbon consumption. The research focus here is to adopt the survey analysis method, collecting primary analysis data through data collection, questionnaires, and interviews. This paper focuses on the validation and practice of this research method. Researches have found that the current direct energy consumption behaviours of residents concentrated in the household scenario and the travel scenario. The low-carbon nature of these scenarios is influenced by two main decisions: the choice of consumption mode at the first consumption stage and the choice of consumption mode at the ongoing consumption stage. The factors that influence these decisions vary from stage to stage, and different strategies can help guide residents' low-carbon consumption behaviour.
In recent years, Chinese tea brands have emerged one after another, and the market has gradually become saturated. This paper explores how MIXUE Ice Cream & Tea has become a leading brand in the low-end tea-drinking industry in China. By applying several basic models, such as Strengths, Weaknesses, Opportunities, Threats (SWOT), Segmentation, Targeting, Positioning (STP), Political, Economic, Social, Technical (PEST), and Porter's Five Forces, the author explores the success of MIXUE Ice Cream & Tea and draws a conclusion that MIXUE Ice Cream & Tea attracts a large number of consumers by utilizing its price advantage and multimedia platforms. Meanwhile, the brand can better understand the needs and preferences of the target market, which helps it achieve an improvement in brand awareness and promote the launch of suitable products. Additionally, MIXUE Ice Cream & Tea’s success is also closely associated with a conducive economic environment, policy support, and technology development. However, risks in market competition, franchise business, inherent brand impression, substitutes, etc. exist as well, and MIXUE Ice Cream & Tea still needs to overcome these difficulties in the future.
This paper examines the salient problems related to the separation of ownership and control of modern corporations, as discussed in the books ‘The Wealth of Nations’ and ‘The Modern Corporation and Private Property’. It qualitatively analyzes past empirical studies and evaluates the effectiveness of various solutions aimed at alleviating the separation between ownership and control. The analysis reveals that the agency relationship between shareholders and organizational managers underpins the phenomenon of ownership and control separation in modern companies. This separation offers several benefits, including ensuring business sustainability through professional management, efficient decision-making, and maximizing physical capital. Additionally, numerous mechanisms have proven effective in managing these issues. The paper explores management ownership and incentives, independent ownership, and institutional ownership as solutions that they have been found to reduce institutional problems and manage risk avoidance.