Proceedings of the 2nd International Conference on Financial Technology and Business Analysis
Javier Cifuentes-Faura, University of Murcia
McDonald’s has a high global brand awareness directly tied to its country-specific marketing initiatives. This study aims to compare McDonald’s marketing tactics in European and Asian markets by delving into McDonald’s global strategic marketing model, which includes. However, it is not limited to product categories, promotional techniques, and distribution networks. This study fosters reciprocal learning and provides additional ideas and methodologies for McDonald’s globalization strategy development in the future by looking for marketing variations in other regions and analyzing the strengths and shortcomings of both sides. By reviewing the literature and various secondary sources of previous relevant studies, it can be concluded that both the European and Asian markets have distinctive marketing methods that the other side lacks, which is inextricably linked to McDonald’s global customization strategy; thus, by analyzing the markets of the two sides, it may be possible to learn from the other side’s advantageous marketing modes through continuous optimization and improvement to attract more consumers. McDonald’s in other regions can also learn from its strategies to respond flexibly to different markets.
This research paper delves into the critical relationship between Starbucks’ sustainable marketing practices and its impact on brand loyalty. The case description of Starbucks as a leading global coffeehouse chain provides the context for analyzing the challenges and opportunities in its sustainable marketing endeavors. The research background highlights the increasing significance of sustainability in contemporary business practices and the potential for sustainable marketing to foster brand loyalty. Drawing upon a literature review, the study summarizes and synthesizes relevant findings from multiple articles that explore the interplay between sustainability initiatives and consumer loyalty. Through a comprehensive analysis of problems, the research identifies key challenges Starbucks faces in implementing and communicating sustainability efforts. The paper then offers strategic recommendations, such as enhancing transparency, engaging consumers through education, and innovating packaging solutions, to improve the effectiveness of Starbucks’ sustainability initiatives and strengthen brand loyalty. The value of this paper lies in its practical implications for Starbucks and other businesses seeking to create meaningful connections with consumers through sustainability. By examining the potential benefits and results of implementing the proposed solutions, the research provides insight into how companies can enhance their brand reputation, consumer trust, and environmental impact. Furthermore, it addresses potential challenges and constraints in executing sustainability strategies, aiding decision-makers in navigating the complexities of integrating sustainability into their marketing approaches. Overall, this paper contributes to the literature on sustainable marketing and brand loyalty, providing a solid foundation for future research and guiding businesses toward a more sustainable and loyal customer base.
As one of the most popular forms of entertainment, video games have become a major relaxation method for people nowadays. Genshin Impact is one of the game people play most rapidly. The performance is extremely remarkable and significant; within two years, its popularity has exceeded many classic video games like a counter strike or League of Legends. Thus, in this paper, It is worth investigating the marketing strategy behind this game as the number of players this game has attracted is enormous. Nevertheless, despite all these performances, many problems also existed in the mechanism and mode of the game. The analysis of problems will discuss complex and tedious plots, shortage of resources, and low rates of getting certain characters. The following paper has suggestions corresponding to the 3 problems: adding interactions between the game and the players, periodic resources refreshing mode, and adding more ways for players to get the five-star characters. In this study, the author will use the CAS method to discuss the abovementioned things.
Currently, Chinese billiards is developing particularly well in China, plus the billiards industry itself has a low threshold, so the billiards industry can be used as an employment direction for people with low education. This paper investigates and researches the educational backgrounds of the top 8 active international and nine world-class players in China, the abilities and incomes of active international players, and the analysis of the commercial marketization of the sport of billiards. It first discusses the commercial ecology of Chinese billiards and the advantages of its current development and promotion in China, and then discusses the educational backgrounds and incomes of professional billiards players. Finally, it analyzes the comparison of the status of international and domestic snooker and Chinese billiards. As well as the volume of non-higher education groups aged 15-22 years old at this stage and the necessity of the introduction of Chinese billiards, this paper draws the conclusion that the education level of billiards professional players is completely irrelevant to their career development. Combined with the current situation of junior and senior high school education in China, this paper finally concludes that billiards can be used as a useful supplement to the vocational education of young people.
Supply chain finance is one of the key topics in today's research. Recently, the number and size of enterprises of various types have increased, and the demand for financing has grown significantly. At the same time, banks are providing supply chain finance services to enterprises to meet the demand for enterprise financing. Some researchers have found that banks play an important role in the supply chain finance business, and the amount of financing is also increasing. However, there are still some unresolved problems in the process of enterprise financing by banks. Therefore, this study focuses on the difficulties in the process of enterprise financing from the perspective of banks and the countermeasures to solve them. The research methodology of this paper is as follows: firstly, to collect the transaction data of Ping An Bank's supply chain financial services in the past ten years, and secondly, to analyse the data and identify the problems in the financing process from the changes in the data. It is found that the current Ping An Bank has problems such as financing cost is still high, it is difficult to guarantee the financing security and transaction transparency, and the financing mode innovation is insufficient, and these problems also limit the further development of the enterprise. In order to better improve the domestic supply chain financial system, the government, enterprises, banks and other parties should work together to overcome the current difficulties.
The past decade has witnessed a growing recognition of ESG (Environmental, Social, and Governance) investing, yet the application of ESG principles within Berkshire Hathaway, under the leadership of renowned investor Warren Buffett, has not been prominently evident. This study seeks to ascertain whether the historical investment decisions made by Buffett's Berkshire Hathaway, seemingly disassociated from active ESG involvement, may have nonetheless been subject to the influence of ESG factors. Employing methodologies encompassing correlation and regression analyses, this research unveils a discernible positive relationship between Berkshire Hathaway's investment quantities and the ESG scores of the invested firms. Moreover, companies demonstrating a positive trajectory in ESG considerations tend to be more aptly enlisted in the subsequent year's investment roster, while those witnessing a decline in ESG scores are prone to exclusion. In a comparative exploration of financial variables and ESG metrics, this study chose profit margin, ultimately revealing that ESG scores exert a notably more pronounced impact on Berkshire Hathaway's investment deliberations, specifically pertaining to share quantities, in contrast to the effect exerted by profit margin.
This paper illustrates the working process of predicting the Bitcoin price applying ARIMA, SARIMA and linear regression. Since more and more machine learning models were developed and tested in the financial field, these three models are selected to examine their reliabilities. In this study, three methodologies have been used for the Bitcoin predictions under the data set of Bitcoin historical prices. With the help of python notebook, order (1, 1, 1) and seasonal order (0, 1, 1, 12) were applied to the predictions in ARIMA and SARIMA respectively. In terms of linear regression, this paper used two independent variables including historical data and trading volume to predict the Bitcoin prices. It was discovered that the predictive graph for these three methodologies can match the actual value well, and linear regression performs the best. Considering the rapid development of machine learning methods, adopting alternative methods deserve in-depth investigations.
A sincere commitment to establishing a community of human destiny is made through achieving carbon peak and carbon neutrality. Following the pace of affluent nations, China has incorporated carbon peak and carbon neutrality into overall economic and social development. Environmental information disclosure is the most basic and major part of environmental accounting, following the sustainable development strategy because of daily accounting supervision and accounting. In this research, a case study of SAIC Group's environmental information disclosure in China from 2019 to 2022 is chosen, and analyzes SAIC Group's operating capacity, profitability, solvency and development capacity by combining information asymmetry theory and stakeholder theory. This paper investigates the roots of environmental information disclosure in the automobile manufacturing industry, chooses a representative SAIC Group to summarize its environmental information disclosure, assesses the current level of corporate environmental information disclosure, and provides relevant departments with pointers and relates for the development of an environmental information disclosure system for enterprises in this industry. Finally, optimization recommendations are offered for the growth of listed businesses' environmental information disclosure in China's vehicle manufacturing sector.
The research background is Lehman Brothers Holdings Inc. (Lehman Bro.) sending Chapter 11 bankruptcy protection application due to extremely high leverage and liquidity lacking towards New York Court in the Subprime Lending Crisis, 2008. This research illustrates the event’s economic background, three critical factors to final bankruptcy and practical solutions from Macro and Micro perspectives. This research adopts the typical case analysis method and concludes that mismatching capital structure with high insolvency risk, not be acquired or purchased for its qualified and inferior assets by other financial institutions or provided timely adequate capital support by American Federal Reserves, agency problem as conflicts of interests between the chief executive officer and his employment's shareholders in risk preference, and finally inappropriate accounting policies without complete disclosure of misconducting Repo105 to reduce debt-equity ratio for external investors are essential reasons account for Lehman Bro. Bankruptcy in 2008 with concrete illustrations in this case analysis research papers.
This study investigates the elements of return and chance in the very good-quality hardware manufacturing sector by directing empirical research and far-reaching analysis. The research centers around building six portfolios considering market esteem scale and book-to-market ratio to explore the relevance of the Fama-French factor analysis model. Our discoveries uncover important patterns, including a backward connection between the book-to-market ratio and rates of return, as well as higher paces of return for bigger scope portfolios, featuring a scale impact. The meaning of positive month-to-month yields across all portfolio blends underlines the business' general profitability from 2015 to 2021. Thorough stationarity tests approve our relapse analysis, where market risk factor(MKT), scale factor (SMB), and book-to-market ratio factor (HML) altogether impact stock portfolio returns. By adjusting our discoveries to past writing and featuring the model's viability in various sectors, our review adds to a more extensive comprehension of asset pricing and illuminates powerful investment methodologies custom-fitted to explicit market elements.
As the capital market develops and matures, the requirements for the quality of accounting information of listed companies gradually increase. Under these circumstances, it is essential to verify whether implementing this system can improve the quality of accounting information and realize the effective allocation of resources. In order to empirically test the governance effect of short selling on the accounting information disclosure of listed companies, this paper uses the data of A-share listed companies from 2009 to 2011, uses the differential and probit model, and finds that short selling has no significant impact on the quality of information disclosure. China officially implemented the short-selling system on March 31, 2010. During the early stages of implementation, the trading volume of short-selling was small, and the scale of short-selling of listed companies was also small. Therefore, short-selling transactions in a short period of time will not significantly affect the quality of accounting information disclosure.
The digital transformation of the 21st century has catalyzed the emergence of two dominant phenomena: the sharing economy and the expansive growth of social networks. This paper meticulously delves into the intricate relationship between these two transformative domains, offering a comprehensive exploration of their convergence and the profound impact this has on consumer behavior, evolving business models, and overarching societal norms. Presently, the landscape is distinctly marked by the democratization of resources, enabling greater access and utilization, coupled with a significant shift in communication paradigms. This shift is predominantly driven by innovative platforms such as Airbnb, Uber, and Facebook, which have redefined user engagement and economic transactions. Peering into the future, it becomes evident that technological advancements, stand at the forefront of refining this convergence. These technologies promise to address and offer robust solutions to prevailing challenges, especially those related to trust, transparency, and tailored personalization. Yet, this promising horizon is interspersed with challenges. Pressing issues such as regulatory frameworks, the imperative of data privacy, and the consistent demand for quality assurance necessitate astute navigation by all stakeholders involved. In summation, the dynamic interplay between the sharing economy and social networks heralds a transformative paradigm shift in the digital age. This shift places a spotlight on sustainability, fosters community collaboration, and champions the ethos of shared value. Stakeholders, ranging from businesses to end consumers, are emphatically encouraged to navigate this evolving digital terrain with adaptability, strategic foresight, and informed prudence.
Short videos refer to video content uploaded, shared, and watched through mobile internet platforms with a duration of between 15 seconds and 10 minutes. Since the popularity of 4G networks, the short video industry has achieved rapid development, and hundreds of millions of user-level platforms such as TikTok and Kwai have been born, which has established a strong influence in the mobile Internet era. This paper uses SWOT writing models to analyze TikTok's features and strengths, weaknesses, opportunities opportunities and potential risks. It provides rationalization suggestions for future growth and development. This is because TikTok is not only in the absolute leading position with other short video apps, such as Kwai but also has strong learning and reference value, so readers can not only learn a series of successful methods, such as business models, operation concepts, timing, etc. but also learn how to use the short video platform as a marketing tool. For some marketers, it is also possible to understand consumer behavior and they can adjust their marketing campaigns to better understand the audience segments they need.
With the progress of machine learning, its application fields are gradually increasing, especially in the field of quantitative finance, which is particularly outstanding, the Portfolio optimization combine with time series prediction and machine learning has brought great development prospects for investors. This study mainly employed the LSTM model and Mean-Variance Model to predict stock return and build an optimal combination respectively. This study selects relatively high weight stocks on the NASDAQ index, 'AAPL', 'AMZN',' ASML', 'AVGO', 'GOOG', from December 31, 2019, to July 1, 2023. First, the study obtained the predicted stock prices of five stocks through LSTM Model and based calculated the predicted returns on a rolling basis. Second, based on the modern investment theory, this study uses the predicted rate of return to construct the optimal daily investment ratio through Mean-Variance Model. Finally, this study compared cumulative return of optimal portfolios with the NASDAQ within the same time frame. This study draws a conclusion that hybrid model which combine the stock price forecasting with asset allocation can indeed bring excess returns.
In the era of big data and advanced computational capabilities, financial market participants are continuously searching for innovative strategies to gain a competitive edge. A potential pathway emerges in the domain of deep learning, especially with regard to the LSTM neural architectures, which are renowned for their ability to handle and make predictions based on time series data. This study delves into the utilization of LSTM for predicting stock prices, emphasizing the advantages of dynamic investment portfolios in the rapidly fluctuating market conditions. Utilizing a dynamic window approach for time series data preprocessing, a self-attention mechanism - LSTM model was designed to anticipate the tendency of annual closing prices for five stocks from 2022 to 2023, Utilizing the initial 80% of stock price data as training set and allocating the residual 20% for validation. The performance of the dynamic optimization portfolio model was assessed by dynamically adjusting the weights of the stocks based on the last 20% of the data, and was subsequently compared to actual market cumulative returns. The findings indicate not only that the LSTM model offers a commendable level of accuracy in predicting stock prices, but also that the recursive algorithm for the dynamic optimization portfolio, constrained by maximum returns and minimal standard deviation, consistently outperforms the general market.
The automobile industry has a long and fascinating history. In the past ten years, the automobile industry has been developing continuously to adapt to changing market conditions and technological progress. With the growth of networked devices and Internet of Things (IoT), the automotive industry pays more attention to data analysis. Companies are using data to optimize operations, improve maintenance and improve security. In recent ten years, there has been a trend of integration in the automobile industry, and a few large companies have monopolized the market. This leads to the intensification of competition and the pressure of innovation. Ford Motor Company is a global automobile company, which has undergone major changes in recent years as part of the restructuring aimed at improving its financial performance and preparing for the company's future growth. This paper will outline the reorganization of Ford Motor Company, including the reasons, main measures involved and financial results of the reorganization. Enterprise reorganization is an effective strategic choice for Ford Motor Company, which can help enterprises adapt to changes in the external environment and improve their competitiveness and profitability. The case analysis of two reorganizations of Ford Motor Company in this paper can not only provide reference and enlightenment for other enterprises to reorganize, but also provide reference and basis for the in-depth study of enterprise reorganization in academic circles.
With the growth of China's national economy, the catering market has shown a strong momentum of development. People's demand for catering is growing, and catering chain stores provide consumers with diversified choices, so catering chain stores have been developed. Chain restaurants are, to a certain extent, the inevitable product of the development of the catering industry. It is a form of business organization and business model, referring to the expansion of catering enterprises through chain operation and franchising. This paper takes Haidilao and Xiangcai brand Nongchengji as examples, based on their successful business models. The study was conducted on the market size, competition, franchise mode, and management mode of chain catering. In addition, the development process and existing problems of Chinese chain catering were analyzed, and finally improvement suggestions were proposed, which provide examples and references for China's mechanization, and China's chain catering industry will enter a new era of development in the future
Inequality in the sex ratio is a significant issue in China. Some illegal sex determination mechanisms that give parents of young children gender information significantly contribute to the sex ratio imbalance. As a result, some parents make prejudiced decisions to abort girls. Regulation of prenatal sex determination by governmental and medical institutions will be challenging due to the high degree of information asymmetry. A few of the government’s initiatives have yet to show obvious results. Medical institutions and women of childbearing age automatically cooperate in spite of information asymmetry. The “prenatal sex determination” regulation model is given a new interpretation in this paper using the game theory analysis method. Additionally, the paper explains the causes of the regulation’s difficulty and proposes ideas, countermeasures, and solutions. The idea of cooperation contained in game theory is of great significance in resolving interest conflicts and achieving social harmony under the condition of interest differentiation.
To study the current situation of China's A-share market, this study contains a lot of literature and summarizes and analyzes the most prominent phenomena of China's A-share market at present. China's A-share market has been turbulent due to economic reforms, and the share of long money is still too small overall. Secondly, China's A-share market continues to open up, bringing fresh blood to the market and improving the long money problem. Third, China's introduction of ESG indicators has brought more references to both companies and shareholders. It also brings more foreign investment institutions to China, which can bring more stable long-term investment to the unstable A-share market and improve the status of having too many retail investors in A-shares now. Last but not least, China has started the era of full registration system, which brings more opportunities and challenges to the A-share market. There are also many problems, which will be pointed out in the text and suggestions given to solve them.
Along with the development of technology, machine learning would take up a higher role in analyzing categories. Among those categories, predicting stock price meets the needs of most people–or most people who trade stocks. By referring to the predicting model, stock traders can decide whether they should trade in or trade out to make a profit in the stock market. Therefore, it is necessary to testify which model can make the prediction with higher accuracy. To analyze this problem, this article examines the performance of different models under different size of datasets. This paper compared XGBoost and LSTM model by collecting stock price data that are 3 years, 6 years, and 9 years ago from the year 2023. Then analyze the close price of stock prices those models. By comparing the figures and calculated rmse value in each year and each model, the impact of different dataset sizes on each model would be revealed. This paper discovered that XGBoost model has greater accuracy under large-size dataset overall, but LSTM can predict more accurate stock price under small-size dataset.