bluuebunny/crossref_metadata_2025 huggingface dataset:
Islam dan kesejahteraan antara representasi dan inspirasi dalam demokrasi
Authors: Sulaiman Surpani , Anshor Maria Ulfah
Published: November 2023

Social welfare is often measured based on aspects such as income, population, health, education, employment, consumption, housing, and social culture. However, some people who already have a luxurious house, vehicle, deposit, and various other forms of wealth actually feel anxious, restless, fearful, and even commit suicide. This fact shows that there is something lacking in measuring social welfare. Islamic economics offers a different concept of welfare. Welfare in Islam is not only measured from the material aspect, but also from the spiritual aspect. In Islam, the happiness of life is given by Allah SWT to anyone who is willing to do good deeds accompanied by faith in Allah SWT. Three indicators for measuring welfare and happiness in Islam are mental formation (tauhid), consumption, and the disappearance of fear and all forms of anxiety. Zakat is one of the important instruments in Islamic economics to achieve social welfare. Zakat is an obligation for every Muslim who is able to give out a portion of his wealth to be given to those in need. Zakat can make a significant contribution to the economy of society, especially for rural communities. Zakat can also help reduce poverty and social inequality. By applying the concept of welfare offered by Islam, we can create a prosperous society, both materially and spiritually.
Information Fusion from Multimodal Clinical Sensors for Effective Supplier Decision-Making in Healthcare
Authors:   Director de la Universidad Regional Autónoma de los Andes (UNIANDES) Sede Santo Domingo, Eccuador, Galarza Fredy Cañizares ,   Docente de la carrera de Medicina de la Universidad Regional Autónoma de los Andes (UNIANDES Ambato), Ecuador,   Docente de la carrera de Medicina de la Universidad Regional Autónoma de los Andes (UNIANDES Ambato), Ecuador, Mullo Becker Neto , Argilagos Miguel Ramos
Published: November 2023

Effective procurement of clinical devices in healthcare demands a sophisticated decision-making approach integrating diverse data sources from multiple devices, brands, and suppliers, particularly within the context of information fusion. This study addresses this challenge by proposing an improved best-worst method harmonized with information fusion techniques and multi-criteria decision-making methodologies. The background emphasizes the dynamic nature of healthcare procurement, necessitating systematic strategies for navigating the complexities of device selection and integration. Recognizing the intricacies inherent in this challenge, the problem statement revolves around enhancing the best-worst method to amalgamate data from clinical devices while concurrently evaluating brands and suppliers. This aims to optimize performance and minimize costs within the information fusion paradigm. Our proposed methodology introduces an augmented best-worst approach, encompassing weighted criteria assessment for clinical devices, brands, and suppliers, providing a more adaptable and nuanced decision-making framework tailored to the information fusion landscape. The results showcase a structured evaluation matrix derived from refined weighted criteria, elucidating the relative performance and strengths across various entities within the healthcare procurement ecosystem. Emphasizing reliability, compatibility, innovation, and quality assurance, this process highlights pivotal factors influencing procurement decisions within the realm of information fusion.
Integrative Multi-Information Fusion for Enhanced Risk Assessment: A Multi-Criteria Decision-Making Framework
Authors:   Docente de la carrera de Software de la Universidad Regional Autónoma de los Andes (UNIANDES), Ecuador, Zambrano Luis Albarracín ,   Docente de la carrera de Software de la Universidad Regional Autónoma de los Andes (UNIANDES), Ecuador, Jadan Bolívar Villalta
Published: November 2023

This study addresses the burgeoning challenges in autonomous Maritime navigation by employing information fusion methodologies to assess and manage multifaceted risks. The proliferation of autonomous maritime systems has led to a complex interplay among maritime-related, shore-based remote control, environmental, and emergency management factors, necessitating a comprehensive risk evaluation framework. Leveraging a multi-criteria decision-making approach and employing the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), this research presents a methodical analysis of the coupling coordination degree among these risk variables. Through a meticulous examination of historical accident data and information fusion techniques, our study reveals dynamic trends in the comprehensive risk evaluation index, showcasing the evolving nature of risks inherent in autonomous Maritime navigation. The predictive insights gleaned from these analyses forecast an initial increase followed by a peak in accidents, underscoring the urgency for proactive risk mitigation strategies. This study's conclusions emphasize the pivotal role of information fusion methodologies in comprehensively assessing, understanding, and managing risks within autonomous Maritime navigation.
Assessment of the Educational Live Action in Uncertainty Environment under Single-Valued Neutrosophic Sets
Authors:   Rector de la Universidad Regional Autónoma de los Andes (UNIANDES), Gómez Gustavo Alvarez G� ,   Vicerrectora General de la Universidad Regional Autónoma de los Andes (UNIANDES),   Director de Investigación de la Universidad Regional Autónoma de los Andes (UNIANDES), Armijos Corona Gómez , Fernández Ariel Romero Fern�
Published: November 2023

Health professional educators are increasingly using escape rooms as a teaching tool. Given the fast development in their use, investigators have chosen varied assessment approaches to evaluate the instructional rooms. Considering educational escape rooms is a multi-attribute decision-making (MADM) process based on various criteria. This study proposed a MADM model to assess the educational escape rooms. This study used the VIKOR MADM method to evaluate the criteria and alternatives. This evaluation is made under a neutrosophic set to overcome the uncertainty information. We collected fourteen criteria and ten alternatives in this study. We employed a sensitivity analysis to show the proposed model's effectiveness and the results' stability. The analysis shows the results are stable.
Predict Stock Price Trend by Using Classification Model
Authors: Guo Jiaqi
Published: November 2023

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 Corporations 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.
Stock Market Price Prediction Using Machine Learning Models
Authors: Guo Zijie
Published: November 2023

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.
The Impact of Federal Reserve Interest Rate Hike on the Entertainment Industry: An Empirical Evidence
Authors: Lu Kuiyuan
Published: November 2023

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.
The Influence of Federal Reserves Interest Rates Hike Cycle on Transnational Corporations
Authors: He Wentao
Published: November 2023

The monetary policy of the United States has a broad and profound impact on the world economy and financial markets. Due to the critical position of the US dollar in the world economy, the US monetary policy will directly affect countries and economies worldwide, and the liquidity of the US dollar is also the foundation of global liquidity. The series of interest rate increases implemented by the Federal Reserve starting in 2022 has led to the appreciation of the US dollar index, which has significantly impacted the import costs and overseas sales of domestic and multinational companies in the United States. This article selects the global multinational technology company Apple and extracts daily closing data from June 2022 to April 2023 and the USD index for the same period. This study applies the VAR model to assess the correlation between both and uses the ARMA-GARCH-X model to analyze the impact of USD index on the volatility of Apple stocks. This study analyzes the impact of interest rate hikes on multinational corporations and provides management and investment advice to their stakeholders.
Forecasting Exchange Rates and Trade Balances: An ARIMA Analysis of USD/RMB and China's Trade Dynamics
Authors: Yin Jianyi
Published: November 2023

This paper utilizes an ARIMA model to predict the future exchange rate and trade balance of China. The primary data is the monthly average exchange rate of USD/RMB, while China's monthly trade balance serves as auxiliary data. The findings indicate a projected downward trend in the USD/RMB exchange rate, continuing from 2023 to 2024 and stabilizing around 6.7 from 2024 to 2025. This implies a depreciation of the Chinese currency against the US dollar. Additionally, China's trade balance is expected to experience modest growth over the next two years, albeit at a significantly reduced rate compared to previous years. These projections highlight the challenges faced by Chinese exporters and suggest evolving global trade dynamics. The paper discusses policy implications for managing exchange rate fluctuations and sustaining balanced trade relations. It emphasizes the usefulness of the ARIMA model for forecasting exchange rates and trade balances while acknowledging the limitations and potential impact of unforeseen events or policy changes on the outcomes. The study concludes by suggesting avenues for future research to improve the accuracy and robustness of such forecasts, encouraging continued exploration in this dynamic field of study.
The Impact of Internet Finance on the Real Economy
Authors: Xu Jiayu
Published: November 2023

Due to the improvement of information technology, the emergence of 5G network, and the rapid development of global informatization, the Internet economy, as an emerging force, has developed rapidly, and gradually formed a network economy and society with Internet as the center. By April 2023, the number of Internet users worldwide has reached 8.5 billion. In this case, more and more people advocate Internet finance, the real economy has been affected to a certain extent. The real economy is the source and cornerstone of social development, marking the economic development level of a country or region. How to improve the growth of the real economy has been the focus of the municipal government. Therefore, the purpose of this study is to explore the impact of Internet finance on the real economy, find out the reasons for the slow development of the real economy, and explore how to use Internet finance to promote the development of the real economy. By using iterature review and some data from the Internet, this article finds that Internet finance will promote and hinder the development of the real economy.
The Impact of the Covid-19 on Real Estate Companies: Vanke A and Poly Development
Authors: Di Boyu , Huang Shuokai , Jia Qingkai
Published: November 2023

The COVID-19 pandemic has had a profound impact on the global macroeconomic landscape, social order, and corporate operations. In light of the prevailing trend of economic globalization, the pandemic has significantly affected this process, but it will not deter the progress of economic globalization. The COVID-19 pandemic has not only had a considerable impact on various countries worldwide but has also affected China. To protect the lives and health of our citizens, China has implemented strict containment policies. The pandemic has also had a significant impression on company operations. Our research focuses on two major Chinese real estate companies, "Poly Developments" and "Vanke A." Both companies have publicly available and transparent financial reports that include information on profit figures, profit margins, cash flow, and debt situations. Despite the impact of the COVID-19 pandemic, Vanke witnessed a downward trend in total revenue and cash flow from 2019 to 2021, but a slight recovery was observed in 2022. Vanke displayed stable revenue growth in its development activities and gradually stabilized its profitability. The company also prioritized effective cash flow management and pursued a diversified development strategy, leading to a significant year-on-year increase in investment cash flow. Overall, Vanke exhibited remarkable performance in sustainable strategic planning, collaboration, and operational activities, maintaining its position as an industry leader.
The Impact of External Factor on Cryptocurrency: Explicit and Implicit Policy
Authors: Zhang Xiaoxuan
Published: November 2023

In recent years, the view of economists and issuers of cryptocurrencies has changed a lot, from the original price of cryptocurrencies is unpredictable, to the view that cryptocurrencies are affected by several key factors. Confidence is one of the key factors, and it is thought to be controllable. However, many economists and cryptocurrency issuers have acknowledged that the support for confidence in the cryptocurrency market is complex and volatile. The goal of this paper is to propose ways to stabilize confidence in cryptocurrencies based on these studies. Explicit and implicit policies are one of the important factors affecting cryptocurrencies. The purpose of this article is to illustrate the close connection between cryptocurrencies and information, and to analyze how explicit and implicit policies affect cryptocurrency information and markets. The impact of explicit and implicit policies on the price of cryptocurrencies is unpredictable, but it still has regularity in the impact on the price of cryptocurrencies. This article analyzes several notable cryptocurrency explicit and implicit policy events to illustrate the differences and characteristics between the two types of policies. The influence of explicit policy is not positively correlated with its propaganda intensity in the short term but will be maintained and stable in the long term. Stealth policies tend to show dramatic changes in a short period of time and are highly correlated with popularity. While hidden policies can cause sudden fluctuations, issuers of cryptocurrencies should pay more attention to the effects of explicit policies because they are more durable.
An Analysis of the Energy Crisis under the Ukraine Conflict
Authors: Qi Junhao
Published: November 2023

The Ukraine Conflict has had far-reaching implications for energy security, geopolitical dynamics, and various sectors. This study examines the impact of the conflict on Russias energy supply chain to Europe, specifically in terms of natural gas, oil, and coal. Before the conflict, Russia played a crucial role in supplying Europe with natural gas, oil, and coal through pipeline networks, maritime transportation, and export routes. However, the conflict disrupted these supply chains and resulted in significant price fluctuations. The conflict led to economic and energy sanctions imposed on Russia by the United States, the European Union, and Japan, causing a decline in Russias oil exports. The natural gas supply faced disruptions, leading to increased prices and supply shortages in Europe and globally. The coal market also experienced price hikes and supply disruptions. In response to the energy crisis, countries implemented measures to diversify their energy sources, reduce dependence on Russian imports, and accelerate the transition to renewable energy. The European Union, in particular, introduced the REPowerEU plan to enhance energy independence and promote green energy. The conflict also had implications for various sectors, including agriculture, manufacturing, and defense. Additionally, the US dollar strengthened as it became a safe haven currency during the conflict. Looking ahead, the Ukraine conflict will continue to shape the global energy landscape and geopolitical relationships, influencing strategies for energy resilience, security, and sustainability.
Systematic Risk Assessment in the Banking Industry
Authors: Bai Yifeng
Published: November 2023

The article delves into the intricate realm of systematic risk, a formidable peril that casts its shadow upon the entirety of the market or vast segments thereof. It traverses the annals of history, unearths the weighty import, origins, and reverberations of systematic risk, while shedding light on the intricate models and methodologies employed for its quantification and prudent handling. The article channels its focus towards three cardinal manifestations of systematic risk, namely liquidity risk, credit risk, and market risk. Moreover, it scrutinizes the perilous path these risks tread, culminating in cataclysmic systemic disasters that ruthlessly disrupt the harmonious functioning and unwavering stability of financial systems. To accentuate the gravity of the subject matter, the article cites poignant examples of such calamities, with the 2008 financial crisis standing as a haunting epitome. Furthermore, the article imparts sagacious guidelines and best practices, serving as a beacon for assuaging the ever-looming specter of systematic risk. Ultimately, the article culminates with an emphatic crescendo, underscoring the paramount significance of systematic risk management for discerning investors and astute policymakers alike.
Strategic Analysis of the Merger Between Ctrip and Go.com: Motivation, Market and Outlook
Authors: Zhang Xiaotian
Published: November 2023

With the increasing growth of the travel industry, many travel companies are partnering with other companies commercially because of their future growth. Ctrip and Where to Go considered that reason and the two companies merged in 2015. This article analyzes the merger of the two companies and through this analysis many methods of cooperation at the financial level can be derived, among others. in the process of the rapid development of Internet businesses driven by the Internet, the development of businesses from hostile competitive relationships to mergers and the gradual formation of new market patterns is also a growth process in the development of the Internet business market. By studying this article, it can be found that in many cases, companies that want to go to the next level in all aspects can choose to collaborate with various resources to amplify their respective strengths for mutual benefit. At the same time, only by maintaining the quality of service, the company will develop better.
A Novel Ensemble Machine Learning Model for Credit Risk Prediction
Authors: Chen Songtao
Published: November 2023

Due to the increasing uncertainty of credit risk in today's society, assessing the size of credit risk has become an indispensable part of the lender, and whether it can accurately assess the size of credit risk has become extremely important, which is directly related to the benefit loss of the lender. In light of this, numerous machine learning models have been employed to enhance the prediction accuracy and robustness of credit risk assessments. This paper proposes a hybrid model to better improve the prediction accuracy and robustness of the model. Firstly, this paper collected a data set about credit risk from kaggle, which has 11 independent variables and 1 dependent variable. The dependent variable is a binary variable, representing default or not. Then the data set is cleaned and sorted, and the data set is divided into training set and test set. Then seven machine learning models were used to fit and predict the data, and the three models with the best fitting effect were found through the two indexes of Aread Under Curve (AUC) and Accuracy: Random Forest, Gradient Boosting and Categorical Naive Bayes, and then mix these three models to obtain a mixed model. The experimental results show that compared with the seven machine learning models, the hybrid model has improved in AUC and still ranks first in Accuracy. Therefore, the hybrid model can well improve the accuracy of predicting credit risk and the robustness of the model.
The Prediction of Tesla Stock Price During COVID-19 Utilizing Machine Learning Methods
Authors: Li Lijia
Published: November 2023

Stock forecasting is an act of predicting the future trend of the stock price based on the fluctuation of the stock price over a period of time. Due to the economic lockdown caused by the COVID-19 pandemic, it is challenging to provide accurate forecasts of price movements to ensure the reliability of investments. This study used Long Short-Term Memory (LSTM), Gate Recurrent Unit (GRU), Random Forest and K-Nearest Neighbor (KNN) algorithms to predict the stock price of Tesla in the special period of the epidemic. In the training data set, the fluctuation situation during the epidemic period was added as the basis for prediction, and as expected, the stock trend of Tesla in the special period was more accurately predicted. According to the results, LSTM and GRU models have high accuracy. They can provide investors with more reliable information about stock prices, and their R-squared values reach nearly 0.95, while the rest two models had poor performances.
Credit Card Fraud Prediction Based on Machine Learning Algorithms
Authors: Wang Xinman
Published: November 2023

The escalating use of the Internet has led to a surge in online shopping and e-commerce, resulting in a corresponding increase in credit card fraud incidents. Therefore, this research focuses on employing machine learning techniques, which offer enhanced precision and efficiency compared to manual detection, to identify fraudulent activities. To establish the association between credit card transaction attributes and the presence of fraudsters, this study initially gathers data from Kaggle, subsequently normalizing the collected data. Furthermore, the data exhibits severe imbalance, leading to overfitting concerns. To ascertain feature correlations, a correlation heatmap is constructed. Moreover, this investigation selects three models for analysis. Finally, the performance of each model is evaluated using a confusion matrix and derived metrics. The findings reveal that both the decision tree and random forest models exhibit optimal performance, achieving 100% across all indicators. The most influential factors in determining credit card fraud involve the ratio to median purchase price and the geographical proximity of the transaction location to the cardholder's residence.
Impact of Models and Eigenvalues on Gold Price Forecasting
Authors: Zhang Jin
Published: November 2023

The burgeoning synergy between computer science and finance has fostered an increasing integration of these domains. Machine learning has become a prevalent tool in aiding financial analysis and forecasting. Compared to traditional forecasting techniques, machine learning-based models exhibit enhanced accuracy and broader applicability. This study introduces three models, namely linear regression, random forest, and support vector machine, to analyze and predict gold prices. The influence of Eigenvalues on model performance is also examined. In the end, the support vector machine model constructed by using two kinds of US dollar exchange rates, US Treasury bond interest rates, and the 10-day moving average of gold prices and passed cross-validation obtained the best model performance evaluation index, and its R2 index reached nearly 0.99. It can be concluded from this study that the performance of the model is poor when only one eigenvalue is used to build the model, while for the case of building a model with multiple eigenvalues, the contribution of the U.S. Treasury bond rate to the improvement of the performance of the prediction model is the smallest. Therefore, appropriately increasing the number of eigenvalues is conducive to improving the performance of the model, and selecting the types of eigenvalues reasonably is also conducive to improving the accuracy of the model.
The Relationship Between Capital Structure and Firm Performance
Authors: Wang Jingyi
Published: November 2023

The composition of the enterprise's entire capital is referred to as its capital structure. It is also referred to as a financial framework at this time. Not just long-term capital structures are included. Short-term capital structures are also included. It also includes dynamic capital structure in addition to static capital structure. Before this article, much research have studied and integrated the relationship between capital structure and company. This article hopes to absorb the results of past research and conduct a research expansion and give the view that belongs to this article. The research topic of this paper is the impact of capital structure on firm performance. This paper compares the impact of different companies through different capital structures and start with different types of companies to observe its capital structure. In this paper, a comparative analysis is mainly adopted, and the capital structure of companies in different types, different regions and social conditions is compared, so as to analyze the impact of the company's impact on the capital structure and finally conclude and give suggestions.
Analysis of Bandai Marketing Strategy Based on Consumer Psychology
Authors: Hu Zheqi
Published: November 2023

With the growth of the world economy, the economic growth of Japan's entertainment industry, especially the electronic game and animation industry, has gradually accelerated. Since 2005, Bandai Company has made a series of adjustments to its business strategy, which not only greatly improved the company's performance, but also had inspiration for other companies. However, there is only little analysis of the company's strategy exists. On this basis, the research uses literature review and case analysis methods to analyze the recent marketing strategies of Bandai Company. The results show that Bandai Company has captured consumers' psychology in social media marketing and limited-edition product sales, but its attractiveness to adult consumers may not be sufficient. This may be due to Bandai's excessive focus on the children's market. For Bandai Company, the research roposes three suggestions, starting from the finished product section, assembling model section, and toy section, to enhance consumers' purchasing intention. It is recommended to improve the process and quality of products and develop more representative products. It is suggested that up to take ladder pricing in pricing and expand the market share of low price and high cost performance.
Portfolio Management Strategy Based on LSTM
Authors: Liu Hailong
Published: November 2023

Mean-variance theory emphasizes the consideration of returns and risks simultaneously. However, traditional optimization of the risk-return trade-off may not always align with the preferences of individual investors. Given that outcomes about daily LSTM predictions have been inconclusive, this study uses weekly price dataset for prediction aiming to improve the signal-to-noise ratio. The objective is to develop a portfolio management strategy that prioritizes achieving reasonable returns while effectively managing risk. To achieve this, a selection process identifies 12 stocks from different sectors. LSTM models are trained using historical stock price data, and these models predict future stock prices. The predictions guide the weekly rebalancing of the portfolio based on a designed principle and the performance of the strategy-based portfolio is evaluated by comparing it to two benchmarks: the NASDAQ index and a 1/N investment portfolio. The results demonstrate that the strategy portfolio outperforms both benchmarks, achieving significant improvements in returns. The strategy portfolio exhibits a remarkable return of 116.21%, a Sharpe ratio of 2.944 and a maximum drawdown of 13.1%. Although there is a slight increase in volatility, the strategy effectively manages risk while delivering superior returns.
The Impact of Population Aging on China's Macroeconomy
Authors: Lang Yaqing
Published: November 2023

China's aging population is a growing concern and the country will become a super-aged society by 2050. Aging has a number of important implications for various macroeconomic indicators in China. From a labor market perspective, aging will have a negative impact on real labor supply and labor productivity in the Chinese market. And the impact on human capital will depend on the relative magnitude of its shrinking effect on educational human capital and its boosting effect on healthy human capital. From an investment demand perspective, an aging population will dampen investment demand in the Chinese market by affecting investment returns and social investment preferences. From a balance of payments perspective, aging may have a negative impact on both China's current account and international capital account, leading to a double deficit in the balance of payments. From a fiscal balance perspective, aging will increase the Chinese government's fiscal burden while decreasing its fiscal revenue, thus increasing the fiscal pressure on the Chinese government.
Enhancing Portfolio Allocation by LSTM Model
Authors: Liu Zibing , Yang Binyu
Published: November 2023

Accurate forecasting of stock prices and the construction of optimized portfolios are essential for investors in the dynamic technology sector. This paper proposes a comprehensive approach that combines Long Short-Term Memory (LSTM) models with portfolio construction techniques specifically tailored to the stocks of Apple Inc. (AAPL), Meta Platforms Inc. (META), Amazon.com Inc. (AMZN), Microsoft Corporation (MSFT), and NVIDIA Corporation (NVDA) from January 1st ,2018 to May 31st, 2023. By leveraging the sequential nature of historical stock price data(80% of data), LSTM models capture complex patterns and dependencies, enabling more precise predictions of Adjusted Close (Adj Close) prices. Subsequently, the forecasted prices (20% of data)are utilized to construct optimized portfolios that maximize returns and minimize risks within the technology sector using Monte Carlo simulations, efficient frontier analysis, and key risk-return metrics. The overall result of the prediction data is similar to the actual data which implies that the integration of LSTM-based forecasting and portfolio construction provides a robust framework for informed investment decision-making and risk management. And the application of Monte Carlo simulations, efficient frontier analysis, and key risk-return metrics gave us two portfolio allocation options : Minimum Variance model (40% of AAPL,60% of MSFT) and Maximum Sharpe Ratio model ( 47% of META, 53% of NVDA). The evaluation of the two portfolios show that the strategy can significantly beat the SP500 index.
Analysis of the Sales of Surplus Food Blind Box
Authors: Pu Yawen
Published: November 2023

With the improvement of living standards, people have higher and higher requirements for food quality and health. However, the global problem of surplus food is becoming increasingly prominent, especially in China, where a staggering amount of foodstuffs are wasted every year. As a new consumption mode, the surplus food blind box can not only reduce waste, save costs, but also meet the needs of consumers, but how to open new sales ideas is still a problem that needs to be discussed. This paper aims to explore a new idea of how to open the surplus food blind box through diversified product portfolios, social media marketing, online and offline combination and other ways. With the increasing concern of people on environmental protection, surplus food and healthy living, the surplus food blind box has a broad market prospect as a consumption way to use leftover ingredients. In this context, this paper puts forward some innovative sales ideas, including the combination of different flavors of surplus food blind boxes, the combination of online and offline sales models, social media marketing strategies, etc., to meet the needs of different consumers and improve sales and brand influence.
Research on the Current Situation and Strategies of Independent Designer Brand Marketing
Authors: Wen Simin
Published: November 2023

Independent designer brands are a large part of the market, and there is few analysis and research on their marketing strategies. It is necessary to analyze the successful brands in different marketing positions as models and evaluate the problems of their strategies for other new designer brands to consider further promotion, helping them avoid predictable failures and make appropriate choices. The author considers existing issues from the perspective of designer brand operators and provides some suggestions for the future development of independent designer brands. The paper reviewed some literature about designer brands from the past years and used the 4P model to analyze two typical independent designer brands, Alexanderwang and Ami Paris. The author indicated some problems with the marketing strategies of these brands, including the strong binding between designers and brands, the excessive online commercial, and unreasonable pricing, and analyzed the causes. This paper provided the methods of finding brand spokespersons and creating virtual characters for promotion as solutions for diluting designers bonding with brands. According to the article, Co-branding is a way to lower production costs and increase the popularity of independent designer brands.
Research on Footwear Industry Marketing Strategy in Artificial Intelligence Era Take Adidas as Example
Authors: Xu Ye
Published: November 2023

AI has been used in a variety of industries to help brand to enhance brand construction and marketing. But confused by the AIs powerful analysis and collection ability, the brand easily ignores the center of marketing: the customer. Its necessary to consider the role of AI in digital marketing. Adidas is a typical case that gets into the marketing trouble, makes a wrong orientation, and then makes an unsuitable digital marketing strategy. Simply attribution model leads Adidas to over-focus on superficial financial data; sub-brand provides Adidas mAIn financial profits, slighting of greater china market. These factors led to Adidas suffering a revenue decline in the last three years. Adidas should quickly adjust its value orientation, keeping the customer orientation all the time, changing its influencer strategy, inviting more sporters as sub-brand spokesmen or brand owners, and enhancing the investment in Greater China. A little negligence in digital marketing will result in the brand being on the AI eras skids.
Starbucks Marketing Analysis Based on Consumer Psychology Theory
Authors: Zhang Yutong
Published: November 2023

Due to the development of the modern market, the old economic system was gradually being replaced. As the world's largest coffee retail company, Starbucks should make timely transformation and innovation with the development of emerging economy. This paper used indirect investigation method and so on to analyze the marketing operation of Starbucks. The research found that Starbucks had advantages in product market and paid attention to atmosphere consumption, this pattern satisfied consumers' consumption experience. But at the same time, Starbucks should pay more attention to innovation and explore foreign markets in order to maintain a certain market position. On the basis of analyzing Starbucks' marketing strategy from multiple perspectives and the consumption theory behind it, the author found that this research played such an important role in whether Starbucks can establish itself in the product marketIf Starbucks can change and innovate in time, they will remain at the forefront of coffee retailing.
A Review of the Phenomenon of Algorithm-facilitated Systematic Price Discrimination
Authors: Meng Zhen
Published: November 2023

As the Internet environment has rapidly developed, algorithms and programs have contributed to an ever-increasing level of convenience in day-to-day life. However, it is of the utmost importance to put an end to discriminatory pricing practices that are brought about by improper application of algorithms. Some retailers employ a strategy known as algorithmic price discrimination in order to boost their profits; nevertheless, this strategy has unfavorable effects on both the consumers' right to privacy and the reputation of the Internet. In order to achieve social and economic justice, it is essential and urgent to do away with algorithmic price discrimination. The purpose of this study is to have a better understanding of the current situation of algorithmic price discrimination by analyzing its history and discussing it. In addition, the research offers actions that authorities can take and measures that might be implemented in order to combat algorithmic price discrimination.
Research on the Marketing Strategy of LVMH in the Chinese Market under the Background of New Media
Authors: Li Sixuan
Published: November 2023

Chinas new media has developed rapidly in recent years, and society has entered the era of new media. At the same time, Chinas economic level is developing rapidly, and peoples living standards are also greatly improved. The demand for luxury goods is greatly increased. The huge population makes China become the largest consumer of luxury goods. In this context, various luxury brands have different marketing strategies for the China market. As one of the worlds largest luxury goods companies, LVMH is bound to change its marketing strategy for China market. In addition to an overview of the topics and trends covered in the relevant literature, this paper mainly discusses the impact of new media on the luxury industry and how the luxury industry should change in the new media environment. For some academics and marketing managers of luxury brands, this paper can provide some theoretical basis and is of great reference for changing marketing strategies.
An Analysis of the Financial Gains from Colonialism of Great Britain
Authors: Jiang Zhiyan
Published: November 2023

The British Empire is known for its large territory ranging all the way from the Americas to Oceania, with dozens of colonies that brought the empire great wealth and capacity to industrialize and mass produce. Based on data and information on the amount of British colonial production, this article focuses on calculations and estimations of the total colonial gain of the empire above. By estimating the amount of production in selected significant colonies, and then converting the results into the desired unit of measurement, a calculated value of colonial wealth can be achieved. The resulting values reveal that the British Empire is estimated to have generated more than 35 trillion British pounds, 56 million pounds of tobacco, 54 million tons of sugar, 173 million tons of rubber, 39 million tons of wool, and 26 million Terawatt hours of oil. Through calculations of the total colonial gains of the British Empire, the effect of colonization on the mother country and its influence on the modern world can be more easily and directly seen.
Analysis of Regional Differences and Influencing Factors of Real Estate Prices: An Empirical Study Based on 31 Provinces in China
Authors: Xiaodi Liu
Published: November 2023

Currently, the continuous rise in housing prices in China has brought about social issues regarding housing fairness. Therefore, it is of practical significance to study the influencing factors of China's real estate prices and the effects of these factors. This article first identifies ten variables that have a significant impact on housing prices through analysis, and then conducts empirical analysis separately for the eastern and central-western regions. Based on the correlation and model multicollinearity tests, two corresponding models are established. Subsequently, this article uses a multiple linear regression model for regression and uses a test set to verify the model's effectiveness, conducting quantitative analysis. Combining the analysis results, the factors that have a significant impact on housing prices in the eastern region of China are identified as the sales area of commercial housing, rental prices, and unemployment rates. In the central-western region, the factors with a significant impact on housing prices are disposable income and real estate development costs. Based on these conclusions, this article proposes relevant policy suggestions, which are expected to provide a basis for differentiated macro-control policies by the government.
Behavioral Finance Analysis of the Spillover Effect of Major Health Emergencies on Shipping and China's Investment Market
Authors: Han Sijie
Published: November 2023

Cross-market risk conduction is an important risk source in the global investment market. In recent years, the global market risk linkage cause-d by major public health emergencies has attracted much attention. This paper selects the COVID-19 epidemic as an example, and uses the shipping market as a representative indicator of the global economy to try to analyze the complete path of the epidemic impact from the global economy to the Chinese stock market. From the perspective of behavioral finance, it analyzes the role of investor sentiment in this risk linkage. The empirical results show that the transmission of epidemic risk between Chinese stock markets deviates from the global economic fluctuations but has regularity, and the momentum effect brought by investors ' overconfidence and overreaction makes the stock market reaction short-term and volatility amplification. Risk aversion and limited attention make the market volatility caused by the deterioration of the epidemic greater than the mitigation. This paper creatively conducts a full-path study on the transmission of epidemic risk between macro-economic and stock markets, and uses behavioral finance theory to quantitatively verify the impact of investor sentiment, which provides a theoretical basis for cross-market risk transmission research.
ESG Transformation Strategy Case Analysis: Taking Sands China Limited as an Example
Authors: Zeng Peng , Zhao Lijia
Published: November 2023

Sands China Limited is a comprehensive entertainment company operating in Macau. This paper evaluates the sustainable development of Sands China by analyzing its performance in environmental, social, and governance (ESG) aspects, as well as financial performance indicators. In terms of the environment, Sands China has implemented multiple measures to improve energy efficiency, reduce carbon emissions, and prioritize water resource management. In the social aspect, the company focuses on employee welfare, community development, and social responsibility, enhancing its social impact through various projects and activities. Regarding governance, Sands China is committed to establishing a transparent and responsible corporate governance system. In terms of financial performance, Sands China achieved relatively stable growth from 2017 to 2019 and addressed market challenges through cost control and risk management measures. However, the company's performance was severely impacted by the COVID-19 pandemic. To adapt to the current uncertainty, Sands China needs to embrace market changes, diversify its business, strengthen risk management, and actively explore new markets. Through these efforts, Sands China will maintain a stable development trajectory and contribute to the economic recovery of Macau.
Explore the Success of HEYTEA Marketing In-Depth Research on the Innovation and Fashion of Marketing Methods
Authors: Jiang Ran , Shi Yunyao
Published: November 2023

HEYTEA, a leading Chinese milk tea industry brand, has achieved significant success through innovative marketing strategies. The companys introduction of a cheese and fruit tea drink revolutionized the beverage industry. 2016 the brand rebranded as HEYTEA to signify its dedication to ongoing development and advancement. HEYTEA prioritizes creating unique flavors that resonate with consumers in order to revolutionize the tea-drinking experience. The analysis emphasizes the importance of social media influencers, especially on new media platforms, in increasing brand influence. HEYTEAs marketing strategy heavily relies on online platforms through collaborations with bloggers and influential individuals who document their visits and endorse the brand. HEYTEAs offline outlets utilize scene marketing to create immersive experiences that engage customers senses. This study investigates the revival of HEYTEAs happy tea marketing strategy using brand portfolio analysis, supply chain management, and research and development efforts. The case study emphasizes the brands dedication to innovation and quality. It engages consumers through social media, word-of-mouth advertising, and co-branding with established intellectual property and fashion companies. Moreover, the Urban Cultural Tourism series incorporates local cultural elements into shop design and offerings, promoting stronger community relationships. HEYTEAs success is attributed to its ability to identify development trends, comprehend consumer behavior, explore diverse markets through co-branding, establish regional brand characteristics, and enhance overall consumption. HEYTEA has established itself as a leading player in the fiercely competitive milk tea industry by distinguishing itself from competitors and building a sizable and devoted customer following.