bluuebunny/crossref_metadata_2025 huggingface dataset:
Association between screen-time use and cardiometabolic-related lifestyle characteristics, in Greek young adults
Authors: Kasimatis Emmanouil , Psykou Sofia , Kokkinelou Evgenia , Tsourti Pasifai , Gkoletsos Evangelos-Marios , Panagiotakos Demosthenes
Published: November 2023

Background: studies evaluating screen time with a variety of lifestyle determinants have been focused, mostly on children and adolescents. To the best of our knowledge, very few studies have been conducted in adult populations, worldwide. Aim: to investigate the association between time spent on screen, for work or and entertainment, with fast food consumption, and certain lifestyle behaviors (physical activity, smoking, dietary habits), in Greek young adults. Methods: a cross-sectional, observational study with convenience sampling of 687 Greek adults, 18-30 years old, was conducted in May, 2023, using a structured web-based questionnaire. Participants were asked regarding time spent on screens (i.e., cellphone/tablet, television, computer/laptop), separately for work and entertainment, as well as various sociodemographic, clinical and lifestyle characteristics, including adherence to Mediterranean diet (through MedDietScore, ranged 0-55). Results: mean daily screen time was 5±4 hours for work and 6±3.5 hours for entertainment. Screen time for entertainment was inversely associated with a participant’s adherence to the Mediterranean diet (OR per 1 h, 0.93, 95%CI 0.89, 0.97, p=0.002). A positive association was found between fast food, sweet and salty snacks consumption with overall screen time (all p-values<0.05), while recreational screen time was inversely associated with the likelihood of a person being physically active (OR per 1 h, 0.90, 95%CI 0.85, 0.95,p=0.001). Body mass index was also positively associated with remote work screen time (rho=0.27, p=0.027). No associations were observed regarding smoking habits. Conclusion: a potential link between screen time and devaluation of the quality of life was revealed, which, in the long-term, can impact human health status.
Semantic aspects of musical language
Authors: Y. Vereshchahina-Biliavska Olena , Mazur Irina , Cherkashyna Olesia , Burska Olena , Hrinchenko Tetiana
Published: November 2023

Music is a multifaceted phenomenon: it carries the mission of aesthetic education and enlightenment, performs therapeutic functions and is not only a source of information, but also the subject of filling human consciousness with a sense of joy, drama, and a high spiritual beginning. All the above is provided due to the many components that constitute the whole picture of musical art. These include intonation and rhythm, tempo and dynamics, timbre and texture, and more voluminous components: genre and form. Each of its elements represents a certain category, which, in turn, carries a semantic load or semantic aspect belonging to it. Having a centuries-old history of existence, music and, accordingly, its components have undergone considerable evolution. The purpose of the study is to show the semantic aspects of musical language as an integral system in their continuous dynamics of development, updating, and complication of content. The history of sound creativity is a vivid picture of styles and techniques of writing, images, and themes inherent in a particular era. The above is inextricably linked with the deep evolution of semantic aspects that convey the content and importance of all the leading categories of music. They represent, regardless of the period, an integral system of elements, the transformation of which is discussed in this article. The definition of the boundaries of "evolution" in relation to music, the disclosure of its action at various levels (the evolution of musical thinking, the evolution of national style, artistic area, the evolution of individual compositional style), the development of the theory of evolution in the field of functioning of musical phenomena – these and other problems are priorities for modern scientific musicology and art criticism. Realising the complexity and globality of the problem posed, the basis of which is the consideration of musical phenomena in the aspect of the theory of evolution, this article includes one of the possible approaches to solving this issue, involving the "general theory of systems" created in the 20th century. For that reason, this publication is relevant. It combines the most important theses of researchers investigating the problem of the semantics of the musical language in the context of such disciplines as pedagogy, quantum physics, medicine, social science. The study searches for the materials on the studied subject, their systematisation, and provides a panoramic display of the analysis of the semantic aspects of musical art. The author also shares the prospects of analysis of the mentioned phenomenon. The need for this research is conditioned by the essence of the subject, since it constantly changes, gets enriched with new qualities, actively responding to what is happening in real-time.
Pengaruh Media Edukasi Video terhadap Keberhasilan Teknik Menyusui pada Ibu Post Sectio Caesarea dengan Metode Eracs
Authors: Indah Seri , Afrina Rina
Published: November 2023

Latar Belakang: Kesulitan menyusui terjadi pada pasien post ERACS dikarenakan cepatnya lama perawat post ERACS yang hanya 1- 2 hari setalah SC ERACS sehingga ibu bersalin kurang mendapatkan edukasi dari tenaga kesehatan untuk posisi menyusui yang baik dan benar.
Tujuan: Untuk mengetahui pengaruh media edukasi video terhadap keberhasilan teknik menyusui pada ibu post SC Dengan Metode Eracs.
Metode: Penelitian menggunakan pre-experimental design one-group pre-test and post-test design. Populasi dalam penelitian ini adalah Ibu yang melahirkan SC dengan Metode Eracs selama 3 bulan sebanyak 30  orang. Teknik sampling dalam penelitian ini adalah Accidental Sampling. Sampel dalam penelitian ini sebanyak 20 orang. Instrumen penelitian menggunakan wawancara dan lembar observasi. Uji statistik yang digunakan adalah uji Wilcoxon.
Hasil: Berdasarkan hasil penelitian nilai rata rata (mean) sebelum diberikan media edukasi video sebesar 4,900 dan nilai rata rata (mean) sesudah diberikan media edukasi video sebesar 11,55. jika dilihat dari hasil uji statistik nilai p-value 0,000 berarti P>0,05.
Kesimpulan: Ada pengaruh media edukasi video terhadap keberhasilan teknik menyusui pada ibu post Sc dengan Metode Eracs.
Implante de catéteres con puerto subcutáneo por radiólogo intervencionista en pacientes oncológicos
Authors: Ariza Fonseca Álvaro , Martínez Álvaro , Leal Contreras Diana
Published: November 2023

Introducción: El implante de catéteres con puerto subcutáneo es un procedimiento que ha demostrado beneficios para los pacientes que requieren quimioterapia. A lo largo del tiempo este procedimiento se ha hecho cada vez más frecuente en manos del radiólogo intervencionista.Objetivo: Revisar la incidencia de complicaciones del implante de catéteres con reservorio subcutáneo para quimioterapia por radiología intervencionista en nuestra institución. Métodos: Se realiza una revisión retrospectiva de los registros electrónicos en un periodo de 31 meses desde el inicio de la realización del procedimiento en nuestra institución para evaluar la incidencia de complicaciones. Resultados: Entre marzo de 2019 y septiembre de 2021 se implantaron un total de 377 catéteres para quimioterapia, procedimiento llevado a cabo en la sala de fluoroscopia bajo anestesia local, excepto uno que se realizó en salas de cirugía bajo anestesia general. La incidencia de complicaciones fue relativamente baja alcanzando un 8,2% y el espectro de complicacionesincluyó neumotórax, dehiscencia de suturas, infección, rotación y extrusión del puerto. Conclusión: Nuestros resultados indican que los catéteres centrales con puerto subcutáneo pueden ser rutinariamente implantados en forma segura en la sala de fluoroscopia bajo anestesia local por el radiólogo intervencionista bajo guía ecográfica y fluoroscópica.
Enfermedad de Van Neck Odelberg. Presentación de caso
Authors: Jácome Pérez Natalia , Salcedo Hernández Carlos Julio
Published: November 2023

La osteocondritis isquiopúbica o enfermedad de Van Neck es una patología ósea benigna infrecuente que afecta a menores de 16 años de edad. Clásicamente, se describe como dolor y limitación del movimiento en la región isquiopúbica asociados a edema óseo y asimetría en la sincondrosis. El conocimiento de esta entidad es importante en la orientación diagnóstica de cojera en niños. El objetivo de este artículo es presentar los hallazgos principales de la enfermedad de Van Neck y sus diagnósticos diferenciales a partir de un caso clínico.
On Frontier Portfolio in Shanghai Stock Exchange Based on Mean Variance Model
Authors: Yu Jiaao
Published: November 2023

In security markets, how to achieve optimal allocation of asset has become the focus of investors. The mean-variance model is a method to achieve revenue maximization and risk minimization on investing stock portfolio. This paper will research on investment problem in Shanghai Stock Exchange. It involves research result about mean variance model since 1952. To take the experiment, 10 stocks are chosen from Shanghai Stock Exchange. Organizing rates of return for 360 days and using the average daily rate of return as the expected rate of return. Applying mean variance model, selecting data, the curve of frontier portfolio is obtained by experiment. Based on the result, the investment advice is given. It shows that higher expected rate of return accompanies by higher risk. To achieve higher returns, investor could analyse the variation of each asset along with the rise of the expected rate of change. When negative weight appears, there exists short selling. If short selling is not allowed, investor could shrink the interval of the expected rate of return.
Analyzing the Strategy of Chicecream's Brand Image Based on Marketing Mix Theory
Authors: Wang Xining
Published: November 2023

Influencers refer to people who spread information through the internet and become popular due to their own attention from netizens. This idea is expanded by the term "Internet celebrity brand," which refers to a brand that has become popular with consumers through online marketing. The aim of this study was to explore how Internet celebrity brands could improve their brand image and foster consumer goodwill. This paper further developed Marketing Mix Theory by exploring the effectiveness of dimensions of Marketing Mix Theory and brand image. Besides, the research also exploited the SWOT method to analyze the product, price, place, and promotion of the Chicecream. This research concluded that Chicecream' s high pricing can generate negative perceptions among consumers, and inappropriate positioning strategies can lead to fragmentation of the brand image and detract from the effectiveness of brand marketing. Through the above summary and analysis, the article provided ideas on how to cultivate a good brand image for online celebrity brands from the perspectives of price, product, place, and promotion.
Research on the Effectiveness of Traditional Education Service Provider Entering New Market on Brand Group
Authors: Liu Guanzhou
Published: November 2023

Starting in July 2021, a far-reaching event occurred in the education industry: the Double Reduction Policy (DRP) was fully implemented in China. This policy has a great impact on the vast majority of families with children, as well as on the operation of public schools and private education institutions. In this paper, New Oriental Education & Technology Group (New Oriental) was used as a case study to conduct SWOT research and analyze the long-term impact of this behavior on its brand image (BI) and brand loyalty (BL) after the group entered the live-commerce domain under the DRP. Through the detailed investigation of the case, it was proven that the strategic decision of entering the live-commerce business can bring financial advantages to New Oriental in the short term, but it had a negative impact on its BI and BL in the long term. Through reviewing the literature and analyzing this specific case, this paper believed that entering a new business field, especially when selling products or services at lower prices, will have a negative impact on the BI and then affect the BL.
Stock Price Forecasting with Machine Learning
Authors: Ying Shichen
Published: November 2023

Stock price prediction has always been a problem that investors are very concerned about. This paper studies the use of financial information of listed companies to predict whether the stock price will rise after the release of its financial report. This paper extracts the financial information of listed companies from the Guotaian database, obtains their stock price data from Yahoo Finance, and calculates the corresponding technical indicators. And exploring the effect of these indicators on the stock price prediction. The study found that there is a small gap between forecasting with financial information alone and forecasting with technical indicators alone. The combined model performs slightly better than the single model. This study demonstrates that financial information can effectively aid in predicting stock prices and overcome the limitations of certain technical indicators. By incorporating both financial data and stock price information, investors can make more accurate prediction regarding fluctuations in the stock market.
Predicting Tencent's Stock Price: A Comparative Analysis of Machine Learning Algorithms
Authors: Li Han
Published: November 2023

Chinese internet companies, such as Tencent, are bringing a new energy to the global market. As one of the largest internet companies in China, Tencent's stock price fluctuations hold significant importance for investors and the market. Accurate forecasting of stock prices is of great importance to make high-yield decisions and manage risk. This paper aims to provide investors with effective and scientific references for decision-making by analyzing various forecasting methods. The dataset China-Techgiant-Stock-Data-in-HK-Market-2022 from Kaggle is used in this paper. This study utilizes various machine learning and time series analysis methods, including linear regression, SVM, random forest, LSTM neural network, and ARIMA, to predict stock prices using historical data. The models' accuracy and performance are compared, with traditional machine learning methods (linear regression, SVM, and random forest) contrasted against deep learning and time series analysis methods (LSTM neural network and ARIMA). It turns out that LSTM has the best prediction results. This can better guide investors in their stock decisions.
Artificial Intelligence and Corporate Innovation: Intelligent Transformation and Development Trends under Technological Empowerment
Authors: Luo Xudong
Published: November 2023

In the context of continuous technological advancements, business operations and value creation face new challenges. This paper investigates the impact of artificial intelligence on corporate innovation, with a focus on analyzing the intelligent transformation and development trends under technological empowerment. The research reveals that artificial intelligence plays a significant role in fostering innovation within enterprises across operational management, product design, and customer service. Moving forward, businesses need to strengthen their application of AI technologies to achieve intelligent transformation, ultimately enhancing operational efficiency and value creation capabilities.
The Business Analysis of Pop Mart
Authors: Xiao Liwen
Published: November 2023

Beijing POP MART is a leading cultural and entertainment company in China. Over the past decade, POP MART has built an integrated platform covering the whole industry chain of pop toys. In 2019, the company combined with blind boxes economy. It has become popular in the toy market. At the end of 2020, the Pop Marts shares are listed. Based on the pandemic background, the article uses some strange financial indicators collected from the companys annual and semi-annual reports to figure out why its stock price decrease in recent years. The research finally concludes about this phenomenon.
The Impact of Population Aging on GDP and Medical Expenditure in China
Authors: Li Rou
Published: November 2023

Population aging is a major social problem in the 21st century, which is also the main feature of human social development in the new century. Since the reform and opening up in China, with the significant development of social economy and the rapid progress of medical level, the average life expectancy has been improved to a large extend, which cause that the problem of population aging has become increasingly serious. This paper takes the proportion of elderly population in China as research object, applies VAR model to analyze and forecast the data with the aim to study its impact on GDP and medical expenditure. As is well-known, it is inevitable that proportion aging will inhibit economic growth and increase healthcare spending. This paper finds that this kind of effect is not immediate. Based on this lag, in order to reduce the negative impact of population aging, suggestions for the future work of government leaders and medical practitioners can be put forward.
The Impact of the Opening Policy of the COVID-19 on Catering, Tourism and Hotel Industries
Authors: Chen Yutong , Qin Zichen , Yin Xiaoxuan
Published: November 2023

On December 7, 2022, China officially released management of the COVID-19, and the economic level of various industries began to gradually rebound, and people's consumption levels also gradually improved. In the consumer industry, the recovery of the catering, tourism, and hotel industries was first perceived by the market. In order to explore the impact of the COVID-19 liberalization policy on these three industries, this paper used the ARIMA model to analyze the stock closing price data of the industries before the COVID-19 liberalization, simulated the stock price trend without the implementation of the COVID-19 liberalization policy, and compared it with the actual stock price. Through the analysis, it was found that the policy of comprehensive COVID-19 control has promoted the economic development of China's catering, tourism, and hotel industry. The economic situation of all three industries has rebounded to a certain extent. Especially for the catering industry, the promotion effect is most obvious, followed by the hotel industry, and finally the tourism industry. The research in this article can help the government better respond to the current economic recovery situation, propose corresponding incentive policies, and promote the healthy development of China's catering, tourism, and hotel industries.
The Impact of the Deregulation of Covid-19 on the Pharmaceutical Industry
Authors: Chen Yixuan
Published: November 2023

The current COVID-19 has lasted for a long time, and the Chinese government has relaxed the control of the pandemic after many considerations. Numerous researches about the COVID-19's effects on the economy and business are currently available. But few studies are on the pharmaceutical industry and the Deregulation. Therefore, this paper selects the Pharmaceutical and Biological Full Benefit as the research object. And then uses the ARIMA model to perform time series fitting and prediction of the closing price series in the past period. By comparing the predicted and real values, how the Deregulation affected the stock price of the biopharmaceutical sector could be determined. After modeling analysis, it is concluded that a positive effect has been produced by the Deregulation on the stock price of the pharmaceutical industry. Further study shows that there is a short-term lag in the impact, and the relative growth rate is not large. And the impact has been observed with certain short-term nature through the prediction results. This paper innovatively uses stock price fluctuations as an insertion point to explore the impact of the Deregulation on the pharmaceutical industry, which has profound practical guiding significance. For Chinese policy makers, it is suggested that the country should cooperate with the improvement and upgrading of the pharmaceutical industry. For investors, it is suggested that they should maintain a more stable and positive wait-and-see attitude towards the pharmaceutical industry.
High-Frequency Data Analysis for Volatility Prediction of the CSI 300 ETF: An Empirical Research Using HAR Family Models
Authors: Lin Xiao
Published: November 2023

In this paper, four HAR family models are used to study them and five-minute high-frequency trading data of the CSI 300 ETF is the target. Descriptive statistics is firstly conducted, and it is found that all variables have obvious autocorrelation and long-term memory which are suitable for time series analysis. Then, in-sample and out-of-sample analyses are implemented to test the predictive effect of each component on the volatility of the CSI 300 ETF and to compare the predictive ability of each model. The empirical results of the in-sample data show that daily and weekly realized volatility, continuous volatility, daily and monthly jump volatility, daily and monthly realized positive semi-variance, weekly and monthly realized negative semi-variance and positive and negative signed jump variation have stronger predictive effects on volatility of the CSI 300 ETF, while weekly realized volatility, jump volatility and realized positive semi-variance, and daily realized negative semi-variance have a weaker predictive effect on the CSI 300 ETF volatility. The results of the MCS test for out-of-sample prediction show that the HAR-RV-CJ model and HAR-RV-RSV model have significantly better out-of-sample predictive ability on the CSI 300 ETF volatility than the other two HAR family models, with HAR-RV-RSV model exhibiting the highest predictive accuracy in most cases. The above results indicate that the jump factor and the asymmetric factor of positive and negative returns can effectively improve the forecasting accuracy of HAR family models, so these two factors cannot be ignored in the construction of future HAR family models.
The Rise of Digital Finance and Systemic Risk: Implications, Challenges, and Coping Strategies
Authors: Wu Xiaoxu
Published: November 2023

With the continuous development of Internet technology, digital finance has become increasingly popular. The digital transformation of finance has brought huge shocks to the traditional financial industry. This paper aims to examine the interrelationship between digital finance and systemic risk and explore in depth its implications for the global financial system's stability. This paper examines key areas such as digital payments, P2P lending, and virtual currencies. Due to the rapid development of digital finance, while bringing great efficiency improvements, new systemic risks also follow. This study attempts to suggest a range of regulatory and risk management strategies. These strategies are designed to deal with the systemic risks brought about by digital finance so as to ensure the stability and sustainable development of the financial system. This study is expected to provide strong references and recommendations for the financial industry and policymakers to realize the potential value and impact of digital finance while protecting financial stability.
Development Status of Chinas Real Estate Industry
Authors: Wang Jiayao
Published: November 2023

The real estate industry has always been a significant factor in the Chinese economy which can be easily affected by many sectors such as demand and supply imbalance, aging, and information asymmetric between producers and consumers. Under the new economic situation in China, the development and innovation of the real estate industry play a crucial role in the Chinese economy. The paper explores the future trend of real estate by analysing the current situation of the Chinese real estate industry through a method of literature review and data analysis. The result shows that the first, the house vacancy rate is a main factor that the government should control in order to balance the demand and supply. The price of the house would decrease caused by the unbalanced supply and demand. Secondly, the high unemployment rate would also affect the demand, which could lead to decrease in house price. Lastly, the uneven development between different areas would lead to negative impact for the real house industry as well. This paper find that government intervention plays a significant role in the control of demand and supply of the real house industry and The real estate industry ushered in a new stage and began to explore new ways of development that fit publics needs.
Risk Assessment of the Banking Industry under COVID-19
Authors: Jin Shenqi
Published: November 2023

The outbreak of the COVID-19 pandemic in 2020 has brought various risks and threats to the entire banking industry, from which we can also know the importance of bank risk management. This paper mainly describes, analyzes, and summarizes the five aspects of liquidity risk, credit risk, market risk, and systemic risk. With regard to liquidity risk, the article describes the impact of the Federal Reserves continuous interest rate hikes on the banking industry in order to curb the high inflation caused by the COVID-19 pandemic and takes the collapse of Silicon Valley Bank as an example for analysis. Regarding credit risk, this article takes Credit Suisse Bank as an example. Highly leveraged speculative transactions caused huge losses to the company due to the high default rate during the epidemic. For the analysis of market risk, the article synthesizes the deeds of Credit Suisse Bank and Silicon Valley Bank, focuses on the interest rate, and the reasons for the collapse of Credit Suisse and Silicon Valley Bank are different on the surface, but the underlying logic of the two events is traced to the same There is a close relationship between the current interest rate environment and the changes in interest rates in recent years. As for systemic risk, the article mainly writes about the purpose and effect of Basel III and the supervision of the capital adequacy ratio during the epidemic. Overall, commercial banks need to closely monitor these risks and take steps to mitigate them to ensure their stability and continued operations during the pandemic.
Post COVID-19 International Economy Recovery: Forecasting Bilateral Trade Between U.S. and China
Authors: Meng Qingxi
Published: November 2023

This paper aims to use an empirical analysis to study and forecast the bilateral trade between the United States of America and the Peoples Republic of China in the post-pandemic era. This study was conducted from three perspectives: aggregate demand (import), supply (export), and trading potentials. In sections of import and export analysis, the paper focuses on analyzing the aggregate behavior by households with Schmitt-Grohe, Uribe, Woodfords risk-averse demand model in an open economy and the firm production optimization model by Giovanni, Kalemli-zcan, Silva, and Yildirim. In the analysis of the bilateral trade potentials, a gravity model of trade with one least square regression was applied and subsidized with the test method from Liu. From the study, it was found that the bilateral import and export between the U.S. and China are facing challenges from stringent policies, fragmentation of global value chains, and the residual effects of the pandemic on consumption. Thereby, the trading potential between the U.S. and China is restrained to an intermediate level, which is not beneficial for the robust growth of bilateral trade in the future.
Behavioral Biases in Investment Decision-Making
Authors: Wang Yuyang
Published: November 2023

This study delves into the profound impact of behavioral biases on investment decisions and provides valuable insights into methods for counteracting or surmounting these biases. Specifically, it focuses on four prevalent behavioral biases: loss aversion, endowment bias, framing bias, and overconfidence bias, examining how these biases can lead to potential investment mistakes or risks. To mitigate the influence of behavioral biases, the study proposes several strategies. Firstly, it emphasizes the importance of sourcing information from multiple perspectives and cross-referencing data to avoid reliance on biased or limited sources. Objective evaluation of one's skills and knowledge is also highlighted as a crucial step in reducing biases. Leveraging professional advice or feedback can provide an external perspective and help investors make more rational decisions. Furthermore, the study suggests that regular portfolio review and adjustments are essential to address biases and adapt to changing market conditions. Additionally, proactive visualization of potential outcomes can aid in mitigating biases by promoting a more realistic assessment of risk and reward. The study concludes that behavioral biases play a pivotal role in investment decisions. To bolster investment performance and satisfaction, investors are encouraged to comprehend and rectify these biases.
Analysis of Zombie Enterprises' Retention and Departure Based on Game Theory
Authors: Huang Xinyi , Mo Yining
Published: November 2023

Zombie enterprises, predominantly found in state-owned heavy industries and labor-intensive sectors, have emerged as a crucial challenge in recent years' supply-side structural reforms. To address this issue, this study constructs an evolutionary game model involving enterprises, banks, and the government. By employing replicated dynamic equations, the analysis examines the impact of factors such as the cost of enterprise exit, implicit benefits after bankruptcy, on the decision-making of enterprises to stay or exit. The research indicates that the cost of continuing operations for zombie enterprises, bankruptcy costs, corporate income tax rates, the magnitude of implicit benefits from continuing operations, bank loan interest rates, and the expected returns from diverting loan amounts to other purposes can influence the outcomes of the game. Ultimately, from a government perspective, relevant recommendations are proposed to provide insights into effectively addressing the problem of zombie enterprises.
Multiple Stock Prediction Based on Linear and Non-linear Machine Learning Regression Methods
Authors: Chen Shuqi
Published: November 2023

In contemporary times, the pressing issue of global environmental pollution has prompted the exploration of alternative energy sources by various industries, aiming to mitigate the adverse environmental impacts caused by traditional energy production. Correspondingly, investors in the financial market have increasingly redirected their capital towards the new energy sector. Within this context, the present research endeavors to employ machine learning techniques for the prediction of Tesla's stock price. This study leverages multiple linear regression, polynomial regression, and lag models to construct models based on the datasets of TSLA, MPC, and UNG stock prices spanning the period of 2019-2020. By discerning potential patterns among these variables, the objective is to anticipate the future trajectory of TSLA stock price. According to machine learning methods, Tesla's stock price can be predicted, and the daily price of Tesla is influenced by the opening price, high price, low price and trading volume of the stock on that day. In addition, the share prices of energy companies related to Tesla also have an impact on Tesla's share price on that day. Specifically, Tesla's stock price is influenced by Natural Gas Company (UNG), which has an opposite relationship. Although common sense economics says that the crude oil market will be closely related to the new energy market. However, the results of this study demonstrated that Tesla's stock price is less influenced by Crude Oil Company (MPC).
Negative Media Coverage, Financing Constraints and Corporate ESG Performance
Authors: Ge Caixiaoyang , Qu Ruojie , Rui Zihan
Published: November 2023

As investors place increasing emphasis on non-financial information of listed companies, information on corporate environmental, social responsibility and corporate governance (ESG) is increasingly becoming the basis for investors' investment decisions, and the media plays an irreplaceable role in providing information on corporate ESG. This study investigates the relationship between negative media coverage and corporate ESG performance using a two-way fixed effects model with a sample of Chinese a-share listed companies from 2011 to 2020. The findings indicate that negative media coverage can effectively enhance corporate ESG performance. Further research through mediating mechanism analysis shows that negative media coverage increases the financing constraints of companies and motivates them to improve their ESG governance, thus contributing to the positive enhancement of their ESG performance. This study contributes to an in-depth understanding of the impact of negative media coverage on corporate ESG performance, highlights the monitoring role of the media in promoting corporate ESG performance, and provides an empirical basis for the improvement and implementation of ESG-related policies.
Mobile Phone Price Range Prediction Based on Machine Learning Algorithms
Authors: Wang Jun
Published: November 2023

Mobile phones with different price points on the market have different parameter configurations, which provide a lot of information related to the price. Mobile phone prices are also the focus of attention of the mobile phone market, manufacturers and consumers. This study used machine learning technology to investigate the issues in mobile phone sales, mainly the prediction of mobile phone price range and the feature contribution of the price range. The data resource includes the release price of mobile phones in the European market from 2018 to 2021. The data augmentation technologies are also applied in this study. Among the three established machine learning models, the accuracy rate of the training set of the Catboost model exceeds 80%, which is the model with the highest accuracy rate. The accuracy rate of Bagging Classifier is slightly lower than that of CatBoost, but the prediction result is the most stable. Such a result conforms to the characteristics of the algorithm itself. The conclusion of the analysis of feature contribution of numerical data to accuracy rate is that rear camera pixels, number of cameras and screen size have negligible impact on model prediction accuracy, which means feature contribution is minimal. The research results are reasonable and can provide references for merchants to set prices and for users to purchase mobile phones.
Amazon Stock Price Prediction During COVID-19 Based on the LSTM Model and Linear Regression Model
Authors: Tan Jing
Published: November 2023

With COVID-19 sweeping the world in 2019 and the ensuing turmoil in the stock market, the need for accurate stock forecasting becomes particularly crucial during exceptional periods. Utilizing Amazon stock price data from 20022023, this study uses a machine learning approach to create a stock price prediction model in order to more accurately anticipate the trend of the stock price movement during COVID-19. The linear regression model and LSTM model are two models commonly used in forecasting studies, and this paper uses both models to forecast stock prices separately and evaluates the models by RMSE. The linear regression model is constructed directly with all data as independent variables. According to a 9:1 ratio, the LSTM model is segmented into a training set and a test set in this paper, with an overall structure of three layers and an optimizer of Adam optimizer. The test results reveal that the LSTM model's error is significantly lower than the linear regression model's error, proving that the LSTM model has a superior forecasting capacity to that of the linear regression model. As a result, it can be widely applied to the task of predicting stock prices for COVID-19 and offering decision-making advice to the appropriate decision-makers.
Study on the Applicability of LSTM for Predicting Stock Price when Facing Extreme Events
Authors: Dong Dingju
Published: November 2023

The analysis of stock price fluctuations holds considerable significance in the field of economics, particularly given the present environment characterized by unpredictability and rapid changes. Previously, the long short-term memory (LSTM) model has been employed effectively in addressing time series problems, including stock market forecasting. However, in the current dynamic landscape, the ability of LSTM to adapt to volatile conditions and provide accurate predictions is an area that merits further investigation. This study gathers stock data from prominent and representative companies, namely Apple, Google, Amazon, and Microsoft, spanning from January 2012 to March 2023. Specifically, two significant events are examined: the impact of the Covid-19 outbreak on the US stock market on February 26, 2020, and the Russia-Ukraine conflict occurring on February 26, 2022. By dividing the stock data surrounding these events into training and test sets, this research aims to evaluate the differential performance of LSTM in scenarios where it possesses no prior knowledge of these events versus situations where it has already assimilated the influence exerted by them.
Stock Price Prediction During the COVID-19 Pandemic Based on the GRU Algorithm
Authors: Wu Zirui , Huang Mingtao
Published: November 2023

Stock price prediction during the Covid-19 pandemic has emerged as a significant research domain within the financial sector, giving rise to a multitude of neural network-based methods aimed at forecasting stock prices. This paper uses multiple machine learning models and analyzes the stock fund flows to attempt to learn and predict price trends from a dynamic perspective. The data used in this study includes the closing price, change rate, and fund flow of an A-share Pharmaceutical stock in the most recent 1000 trading days. The Mean Squared Error (MSE) is used as the model evaluation metric, and the models MSE can reach 0.013 after training. The predictions generated by the model exhibit a high degree of alignment with the actual price trends, indicating its accuracy in short-term price trend prognostication. These findings substantiate the efficacy of the model for stock price prediction during the pandemic period, thereby contributing to the body of knowledge within the field of financial forecasting.
The Impact of the Lifting of Pandemic Control on the Shanghai and Shenzhen Stock Indexes
Authors: Chen Hongxi
Published: November 2023

Many previous studies have analyzed the pandemic effect to the different fields of economic. This essay analyzes the effect of lifting of global pandemic control to SZSE and SSEC stock market by mainly using ARIMA model and Stata. The essay finds that the lifting the global pandemic has indeed affect both SZSE and SSEC market by accelerating the return rate trend of stocks. The wired point is the sharp decreasing trend initially after the lifting of control. This phenomenon has related to financial theory to find out the reasons. The first reason is because the news is within the market expectation and thus did not stimulate stock price. The second reason is because the overall worse economic situation and market expectation outweigh the effect of this good news effect. The last reason is because many people got sick after the lifting of pandemic control and thus affect economic activity. Finding these reasons, the policymaker can have better way to stimulate the stock market in the next time. Policymaker can be also aware of the significance of building a more efficient market. Investors can have more understanding toward SZSE and SSEC market and be cautious to good news in the next time. Better strategy can be adopted to catch stock return in the similar strategy.
Competitive Advantages and Challenges of E-Commerce
Authors: Cao Meng
Published: November 2023

The rise of e-commerce, driven by the integration of technology into daily life and the increasing consumer preference for online shopping, has revolutionized the global trade of goods. Traditional retailing relied on physical stores, while e-commerce eliminated the need for a physical storefront by hosting merchandise in online stores. This shift reduced operational costs and offered consumers a vast variety of goods. However, e-commerce lacks the social experience of traditional retailing. This paper analyzes the evolution and current stage of e-commerce, evaluates the advantages and shortcomings of online retail, and provides insights into its future development. Understanding the transformative impact of e-commerce on the retail industry is crucial for stakeholders to make informed decisions and adapt to the changing landscape. By examining the past and present of e-commerce, this research contributes to a deeper understanding of its dynamics and implications. This knowledge serves as a foundation for anticipating the future trajectory of e-commerce and its continued growth.
Analysis of the Reorganization of ST Zhangzi Island
Authors: Hou Shengwei , Jiang Haoyi , Wang Xinyue
Published: November 2023

As a matter of fact, ST Zhangzi Island is a Chinese company primarily engaged in marine aquaculture. However, in recent years, the companys stock price has fallen, and it has faced lots of difficulties from the perspectives of operation, financing as well as fraud. One of the main reasons leading to the decline in the companys stock price and its difficulties is its financial problems. This study provides an in-depth analysis of ST Zhangzi Island s financial situation. According to the analysis, it is found that the company has a large amount of inventory backlog and overdue payments. Additionally, there are certain issues in terms of equity structure and internal monitoring. Through a thorough study and analysis of this case, this research summarizes the lessons learned from its restructuring, providing reference and guidance for similar situations in other companies. Overall, these results shed light on guiding further exploration in terms of similar cases in different industries.
Customer Preferences: Exploring the Synergistic Effects of Market Segmentation and Product Differentiation
Authors: Li Mingrui
Published: November 2023

Fast fashion brands and sustainable fashion play different roles in today's fashion market. While fast fashion brands are favored by a wide range of consumers for their quick responsiveness, fashion following, and affordable prices, sustainable fashion brands focus on environmental friendliness, social responsibility, and transparency. In this paper, we will explore the differences between these two types of fashion brands by comparing them in terms of market segmentation and product differentiation. Through a case study approach, the paper will delve into the strategies and practices of several brands to reveal how they differ in terms of market segmentation and product differentiation. This will help to better understand the business models, strengths, weaknesses, and risks faced by fast fashion and sustainable fashion, as well as their roles in meeting different consumer needs and setting fashion trends. Through such a comparative analysis, companies can better recognize the characteristics and trends of fast fashion and sustainable fashion and thus make informed choices in the fashion market.
Valuation Analysis of Lithium Battery Industry: Evidence from Kedali
Authors: Mou Yujiang , Yan Jianfan , Zeng Canfeng
Published: November 2023

In order to solve the problems of energy security and environmental pollution, governments are promoting the development of new energy industries to ensure the sustainable development of the human world. Among the new energy idustries, lithium batteries, as an important part of new energy vehicles, have widely used in recent years with the rise in sales of new energy vehicles. In China, with the increasing competition in the industry, various companies have different performances, and their business conditions affect the development of the entire new energy industry. Therefore, it is very necessary to build a reasonable value evaluation system for lithium battery companies to help related enterprises develop healthily and continue to operate, and market investors better understand the industry situation to make bright investment decisions. Based on the FCFF model, this paper analyzes the valuation of Kedali Company (one of the representative company in China's lithium battery industry), compares the intrinsic value of Kedali with the market value, and comes to the conclusion that the current stock price of Kedali is slightly undervalued. Finally, based on the conclusion, the investment risk analysis is presenetd for market investors, offering them some useful investment decisions.
Portfolio Optimization Strategy Based on Four Deep Learning Models
Authors: Zhang Erchuan
Published: November 2023

Deep learning techniques have provided a fresh outlook on the evergreen subject of portfolio optimization within the finance domain. This article selects the stocks of Google, Tesla, Tractor Supply Company, Analog Devices, and Duke Energy Corporation and deploys four deep learning models to estimate returns and covariance respectively. The mean-variance model is utilized to generate the target portfolio for each deep learning model, incorporating the predicted outcomes. Ultimately, the returns of each portfolio are compared to the market benchmark (S&P 500) returns. The findings demonstrate that the proposed target model outperforms the market benchmark (S&P 500) across multiple financial metrics. This study highlights the groundbreaking and promising applications of deep learning in the financial sector, providing valuable insights into innovative portfolio allocation strategies for risk-averse investors who aim to achieve stable and positive returns even in turbulent market conditions.
Microsoft's Layout in the Field of Speech Recognition: Taking the Acquisition of Nuance as an Example
Authors: Guo Rui
Published: November 2023

This case analysis examines the acquisition of Nuance Communications by Microsoft, with a specific focus on its implications and impact on both companies and the industry. The study reveals that Microsoft's strategic acquisition provides crucial support for its advancements in artificial intelligence and healthcare technology, thereby bolstering its capabilities in medical natural language processing, speech recognition, and electronic health records. Consequently, this acquisition strengthens Microsoft's position in the healthcare sector. For Nuance, the acquisition offers access to greater resources and market channels, facilitating the promotion of its healthcare technologies and solutions. The research emphasizes the significance of this acquisition in comprehending trends within AI and healthcare technology, while exemplifying the positive impact of mergers on driving industry innovation and collaboration. Investors are advised to closely monitor developments in AI and healthcare technology and seize investment opportunities, while leaders should actively foster collaboration and innovation to enhance healthcare services through AI applications. Overall, the acquisition illustrates the potential of this sector and emphasizes the need for strategic positioning in the evolving technological landscape.