The COVID-19 pandemic has posed significant challenges to global public health, necessitating the development of effective predictive models to anticipate future outbreaks and allocate healthcare resources efficiently. This study aims to forecast the number of COVID-19 infections in four European countries—Germany, Italy, Malta and Sweden—during April and May of 2022. Two distinct forecasting models are employed: the Seasonal Autoregressive Integrated Moving Average (SARIMA) model and a Random Forest regression model. The analysis utilized data up to the end of March 2022, incorporating factors such as lagged case numbers, vaccination rates, temperature, and jurisdictional policies. The results indicate that while the SARIMA model captures the general seasonal trends, the Random Forest model outperforms SARIMA in predictive accuracy, as reflected by lower Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) values. Moreover, feature importance analysis from the Random Forest model highlights that recent infection rates (lagcases7) significantly impact future case predictions, suggesting the utility of machine learning techniques in capturing complex interactions within epidemiological data. These findings provide valuable insights for policymakers in planning effective pandemic responses.

This paper investigates the feasibility of using crude oil prices to forecast the future performance of major Exchange-Traded Funds (ETFs) using data spanning from January 1, 2000, to June 1, 2024, which can have a significant impact on investment choices and portfolio management. The chosen ETFs include SPDR S&P 500 ETF (SPY), iShares MSCI Emerging Markets ETF (EEM), iShares MSCI Australia ETF (EWA), iShares MSCI Canada ETF (EWC), iShares China Large-Cap ETF (FXI), and Vanguard FTSE Europe ETF (VGK). The study comprises two main analyses: firstly, investigating the relationship between the Dow Jones Industrial Average Crude Oil Index and various ETFs; secondly, utilizing a predictive trading strategy with crude oil futures to predict ETF returns. The results indicate a notable inverse correlation between crude oil prices and ETF returns, suggesting that as crude oil prices increase, ETF returns tend to decrease. Moreover, the predictive strategy showcases significant annualized returns and favorable risk-adjusted performance, indicating that trading based on crude oil futures can lead to consistent profits. These findings offer valuable insights for both investors and policymakers, emphasizing the potential of crude oil prices as a predictive factor for ETF performance. By integrating crude oil price movements into their strategies, investors can improve their portfolio management and make more informed investment choices.

Given the urgent situation of global climate change, green technological innovation has become a key driver for the achievement of sustainable development goals. Green finance, as an innovative financial solution, provides financial support for green technological innovation through the in-depth integration of policy orientation and market mechanisms. This paper systematically evaluates the effectiveness of green finance in promoting green technological innovation and accelerating the transformation of the green economy and analyzes in depth the implementation effect of green finance policy, its interaction mechanism with green technological innovation, as well as the stability of the green financial market and risk prevention and control strategies. At the same time, there are deficiencies in policy evaluation, interaction mechanism exploration, and market stability maintenance in the field of green finance, and in-depth research and practical exploration should be strengthened, which is of vital significance for promoting the establishment of a sound global green economic system.

The NASDAQ Composite Index, a critical barometer of market trends heavily influenced by technology firms, poses significant forecasting challenges due to various external influences, such as economic conditions and geopolitical developments. This study delves into the predictive capabilities of three prominent time series models—Exponential Smoothing (ETS), Autoregressive Integrated Moving Average (ARIMA), and Linear Regression are key forecasting techniques used for time series analysis—utilizing monthly closing data spanning from September 2019 to August 2024. A comprehensive analysis is conducted to compare these models based on Root Mean Squared Error (RMSE) and Akaike Information Criterion (AIC) are important metrics used for evaluating model performance. The findings indicate that ARIMA, with its robust mechanism for handling non-stationary data, provides superior forecasting accuracy compared to the other models. In contrast, while ETS demonstrates effective forecasting capabilities, particularly in capturing level and trend, Linear Regression is found lacking due to its challenges in accurately capturing the intricate dynamics and temporal dependencies of the market. These findings offer important insights for investors, highlighting the need for sophisticated and detailed forecasting methods in the constantly changing financial environment. Additionally, this study paves the way for future research that could incorporate external factors, like macroeconomic indicators and sentiment analysis, while also examining the promise of machine learning techniques to enhance the precision and robustness of financial market forecasts. This holistic approach could significantly improve the understanding of market behavior and inform better investment strategies.

New quality productive forces (NQPF) focus on progress and innovation. The position of innovative financial mode is crucial in the development of NQPF. This paper focuses on the relationship between NQPF and innovative financial modes led by science and technology (SAT) finance and digital finance (DF). It discusses why and how innovative financial modes can play a strong part in developing NQPF. The comprehensive analysis results of this paper show that SAT finance invests financial resources into enterprises, and takes enterprises as the core carrier to achieve the rapid development of NQPF, which is embodied in the capital chain, talented people chain, and other chains. DF has strong universality, intelligence, and low costs, which can profoundly reshape industrial ecology and optimize resource allocation. Meanwhile, DF focuses on research and development, achievement transformation, and market promotion, and creates a whole process service mechanism of DF to help the development of NQPF. Finally, this paper discusses two problems of innovative financial mode, which are policy problems and talented people problems. To solve the policy problem, countries should establish a unified finance policy and reduce unnecessary sanctions. To solve the talented people problem, local governments should introduce preferential policies and actively explore and cultivate talents.

With the development of digital technology, the rise of several online financial platforms has had a huge impact on traditional commercial banking. Compared with traditional commercial banks (TCB), digital finance has high efficiency, low risk, low threshold, and diversified services, which has caused huge competitive pressure on commercial banks. For the high-speed and high-quality development of the national economy, the transformation of TCB has been urgent. However, there is a lack of summary on the specific transformation methods and feasibility of TCB in the existing studies. This paper reviews the development process of TCB and digital finance, compares the advantages and disadvantages of digital finance compared with TCB, and tries to analyze the reasons and methods for the transformation of commercial banks. Finally, the conclusion is drawn: Although the emergence of digital finance has squeezed the ecological niche of some TCBs, the transformation process of TCBs is full of risks and challenges. However, commercial banks can achieve transformation by increasing initial capital investment, optimizing talent selection and management mode, and expanding business diversity to absorb and integrate the advantages of digital finance to promote their own and national economic prosperity and development.

This paper explores the intricate relationship between globalization, urbanization, and rural economies, with a focus on the Global South and a specific case study of China. It examines how globalization has driven significant transformations in rural economies, leading to accelerated urbanization trends. The paper provides a comprehensive analysis from two major perspectives—urbanization and globalization—highlighting the rapid pace of development and its associated challenges. By analyzing China’s experience and comparing it with neighboring countries, the paper delves into the side effects of these modern developments, such as intensified exploitation and the loss of species diversity. The study also offers a multi-faceted analysis that considers geographic, political, and economic factors to understand the broader impacts of globalization. Moreover, this paper emphasizes the importance of environmental protection in the context of rapid development and provides valuable insights into the role of agricultural trade in economic globalization. It assesses the influence of agricultural trade between China and its neighbors on local residents, domestic political stability, and economic policies. The paper concludes with practical suggestions to address the challenges posed by globalization and urbanization, contributing to the ongoing discourse on sustainable development in rural economies.

This paper investigates the role of blockchain technology in revolutionizing start-up financing, focusing on the integration of cryptocurrencies through Initial Coin Offerings (ICOs) and Security Token Offerings (STOs). The research aims to explore how decentralized financial systems provide innovative alternatives to traditional funding methods like venture capital. Key topics of analysis include the advantages of blockchain technology, such as enhanced transparency, security, and global accessibility for start-ups. At the same time, the paper addresses significant challenges, including legal uncertainties and operational risks. Case studies of companies like Blockstack and Telegram illustrate the practical applications of ICOs and STOs, shedding light on both their potential and associated risks. The findings suggest that while blockchain can democratize access to capital, its long-term viability will largely depend on the development of comprehensive regulatory frameworks and greater investor protections. Overall, the study provides valuable insights into how blockchain technology could transform the future of start-up financing, fostering a more efficient, secure, and inclusive financial ecosystem.

In the context of globalization, China’s stock market (CSM) is increasingly impacted by the risks in international financial markets, especially the two important commodities, oil and gold. This article started from the perspective of stock market styles and used the model to explore the dynamic conditional correlation between international oil, gold markets, and CSM. The study found a positive mean dynamic conditional correlation (DCC) between the international oil market and CSM, which is heterogeneous for different market styles. On average, Oil price shocks are more correlated with large-cap, value, and dividend stocks. After further dividing the sample interval, the study found that during the occurrence of external shocks, the DCC between assets generally increases. As for gold, the research also found a positive mean DCC between the international gold market and CSM, which expands when external shocks occur as well, failing to reflect its hedging properties. The research not only enriches the understanding of the linkage mechanism among financial markets but also provides an empirical basis for investors to formulate more precise risk management and investment strategies in the changing international market environment.

At present, China is in a new normal of population development characterized by low birth rates and a highly aging population, and the trend is increasingly deepening. In this context, this article first classifies the reasons for the low fertility rate through theory and then analyzes the pressure of raising children and taking care of the elderly from the perspective of people during the fertility window period. Through data and analysis, it has been found that the economic pressure on contemporary young people is particularly concentrated on the education expenditure for children and the dependency ratio for the elderly. Finally, this article proposes some practical solutions, attempting to develop the role of the elderly labor force in intergenerational education through economic policies and social support. These plans can simultaneously utilize the two demographic characteristics of China's low fertility rate and high aging population, and accelerate the improvement of the fertility support system by helping young people obtain intergenerational care.