In this paper, a kernel Extreme Learning Machine (KELM) model based on vector weighted average algorithm is proposed for the prediction of national tax revenue ratio, which provides a new way of thinking and method for tax revenue prediction. By analyzing the correlation between each index and tax share, it is found that gasoline price and life expectancy are significantly positively correlated with tax share, while fertility rate and birth rate are significantly negatively correlated. The model shows excellent predictive performance on both training set and test set, with an R² of 0.995 in training set and 0.994 in test set, indicating that the model has excellent generalization ability. In addition, the root mean square error (RMSE) of the training set and the test set are 0.185 and 0.177, respectively, and the relative prediction deviation (RPD) is 14.234 and 13.178, respectively, which further verifies the high accuracy and stability of the model. Scatter plots of actual predicted versus actual values show that the model is able to accurately capture trends in tax shares with little prediction error. In summary, the optimized KELM model proposed in this paper not only has excellent performance on known data, but also has good expansion ability, and can be effectively applied to the tax share prediction of unknown data, providing a reliable tool for relevant policy making and economic analysis. The research of this paper provides a new technical path for the field of tax forecasting, which has important theoretical significance and practical value.

Show more

This study primarily investigates the application of the ARIMA-GARCH model in forecasting pharmaceutical industry stocks, with Apeloa Pharmaceutical chosen as the sample stock and the sample period spanning from 2020 to 2024. Initially, this research determines the order of the ARIMA model using information criteria, and subsequently conducts joint volatility modeling on the residual series of the ARIMA model. The modeling results demonstrate that all parameters exhibit favorable statistical significance. Based on the established model, backward forecasting is performed, yielding the MAPE of 3.81% between the forecasted values and actual values for the subsequent five periods. This indicates that the ARIMA-GARCH model can effectively contribute to the process of stock price forecasting. The empirical conclusions of this study provide robust theoretical underpinnings by validating the ARIMA-GARCH model's efficacy for pharmaceutical stocks. Practically, these findings enable more accurate equity price forecasts, refine portfolio risk management strategies through volatility insights, and guide dynamic asset allocation optimization.

Show more

The Guangdong-Hong Kong-Macau Greater Bay Area (GBA) achieves the goal of integration and sustainable development of all regions through the creation of comprehensive smart cities. Leveraging the smart city effect plays a crucial role in the development of the GBA. However, the key to creating a good smart city in each region lies in whether the region’s public finance system can be effectively utilised to achieve the fundamental objective of promoting smart city development through the key role of digital technology for governance, infrastructure and innovation. This study examines findings on local development disparities in the GBA and their impact on infrastructure, public services, and industrial innovation, and proposes measures to improve cross-regional fiscal coordination. The measures are developed through case studies and policy comparisons, with a focus on Shenzhen’s intelligent transport. Recommendations include the establishment of a joint municipal financing mechanism, the promotion of inter-city cooperation, and the enhancement of private sector participation through a public-private partnership model. The paper highlights the importance of establishing institutionalised fiscal relations to ensure the building of equitable and efficient smart cities in the GBA.

Show more

Stock market investment involves various levels of risk. This study attempts to solve the problem of classifying stocks into specific risk categories using fundamental and technical indicators. This study helps bridge the gap between theoretical classification of risks and practical trading approaches for various investment styles. The author employs quantitative methods such as descriptive statistics, risk scoring, time series analysis, and Auto-Regressive Integrated Moving Average (ARIMA) modeling on 10 diverse U.S. stocks from January 2020 to current date. These analyses provided evidence of three risk categories with distinct volatility patterns. Relative Strength Index (RSI) has been found to be the most statistically relevant variable for all returns, while volatility had the largest absolute coefficient of returns. The author found that ARIMA(0,1,0) best fit all stocks. This indicated that all stocks followed a random walk with drift, regardless of risk category, but with regular risk stocks exhibiting slightly more predictability than conservative or aggressive. These results will be useful to portfolio managers and investors who wish to adjust their stock portfolios according to their risk appetite by helping align stock selection with risk tolerance while indicating which technical indicators are useful for different styles.

Show more