Existing research suggests that elite clubs exist in venture capital markets, but a standard for determining their size and composition is lacking. This paper addresses this challenge by using the weighted k-means sorting algorithm to construct a research framework for elite clubs. Validating the framework with investment events data from China’s venture capital market (2001–2018), intriguing findings emerge. The ranking of Venture Capitalists (VCs) follows a power-law distribution, providing evidence for elite clubs’ existence. The analysis identifies a turning point in the score curve, serving as a valuable indicator for club boundaries. Elite clubs demonstrate relatively high stability, maintaining advantages and elite status in future competitions. Empirical validation confirms the proposed framework’s superior stability compared to existing methods. Importantly, elite club members outperform non-elites significantly. This paper effectively identifies elite clubs in the Chinese venture capital market, helping other VCs recognize potential partners, access high-quality information, and enhance investment performance.

The value-added tax (VAT) in China is levied and allocated based on the origin principle. Under the background of the increasing substitution of online retail for traditional offline retail, this mechanism will exacerbate the disparity of regional tax revenue, and intensify tax competition among local governments. Therefore, reconsidering the allocation mechanism of value-added tax in China can be an important policy decision, and it is influenced by various economic and social factors. Firstly, we utilize large-scale retail transaction data from an e-commerce platform to measure regional disparities in retail and consumption among different regions and then reveals present tax policy results in revenue imbalance in different regions. Secondly, we establish a model based on game theory to illustrate how the origin principle leads to fierce tax competition among regions. Furthermore, by establishing and solving tax allocation models between local governments and the central government, this study simulates and calculates the degree of revenue imbalance under different scenarios and attempts to propose policy measures. The results indicate that implementing the destination principle will reduce regional tax imbalances. Moreover, adjusting the allocation ratio between the central government and local governments based on city levels is advantageous for further reducing regional tax revenue disparities.

Gasoline is the lifeblood of the national economy. The forecasting of gasoline prices is difficult because of frequent price fluctuations, its complex nature, diverse influencing factors, and low accuracy of prediction results. Previous studies mainly focus on forecasting gasoline prices in a single region by single time series analysis which ignores the daily price co-movement of different series from multiple regions. Because price co-movement may contain useful information for price forecasting, this paper proposes the Lasso-CNN ensemble model that combines statistical models and deep neural networks to forecast gasoline prices. In this model, the Least Absolute Shrinkage and Selection Operator (Lasso) screens and chooses the correlated time series to enhance the performance of forecasting and avoid overfitting, while Convolutional Neural Network (CNN) takes the selected multiple series as its input and then forecasts the gasoline prices in a certain region. Forecasting results of gasoline prices at the national level and regional levels by using the new method demonstrate that the new approach provides more accurate results for the predictions of gasoline prices than those results generated by alternative methods. Thus, the relevant series can enhance the performance of forecasting and help to gain better results.