ISSN 2512-6660 (Online) ISSN 1478-9906 (Print)
CN 10-1192/N
CN 10-1192/N
This study investigates the ex-ante information on initial public offering (IPO) underpricing in Malaysia using multiple machine learning techniques. The paper analyzes a sample of 350 fixed-price IPOs from 2004 to 2021, applying five machine learning models: artificial neural networks, random forest, gradient boosting, extra trees, and linear regression. The results indicate that random forests demonstrates superior performance, with a test
Financial holding companies (FHCs) in China leverage equity control to enhance operational efficiency and synergies, yet excessive equity concentration often undermines these benefits. This study investigates the impact of equity structure —specifically concentration and balance — on the performance of 17 A-share listed Chinese FHCs from 2010 to 2022, using data from the CSMAR database. Empirical results reveal an inverted U-shaped relationship between equity concentration and performance, with moderate concentration optimizing decision-making efficiency, while excessive levels risk power abuse. Equity balance, however, negatively affects performance by fostering power struggles and delaying decisions. These findings underscore the need for a balanced equity structure in Chinese FHCs. Policy recommendations include listing parent companies to diversify equity, keeping subsidiaries unlisted with concentrated ownership for synergy, strengthening regulation, and encouraging small shareholder participation to enhance governance and stability.
The development of the new energy vehicle (NEV) industry is pivotal in addressing China’s energy security challenges and restructuring its automotive sector. It is a critical measure for achieving China’s carbon neutrality goals. Accurately analyzing and forecasting NEV sales volumes carries significant implications for industry planning and policy formulation. This paper proposes an integrated modeling framework that combines variable selection techniques with the XGBoost algorithm to forecast NEV sales in China. We compare three distinct variable selection methods and evaluate their performance against commonly used benchmark forecasting models. The prediction performance is assessed using two metrics: root mean squared error (RMSE) and mean absolute percentage error (MAPE). Our empirical results demonstrate that the variable selection-XGBoost integrated model outperforms both univariate models and models that do not incorporate core factor extraction, showing superior accuracy in both in-sample and out-of-sample predictions. Among the variable selection-XGBoost models, the SSL-XGBoost model yields the best performance, followed by GLMNET-XGBoost and LARS-XGBoost. The SSL method selects the greatest number of core variables. Cross-validation results indicate that integrating XGBoost with variable selection significantly reduces prediction errors. The variable selection-XGBoost integrated model surpasses traditional models in terms of both accuracy and reliability.
With the rapid advancement of the Internet of Things, the generation and sharing of massive data have become a significant trend. However, the pervasive free-riding behavior among stakeholders has adversely impacted data quality. To address this issue, this study employs tripartite evolutionary game theory to construct a decision-making model involving data providers, data brokers, and regulators. This model depicts the strategic choices of these three parties in data quality management: data providers and brokers may opt for proactive investment or passive free-riding, while regulators may choose between stringent oversight or routine inspections. By constructing replicator dynamics equations, employing Jacobian matrix analysis to examine equilibrium points, and combining numerical simulations with sensitivity analysis, this paper explores the evolution of stakeholder strategies and the impact of key parameters on system stability. Results indicate that free-riding behavior significantly compromises data quality. When the net benefit of active quality control falls below the free-riding payoff, the system converges toward a fully passive equilibrium point (0, 0). However, appropriately calibrated incentive and penalty mechanisms can effectively promote active behavior. This study provides a quantitative foundation for understanding multi-agent interactions in data sharing and offers actionable strategic insights for optimizing IoT data sharing mechanisms.
In recent years, extreme weather events and pest/disease issues have made the resilience of the Agri-food supply chain a focus of social concern. Enterprises typically adopt two primary strategies to enhance the supply chain’s resilience, namely maintaining high inventory levels and improving logistics timeliness. The former, particularly through the implementation of the safety stock strategy, appears more feasible in the short term but incurs significant costs, especially for Agri-food. Therefore, striking a balance between resilience and cost efficiency is essential. This paper proposes a system dynamics model to collaboratively optimize resilience and holding costs in a three-level Agri-food supply chain. Using demand fulfillment rate as a resilience indicator, six simulation scenarios with varying inventory and transportation time configurations are designed. The dynamic impacts of these factors on both costs and resilience are analyzed. Optimization is performed using the Powell hill climbing algorithm in Vensim® DSS to adjust the safety stock strategy. Results show that: Reducing distributors’ transport time enhances resilience more, but at higher costs; increasing the inventory levels of retailers and distributors is more effective in improving resilience, though also accompanied by increased costs; Collaborative optimization among supply chain members can maximize both resilience and cost efficiency.
The level of intelligence in weapon systems and equipment will be one of the key factors determining the victory or defeat of future wars. Methods to incorporate the level of intelligence as an incentive factor into the pricing system of weapons and equipment were explored, which can motivate contractors to strive to improve the level of intelligence in weapons and equipment. Based on the core combat capabilities of weapons and equipment in the context of intelligent warfare, a comprehensive evaluation index system for equipment intelligence level is constructed, which includes 6 primary indicators and 18 secondary indicators. Taking the intelligence index as the intelligence level of equipment, the calculation method of intelligence index is given by using the closeness in TOPSIS. On this basis, the equipment intelligence index is included as the main incentive factor in the equipment incentive pricing model, forming an incentive pricing method based on the level of equipment intelligence. The feasibility of the method was verified through simulated data and compared with the pricing method that only considers cost incentives. The results indicate the method proposed in this paper can obtain differentiated prices according to the market environment, which is more flexible and applicable.
Enhancing industrial linkage is an effective way to optimize the industrial structure, improve the efficiency of industrial resource allocation, and prompt the high-quality development of the industrial economy; therefore, analysing industrial linkage is highly important. On the basis of the complex network perspective, this paper uses the input-output table of Henan Province from 2017 to explore the topology and association characteristics of the industrial network in Henan Province, and the results show that the industrial network in Henan Province has some small-world nature and scale-free network characteristics; however, it is not completely consistent with the stochastic network model and the BA model, and the stochastic block model is able to fit the data better; however, there are some difficulties in model estimation.
This paper applies the WSR systems methodology to explore how the quality monitoring and evaluation system in regional elementary education management acts as a carrier of value orientation and promotes high-quality development through feedback control. Based on the static Wuli (physical), Shili (practical), and Renli (human) elements, a time factor is introduced by adding a feedback control mechanism between cycles, creating a continuous dynamic closed-loop model for improving education quality. This ensures alignment with the system’s value and overall goals. Using student development data from the regional quality monitoring platform, the study implements personnel and distribution reforms, breaking system equilibrium and aligning individual and organizational goals. Based on data from 21 high schools in J City, H Province (2008–2019), the difference-in-differences (DiD) method is used to analyze the system’s impact on education quality. The results show a significant positive effect, with conclusions remaining robust after stability tests. This study enriches WSR’s application in elementary education, fills a research gap on policy effects, and offers practical insights for education managers.
