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 R2 of 0.2292,a CV R2 mean of 0.529,and a CV R2 standard deviation of 0.1293,indicating moderate but reliable predictive power suitable for noisy financial data,such as IPO underpricing,where feature importance insights are more valuable than precise predictions.To reconcile and aggregate different outcomes from these multiple models,we implemented a voting algorithm to identify robust and reliable feature ranking for ex-ante determinants of IPO underpricing.Among the features,investor demand and divergence of opinions consistently emerged as the top two influential predictors of IPO underpricing,highlighting the key role investor sentiment and information asymmetry play in determining IPO pricing.These findings offer insights to investors,issuers,and policymakers,enabling a deeper understanding and effective management of the ex-ante drivers of underpricing in fixed-price IPOs,which lack market-based price discovery.
The research purpose is to contribute to the field of forecasting foreign exchange.This is due to the ever-changing economic conditions under which analysts can observe the significant volatility of exchange rate forecasts,as exchange rate forecasting has been challenging for analysts for many years.Stakeholders(central banks,governments,and investors)will seek to maximize asset returns and minimize risk in their decision-making using exchange rate forecasting.Therefore,this study proposes a new approach from the Black-Scholes model to forecast daily,weekly,monthly,and yearly exchange rates for the domestic currency pair the indonesian rupiah(IDR)to the United States dollar(USD)traded.The Black-Scholes model,originally referred to as a partial differential equation,was changed to an ordinary differential equation,which is used to approximate the numerical solution of the new Black-Scholes equation.The numerical solution used in this research is the fourth-order Runge-Kutta method.This study uses actual exchange rate data for more than one year,and the prediction results show that the proposed methodology can be an effective method for forecasting the exchange rate(IDR/USD).It is indicated by a small error,of less than 5％,and a mean absolute percentage error of less than 2％.
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 regu-lators.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 pro-vides a quantitative foundation for understanding multi-agent interactions in data sharing and offers actionable strategic insights for optimizing IoT data sharing mechanisms.
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 sig-nificant 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 perfor-mance 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 accu-racy 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.
This study develops a systematic framework to evaluate regional service science and tech-nology innovation(SSTI)in China.It aims to address the"manufacturing paradigm"bias in existing evaluations.Drawing on the national innovation system(NIS)theory,a multi-level indicator system is constructed with three dimensions:Innovation input,innovation output,and innovation environment.The analytic hierarchy process(AHP)is applied to determine indicator weights,and the SSTI perfor-mance of eight provinces and municipalities in China from 2011 to 2023 is systematically measured and comparatively analyzed.The results reveal a sustained upward trend in the overall level of SSTI.However,a pronounced stratified pattern of"the strong get stronger"persists,reflecting significant and widening regional disparities.Moreover,regional innovation modes exhibit marked structural hetero-geneity.These findings advance the operational evaluation of SSTI and provide diagnostic insights for identifying systemic bottlenecks,optimizing resource allocation,and formulating region-specific innova-tion strategies.
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.
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.
We introduce a mathematical model for a voting system called fuzzy threshold voting(FTV)intended for use primarily in cryptocurrency governance systems.The main difference between this voting model and approval voting is that the voting outcome depends not only on the scores of candidates but also on other factors-budgets of the corresponding projects(actually,projects are the candidates).The proposed FTV voting model is considered to be some generalization of the yes-no voting model with expanded features.In addition to building an adequate mathematical model for the new voting system,we also analyze its main properties,such as the optimality(in some sense)of the voting procedure,existence of admissible strategies,advantages of sincere voting,and necessity of secret voting.We demonstrate that the presented FTV system satisfies one of the most important properties of the voting systems-the monotone rule,and prove several statements regarding possible voting strategies.
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 study aims to analyse transformational changes in banking risk management and mar-keting policy of banks caused by digitalisation.The study addressed examples of successful digital technology implementation in the banking sector around the world and conducted a comparative analy-sis of the current situation in Kyrgyz banking.The study addresses the process of digital transformation of the banking sector,with a focus on Kyrgyzstan.Digital transformation involves the integration of technologies such as artificial intelligence,big data and blockchain into all aspects of banking operations,leading to significant changes in their functioning and customer experience.The study analysed au-tomating processes,improvement of customer service,enhancement of transaction security and creation of new business models.The study included examples of successful digitalisation in banks around the world,such as the use of artificial intelligence to automate and analyse data,which helps to predict risks and improve customer experience.Based on the analysis,recommendations for banks in Kyrgyzstan were proposed,including investments in IT infrastructure,literacy programmes and enhanced cyber-security.The results show that digitalisation can significantly increase the accessibility and quality of banking services,improving the overall standard of living of the population.
Financial holding companies(FHCs)in China leverage equity control to enhance oper-ational 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 encourag-ing small shareholder participation to enhance governance and stability.
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.
Efficient cross-border tourism flows are a critical dimension of a country's economic in-tegration into the global economy.This paper introduces an innovative application of the stochastic frontier gravity model(SFGM)to analyze and measure international tourism efficiency.By integrat-ing natural determinants(e.g.,geographical distance,economic size,and price indices)with man-made factors(e.g.,social,political,economic,and policy preferences),the study provides a comprehensive framework for assessing tourism efficiency relative to theoretical gravity frontier levels.The findings reveal that,while China has achieved approximately 80％of its tourism potential on average,significant inefficiencies persist,particularly across different origin countries and regions.The study highlights the complementary relationship between human and goods flows,emphasizing the importance of cultural proximity and trade intensity in reducing inefficiencies.Furthermore,it demonstrates the robustness of the SFGM framework in capturing the dynamic and uneven patterns of tourism efficiency over time.By addressing gaps in the application of SFGM to tourism research,this paper advances the theoretical and methodological understanding of tourism efficiency and provides actionable policy recommendations for enhancing China's tourism market integration.
In this paper,we discuss two cases of two-player repeated games in an environment of incomplete information with randomness and cognitive uncertainty,where incomplete information refers to the situation in which the transition probabilities and player payoffs are uncertain.We use robust optimization techniques to handle data uncertainty and determine the optimal solution within this framework.Our contributions are threefold:Firstly,we apply Markov processes to the repeated game model.Secondly,we propose an effective robust optimization method that can handle uncertain data in the context of incomplete information and solve the uncertainty problem in different types of repeated games.Finally,our method is more general than previous methods and can adapt to various types of data,providing decision support for risk-averse players.
This paper investigates an M/G/1 queueing system that integrates preventive maintenance,randomized vacation policy,and Bernoulli feedback mechanism.By applying the law of total probability decomposition and Laplace transform(LT)technique,we derive explicit expressions for the LT of the transient queue length distribution starting from arbitrary initial states.Based on the transient results,the steady-state queue length distribution and the corresponding additional queue length distribution are also obtained.Furthermore,we show that the proposed model reduces to several classical systems under specific parameter settings.Finally,numerical experiments are conducted to compare the proposed model with classical vacation models,to examine the system capacity optimization design under loss constraints,and to illustrate the transient behavior of the system.These findings verify the validity and practical relevance of the considered model.
This paper examines the impacts of retailer fairness concern and consumer fairness concern on the profit under a supply-chain framework consisting of a manufacturer and multiple retailers.Firstly,the case in which the manufacturer declares a unified wholesale price under retailer fairness concern is discussed.The results show that the profit of the manufacturer decreases and the total profit of retailers increases under retailer fairness concern.In the meanwhile,an interesting phenomenon is revealed:Despite a constraint of retailer fairness concern is added to the supply chain,the total profit rises rather than falls.Secondly,we consider both retailer fairness concern and consumer fairness concern.In this case,the manufacturer declares a unified wholesale price,and all retailers declare a unified retail price.Compared with the case in which only retailer fairness concern is considered,consumer fairness concern lowers the total profit of all retailers,while the profit of the manufacturer remains unchanged.Because pricing coordination is needed during the unified pricing of retailers,this study puts forward the definition of extreme-value-proportion,and synthetically applies Nash bargaining solution and proportion sharing solution to obtain the profit distribution scheme for the retailer-pricing alliance based on absolute value average and relative value average.
In this paper,based on the advantages of bipolar fuzzy sets(BFSs)and hesitant fuzzy set(HFS),the concept of dual hesitant bipolar fuzzy sets(DHBFSs)is introduced and their operation rules and properties are discussed.The objective of this paper was to develop some novel Heronian Mean(HM)operators for any dual hesitant bipolar fuzzy elements(DHBFEs).The advantage of em-ploying Heronian Mean operator mainly lie in considering the interrelationships between parameters and handling uncertainty and ambiguity information that exist in the real world.Thus,based on the DHBFS environment,we develop a family of dual hesitant bipolar fuzzy Heronian Mean aggregation operators,such as the generalized dual hesitant bipolar fuzzy Heronian Mean(GDHBFHM)operator,the generalized dual hesitant bipolar fuzzy weighted Heronian Mean(GDHBFWHM)operator,the dual hesitant bipolar fuzzy geometric Heronian Mean(DHBFGHM)operator,the dual hesitant bipolar fuzzy weighted geometric Heronian Mean(DHBFWGHM)operator.And the properties of these pro-posed operators are studied.Furthermore,an approach based on the GDHBFWHM operator and the DHBFWGHM operator is proposed for multiple attribute decision making problems under dual hesitant bipolar fuzzy environment.Finally,a numerical example and comparative analysis is given to illustrate the application of the proposed approach.
Personalized recommendation services have recently been widely provided in online social networks(OSNs).OSNs have large number of users,and users' information and browsing data are saved in online services due to frequent communication between users.To achievebetter performance,personalized recommendation services need users' characteristics and behavior information,which brings privacy issues into concern.Therefore,balancing privacy preservation and recommendation results has become the main focus in this field.In this paper,we combine a privacy preserving method with the social network model and make full use of the user's attribute information in the social network to improve personalized recommendation results based on the privacy-preserving link prediction(PPLP)framework.Additionally,a link prediction algorithm with attribute classification is proposed in this paper,which considers the connections between user attributes and the similarity between users.The improved PPLP was evaluated on Google+datasets and the results show that it can improve the accuracy of recommendation results while protecting users' information.
This research introduces a novel framework that integrates graph convolutional networks(GCNs)with clustering techniques to examine the intricate spatial structure of contemporary service industries.Utilizing 2023 point-of-interest(POI)data from Xi'an,the study extends beyond analyz-ing single industries to uncover 18 unique multi-industry composite clusters,highlighting significant patterns such as the blending of education with real estate and the merging of business and financial services.Additionally,by employing DBSCAN,the research identifies the high-density core regions within these clusters and their spatial coexistence patterns,pinpointing multifunctional areas.These results contribute to the theory of urban polycentricity and offer data-driven guidance for planners to promote evidence-based zoning,encourage mixed-use development,and enhance functional integration in service-focused urban economies.
Smart manufacturing is a key aspect of current urban sustainability concerns,with urban skills impacting the growth of smart manufacturing.This raises questions for sustainable urban de-velopment regarding the polarization of skills between cities.This study investigates the influence of inter-city skills polarization on the wages and employment of workers in smart manufacturing in China by examining social-cognitive skill score data.The regression results show that the social cognitive skill score of the city has a significant positive effect on local manufacturing wages.However,it reduces the number of local manufacturing jobs and the proportion of manufacturing in the industrial structure.Smart manufacturing development policies have a significant impact on local manufacturing employ-ment but do not influence the wage levels of local manufacturing workers.In addition,productivity in the secondary industry may reduce local manufacturing wages and employment.Nevertheless,it has a negative skew when mediating the connection between intercity skill polarization and local man-ufacturing wages.The study reveals the reasons for workers participating in production during the smart manufacturing era,predicts future wage changes for these workers,and examines the differences in industrial layouts across cities.
