This paper proposes an assumption of quasi-variable discount rates to explain the excess volatility puzzle of stock market. Under the assumption, the ARMAX model is derived based on the CCAPM model and CRRA utility function to describe the linear relationship between the discount rate and the consumption growth rate. We conducted empirical research on this model using historical data of the US stock market. The results confirm a significantly negative relationship between consumption growth rate and discount rate. Subsequently, the results of Monte Carlo simulation show that given the risk preference coefficient and dividend sequence, the rational expectations price fluctuation obtained under the assumption of quasivariable discount rate is the largest.

With e-commerce concentrating retailers and customers onto one platform, logistics companies (e.g., JD Logistics) have launched integrated supply chain solutions for corporate customers (e.g., online retailers) with warehousing, transportation, last-mile delivery, and other value-added services. The platform’s concentration of business flows leads to the consolidation of logistics resources, which allows us to coordinate supply chain operations across different corporate customers. This paper studies the stochastic joint replenishment problem of coordinating multiple suppliers and multiple products to gain the economies of scale of the replenishment setup cost and the warehouse inbound operational cost. To this end, we develop stochastic joint replenishment models based on the general-integer policy (SJRM-GIP) for the multi-supplier and multi-product problems and further reformulate the resulted nonlinear optimization models into equivalent mixed integer second-order conic programs (MISOCPs) when the inbound operational cost takes the square-root form. Then, we propose generalized Benders decomposition (GBD) algorithms to solve the MISOCPs by exploiting the Lagrangian duality, convexity, and submodularity of the sub-problems. To reduce the computational burden of the SJRM-GIP, we further propose an SJRM based on the power-of-two policy and extend the proposed GBD algorithms. Extensive numerical experiments based on practical datasets show that the stochastic joint replenishment across multiple suppliers and multiple products would deliver 13∼20% cost savings compared to the independent replenishment benchmark, and on average the proposed GBD algorithm based on the enhanced gradient cut can achieve more than 90% computational time reduction for large-size problem instances compared to the Gurobi solver. The power-of-two policy is capable of providing high-quality solutions with high computational efficiency.

Order release is a key production planning and control function, specifically in high variety contexts. A large literature on release methods that balance the workload consequently emerged. These Workload Control methods can be rule based, using a simple greedy heuristic, optimization based or optimization based with lead times that are exogenous. Although all three types of methods have the same objective, their performance has never been compared. Using simulation, this study shows that a better on time delivery performance of jobs can be achieved by the two optimization based release methods. Most importantly, optimization based methods that assume lead times to be exogenous significantly outperform alternative methods in terms of tardiness performance. Rule based and optimization based Workload Control without exogenous lead times overemphasize average lateness reduction, which leads to sequence deviations that offset performance improvements through balancing. In contrast, Workload Control methods that assume lead times to be exogenous limit sequence deviations, which leads to a significant reduction in dispersion of lateness. This has important implication for the future design of order release methods, and managerial practice.

Recognition of psychological characteristics based on massive data and computer machine learning algorithms has gradually become a new way for psychological research. As we all know, person-job fit is an important consideration in recruitment and selection. Most existing selection process can reliably measure skills fit, i.e., matching job seekers’ skills/work experience with job demand. What is often harder to assess is the compatibility between job seekers’ motivational needs/career aspirations and job characteristics, which will ultimately determine their career progress and job satisfaction. With the increasing application of machine learning methods in psychology, this paper constructed classification models to predict individuals’ needs, career aspiration, and occupation through their personality traits. This enables automatic access to individuals’ psychological indicators, with the MLP (Multi-Layer Perceptron) method showing the highest prediction accuracy. In addition, it conducted a comparative analysis of the distribution of personality characteristics in different occupations. Based on the study results, we put forward some countermeasures and suggestions for application in human resource management.

As one of the key technologies of intelligent transportation systems, short-term traffic volume prediction plays an increasingly important role in solving urban traffic problems. In the last decade, many approaches were proposed for the traffic volume prediction from different perspectives. However, most of these approaches are based on a large amount of historical data. When there are only finite collected traffic data, they cannot be well trained, so the prediction accuracy of these approaches will be poor. In this paper, a tensor model is proposed to capture the change patterns of continuous traffic volumes. From collected traffic volume data, the element data are extracted to update the corresponding elements of the tensor model. Then, a tucker decomposition and gradient descent based algorithm is employed to impute the missing elements of the tensor model. After missing element imputation, the tensor model can be directly applied to the short-term traffic volume prediction through searching the corresponding elements of the model and the storage cost of the model is low. Our model is evaluated on real traffic volume data from PeMS dataset, which indicates that our model has higher traffic volume prediction accuracy than other approaches in the situation of finite traffic volume data.

In this paper, we construct a company value model based on the tax shield effect for overseas listing and privatization scenarios. The trade-off process of privatization decisions is simulated in the context of China concept stock companies’ reality. The results indicate that the value of tax shields, the degree of undervaluation, the ability to obtain cash flows, the risk of short selling, the cost of listing transactions, and fraud penalties are critical factors influencing the choice of privatization. The company value analysis shows that tax shield effect positively affects the probability of privatization. Furthermore, the weaker the ability of a company to obtain cash flow when listed overseas, the lower the WACC, the higher the risk of being shorted, and the higher the cost of listing transactions, the higher the probability that a company will choose to go private. Finally, numerical simulations are adopted to validate the validity of the theoretical model and the findings using SINA’s privatization as a case study. The findings can provide academic guidance and a decision-making basis on trading arrangements for CCS companies.