This article theoretically and empirically studies the effects of investor limited attention on information diffusion and stock pricing on industry level, and concludes that industrial information diffuses only gradually among investors. Industrial information gradual diffusion causes the return lead-lag effects between stocks with high attention and those with low attention within the same industry. It also leads that industry returns affect future market returns significantly. The less attention is paid to the industry, the more it affects future market returns. Industrial information gradual diffusion also causes industrial momentum. Strategy buying winner industry and selling loser industry correspondingly is significantly profitable.
Margin-trading is one kind of short purchase mechanism which implies leverage buying. Since the launch of margin-trading and short-selling in Chinese security market, the special trading mechanism and the characteristics of Chinese stock market together have produced profound and complicated influence on investor behavior. In essence, margin-trading is a kind of leverage trading. It is not only a means of trading method, its business characteristics and trading mechanism has a profound impact on investor psychology, and thus affect the stock market. Previous study proves that individual investors in China market have shown obviously overtrading. Do margin-trading investors have significant differences in overtrading with ordinary individual investors? Using account data and trading history of all investors from a large security company in China, this paper makes an empirical study on the overtrading behavior of margin-trading investors. We document that compared with ordinary investors, 1) investors who trading in credit account trade more frequently, showing more prominent overtrading; 2) and thereby reduce their returns more so than do ordinary investors.
This paper captures the intraday price dynamics and the price-volume relation by modeling the intensities of the price rising and price falling events. We find that when the price is rising the trading volume drives the price up, while when the price is falling the trading volume pulls the price down. Moreover, the magnitude of the former effect is stronger than the latter's. The trading strategies based on our price intensity models earn remarkably high excess returns, which suggests the weak-form market inefficiency of the Chinese A-share market. In the trading strategies, the trading volume contains unique information and improves the profitability significantly.
This paper uses a special perspective to document the difference phenomenon in bull and bear markets by studying the information transmission between the Chinese stock index futures and spot market. We extract the bull and bear market data samples respectively based on the historical market performance and apply BVGJR-GARCH-BEKK model and LM jump test method to examine the price discover, volatility spillover and jump risk. The empirical results indicate that futures market plays the leading role in the long-term price discovery in the bull market while price discovery is mutual in the bear market. Overconfidence effect can provide explanation of the difference. Furthermore, we find that there is an asymmetric volatility spillover effect between futures and spot markets. Besides, there are more jumps in the bear market from the unexpected information.
Based on a sample of Chinese city commercial banks during 2007 and 2015, this paper investigates the influence of political resources on banks' geographic diversification. The empirical results indicate that city commercial banks with political resources are more likely to set up trans-area branches than banks without political resources, and the effect is more pronounced in banks with high-level political resources. Further results show that the positive relation between political resources and geographic diversification is weakened when firms are located on areas with higher marketization level. Our paper serves as reference for the determinants of city commercial banks' geographic diversification, and provides city commercial banks with practical guidance on the realization of geographic diversification.
In response to frequent disasters, the cooperation in disaster relief between government and enterprises has caused wide public concern. But in the process of disaster relief, the motivation of enterprise charity is not simple. Considering a disaster relief system consisted of the government and one enterprise. Assuming that the government plays a dominant role, the relief efforts of the government and enterprise can increase the charity goodwill. The enterprises' relief efforts can also generate advertising effect, the consumer demand is effected by the two factors above. Then we established three differential game models. And it is founded that under the cost-sharing contract profits can achieve Pareto improvement for the government, the enterprise and the whole system under certain conditions; under the cooperation contract, profits can achieve optimum for both the government, the enterprise and the whole system. Finally, we verified the validity of conclusion through a numeral analysis.
Considering two production modes of products which are make-to-order (MTO) and make-to-stock (MTS) and ignoring setup costs which are from production mode change and other fixed costs, this paper develop a finite Markov decision process (FMDP) model to analyse the hybrid production decision of these two products. A comparative research is done under the situation of hybrid production of MTO and MTS products in a production system with stochastic demand about the efficiency of MTO priority mode, MTS priority mode and hybrid MTO/MTS production decision based on FMDP model (FMDP mode). The results show that the number of MTO order, MTS inventory and product demand intensity are essential to the decision of FMDP mode, while remaining decision periods, penalty of unit MTO order for late delivery and the cost of handling remaining MTS production impacts are only effective in the short term. When the MTO orders or the demand intensity of MTS production is reduced, FMDP mode is better than the MTS priority mode. With respect to MTO priority mode, the same conclusion is drawn when the stock level of MTS production is reduced or the demand intensity of MTO product is lowered.
On a monopolistic online trading platform where sellers make advertising investment decisions through the platform endogenously and the investments will be part of the platform's revenue, this paper build a two-stage dynamic game model to study a classic pricing problem in this common two-sided platform. The main conclusions show that the platform's pricing strategy is influenced by the effect of advertising and the inherent effect of the platform. When the effect of advertising is relatively small, the sellers' strength gap related to the investment budget will therefore be less, and the pricing strategy is to let all sellers join. Alternatively, when it is relatively large, the pricing strategy is decided according to the size of sellers' strength gap, and the smaller the gap, the lower the sellers' market-entry barrier. It means the platform should attract more small sellers if the gap is small. In addition, the optimal price (seller's membership fee) and profit of the platform decrease with sellers' sensitivity of membership fee, and the platform should attract more sellers with a high seller's externality. Specifically, the platform's optimal price increases with the seller's externality when the effect of advertising is relatively small.
With respect to the problem of advertising budget allocation considering uncertain consumer preferences, firstly, a multi-attribute utility function is used to measure the consumer utility. Secondly, the advertising response function is built to specify the relationship between advertising expenditure and consumer demand. Based on this, the consumer purchase decision model and the expected profit model of the enterprise are established, respectively. Lastly, a two-period game-theoretic model with incomplete information is built and analyzed. The outcomes show that as the uncertain degree of consumer preference information is reduced, the allocation strategy of the advertising budget is more targeted and thus to gain greater profits for the enterprise; for the consumer, only when the product that the consumer preferred can bring relatively larger profits, could he/she benefit from the advertising budget allocation. Besides, the expected profit of the enterprise increases, as the product profit increases. In addition, through further analysis of the game model, under different conditions, with increase of the product profit, the marginal profit of the enterprise gained decreases or increases.
In ratemaking of automobile insurance, insurers usually first charge the premium by using generalized linear models that can accounts for the prior information of individual polices and then adjust the premium based on the numbers of his claims in the past years by using bonus-malus system. This paper establishes the optimal bonus-malus system based on Poisson-Gamma distribution and Negative Binomial-Beta distribution to analyze the datasets collected by an insurance company in China, which contains claim information from year 2010 to 2015. We apply both the maximum likelihood method and Bayesian approach to calculate the bonus-malus factors. The paper shows that the current bonus-malus system in China is too simple and the claim information of insureds is not completely used. Moreover, the current BMS in China gives too less bonus for good drivers and too less malus for bad drivers. The optimal BMS we proposed in the paper takes into account simultaneously the prior characteristics and claim numbers of each policyholder, effectively avoiding the repeated bonus or malus for the premium, making the ratemaking process more reasonable and more accurate.
For the ranking problem of interval additive linguistic preference relation, we propose a new method to calculate the priority vector based on cross efficiency DEA and stochastic simulation. Firstly, the output-oriented DEA model is constructed for the additive consistent preference relations, and the intrinsic correlation between the relative efficiency score and the ranking vector is analyzed. Secondly, we develop a revised benevolent cross-evaluation DEA model for the inconsistent additive linguistic preference relation and a sorting method of additive linguistic preference relation is proposed based on this model. Finally, the proposed DEA model is combined with stochastic preference analysis, and a Monte Carlo calculation method for the expected priority vector and confidence of the interval additive linguistic preference relations is proposed. The numerical results show that the proposed method can effectively avoid the loss of information and has good reliability and applicability.
In order to further enhance the efficiency of the multi-objective decision-making problem with an enormous solution space, an effective and fast construction approach of the non-dominated solution set is worth to explore. At first, the properties of non-dominated relationship, the initial set of non-dominated solution, the definitions and theorems related to the set of non-dominated solution construction are given. In this paper, then, in terms of ordered set theory and operation rules, a new kind of construction approach of Pareto non-dominated solution set is proposed based on the initial non-dominated solution set sorting approach. This algorithm applies set sorting approach to attain the optimal solutions of MOP through comparing the ordered feasible solution set with the ordered non-dominated solution set. On the one hand, the ordered feasible solution set which not include the initial non-dominated solutions is built. On the other hand, some rules of algorithm are designed, which include the sorting criterion of initial non-dominated solutions, the insertion criterion and the find criterion of non-dominated solution. The time complexity in different situations of the presented algorithm and the common non-dominated sorting approaches are analyzed. At last, the experiments to construct the set of non-dominated solutions have been done through the test functions of ZDT1~ZDT3, DTLZ1 and DTLZ3, compared with the approach of NTCM proposed by Wang and the other three kinds of approaches, the algorithm mentioned above has a higher effectivity to find the non-dominated solution correctly, it has the more lower time complexity and the more shorter computation time to construct the set of non-dominated solutions.
In order to capture not only the interrelationship between the input arguments, but also the overall balance of aggregate object in information fusion problem, in this paper, we combine Heronian mean with the power average operator under intuitionistic fuzzy environment and present the intuitionistic fuzzy power Heronian mean operator (IFPHM) and the intuitionistic fuzzy weighted power Heronian mean operator (IFWPHM). The new operators use the crossover operation of Heronian mean to represent the correlation of variables, and introduce the support degree coefficient to mine the relative closeness degree of information, so as to embody the integrity in the process of information fusion. The desirable properties of these new extensions of Heronian means and their special cases are investigated. Finally, based on the IFWPHM operator, we present an approach to multiple attribute decision making and illustrate its validity and feasibility with a practical example.
The parameter learning and structure learning for the belief rule base (BRB) have been the two major aspects which co-affect the modeling accuracy and the modeling complexity of BRB. So far, most BRB related studies can be categorized to either one of the two aspects. In this study, a bi-level approach of BRB parameter and structure joint optimization is proposed. First, based on the characteristics of BRB, the Akaike information criterion (AIC) has been applied to deduce a single objective which can represent both the modeling accuracy and the modeling complexity of BRB. With the AIC-based objective, a bi-level model and a corresponding algorithm for BRB joint optimization are developed under the traditional conjunctive assumption. Second, a new disjunctive assumption is proposed with new rule activation and weight calculation procedures. Furthermore, the bi-level optimization model and the optimization algorithm are updated to fit the disjunctive assumption. After BRB joint optimization, the best decision structure of BRR can be derived. Finally, the pipeline leak detection case is investigated to validate the efficiency of the proposed bi-level optimization approach. In comparison with the results of previous studies as well as that of the adaptive neural fuzzy system and the support vector machine, the BRB joint optimization approach has shown superior performance in both improving the modeling accuracy and reducing the modeling complexity.
It is a strategic choice for China to develop public transportation with priorities, based on the relative relation between available resources and the population to support in cities. Due to the special characteristics of public transit operations, it is an important means to safeguard the healthy development of the public transportation industry and enhance the overall social welfare, through regulating transit operators and subsidizing transit users. This paper intends to explore how the government should combine two means of interventions-regulation and subsidization-to maximize social welfare in a monopolistic market. A mathematical programming model is proposed for public transit regulation and subsidization, after which the model's structure is simplified. Key analytical relations about fare, headway, and vehicle capacity are identified, which are supported numerically and theoretically. It is shown that it is welfare-improving to subsidize transit users only when the cost of subsidy is below a threshold.
Road bottleneck in the morning commute is one of the main reasons of traffic congestion. This paper studies travel behavior of travelers with different value of time (VOT) in morning rush hours, when there exist bottlenecks on highway (including high occupancy vehicle lanes) and ordinary road. Based on the equilibrium condition of the bottleneck model, the critical value of the individual early schedule delay is deduced when the travelers choose the travel mode under different toll levels. In the situation of lower toll level, the number of travelers with low VOT will increase. When the toll reaches a certain level, even the high VOT travelers will change the travel mode with the high occupancy vehicle. Numerical examples are presented to verify the results, and indicate that setting the appropriate toll can reduce the system total travel time.
This paper investigates a drayage problem with foldable containers and time windows. In this problem, foldable containers are used to collect and distribute freight between customers and the depot, and a truck could carry one loaded container or several (folded) empty containers. The objective of this problem is to minimize the total working time of trucks. Inspired by the determined-activities-on-vertex graph, the problem is decomposed into a loaded container sub-problem and an empty container sub-problem. The former one is similar to multi-traveling salesman problem with time windows. The latter one differs from vehicle routing problem because the amount of freight of customers could be negative. Furthermore, they couple each other in several aspects such as the visiting time of customers. The problem is mathematically formulated and then solved based on reactive tabu search (RTS) algorithm. A large number of randomly generated instances validate the model and algorithm presented above. The results indicate that the RTS algorithm can provide much better solutions of the problem in a much shorter time than typical optimization software such as CPLEX and that the use of foldable containers can save approximately 13% of drayage costs compared to the use of standard containers.
This paper uses niche theory and inframarginal model to research port enterprise value chain division decisions and the requirements of port enterprise value chain when it achieves the optimal adaptive state respectively. The result shows that the basic niche of port enterprise affects the extension of value chain through the mixed functions of interior niche factors such as scale factor, operation factor and profit factor, the relations between the comprehensive index of niche factor and the exchange rate in port enterprise colony determine the better basic conditions of basic niche's three corner decisions and further affect the division decisions of products and service supply in value chain. The results provide a theoretical reference to enhance international competitiveness through value chain division and construction.
In order to understand the deterioration law of product performance and rationally arrange the maintenance actions. According to the deterioration process, the performance deterioration characteristics of product on servicing are analyzed by cumulating failure theory. The deterioration model is established based on compound Poisson process, and the example of water pump rotor is introduced to illustrate the feasibility and validity of model. The propability model of maintenance actions which combines servicing with condition-based repair is estabilished, and the analytical expression of failure risk is given. Finally, the optimization model of combined maintenance strategy is proposed with an objective to minimize the long-run average cost rate and a constraint on failure risk. The platform inertial navigation system is taken as numerical example. The results demonstrate it can prolong the operational time, control the failure probability, guarantee high safety grade and decrease the maintenance cost.
In this paper, the optimal maintenance policy is investigated for a system with repairable repair facility. When the system fails, it will be repaired by the repair facility. The repair facility may be subject to failure during the repair period, and it can be repaired by a repairman if it fails. The successive survival times of the system and repair facility form different stochastically decreasing geometric processes respectively, and the consecutive repair times after failures of the system and repair facility form different stochastically increasing geometric processes respectively. When the failure number of the system reaches an integer number N, the system and repair facility will be replaced by a new and identical one. By using the renewal process theory and geometric process theory, the explicit expression of the long-run average cost per unit time under policy N is derived, and the corresponding optimal replacement policy can be found analytically. It is proved that the optimal replacement policy exists uniquely. Finally, the numerical examples are given.
The accuracy of monthly load forecasting will be affect by the outliers and holidays such as Spring Festival. So, two kinds of monthly load forecasting model based on seasonal adjustment and BP neural network was put forward. One, the original load series was pre-adjusted by using the seasonal adjustment method, the impact of outliers and the Spring Festival effect was eliminated; then BP neural network was used on the regression residuals series, the forecasting results can be attained. Another, the original load series was pre-adjusted as same as above, then the BP network was used on seasonally adjusted series and seasonal component series separately, the final forecasting results attained through reconstruct the forecasting result of component series. Case study results show that the prediction accuracy of proposed methods was better than BP neural network, SARIMA, support vector machines and other methods.
A novel autopilot based on L1 adaptive control theory is designed for the portable homing anti-tank missile, and trajectory simulation is made to verify the performance of the system. Firstly, the mathematical model of the missile oriented to L1 adaptive control is established. Secondly, a novel acceleration autopilot based on the L1 adaptive control theory and gain-scheduled technology is designed and validation simulations are made. Finally, an improved trajectory shaping guidance law (TSG) with varying velocity is proposed. And the trajectory simulation using the L1 adaptive autopilot and the TSG is carried out. Simulation results show that the autopilot can track the expected accelerated signal very well even in the presence of time-varying and uncertain dynamics. The improved TSG can make the missile attack the target from the top in short rang with large impact angle, while the requirement of attack angle, frame angle and acceleration constraints are satisfied.
The ever-increasing amount of digital components and the ever more complex systems have brought more challenges to safety. It's thus believed that systems theory once being implied into accidents prevention, will serve as an effective way to understand accident causation. Systems-theoretic accident model and processes (STAMP) is an accident model based upon systems theory, and has been widely applied to fields such as astronomy, chemical industry, medical services and transportation safety. This paper attempts to use the STAMP model to a coal mine for the first time, and analyzes the control flaws leading the accidents in the dimension of physical progress, first-level operation, direct regulatory agencies, company supervisory and system design, provincial government, and dynamics process. Results show that apart from technical reasons and the rule-breaking behaviors from the coal mine workers, there have been controlling mistakes in 12 organizational structures, resulting mainly from the changes of the feedbacks in different layers of the multiple-layer control structures after the shifts of the outside environment. All in all, given that coal mine accidents take up a large percentage of production accidents in China, we hope that the paper will provide a more systematic and comprehensive view on spotting ever more complex hazards so as to prevent future accidents.
Due to the limitation of the oil reservoir, the well needs to be fractured in the large batch of Q oilfield, some wells have entered two or more than two times, so the selection of layer for fracturing has become the important work of the oil field. In order to avoid the traditional selection of layer for fracturing of empirical and subjective, it base on the relationship between multiport fracturing wells and fracturing parameters and apply analytic hierarchy process (AHP) to establish for the Q oilfield selecting fractured layer model. The layer selection method is given by solving and the practical application, and the advantages and disadvantages of the candidate layer are sorted. The research results can provide important guiding significance for Q oilfield fracturing selection work, and reducing the human factors, improve the accuracy and timeliness of the layer.