Quantitative investment is an important application of system engineering in the financial investment area. Quantitative investment tries to automatically invest on securities using computational algorithms, and to obtain excess return. This paper proposes a novel quantitative trading algorithm based on machine learning and technical analysis, named "ML-TEA" (machine learning and technical analysis). ML-TEA predicts the stock's movements using the technical indicators calculated by prices and volumes. The empirical results show that firstly, three strategies can obtain an annual return of 25%, which outperforms the index's 10.60% and buy and hold's 3%, and the state of the art algorithms. The three algorithms also significantly outperform the benchmarks and the state of the art in terms of risk adjusted return, i.e., Sharpe ratio, Treynor ratio, and Jensen's alpha. Secondly, Ada-TEA and SVM-TEA can resist reasonable transaction costs that are much higher than the actual transaction costs.
Grasping the characteristics of stock market performance is the key to maintaining its operation and development. This paper applies approaches in complex system to study the risk response problem of stock market. Based on the price interdependencies among stocks, we build stock network to represent the microstructure of the market. According to occurrence probability of different types and levels of risks, we propose resilience computing model to measure the characteristics of stock network performance macroscopically. Taking Shanghai stock market as an example, grounded on the rules of risk occurrence and stock price change, this study investigates the effects of three risk response strategies on network resilience. The results show that different strategies are appropriate to different types of risk. The research is helpful to grasp the operational mechanism of the stock market, and provides decision supports to the construction of risk response strategy.
The evolutionary process of the behavior of internet finance platform and regulation strategy is discussed from the perspective of evolutionary game theory. Also the influencing factor of the evolutionary process is analyzed systematically. And evolutionary equilibriums of the behavior of internet finance platform and regulation strategy are compared under fixed punishment mechanism and dynamic punishment mechanism. The findings are the evolutionary process of the behavior of internet finance platform and regulation strategy can't reach equilibrium state and the behavior of the two groups presents cycle mode under fixed punishment mechanism. In contrast, the evolutionary process of the behavior of internet finance platform and regulation strategy presents spiral convergence under dynamic punishment mechanism. If the initial probability of self-discipline behavior of internet financial platform is different, the value of evolutionary equilibrium convergence is different. The probability of self-discipline behavior of internet financial platform will increase improving punishment degree limit.
The incentives of control right transfer is an important element of the principal-agent incentives contracts, but the principal-agent incentives contract mechanism design under the dynamic learning mechanism still lack of in-depth study. Thus based on existing research, this paper considered the interaction between the project income risk and the professional managers' moral hazard, and integrated with the control right transfer incentives and the risk-averse behavior of professional managers. And based on those, this paper established the two-stage optimization and learning model of the principal-agent incentives contract including capital and labor input of professional managers of risk-aversion, and explored the learning mechanism and influential factors of venture investors, and found the interaction mechanism among the control right transfer incentives, the risk-averse professional managers' moral hazard behavior and the project income risk. The study found that the positive correlation between control right transfer incentives and project risk, and the scale compensation effect under the learning effects of risk investors. In addition, the study also found the significant enhanced effect of on-the-job consumption and the strategic benefits of risk project on the labor utility of professional managers of risk aversion through calculating the experiment, but the significant enhancement effect of the strategic benefits of risk project on the investment income of venture investors is sole. Integrating the control right transfer incentives, the risk-averse professional managers' moral risk and project income risk into the design of risk investment principal-agent contract has stronger theory value, also have a certain reference significance for venture investors and professional managers.
Based on Ho-Lee model, we discussed the evolution of the prices of zero-coupon. A discrete time regime switching binomial model of the term structure where the regime switches are governed by a discrete time semi-Markov process is introduced by applying the arbitrage free principle and martingale measure method. This paper use minimal Tsallis entropy martingale measure (MTEMM) to deal with the above model, and give an application to the pricing of a European bond option in Markov and semi-Markov regime switching framework. The study found the model result is consistent with the result under minimal entropy martingale measure.
This paper aims to study how much exchange rate volatility affect China's value-added in exports from the perspective of global value chains. Econometric models are established to estimate the influence of RMB volatility on exports and imports demand. By combining these econometric models with non-competitive input-output model capturing processing trade, this paper further measure the impact of RMB volatility on China's value-added in exports. The results show that RMB volatility not only affect the direct value-added in exports, but also affect the indirect value-added in exports because it affects the substitution of imports for domestic products. Additionally, the results reveal that processing trade mitigate the influence of RMB volatility on value-added in exports. As for the sectors, sectors with higher share of processing trade are less affected. On the contrary, sectors which have higher import price elasticities tend to be more strongly affected.
A country's balance of payments and its structure not only reflect the development stage of balance of payments of the country, but also reflect a country's resources endowment, competitiveness and external economic openness. The research about balance of payments structure, stage evolution and its influential factors has important practical significance and academic value. Firstly, based on the traditional balance of payments stages hypothesis (BPSH), this paper analyzes the influencing factor about balance of payments stage evolution; Secondly, based on the balance of payments stages hypothesis, this paper does empirical stages classification using the data from 50 countries over 1998-2014; Lastly, based on the above, this paper does the empirical research with the method of orderly logistic regression model. The empirical results imply that the GDP per capita has a significant positive effect on the stages evolution, which is correspond with the balance of payments stages hypothesis; real effective exchange rate index has a significant negative effect on stages evolution, M2/GDP has a significant positive effect on the stages evolution, and the significance of the two variables do not appear in the existing literatures, which is an important innovation and contribution in this paper.
With the rapid development of bulk commodities and electronic technology, the network information carried by the Internet delivers quickly to the market and the participants in it. Using search engines that equip with massive open-source data, we propose in this paper a prediction model of the price of bulk commodities, by constructing Internet concern indices from the key searching information. Due to the support vector regression (SVR) model with different kernel functions, we build a prediction model respectively for the single market of crude oil, copper and corn. In addition, considering the co-movement among commodity markets, we further present a model with Internet concerns in terms of multiple markets. Empirical results demonstrate that the Internet concerns present a significant Granger causality on the variation of market price. Meanwhile, taking into account the Internet concern indices as well as information from related markets can improve the prediction accuracy in a remarkable amount.
The accounting of embodied energy in China's international trade has attracted a lot of attention in recent years. But how to reduce China's embodied export scale within the bearable costs requires in-depth analysis. This research analyse how to decrease the net export of embodied energy while keeping the least loss of China's GDP and unemployment rate by optimizing the embodied energy export-import structure though the combined input-output model and multi-objective programming model. According to the different constraints, designed two different scenarios. The unconstrained scenario optimization result shows that the export of embodied energy decreased by 19.99%. But at the same time resulting in 9.01% unemployment rate increase and 4.6% loss of China's GDP. By contrast, in reasonable constraint scenario, the export of embodied energy decreased by 4.41%, resulting in 4.19% unemployment rate increase and 0.47% loss of China's GDP. Therefore, under the current China's economic structure, it is difficult to fundamentally change the status of net exporter of embodied energy simply by changing the international import and export trade.
A global climate agreement sometimes produces accelerating climate change as a result, in the future under the new governance framework of the Paris Agreement, we should try to avoid the "green paradox" happen again. In this paper, based on the supply side structural reform in our national condition, through the theoretical models, empirical test and prediction research of the "green paradox" effect, we have comprehensively introduced the trigger mechanism of the "green paradox" effect. Theoretical model results show that in a perfectly competitive market, with fossil energy mining Hotelling rule, when interest rates are exogenous given and remain the same in two stages, the gradually rising carbon taxes is expected to lead to the "green paradox" effect; Under the increasing interest rate changes, the change degree of a carbon tax would be faster than the rate change, the "green paradox" effect will become irreversible; The carbon price policy will make the pollutant discharge has the general nature of commodity market and capital market, the government should prevent the abnormal price fluctuations and the potential inflation problems maybe caused by the carbon market. Empirical results show that China after 1999 by "The China National Plan to Phase out Ozone-Depleting Substances (Revised)" does not appear a continuous decline in energy price, which does not exist the "green paradox" effect, but this conclusion also with our country has not yet formed a unified system to reduce emissions and the marketization level of energy and other factors. By CGE simulation study, we have found that among the shocks of reform on the supply side, a carbon tax policy and a R&D subsidy policy theses impacts of the different external conditions, the supply side reform has the largest macroeconomic effect for the future, a single carbon tax policy will not trigger the "green paradox" effect, the carbon tax policy and consider the supply side of the capital markets reflect the reform scenario, the possibility trigger the "green paradox" effect is enhanced greatly, which China will face the risk of a conditional "green paradox" effect, the combination of a carbon tax and R&D subsidy policy has the best effect on energy conservation and emissions reduction. Finally, we give the policy recommendations to reduce the market risk for China reduction policies.
This paper employs input-output structural decomposition analysis (IO-SDA) to explore the driving factors of Beijing's energy intensity, including energy input coefficient, technology coefficient (Leontief inverse coefficient), final demands structure by product, final demands by category, and final energy consumption coefficient. Based on the constant price energy-input-output tables from 1997 to 2012, the analysis results show that 1) in general, Beijing's energy intensity declined gradually; 2) the major driving factors were energy input coefficient and final demands by category; 3) the impacts of energy input coefficient and technology coefficient on energy-intensity decrease varied positively or negatively with time; 4) the impact of final energy consumption coefficient was somewhat weak.
Based on the feedback mechanism between China's coal production capacity (CPC) and its influence factors, the CPC system dynamics model is constructed to forecast the change of China's CPC and its related factors' regulation potentiality. Research indicates that: 1) China's CPC will continue growing during the period of the 13th Five-Year Plan, and it will reach 4.635 billion tons in 2020 in the Policy Scenario (PS), a reduction of 509 million tons compared with that in the Baseline Scenario (BS). 2) The gap between China's coal supply and demand in PS will reach 646 million tons in 2020. And the pressure from coal supply over demand in the PS is significantly greater than that in BS, which is because the coal demand is effectively controlled in the PS. 3) China's CPC will reach 4.420 billion tons in 2020 in the regulation scenario, a reduction of 723 million tons compared with that in the benchmark scenario. The key to CPC regulation lies in backward capacity elimination, coal import quota, coal price controls and coal investment constraints.
Considering regime switching, this paper establishes an optimal decision model of strategy adjustment based on a widely-used operating cash flow model. We provide closed-form solutions for the firm value, optimal trigger value of the relative strategy level and the corresponding investment decision by deriving and solving HJB equation. The conclusions are summed up as follows: strategy regime cannot influence the optimal timings of strategy adjustments, but have influences on the optimal investment expenditures; when there exist regime switching, transition probability has significant effects on optimal timings of strategy adjustment, optimal investment expenditures at the instant of strategy adjusting, Tobin's Q, and the ratio of the growth option to the firm value; the optimal trigger value of the relative strategy level is a monotone increasing function of transition probability, which shows that the firm maintains current strategy much longer with the bigger transition probability.
In this paper, we formulate a two-period model in which an OM decides the remanufacturability level of products in product design in the first period, and a remanufacturer enters the product market to compete with the OM in the second period. The OM can control the supply of used remanufacturable products by changing the remanufacturability level in product design. A product with higher remanufacturability will directly decrease the production cost of the remanufacturer, increase fixed costs and variable costs of new products for the OM, and lower the producing tax to the government. Making use of game theory, we derive the equilibrium decisons for the OM and remanufacturer in the two scenarios that the collection is unconstrained or constrained. We characterize the equilibrium decisions to describe how the OM and remanufacturer behave with respect to changes in government producing tax. We analyse the sensitivity of the equilibrium decisions, and consider the trends of the equilibrium production of remanufactured products, the equilibrium profits of remanufacturer and OM with regard to the percent changes in related parameters.
Considering both altruism preferences of supply chain members and freshness and transportation loss of fresh agriculture products, the paper establishes the decision model of supply chain of fresh agricultural products. We study supply chain decision when the supplier owns altruistic preference, retailer owns altruistic preference as well as both supplier and retailer own altruism preferences, and then obtain optimal order quantity and optimal order price. Furthermore, we analyze the effect of altruism preference on both optimal decision and efficiency of supply chain. The results show that the altruism preference can significantly affect the optimal decision of supply chain. When both supplier and retailer own altruism preference and their altruistic preferences are within a certain range, their altruistic preferences will improve the efficiency of supply chain of fresh agricultural products. The higher degree of altruistic preference of supplier will increase the efficiency of supply chain, but the higher degree of altruistic preference of retailer will decrease the efficiency of supply chain. The results also show that the freshness degree and transportation effective factor can affect both the optimal decisions and supply chain profits. Finally, a numerical example is provided to support the findings.
In the uncertain demand market, three different contract strategies of the dual-channel manufacturer were researched when the retailer is capital constraint. By solving the model, the optimal order quantity and profit of the two partners were obtained. The results pointed out: when the retailer is capital constraint, the manufacturer's trade credit (TC) contract can effectively increases the two partners' profit. Furthermore, when the retailer is capital constraint and the manufacturer provides the advance booking discount (ABD) contract in his online channel, the manufacturer's profit always higher than the profit when there is no contract existed, and the retailer's profit always no higher than the profit when there is no contract existed. Finally, when the retailer is capital constraint and the manufacturer jointly uses the two different contracts in the two channels, the manufacturer can always get higher profit than the profit when retailer is no capital constraint.
The frequent occurrence of food safety incidents is not only to be hazardous to the health of consumers, but also impacts the market of food consumption. Integrating the consumers' willingness to pay for traceability and the difference between suppliers' traceability, this paper provides four sourcing strategies for the retailer. Then, we investigate the sourcing decision of the retailer considering consumers' willingness to pay for traceability, the portion of consumers with traceability consciousness, probability of food safety incident, and additional penalty incurred by food safety incident. We find that increasing consumers' willingness to pay for traceability or expanding the portion of consumers with traceability consciousness may incur unintended consequence, such as an increase in sourcing quantity of non-traceable food product, while efforts on strengthening inspection and penalizing the retailer always lead to more supply of traceable food in the market.
The steel box girders' production and installation play a great role in construction of the Hongkong-Zhuhai-Macao Bridge. We develop an early/tardy scheduling model for the production of steel box girders. And then an improved genetic algorithm based on heuristic rules has been proposed. A tardiness job filtering rule has been integrated to the genetic algorithm on the basis of the optimal time algorithm, which effectively solves the early/tardy problem without ex ante sequencing the jobs. The numerical study from Hongkong-Zhuhai-Macao examples proves that our proposed algorithm can improve the cost and to speed up the convergence.
Business intelligence systems has become an important tool to solve complex problems and improve management efficiency for retailers. Based on the demand for practices and existed information platform within dominated information provider, we design and propose a novel framework of the big data and business intelligence system, i.e., HDBI system, which can integrate with other more than 20 retail information systems and apply to cross-regional retailers with multi-formats. Further, we explore and discuss some design methods and key techniques. The practical application shows the effectiveness and advancements of HDBI.
One of the most difficult problems in operation management for retailers is the circulation safety management of limited shelf life commodities. Based on the circulation characteristics and management difficulties of limited shelf life commodities, we propose a brand new circulation safety management framework, entitled "shelf-life code bar traceability system, SCBTS", which could integrate other systems seamlessly to achieve automation management in production, circulation and sales. Meanwhile, we discuss the detailed process design, integrated solutions, and application value. The practical application shows that SCBTS can improve the efficiency of management for commodity with limited shelf life.
The incentive variable weight vector is researched. Firstly, a definition of the incentive type variable weight vector considering decision maker' risk attitude is proposed, the state weight vector of this variable weight is discussed, it is proved that incentive variable weight vector is induced by utility function; Secondly, the coefficient of risk aversion of incentive variable weight vector is defined, and the relationships between the coefficient of risk aversion and incentive variable weight vector are discussed; Then, a new method for multi-attribute decision making based on incentive variable weight vector is proposed; Finally, the example shows that the proposed method is correct and effective.
Considering the co-opetition relationship in research and development (R&D) network, the paper established the dynamic model of risk propagation, which includes three aspects: the definition of each enterprise risk load, determination of each enterprise's capacity of resisting risk and how failed enterprises nodes effect on the neighboring nodes. Risk recovery factor is introduced into the model. The simulation results show the relationship risk propagation process in co-operation R&D networks can be over rapidly in a very short time; in the three types of risk, opportunism risk propagates fastest and affects widest. The proportion of the competitive enterprises and the competition level have positive effects on the propagation speed and scope of relational risk; the level of corporation and competition both have significant impacts on relationship risk propagation, however, the corporation level has greater influence than the competition level; there exists optimal risk recovery level which can stop the risk from propagation in R&D network. This research has an important enlightenment for improving the capability of resisting risk and keeping the sustainable development of R&D network.
In a multi-class, multi-criteria transportation network, a logit-based stochastic user equilibrium is developed based on regret theory by incorporating travelers' regret aversion. We postulate that the travelers are heterogeneous in terms of their value of time that follows a continuous distribution function. The proposed model is formulated as an equivalent variational inequality problem and solved by the path-based algorithm using the method of successive averages. Finally, a simple illustration is used to prove the reasonableness of the model and the feasibility of the algorithm. It is shown that the travelers' regret aversion level does influence their route choice behavior and that travelers with high value of time change route choice from small degree to large degree and travelers with low value of time from large degree to small degree.
Considering the user heterogeneity based on value of time (VOT), a traffic equilibrium model is proposed with the application of tradable permits policy into a bi-modal transportation system consisting of transit and auto in parallel. It is shown that the permits scheme can erase the externality effect caused by congestion and reduce the system cost. Furthermore, it is found that if the auto mode is faster than the transit mode, the higher-VOT users prefer to use highway and the lower-VOT users choose transit; otherwise, those choosing highway are lower-VOT users. The permits scheme can play the same role with the OD-based tolling policy. Numerical results show that for users who are initially distributed permits, their commuting costs can be further decreased when choosing transit. Even in the case the amount of permits is less than threshold, users can have positive revenue by commuting on transit.
In this paper, considering the increasingly heavy vehicle emission pollution and strict traffic restriction regulations in urban areas, various traffic restriction factors, such as the traffic restriction regions differed by the energy types and carrying capacity of vehicles, are initially introduced to vehicle routing problem. Regarding minimising cost of carbon emission and transportation as objectives, the multi-energy heterogeneous fleet vehicle routing problem under urban traffic restriction is proposed. Since the proposed problem is NP-hard, a variable neighborhood search algorithm (VNS-TR) is developed to solve it. Finally, the results of a numerical example and benchmark instances demonstrate the effectiveness of the model and algorithm.
This study proposed an integrated approach to determine the ALV (automated lifting vehicle) dispatching and container storage, considering the loading and unloading operations simultaneously. An integrated optimization model of ALV scheduling and storage allocation was developed taking the minimum of the maximum completion time as objective function. To solve the model, a heuristic algorithm based on genetic algorithm was designed. Numerical experiments were provided to illustrate the validity of the model and algorithm. Results indicate that the designed algorithm proposed can improve the computation efficiency and obtain near-optimum solution for large-scale problems efficiently. Moreover, the integrated optimization model considers multiple links, which contributes to the operation efficiency in automated container terminals.
Scale breeding ecological energy system is a comprehensive system, providing the environment contamination, in which the pig waste is used as resource, and in which biogas source is developed and used comprehensively. In view of the system and these new system stability problems that the system regional environment being overloaded, the low utility ratio of the biogas source and the biogas projects construction and administration out of line. Having holding closely the two characters that the complication of construction and the complication of behavior by the main body of system, combining with the feature of interdisciplinary, research used the system dynamics studying complex system construction and the game studying complex system behavior to study system complex construction feedback and system game cooperation mechanism respectively. Then the same dynamic replication feature of the evolutionary game dynamic replication system and system dynamics level and rate system will be seized to study integrated theory of rate variable based in-tree model being established from evolutionary game replication system, by which the complex system construction and complex system behavior game was researched synthetically. Results show that any evolutionary game replication system can be transformed to rate variable based in-tree model, in which rate variable is root, level variable being tail, and level variable and external variable control rate variable directly. And then system dynamics evolutionary game rate variable based in-tree model was established to simulate Jiangxi Debang scale pig breeding ecological energy system stability. The manage measures was obtain, which will provide the decision basis of formulating policies providing environment contamination of scale breeding and for the biogas project strategy of our country.
The development of large-scale open-source software (OSS) is investigated in this paper with the case of OpenStack, which is a recently well-known open-source cloud computing platform. The collaborative coding network is constructed by extracting the Hash-code associations in the Git commit data of this software project; and the structure and evolution of this network is examined. The intrinsic organization mode and the mode of collective action of developers is explored accordingly. The results of explorative data analytics indicate that, as a collective knowledge-creation action of a networked system with a stable core, the development of OpenStack is prominently directed by its core developers, while the massive average developers' contributions constitute the largest fraction in the entire project. Furthermore, strong correlation between the subprojects of OpenStack and the communities of the collaboration network is identified, both statically and dynamically. The majority of developers are highly focused in that one developer usually adheres to one single subproject in the full duration of investigate period. The overall results of this work may contribute to deepen our knowledge on the self-organization of collaborative social structure in large-scale OSS projects, and more generally may have some reference value for understanding the underlying modes of massive socialized innovations.
Educational data mining is a research area of using data mining technology in education industry. In the research of EDM, data mining technology is used to modeling dataset samples in the field of education, which aims to study and forecast the testing data set with the help of effective statistical machine learning models. Machine learning models with distributed computing frameworks in the EDM can meet the needs of large-scale data processing meanwhile provide tailored data recommendation and then support decision-making in the future. Based on this background, this study first put all kinds of data models into the data training and predicting for simulation, propose an improved model to ameliorate the classification performance of the data model by adjusting the data model and by using an improved algorithm based on a new equation of information gain when calculating the optimal feature to split. Based on the best-performance data model in previous study combined with the application background of the "big data" era, we proposed a new random forest algorithm model focusing on giving classification to large-scale datasets based on distributed computing framework called MapReduce. By using the MapReduce, we design and realize a new system to meet this requirement. In this system, the model that has been trained can be serialized and deserialization between local disks and the distributed file system.