Valuing pilot projects in a learning by investing framework: An approximate dynamic programming approach

We introduce a general discrete time dynamic framework to value pilot project investments that reduce idiosyncratic uncertainty with respect to the final cost of a project. The model generalizes different settings introduced previously in the literature by incorporating both market and technical uncertainty and differentiating between the commercial phase and the pilot phase of a project. In our model, the pilot phase requires N$N$ stages of investment for completion. With this distinction we are able to frame the problem as a compound perpetual Bermudan option. We work in an incomplete markets setting where market uncertainty is spanned by tradable assets and technical uncertainty is idiosyncratic to the firm. The value of the option to invest as well as the optimal exercise policy are solved by an approximate dynamic programming algorithm that relies on the independence of the state variables increments. We prove the convergence of our algorithm and derive a theoretical bound on how the errors compound as the number of stages of the pilot phase is increased. We implement the algorithm for a simplified version of the model where revenues are fixed, providing an economic interpretation of the effects of the main parameters driving the model. In particular, we explore how the value of the investment opportunity and the optimal investment threshold are affected by changes in market volatility, technical volatility, the learning coefficient, the drift rate of costs and the time to completion of a pilot stage.     

One-side value-added service investment and pricing strategies for a two-sided platform

To widen user participation and increase profit, two-sided platforms may invest on value-added services (VASs) for users. Due to the cross-market network externality, the investment for one side would affect the utility of users on two sides, thus affect the demand and profit of the platform. In this paper, we investigate one-side VAS investment and pricing strategies for a two-sided platform. It is revealed that it is optimal to invest at the maximum level for any marginal investing cost below a certain threshold, and to decrease the investment when the marginal investing cost increases above the threshold. We find that compared with the case of no investment, the invested user side will always be priced higher, while the uninvested user side may either be priced higher or lower, depending on the relative magnitude of mutual cross-market network externalities. If both sides are priced higher after the investment, the price increment for uninvested user side could be larger than that for invested user side.     

Evaluating learners’ motivational and cognitive processing in an online game-based learning environment

This paper describes the process and results of an evaluation on an online game-based learning environment (GBLE) by focusing on learners’ motivational processing and cognitive processing. The goal is to explore how online GBLE might initiate and support learners’ goal-setting activities and impact learners’ cognitive loads. The study surveyed 144 undergraduate students after their autonomous participation in the online game available at the Nobel Prize Foundation website teaching the Heckscher-Ohlin Theory on international trade. Grounded in the integrative theory of motivation, volition, and performance (MVP), the evaluation indicated that participants felt significantly confident in learning the subject. The perceived satisfaction, however, was lower than the rest of motivational components possibly due to heavy cognitive processing. The finding of cognitive load reported that learners perceived a significantly higher level of intrinsic load than the germane load due to the novelty of the subject matter. Data analysis further indicated a significant canonical correlation between learners’ motivational and cognitive processing. This particular finding could inform future research to investigate specific motivational processing components’ effects on learners’ cognitive load levels in online GBLEs.     

基于移动平均线组合和支持向量机的股市趋势研究

证券投资是一种高风险、高收益的理财方式,而对市场运行趋势的良好把握有助于投资者回避风险、把握机会。提出基于多条移动平均线斜率组合和支持向量机算法,判断当前市场运行趋势,从而采取相应的投资策略。模拟实验证明,该方法取得了良好的效果。     

Robust portfolio optimization via solution to the Hamilton–Jacobi–Bellman equation

We consider a problem of dynamic stochastic portfolio optimization modelled by a fully non-linear Hamilton–Jacobi–Bellman (HJB) equation. Using the Riccati transformation, the HJB equation is transformed to a simpler quasi-linear partial differential equation. An auxiliary quadratic programming problem is obtained, which involves a vector of expected asset returns and a covariance matrix of the returns as input parameters. Since this problem can be sensitive to the input data, we modify the problem from fixed input parameters to worst-case optimization over convex or discrete uncertainty sets both for asset mean returns and their covariance matrix. Qualitative as well as quantitative properties of the value function are analysed along with providing illustrative numerical examples. We show application to robust portfolio optimization for the German DAX30 Index.     

A review on cost allocation methods in collaborative transportation

Collaboration in transportation between two or more agents is becoming an important approach to find efficient solutions or plans. Efficiency can be measured in, for example, lower cost or more flexibility. An important aspect of the collaboration is to decide on how to share the benefits—for example, cost, profit, or resources. There are many sharing mechanisms or cost allocations proposed in the literature. Some are based on simple proportional rules and others are based on theoretical concepts found in game theory. We provide a survey on cost allocation methods found in the literature on collaborative transportation, including problems on planning, vehicle routing, traveling salesman, distribution, and inventory. A total of 55 scientific articles compose the main part of the survey, most of them published between 2010 and 2015. We identify more than 40 cost allocation methods used in this stream of literature. We describe the theoretical basis for the main methods as well as the cases where they are used. We also report savings from the collaborations when they are based on industrial data. Some directions for future research are discussed.     

Application of fuzzy sets to manufacturing/distribution planning decisions in supply chains

In the real-world manufacturing/distribution planning decision (MDPD) integration problems in supply chains, the environmental coefficients and parameters are normally imprecise due to incomplete and/or unavailable information. This work presents a fuzzy linear programming approach based on the possibility theory. It applies this approach to solve multi-product and multi-time period MDPD problems with imprecise goals and forecast demand by considering the time value of money of related operating cost categories. The proposed approach attempts to minimize the total manufacturing and distribution costs by considering the levels of inventory, subcontracting and backordering, the available machine capacity and labor levels at each source, forecast demand and available warehouse space at each destination. This study utilizes an industrial case study to demonstrate the feasibility of applying the proposed approach to practical MDPD problems. The primary contribution of this paper is a fuzzy mathematical programming methodology for solving the MDPD integration problems in uncertain environments.     

Adoption of Open Source Software: The role of social identification

While the benefits of incorporating Open Source Software (OSS) into personal and organizational systems have been widely touted, OSS must be adopted and used by end users before these benefits can be realized. Drawing on research in information systems and sociology, this study develops and evaluates an integrated model for the acceptance of OSS. In addition to the traditional technology adoption variables the findings stress the importance of social identification as a key driver of OSS adoption. The proposed model provides a useful decision support tool for assessing and proactively designing interventions targeted at successful OSS adoption and diffusion.     

A comprehensive investigation on the relationship between information technology investments and firm diversification

This paper empirically investigates the role played by information technology in diversified firms by building a demand function for IT investments. First by reviewing the management literature, we briefly examine different types of diversification, including related diversification, unrelated diversification, and geographic diversification. After carefully developing the theoretical arguments we empirically test the relationship between IT investments and different types of diversification. We find that in general diversified firms demand more investments in information technology, but the positive relationship may also depend on the extent to which firms diversify. Our findings show that firms with diversified structures that increase the complexities of coordination and control, e.g. unrelated diversification or extensive geographic diversification, would face a lesser demand for IT investments because of the increased use of financial controls instead of strategic controls by these firms. Overall, we find that information technology can serve as an effective coordination and control mechanism for moderate levels of diversification whereas non-IT mechanisms for coordination and control becomes more suitable in a context of higher levels of diversification. The implications of these findings for research and practice are discussed.

证券投资交易系统技术实现中若干问题的研究

文章给出了一个证券交易与信托投资系统的网络结构框架和技术实现方法,并针对该类系统技术实现过程中所面临的一些关键性技术问题进行了研究与探讨,给出了相应的解决方案。这些问题与解决方案对于基于Internet的证券交易系统以及信托投资管理系统也具有一定意义。