基于实物期权的项目组合投资决策研究

基于项目投资决策的可延迟性特征,将实物期权的决策灵活性思想引入到企业的投资组合决策中,建立了基于实物期权的0-1整数规划模型.模型以项目的期权价值最大化作为投资组合项目选择的标准,通过项目组合投资时机的灵活安排,实现了项目组合的总投资价值最大化.     

项目区域投资的期权特征与决策研究

运用期权的观点和方法研究项目区域投资问题具有重要的现实意义.在衡量投资区域机会价值的基础上,分析了项目区域投资的期权特征,建立了项目区域投资决策的实物期权模型,为实际中的多区域投资决策问题提供了一种量化分析方法.     

风险条件下基于实物期权的研发项目多阶段评价模型

研发项目投资决策是现代企业研发管理的核心内容,对研发项目进行合理、准确的评估,具有高度的战略意义和现实意义.本文基于实物期权理论,考虑到研发项目投资的灵活性,结合研发项目的阶段性特征,综合分析了研发项目寿命期内面临的市场风险、技术风险和突发风险对研发项目潜在现金流的影响,构建了风险条件下研发项目多阶段评价模型.最后通过算例对模型进行验证、横向对比和敏感性分析,证实了模型的正确合理性.     

阶段性投资最优比例问题的实物期权方法

以研发成功的初创企业为例,针对两阶段投资决策问题,描述了柔性决策所具有的实物 期权思想,根据相应的投资准则得到项目决策的执行概率,再结合项目收益过程的分析,建立 起包括决策柔性价值在内的项目总价值的数学模型. 在此基础上可以分析投资比例对项目价 值的影响,并得到最优值. 文章结合案例进行了数值计算,并对结果做了分析.     

不确定环境下自然资源开发项目投资评价模型

以不确定环境下自然资源开发项目投资评价问题为研究对象,以现金收益、开发投资和运营成本的不确定性对投资价值的影响为切入点,在现有实物期权投资评价模型的基础上,同时考虑项目方在投资阶段推迟投资和在生产阶段暂停运营的管理柔性,建立基于多随机变量的自然资源开发项目实物期权投资评价模型． 应用交换型期权和鞅过程等定价理论给出了模型的解析解,并结合某原油开发项目的实例进行了参数的静态分析．     

基于实物期权的新技术投资决策实证研究

本文基于对我国高新技术企业的实际调研,从实物期权视角检验不同类型不确定性对企业新技术投资决策的影响。研究结果表明,市场与政策不确定性对投资的延迟作用与技术不确定性对投资的促进作用得到不同程度验证,而竞争强度与投资不可逆程度对不确定性与投资关系的调节作用则不完全符合实物期权的理论预期。论文结果揭示了我国高新技术企业进行投资决策的一般规律,可为企业应用实物期权方法提供参考。     

基于实物期权法的油气勘探开发投资决策分析

通常油气勘探开发项目的风险性较高,而且投资决策过程极其复杂,在油气勘探开发各阶段存在着多种选择,这种管理的灵活性具有一定的价值。文章在分析传统投资决策评价方法与实物期权法进行类比的基础上,对实物期权理论在油气勘探开发投资决策中的特性进行了分析,评述了现有的国外有关实物期权在油气勘探开发投资决策中的研究特点,并提出了未来可能的主要研究展望。     

实物期权在油气勘探投资项目决策中的应用

油气勘探投资项目具有周期长、风险高、投资不可逆性以及资金分阶段投入等特点。传统的投资决策方法忽视了投资项目中的期权价值,易于造成项目价值的低估,使得具有潜在价值的投资项目被忽略。本文通过实例,将二叉树期权定价模型运用在石油投资项目决策中,比较分析了实物期权法的合理性。     

基于中小板企业项目投资的实物期权方法实证研究

传统投资决策方法往往会低估投资项目的价值,实物期权方法因更充分考虑不确定性和决策柔性的价值而被认为可以弥补传统方法的缺陷.本文通过对中小板企业投资项目的分析对此进行了验证,结果表明实物期权方法比NPV法能够更好地解释股票市场反应和更加准确地体现项目投资的价值.     

基于二叉树期权定价模型的企业R&D项目价值评估研究

本文将实物期权理论引入R&D项目管理领域,以阶段门NPD模型为基础,探讨了应用二叉树期权定价模型评估研发项目价值的具体思路和步骤,并通过比较,证明了由于评估时采用不符合研发项目风险特点的高折现率,NPV法倾向于低估项目价值,这将影响企业做出合理的投资决策。     

风险条件下研发项目改进DEA选择模型

研发项目是企业提高竞争力的关键,研发项目选择关系到企业的战略和未来发展趋势。研发项目在生命周期内面临技术风险、突发风险和市场风险,本文考虑到研发项目的风险特征,结合企业战略,构建了基于效率排序的研发项目选择模型。首先,本文对研发项目的有效性和相对于其它备选项目的相对优势进行分析;然后针对数据包络分析模型排序的不足,结合平衡计分卡理念,规范研发项目投入的比例,引入实物期权理论,改进数据包络分析模型,实现了对研发项目的效率排序;最后通过实际算例对模型进行验证、横向对比和敏感性分析,结果表明：相对于标准数据包络分析模型,本文提出的模型具有更高的效率区分度,并且能够敏感地捕捉到研发项目投入和产出的变化对效率影响的演变过程。     

Probabilistic dominance criteria for comparing uncertain alternatives: A tutorial

This paper is a tutorial which demonstrates the current state-of-the-art methods for incorporating risk into project selection decision making. The projects under consideration might be R&D, IT, or other capital expenditure programs. We will show six decision making methods: 1. mean-variance (MV), 2. mean-semivariance, 3. mean-critical probability, 4. stochastic dominance, 5. almost stochastic dominance (ASD), and 6. mean-Gini. We will also describe the assumptions about the risk attitudes of the decision maker which are associated with each of the techniques. While all these methods have been previously applied elsewhere, this is the first paper which shows all of their applications in the project selection context, together with their interrelationships, strengths and weaknesses. We have applied all six techniques to the same group of five hypothetical projects and evaluated the resulting nondominated sets. Among the methods reviewed here, stochastic dominance is recommended because it requires the least restrictive assumptions. ASD and mean-Gini are recommended when stochastic dominance is not practical or when it does not yield definitive choices. MV, mean-semivariance, and mean-critical probability are shown to be flawed.     

考虑项目关联关系的R&D网络风险传播建模与仿真

考虑同一企业内不同项目间关联关系的情况下,基于研发项目的数量将研发网络分为不同子网络。借鉴SIS模型构建了R&D网络风险传播动力学模型,并对该模型进行了数理解析与仿真分析。结果表明：R&D网络风险传播阈值是由不同子网络的拓扑结构以及企业内不同项目间的关联程度共同决定的;考虑项目关联的研发网络风险传播阈值小于不考虑关联关系的研发网络风险传播阈值;项目间关联关系的企业个数及项目间的关联程度对风险在不同子网络上的传播速度与传播范围均具有影响。该研究成果对于提高研发网络抗风险能力具有重要意义。     

基于项目网络支配和扩散关系的研发项目组合选择

项目组合包含多项目且项目间存在相互作用和依赖关系,针对传统项目组合评价方法忽略了各项目间依赖关系的不足,本文采用复杂网络理论和PageRank算法,提出一种新的项目优先级排序方法（PPRM）。首先,本文建立研发项目多属性评价准则,分析了项目间的支配关系以及技术和经验在项目间的扩散关系。然后,以项目为节点、以支配和扩散关系为边,分别构建了项目支配和扩散网络,进一步,采用设计结构矩阵（DSM）和K-shell方法建立了基于支配网络的项目影响力模型,并建立了考虑项目之间多次扩散传播的综合扩散概率模型。综合项目节点影响力和扩散关系,本文构建了基于PageRank算法的研发项目优先级排序模型。最后,以某研发项目组合选择为例,验证了本文所建立的模型和算法可有效分析项目组合中的排序问题。     

Economic Evaluation of Scale Dependent Technology Investments

We study the effect of financial risk on the economic evaluation of a project with capacity decisions. Capacity decisions have an important effect on the project̂s value through the up‐front investment, the associated operating cost, and constraints on output. However, increased scale also affects the financial risk of the project through its effect on the operating leverage of the investment. Although it has long been recognized in the finance literature that operating leverage affects project risk, this result has not been incorporated in the operations management literature when evaluating projects. We study the decision problem of a firm that must choose project scale. Future cash flow uncertainty is introduced by uncertain future market prices. The firm's capacity decision affects the firm's potential sales, its expected price for output, and its costs. We study the firm's profit maximizing scale decision using the CAPM model for risk adjustment. Our results include that project risk, as measured by the required rate of return, is related to the inverse of the expected profit per unit sold. We also show that project risk is related to the scale choice. In contrast, in traditional discounted cash flow analysis (DCF), a fixed prescribed rate is used to evaluate the project and choose its scale. When a fixed rate is used with DCF, a manager will ignore the effect of scale on risk and choose suboptimal capacity that reduces project value. S/he will also misestimate project value. Use of DCF for choosing scale is studied for two special cases. It is shown that if the manager is directed to use a prescribed discount rate that induces the optimal scale decision, then the manager will greatly undervalue the project. In contrast, if the discount rate is set to the risk of the optimally‐scaled project, the manager will undersize the project by a small amount, and slightly undervalue the project with the economic impact of the error being small. These results underline the importance of understanding the source of financial risk in projects where risk is endogenous to the project design.     

Research and development project selection and resource allocation: a review of quantitative modelling approaches

This paper reviews the literature on quantitative modelling for research and development (R&;D) project selection and resource allocation. The topic has been a subject of operations research for about four decades. Its importance stems from the fact that R&D projects are a core element of corporate renewal, heavily influence a firm's market success and, if not properly chosen and trimmed, may waste large amounts of resources or even ruin the enterprise. Our survey classifies and characterizes the various modelling approaches.     

不同阶段研发投资对企业绩效的动态效应研究:基于中国上市公司的面板数据分析

研发投资是提高企业绩效的重要途径,不同阶段的的研发投资对于企业绩效的改善存在着差异化作用。本文构建了不同阶段研发投资对企业绩效影响效应的理论模型,并以我国103家上市公司2010-2016年的相关数据为样本,从动态效应的实证分析阶段和开发阶段两个阶段的投资强度对企业绩效影响的异同,结果发现:研究阶段和开发阶段的投资对企业绩效影响均具有正向的积极影响作用,开发阶段的投资在当期对企业绩效的影响较强但不可持续,研究阶段的投资具有滞后效应并且对企业绩效的提升有比较长远的影响。因此过多关注开发阶段的投资并不能有效提高企业绩效,企业应加大研究阶段的投资,以更好地实现其在提高企业绩效中的长效作用。     

考虑决策者后悔规避的风险投资项目选择方法

在风险投资项目选择过程中,决策者通常是有限理性的,会尽量避免选择可能会令其感到后悔的风险投资项目,而在涉及多个行业的风险投资项目选择过程中如何考虑决策者后悔规避的心理行为因素,这方面的研究特别需要关注。本文提出了一种涉及多个行业的考虑决策者后悔规避的风险投资项目选择方法。在该方法中,首先计算各风险投资项目在不同市场状态下的项目价值;然后,计算各组合市场状态发生的概率;进一步地,构建后悔函数刻画决策者后悔的心理感知,并通过计算不同行业的各风险投资项目的综合效用值得到风险投资项目的排序结果。最后,通过一个算例说明了本文提出方法的可行性与有效性。     

A fuzzy set approach for R&D portfolio selection using a real options valuation model

Making R&D portfolio decision is difficult, because long lead times of R&D and market and technology dynamics lead to unavailable and unreliable collected data for portfolio management. The objective of this research is to develop a fuzzy R&D portfolio selection model to hedge against the R&D uncertainty. Fuzzy set theory is applied to model uncertain and flexible project information. Since traditional project valuation methods often underestimate the risky project, a fuzzy compound-options model is used to evaluate the value of each R&D project. The R&D portfolio selection problem is formulated as a fuzzy zero–one integer programming model that can handle both uncertain and flexible parameters to determine the optimal project portfolio. A new transformation method based on qualitative possibility theory is developed to convert the fuzzy portfolio selection model into a crisp mathematical model from the risk-averse perspective. The transformed model can be solved by an optimization technique. An example is used to illustrate the proposed approach. We conclude that the proposed approach can assist decision makers in selecting suitable R&D portfolios, while there is a lack of reliable project information.