Valuation of R&;D Sequential Exchange Options Using Monte Carlo Approach

This article describes a methodology for evaluating R&D investment projects using Monte Carlo method. R&D projects generally involve multiple phases with or without overlapping. R&D investments are made often in a phased manner, with the commencement of subsequent phase being dependent on the successful completion of the preceding phase. This is known as sequential investment. Moreover, each stage creates an opportunity (option) for subsequent investment. Therefore, R&D projects can be considered as ‘Compound Options’ in which investments present uncertainty both in the gross project value and in their costs. It is possible to use exchange options to value the R&D investment opportunities. In this paper, we propose to evaluate the European and American Real Compound exchange options through Monte Carlo simulations. We also provide a set of numerical experiments to provide evidence for the accuracy of the proposed methodology.      

Fuzzy R&D portfolio selection of interdependent projects

Global competition of markets has forced firms to invest in targeted R&D projects so that resources can be focused on successful outcomes. A number of options are encountered to select the most appropriate projects in an R&D project portfolio selection problem. The selection is complicated by many factors, such as uncertainty, interdependences between projects, risk and long lead time, that are difficult to measure. Our main concern is how to deal with the uncertainty and interdependences in project portfolio selection when evaluating or estimating future cash flows. This paper presents a fuzzy multi-objective programming approach to facilitate decision making in the selection of R&D projects. Here, we present a fuzzy tri-objective R&D portfolio selection problem which maximizes the outcome and minimizes the cost and risk involved in the problem under the constraints on resources, budget, interdependences, outcome, projects occurring only once, and discuss how our methodology can be used to make decision support tools for optimal R&D project selection in a corporate environment. A case study is provided to illustrate the proposed method where the solution is done by genetic algorithm (GA) as well as by multiple objective genetic algorithm (MOGA).     

R&D项目进展评估及决策选择模型

在分析R＆D项目技术和市场不确定性分布特征的基础上,提出多步骤四项式期权定价模型,用于R＆D项目进展评估.探讨了当出现不可预见信息时,对采取的多个管理决策的选择问题.实例分析表明,多步骤四项式模型能灵敏地监测项目的单因素变化引起的项目投资价值的变化,对管理的灵活性和不确定性能进行量化处理,在很大程度上提高了R＆D中期投资决策的准确性.     

Fuzzy multiattribute evaluation of R&D projects using a real options valuation model

R&D project selection decision is very important in two ways. First, in many organizations, R&D budget represents huge investment. Project selection decisions could be thought with the strategic objectives and plans of the firm. Second, R&D projects' organizational returns are multidimensional in nature and risky in terms of projected outcome. Real options approach helps to calculate this risky side of the selection process. This paper considers that multidimensional side of the R&D project selection process. Another consideration is the vagueness in the evaluation process. The fuzzy analytic hierarchy process, which takes monetary (fuzzy real option value) and nonmonetary (capability, success probability, trends, etc.) criteria into account, is used to make this selection among alternative R&D projects. A real case study is given to illustrate the application of the proposed approach. © 2008 Wiley Periodicals, Inc.     

Real Options and Incremental Search in Pharmaceutical R&D Project Portfolio Management

This paper investigates the role of real options reasoning in R&D project portfolio management and investment decisions of pharmaceutical firms. We analyse a unique dataset that integrates information on initiation and termination of clinical trials at the level of specific medical indications. Consistent with existing literature, we find a positive relationship between market size and firm entry in clinical trials. We also show that the option value of R&D investments, as proxied by the scope of R&D projects, affects the selection of target markets. Moreover, high‐risk research areas attract more entry, in line with the predictions of real options theory. However, we also find that more flexibility in project duration and delayed project discontinuation attract higher rates of entry. Departures from pure real options reasoning are motivated by the presence of incremental learning in pharmaceutical R&D.     

Robust Monte Carlo Method for R&D Real Options Valuation

This paper is devoted to developing a robust numerical analysis of least squares Monte Carlo (LSM) in valuing R&D investment opportunities. As it is well known, R&D projects are characterized by sequential investments and therefore they can be considered as compound options involving a set of interacting American-type options. The basic Monte Carlo simulation takes a long time and it is computationally intensive and inefficient. In this context, LSM method is a powerful and flexible tool for capital budgeting decisions and for valuing R&D investments. In particular way, numerical tests are performed to examine the optimal choice of basis function and polynomial degree in terms of reduction of the execution time, accuracy and improvement in the simulation.     

基于R&D溢出的集中研发联盟合作行为

研究了在联盟合作R&D中R&D溢出、R&D溢出的抑制能力以及创新能力对联盟成员的R&D投资策略的影响, 确定了联盟获得最大期望投入的最优分配方式. 研究表明: 1) R&D溢出和创新能力对联盟成员增加R&D投入有正效应; 企业抑制R&D溢出的力量对R&D投入有负效应, 但影响力很弱. 2) 对于创新能力强的联盟, 平均分配为最优分配方式; 对于创新能力弱的联盟, 市场规模低时选择按投入比例分配方式, 市场规模高时选择平均分配方式.     

A study on framework for effective R&D performance analysis of Korea using the Bayesian network and pairwise comparison of AHP

To effectively evaluate and analyze R&D performance, it is necessary to measure the relative importance of performance analysis factors and quantitative analysis methods that consider the objectivity and relevance of detail factors that constitute performance evaluation. This study suggests a framework for R&D performance evaluations by computing weights through an AHP (Analytical Hierarchy Process) expert survey and by applying a Bayesian Network approach whereby, through which, giving objectivity and allowing inference analyses. This framework can be used as a performance analysis indicator, which uses input and output performance factors in order to perform quantitative analysis for projects. We can quantitatively define the satisfactory level of each project and each performance analysis factor by assigning probability values. It is possible to analyze the relationship between project evaluation results (qualitative evaluation) and performance analysis indicator (quantitative performance). This performance analysis framework can infer posteriori probability using the prior probability and the likelihood function of each performance factor. In addition, by inferring the relationships among performance factors, it allows performing probability analyses on the successful and unsuccessful factors, which can provide further feedback. In conclusion, the framework would improve the national R&D program in terms of financial investment efficiency by aligning budget allocation and performance evaluation.     

基于序贯博弈的装备制造业复杂产品研发补贴和关税决策

增大国内装备制造业的研发投入和引入国外产品带来的研发溢出可提升国内装备制造业科技水平,但同时带来了企业的研发成本负担和国外产品抢占国内市场的问题,因此政府在其中的研发补贴和关税政策调控显得尤为迫切.探究政府研发补贴和关税政策对存在研发溢出的国外装备制造商和国内装备制造商进行序贯博弈时的市场份额、利润和研发水平的影响问题.进一步分析政府以社会福利最大为目标时,政府的最优决策并对关键因素进行敏感性分析.最后通过数值扩展验证结论的稳健性.研究发现:研发补贴系数或关税增大都会提高(降低)国内制造商(国外制造商)的产量、利润和研发水平;政府以社会福利最大进行决策时,随着两种产品竞争强度的增大,总的研发补贴会减小,而单位研发补贴先减后增,关税先增后减.研发成本系数越高,关税和单位研发补贴会越高,但总的研发补贴会减少.     

模糊理论在评价项目不确定性中的应用

项目活动的复杂性和动态性决定了项目的不确定性，概率及概率分布的观点比较适合于处理存在相关历史数据的项目的不确定性。面对很少有或根本没有可供参考的历史数据的项目，使用模糊方法处理不确定性是恰当的。文章使用模糊数来描述项目及活动的不确定性，并举出了一个具体算例。     

A multiobjective integrated multiproject scheduling and multiskilled workforce assignment model considering learning effect under uncertainty

Today, organizations try to decline academically expenses using humans and resources in addition to rising managers and operators' satisfaction. Meantime, a very important step in the process of decision is the assignment of human resources, particularly in connection with research and development (R&D) projects in which the system is highly dependent on the capabilities of human resources. In this study, we tried all the assumptions that come true in the real world, considered a model for applied R&D projects to reduce costs and increase the efficiency of projects. Therefore, an integrated multiproject scheduling and multiskill human resource assignment model under uncertainty has developed for R&D projects. Furthermore, it is assumed that the activity processing time is related to human resources assignment that means the learning effect is considered. To demonstrate the proposed model efficiency, the various dimensions instance problem was solved accurately and efficiently in GAMS software, and the results have been reported. In addition, the proposed model is validated through the input parameter sensitivity analysis. The results indicate a suitable performance of the proposed fuzzy mathematical programming model is due to the complexity of the problem.     

How Should the Chinese Government Invest R&D Funds: Enterprises or Institutions?

With the rapid increase of the government R&D investment in China, the efficient optimization of the R&D investment areas of the Chinese government significantly influences regional innovation productivity and economic growth in the long run. By expanding the R&D-based growth model and using empirical analysis, this paper discusses the optimal combination for the government to coordinate its R&D expenditure as productive public investment in various sectors, under the condition of promoting long-term economic growth, is discussed by considering the aspects of capability for independent innovation, the development capability for basic knowledge, and the absorption capability for the technique. Results are as follows. (1) The level of production efficiency of enterprises, such as the capability for independent innovation, the development capability for basic knowledge, and absorption capability, influences the inclination of the government to invest in enterprises and institutions. When an enterprise has a high production efficiency level, technological level A, and knowledge storage B, increased investment from institutions to the enterprise can significantly promote regional economy growth. Otherwise, the government should increase R&D investment in institutions. (2) The government R&D investment in institutions can indirectly promote economic growth through the promotion of human capital and the understanding and absorption capacity of external knowledge, independent innovation capability, and basic knowledge development capability of enterprises. This paper may help the government to create appropriate policies with higher efficiency in R&D investment, and promote technical progress and economic growth in China.     

A milp bi-objective model for static portfolio selection of R&;D projects with synergies

This paper presents a multi-objective MILP model for portfolio selection of research and development (R&D) projects with synergies. The proposed model incorporates information about the funds assigned to different activities as well as about synergies between projects at the activity and project level. The latter aspects are predominant in the context of portfolio selection of R&D projects in public organizations. Previous works on portfolio selection of R&D projects considered interdependencies mainly at the project level. In a few works considering activity level information the models and solution techniques were restricted to problems with a few projects. We study a generalization of our previous model and show that incorporating interdependencies and activity funding information is useful for obtaining portfolios with better quality. Numerical results are presented to demonstrate the efficiency of the proposed approach for large models.     

Pricing real abandonment options on several R&;D investment projects

Much previous research has shown that the R&D investments can be evaluated by real growth options approach. But few studies have been done on real abandonment options for R&D projects which may not succeed. The contribution of this paper is not only to derive a more general closed-form solution for evaluating real abandonment options, but to put backup project consideration into our model for reality. We show that both Black-Scholes’s and Stulz’s models are special cases of our model under some specifications of parameters. From the simulation results, we explore that the higher the percentage of recovering salvage value, the more investment projects should be carried out. We hope that the results in this study could provide a useful reference for the manager, to make better decisions regarding backup projects.     

Organizing Pharmaceutical Innovation: From Science‐based Knowledge Creators to Drug‐oriented Knowledge Brokers

The pharmaceutical industry is exposed to severe conditions: while a typical R&D process lasts up to 13 years, only one out of 10,000 substances becomes a marketable product. In addition, markets for pharmaceutical products tend to become more fragmented, leading to an increased risk of market failure. At the same time, R&D productivity has been deteriorating for several years. The number of new drug approvals is constantly declining, while R&D expenditures are escalating as a result of high investments in new drug discovery technologies and more complex clinical studies. As a response, pharmaceutical firms have started to focus on balancing the right size and structure of their R&D activities. This leads to several organizational trends: (1) novel management of technologies; (2) R&D internationalization; and (3) open innovation modes.     

An Optimal Balanced Economic Growth and Abatement Pathway for China Under the Carbon Emissions Budget

Arguments over equity during abatement goal setting is the principal obstacle to climate mitigation cooperation, while allocating global emissions to each country as deduced from the climate objective according to certain equitable principles offers an effective alternative to ending this dispute. Such an alternative also endows each country with the freedom to determine and develop its own pathway under its emissions budget while ensuring that global climate targets are met. Within this context, this paper integrated economic growth theory with the optimal control model and simulated the optimal abatement pathway as well as the economic growth trajectory for China within the allocated emissions budget. The study found that research and development (R&D) investment is an effective way of improving energy efficiency. Our simulation showed that the R&D intensity of the gross domestic product (GDP) would see a slight decline for the inceptive period, followed by an aggressive rise to a relatively high level before decreasing again. Under the 450 ppm carbon concentration target, the R&D investment intensity would have to increase significantly beginning from 2014 because of the more stringent demands as compared to other less rigorous targets such as 500 ppm. The economy would continue to grow, although growth would occur less rapidly under rigorous targets: relative to 2007 levels, the GDP would grow by 11-fold and 15-fold under the 450 and 500 ppm scenarios, respectively. Before enforcement of an effective R&D investment, the carbon emissions would increase rapidly, while after enforcement, the speed of carbon emissions would slow down.     

An adaptive crashing policy for stochastic time-cost tradeoff problems

We consider a stochastic time-cost tradeoff problem that determines how much to crash activities with the purpose of minimizing the expected summation of crashing and tardiness costs. We propose a threshold policy that makes a crashing decision contingent on a project׳s current status; crashing an activity to compensate delayed starting time from a predetermined threshold. First, we present a dynamic programming (DP) formulation to find the threshold values, and prove that the resulting threshold policy is optimal for a serial-graph project. Since the above optimality does not hold generally, we develop a DP-based procedure to construct a threshold policy for arbitrary-graph projects.We show through the computational experiments that our threshold policy is rapidly constructed by this procedure, and its cost is not very far from the theoretical lower bound.     

关于波动率与投资概率之间关系的进一步研究

对项目价值波动率与投资概率之间的关系进行了深入研究．通过建立实物期权数学模型,将项目投资临界触发值与项目价值两者有机地溶入投资概率计算中,并利用Matlab语言仿真计算,说明了波动率和有关参数对投资概率的影响．结果表明,当波动率σ较小时,投资概率会随σ的增加而增加;当σ的值较大时则情况相反．所提出的观点对当前我国投资实践活动具有一定的理论指导意义．     

纵向研发合作的研发绩效和成本分担机制

在日益激烈的市场竞争环境下,企业往往会选择研发合作以降低研发的成本和风险,而纵向研发合作已成为其中的重要模式。构建了一个存在两个上游企业和多个下游企业所组成的复杂双层市场结构,对上下游企业实施纵向研发合作的产量变化和利润变化进行了分析,在此基础上探讨了研发合作的最优研发绩效。研究了纵向研发合作下利润匹配型和固定比例型两种分担机制的分担系数和净利润等经济指标,得出了一些符合现实经济活动的结论。     

An exact method for scheduling of the alternative technologies in R&D projects

A fundamental challenge associated with research or new product development projects is identifying that innovative activity that will deliver success. In such projects, it is typically the case that innovative breakthroughs can be achieved by any of several possible alternative technologies, some of which may fail due to the technological risks involved. In some cases, the project payoff is obtained as soon as any single technology is completed successfully. We refer to such a project as alternative-technologies project and in this paper we consider the alternative-technologies project scheduling problem. We examine how to schedule alternative R&D activities in order to maximize the expected net present value, when each technology has a cost and a probability of failure. Although a branch-and-bound algorithm has been presented for this problem in the literature, we reformulate the problem and develop a new and improved branch-and-bound algorithm. We show using computational results that the new algorithm is much more efficient and outperforms the previous one.