The impact of investment lags on investment decision

This paper suggests a valuation framework for an investment project through the concept of real options. Generally, in real asset world, decision time and its payment time are not identical. This so-called investment lag problem should be considered when valuing real assets. When investment lags exist, firms’ accommodation capacities play important roles. In this paper, the real effect of investment lag on investment value is tested upon various conditions. We show the valuation process of real assets under the risk-neutral world. The closed-form formula is also provided for valuing real assets, including R&D project.     

Strategic optimization in R&D investment

We use d'Aspremont and Jacquemin's strategic optimal R&D investment in a duopoly Cournot competition model to construct myopic optimal discrete and continuous R&D dynamics. We show that for some high initial production costs, the success or failure of a firm is very sensitive to small variations in its initial R&D investment strategies.     

Scheduling tests in automotive R&D projects

In automotive R&D projects a major part of development cost is caused by tests which utilize expensive experimental vehicles. In this paper, we introduce an approach for scheduling the individual tests such that the number of required experimental vehicles is minimized. The proposed approach is based on a new type of multi-mode resource-constrained project scheduling model with minimum and maximum time lags as well as renewable and cumulative resources. We propose a MILP formulation, which is solvable for small problem instances, as well as several variants of a priority-rule based method that serve to solve large problem instances. The developed solution methods are examined in a comprehensive computational study. For a real-world problem instance it is shown that the introduced approach may enhance the current methods applied in practice.     

An approach to the valuation and decision of ERP investment projects based on real options

The risks and uncertainties inherent in most enterprise resources planning (ERP) investment projects are vast. Decision making in multistage ERP projects investment is also complex, due mainly to the uncertainties involved and the various managerial and/or physical constraints to be enforced. This paper tackles the problem using a real-option analysis framework, and applies multistage stochastic integer programming in formulating an analytical model whose solution will yield optimum or near-optimum investment decisions for ERP projects. Traditionally, such decision problems were tackled using lattice simulation or finite difference methods to compute the value of simple real options. However, these approaches are incapable of dealing with the more complex compound real options, and their use is thus limited to simple real-option analysis. Multistage stochastic integer programming is particularly suitable for sequential decision making under uncertainty, and is used in this paper and to find near-optimal strategies for complex decision problems. Compared with the traditional approaches, multistage stochastic integer programming is a much more powerful tool in evaluating such compound real options. This paper describes the proposed real-option analysis model and uses an example case study to demonstrate the effectiveness of the proposed approach.     

A generalized real option pricing method of R&D investments: jump diffusion and external competition

ABSTRACT

Numerous studies have assessed Research and Development (R&D) investment using the real option pricing approach. This paper proposes a more general real option pricing method that both considers the specificity of R&D investment (such as uncertainty) and the R&D investment opportunity of a business in a market environment with external competitors. Specifically, we adopt a jump diffusion model to evaluate R&D investments that incorporate the uncertainties of these activities. The model values a pioneer's R&D investment opportunity allowing the chance that competitors may enter the market and the project value may vary with time. By construction and analysis of the model, we then analyse the optimal timing to realize profit on an investment. Overall, this model should facilitate a more comprehensive evaluation for R&D investments.     

Multi-criteria decision analysis as an aid to the strategic planning of Energy R&D

This paper demonstrates the use of multi-criteria decision analysis to assist an advisory council in selecting areas of interest for government-financed Energy R&D, subject to a budget constraint. The benefits of Energy R&D may be difficult to value in monetary terms. A different way of comparing the anticipated impacts of Energy R&D in various technological areas based on the opinions of the council members given a number of judgment criteria is described, followed by maximizing the overall impact of an Energy R&D programme for a given level of expenditure. Since the council members have different backgrounds, their opinions may diverge and so may the solutions to the optimization problem. The decision model can be used as a discussion model highlighting the points of agreement and disagreement amongst them, so that they can concentrate on the latter in order to reach a vindicated compromise.     

A mixed R&D projects and securities portfolio selection model

The business environment is full of uncertainty. Allocating the wealth among various asset classes may lower the risk of overall portfolio and increase the potential for more benefit over the long term. In this paper, we propose a mixed single-stage R&D projects and multi-stage securities portfolio selection model. Specifically, we present a bi-objective mixed-integer stochastic programming model. Moreover, we use semi-absolute deviation risk functions to measure the risk of mixed asset portfolio. Based on the idea of moments approximation method via linear programming, we propose a scenario generation approach for the mixed single-stage R&D projects and multi-stage securities portfolio selection problem. The bi-objective mixed-integer stochastic programming problem can be solved by transforming it into a single objective mixed-integer stochastic programming problem. A numerical example is given to illustrate the behavior of the proposed mixed single stage R&D projects and multi-stage securities portfolio selection model.     

Evaluation of firm’s loss due to incomplete information in real investment decision

We investigate the effect of incomplete information in a model where a start-up with a unique idea and technology pioneers a new market but will eventually be expelled from the market by a large firm’s subsequent entry. We evaluate the start-up’s loss due to incomplete information about the large firm’s behavior. We clarify conditions under which the start-up needs more information about the large firm. The proposed method of evaluating the loss due to incomplete information could also be applied to other real options models involving incomplete information.     

Decision support system for multicriterial R&D and information systems projects selection

Decision support system described here makes it possible to carry out multicriterial selection of hundreds of projects simultaneously, with tens of criterion functions with bivalent variables including polynomial ones and quotients of linear functions, and tens of resources limitations. Stewart's idea [J. Oper. Res. Soc. 42 (1) (1991) 17] of the special scalarizing function based on the modified reference point approach, which has been hitherto applied for linear benefits and for balance criteria functions only, and its optimization by effective gradient method is used here in the situation described by Santhanam and Kyparisis [Comput. Oper. Res. 22 (8) (1995) 807], involving synergistic effects of second- and third-orders in benefit and cost criterion function, and in resource requirements respecting resource sharing and hierarchical contingency relationships among candidate projects. In addition, the system enables us making the dialogue of the solution in a way of adaptive creation of weights of criterion functions and also flexible projects portfolio changes. A test of efficiency is presented.     

The Sprague–Grundy function of the real game Euclid

The game Euclid, introduced and named by Cole and Davie, is played with a pair of nonnegative integers. The two players move alternately, each subtracting a positive integer multiple of one of the integers from the other integer without making the result negative. The player who reduces one of the integers to zero wins. Unfortunately, the name Euclid has also been used for a subtle variation of this game due to Grossman in which the game stops when the two entries are equal. For that game, Straffin showed that the losing positions (a,b) with a<b are precisely the same as those for Cole and Davie’s game. Nevertheless, the Sprague–Grundy functions are not the same for the two games. We give an explicit formula for the Sprague–Grundy function for the original game of Euclid and we explain how the Sprague–Grundy functions of the two games are related.     

具随机折现的博弈期权定价问题

对具随机折现的博弈期权定价问题进行了研究,在满足一个可积性条件的情况下,借用过份函数等工具给出了期权价格的表达式和买卖双方的最优停止策略.对于不满足可积性条件的情况,推广了相关文献的结果,并给出了τ＊存在的条件.最后给出了一个例子.     

Constructing and evaluating balanced portfolios of R&D projects with interactions: A DEA based methodology

We propose and demonstrate a methodology for the construction and analysis of efficient, effective and balanced portfolios of R&D projects with interactions. The methodology is based on an extended data envelopment analysis (DEA) model that quantifies some the qualitative concepts embedded in the balanced scorecard (BSC) approach. The methodology includes a resource allocation scheme, an evaluation of individual projects, screening of projects based on their relative values and on portfolio requirements, and finally a construction and evaluation of portfolios. The DEA–BSC model is employed in two versions, first to evaluate individual R&D projects, and then to evaluate alternative R&D portfolios. To generate portfolio alternatives, we apply a branch-and-bound algorithm, and use an accumulation function that accounts for possible interactions among projects. The entire methodology is illustrated via an example in the context of a governmental agency charged with selecting technological projects.     

The option value of advanced R & D

Existing tools for making R&D investment decisions cannot properly capture the option value in R&D. Since many new products are identified as failures during the R&D stages, the possibility of refraining from market introduction may add a significant value to the NPV of the R&D project. This paper presents new theoretical insight by developing a stochastic jump amplitude model in a real setting. The option value of the proposed model depends on the expected number of jumps and the expected size of the jumps in a particular business. The model is verified with empirical knowledge of current research in the field of multimedia at Philips Corporate Research. This way, the gap between real option theory and the practice of decision making with respect to investments in R&D is diminished.     

On the prisoner’s dilemma in R&D with input spillovers and incentives for R&D cooperation

This paper considers a standard model of strategic R&D with spillovers in R&D inputs, and extends the result that duopoly firms engaged in a standard two-stage game of R&D and Cournot competition end up in a prisoner’s dilemma situation for their R&D decisions, whenever spillover effects and R&D costs are relatively low. In terms of social welfare, this prisoner’s dilemma always works to the advantage of both consumers and society. This result allows a novel and enlightening perspective on some issues of substantial interest in the innovation literature. In particular, the incentive firms face towards R&D cooperation in the form of an R&D cartel is shown to be maximal for the case of zero spillovers, which is when the prisoner’s dilemma has the largest scope.     

The duality of option investment strategies for hedge funds

This paper explores the structure of optimal investment strategies using stochastic programming and duality theory in investment portfolios containing options for a hedge fund manager who attempts to beat a benchmark. Explicit optimal conditions for option investments are obtained for several models. This research was supported by Inquire, the Social Sciences and Humanities Research Council of Canada, the Natural Sciences and Engineering Research Council of Canada, the National Center of Competence in Research FINRISK, a research instrument of the Swiss National Science Foundation, and MIT’s Sloan School of Management. J. R. Rodríguez-Mancilla thanks Gabriel Casillas-Olvera, Deputy Manager of Risk Control at Banco de Mexico, who read several drafts of this paper and made many helpful comments. The opinions in this paper do not necessarily represent those of Banco de México.     

The impact of intermediaries on a negotiation: an approach from game theory

Central European Journal of Operations Research - Standard approaches to model interaction networks are limited in their capacity to describe the nuances of real communication. We present a game...     

A note on the analysis of the expected value of perfect information with respect to a class of R&D projects

This note is concerned with the question if and when to carry out marketing operations that are aimed at completely reducing marketing uncertainties surrounding the value of a stationary R&D project at its technical completion. It is shown that the benefits arising from thise operations can be measured via the EVPI (the expected value of perfect information). In addition, it is observed that the timing of these operations should only be considered at the beginning of the project's life. Finally, a sensitivity analysis with respect to the statistical properties of the EVPI is performed.     

Optimal decision indices for R&D project evaluation in the pharmaceutical industry: Pearson index versus Gittins index

This paper examines issues related to various decision-based analytic approaches to sequential choice of projects, with special motivation from and application in the pharmaceutical industry. In particular, the Pearson index and Gittins index are considered as key strategic decision-making tools for the selection of R&D projects. It presents a proof of optimality of the Pearson index based on the Neyman–Pearson lemma. Emphasis is also given to how a project manager may differentiate between the two indices based on concepts from statistical decision theory. This work demonstrates and justifies the correct use of the Pearson index.