Pricing real growth options when the underlying assets have jump diffusion processes: the case of R&D investments

Numerous previous studies have demonstrated that research and development (R&D) investments can be evaluated by a real growth options approach. However, few studies have constructed evaluating models which consider the important R&D characteristics, including uncertainty regarding the project value, investment cost, and jump diffusion processes. The contribution of this study is not only to derive a model for evaluating R&D investments to conform to these key characteristics of R&D activities but also to build a real option pricing method that is more general than comparative important models, such as the theoretical papers of Black and Scholes (1973), Merton (1976), and Fischer (1978), and the application paper of Brach and Paxson (2001). This study also presents sensitivity analyses which illustrate the dynamic relationship between the real growth option value and the project value, investment cost, and main jump parameters. Hopefully, the results of this study can provide a useful reference for managers, and help them make better evaluations of R&D investments.     

An option approach to the new product development process: a case study at Philips Electronics

The paper considers the product development process as a series of (real) options with reducing uncertainty over time. Criteria are developed to decide on speeding up or delaying the development process. The paper demonstrates how, in the R&D phase, any particular project may be assigned within a 2 × 2 matrix of uncertainty versus R&D option value. A similar matrix can be established for the product launch phase. The matrices support portfolio management throughout the different phases of development and enable management to decide on an appropriate point at which to abandon individual projects. The approach originates from applying real options insights into the product development process at Philips Electronics. The paper is illustrated with some actual R&D projects.     

Flexibility planning for managing R&D projects under risk

Incorporating managerial flexibility in an innovative R&D project is important, because managers face greater uncertainty in today's competitive and dynamic changing environment. It is essential to bring managerial flexibility into R&D project planning to decrease technical and market risks, while increasing potential market value. The objective of this paper is to develop a flexibility planning methodology based on real option analysis to improve managerial flexibility for R&D projects. The proposed methodology identifies potential risks that may occur during every R&D stage. It also recognizes a cascading option structure to resolve the identified risks, and evaluates and selects adequate options that maximize the potential value of the project. Instead of using a traditional option pricing method, a dynamic programming model that considers multidimensional product performance and market payoff is used to evaluate the R&D project value. Using the proposed methodology, managers can identify future scenarios as a function of their management actions. The proposed flexibility planning methodology can help managers improve managerial flexibility of R&D project and increase the success rate of product launch. A drug development project is used to illustrate the proposed methodology.     

DISCRETE VERSUS CONTINUOUS COMPOUNDING - A NOTE

R&;D projects generally involve multiple phases with or without overlapping. Moreover, the investment is usually made in a phased manner, with the commencement of the subsequent phase being dependent on the successful completion of the preceding phase. The aim of this article is to analyze the equilibrium strategies of two firms competing for a two-stage sequential R&;D investment, when a firm may infer a private signal from the strategy played by the other. Thus, firms must formulate their optimal strategies in a context of imperfect information. We evaluate the resulting compound option with information revelation as an American compound exchange option using Monte Carlo simulations. We then show that different equilibria may arise.     

The derivation of R&D return indicators within a real options framework

The purpose of this paper is to rework the building blocks of real option applications and to introduce a basket option framework. We find that the characteristic parameters of the risk neutral density function implied in observed share prices within the real option framework represent a novel category of R&D return indicators. Empirical evidence for a set of 13 US bio-pharmaceutical companies is provided. The novel R&D return indicator can be used to analyse investor's expectations on R&D success of a particular firm. The implications of this indicator on decision making are mainly based on its information content on technological and market risk of the products under development in a particular firm. A proposal for a potential application of the stability index in innovation research is discussed as well. The study thus is at the interface between innovation research and (empirical) finance.     

A gene to drug venture: Poisson options analysis

We provide a Poisson real option model of a gene‐to‐drug venture. First we describe a general new drug discovery programme as well as a specific secretory protein research programme. Then we model both the candidate secretory gene and the 'hot' gene discoveries as Poisson processes. Gene deal value sizes are modeled as lognormal distributions. Then we calculate the expected R&D value (EV) of the Poisson discoveries times the value distributions, for both stages. Finally, for generic collaborating‐funding arrangements, we show the Merton (1976) standard mixed diffusion‐jump option value, compared to a risk neutral 'intrinsic' value. Under simple assumptions, the real option value is substantial, even if there is no intrinsic value.     

Real options valuation: the new frontier in R&D project evaluation?

The real options approach has recently received growing attention in R&D and Technology Management research. Recent empirical findings by Ellis (1997) and Busby and Pitts (1997) also report growing attention and use in practical investment decisions. However, there is a certain concern about the applicability to a wide range of R&D related problems. The theoretical base behind options valuation is derived from the capital markets and thus assumes market conditions that are closer to the theoretical construct of 'perfect competition' than most other settings. Even under these conditions, several assumptions made and difficulties left are subject to controversial discussions. Of course these problems even gain importance when the R&D environment with its discontinuities and lack of regulation or institutionalized trade is assumed. This paper describes some basic properties of the real options approach and sheds light on existing problems for the application in R&D project evaluation. On the other hand, roads to application of the method are shown using the Geske model of option evaluation. One main goal of the paper is to broaden and deepen the discussion on real option models in R&D and Technology Management, which has in some cases been limited to stressing the advantages of the method rather than reflecting on applicability and concrete way of application of the method.     

R&D as an option on market introduction

Discounted cash flow methods for making R&D investment decisions cannot properly capture the option value in R&D. Since market and technology uncertainties change expectations about the viability of many new products, the value of projects is frequently adjusted during the R&D stages. Capturing the adjustment in expectations has an option value that may significantly differ from the Net Present Value of R&D projects. However, there are no historic time series for estimating the uncertainty of the value of R&D projects. As a result, the standard Black and Scholes model for financial option valuation needs to be adjusted. The aim of this paper is to report the application of a particular option pricing model for setting the budget of R&D projects. The option value of the model captures jumps or business shifts in market or technology conditions. The approach originates from applying current insight into the valuation of R&D projects to the field of multimedia research at Philips Corporate Research. This way, the gap between real option theory and R&D practice is further diminished.     

R&D project selection and scheduling for organizations facing product obsolescence

Research and development is very crucial in an organization that faces rapid product obsolescence. Our thesis is that R&D project scheduling and resource planning should be born out of the optimum product launch plan which, in turn, should be in adherence to the growth rate targeted by the organization. Such an approach would ensure that the R&D activities of the organization are in tune with the Organization's goals. The absence of such coordinated R&D planning would hamper the productivity and profitability of the organization. This paper attempts to develop a methodology and model that would enable the streamlining of R&D project schedule. A real life case application has also been illustrated.     

Integrating Real Options with Managerial Cash Flow Estimates

This article presents a real options model that fits managerial cash flow estimates (optimistic, likely, and pessimistic projections) to a continuous geometric Brownian motion (GBM) cash flow process with changing growth and volatility parameters. The cash flows and the value of a project are correlated to a traded asset, so the real option is priced under the risk-neutral measure with a closed-form solution. The analysis is extended to a sequential compound call option for investments over multiple periods. If the project is correlated to the market, then some of the risk may be mitigated by a delta-hedging strategy. A numerical example shows that the effect of the correlated asset on the real option value is significant, and the relationship between the volatility of the project and the real option value is not analogous to the typical relationship found in financial option pricing. Integrating the expertise and industry knowledge of management, this approach makes possible a more rigorous estimation of model inputs for real option pricing.     

Organization of industrial R&D on a global scale

Although the globalization of industrial R&D processes has increasingly attracted management scientists and considerable work on R&D internationalization has been done since the 70s, important problems in organizing geographically distributed R&D sites have not yet been resolved. This analysis is based on 165 interviews in 31 technology-based multinationals from Switzerland, Germany, USA, and Japan conducted between 1994 and 1997. We describe the extent of R&D internationalization, its key drivers and principal impediments. Two trends are identified: (i) a geographic concentration on leading technology centres, and (ii) a tightened coordination of R&D units. Overlaying structures, superimposed to regional and functional structures, play a central role in overcoming current deficits in global R&D management. Organizational constituents of this structure are technology offices (Schering), technology management as a competence center (Schindler), central project offices (IBM), central project management (Bayer), project oriented career development (Bosch), virtual project management departments (Roche), and cross-cultural job rotation (ATR).     

Structuring innovation funnels for R&D projects under uncertainty

An innovative R&D project that creates a great business opportunity usually involves high technological and market uncertainty. It is easy to see that developing only one solution approach in the R&D project is too risky. The selectionism or the parallel development strategy can be applied to construct an innovation funnel that increases the flexibility to hedge against the uncertainty. However, little research has been devoted to selection of which alternative solution approaches should be included for the innovation funnel. This research aims to develop a simulation-based methodology that can help R&D managers evaluate and construct an innovation funnel containing promising alternative approaches for a novel R&D project to maximize project profit, while minimizing downside risk within an allowable loss. A new risk measure based on the concept of conditional value-at-risk from the finance literature is defined to evaluate the project downside risk. An example of drug development project is used to illustrate the proposed methodology. We recommend that firms should improve their capabilities on market research, concept screening, and R&D efficiency for taking full advantages of selectionism.     

Modeling the firm's response to research & development tax credit policies

In this article, we develop a microeconomic model of normative firm behavior under the incentive of a research and development (R&D) tax credit. The model is based on the well-known concept of a two-factor learning model in which R&D expenditures and manufacturing capacity expansion are the principle determinants of cost reduction in a new technology product. We distinguish between the behavior of start-up firms and ongoing firms and study the potential impacts of progressively larger R&D tax credits. We find highly significant differences in the potential impact of the credit on start-up firms versus ongoing firms. We also find that the credit can significantly impact optimal product pricing of the technology when introduced into the marketplace. We examine the implications of this latter fact on the overall social cost of the R&D tax credit.     

The role of taxes in corporate research and development spending

This study replicates prior research regarding research and development (R&D) spending by sampling R&D spending for a cross‐section of firms in non‐service related industries. Compustat data for 231 firms from 1992 to 1998 are used to test whether the US Federal tax credit for R&D meaningfully influenced R&D spending of the sampled firms. Firms' (1) effective rate of R&D tax credit, (2) rate of decay in R&D capital for firms' primary industry affiliation, (3) financial cost of capital, and (4) marginal tax rate are used to compute firms' user‐cost of capital for in‐house R&D. Results show that firms that were eligible for the tax credit spent more on R&D than non‐eligible firms as the user‐cost of in‐house R&D increased. These results add further evidence regarding the role of the tax credit in stimulating R&D activity and suggest that a tax credit for incremental research can be used to boost private‐sector R&D spending.     

Exploring the Motivational and Behavioral Foundations of External Technology Experts’ Knowledge Sharing in Collaborative R&D Projects: The Contingency Role of Project Formalization

High‐tech manufacturers increasingly rely on the knowledge contributions of external technology experts (ETEs), who contribute to collaborative R&D projects on behalf of suppliers. Many scholars have considered knowledge sharing in R&D collaborations from a firm‐level or project‐level perspective and focused on formalization as a potential remedy. While individual supplier employees at the operative level make the decision to share critical knowledge, the individual‐level perspective in literature on knowledge sharing in collaborative R&D projects is virtually nonexistent. Because knowledge sharing in collaborative R&D is a largely discretionary act on behalf of the supplier employee, personal motivations rather than inter‐firm relationship elements (e.g., network position or dependency) become the primary determinant of one’s sharing behavior. Abstracting from or ignoring these motivations of supplier employees in studies on collaborative R&D may obscure important insights for R&D managers. This study is an important first step in providing the empirical evidence needed to uncover the motivational and behavioral foundations for ETEs’ knowledge sharing in a collaborative R&D setting. Building on theories of gift and social exchange, this article identifies customer stewardship and distributive fairness as two important personal motivations of ETEs to share knowledge. Project formalization is considered as a key contingency condition. Analyzing survey responses of 186 ETEs, a multilevel regression‐based moderated‐mediation analysis of direct and indirect effects shows that customer stewardship predicts an ETE’s knowledge sharing behavior under (very) low levels of project formalization, and distributive fairness predicts knowledge sharing behavior under medium to high levels of formalization. Together, the results provide R&D project managers who aim to leverage external knowledge contributions with valuable insights that have been obscured in past firm‐level collaborative R&D studies.     

Scale and performance in publicly funded collaborative research and development

This paper reports the results of an extensive empirical investigation on the role of project scale (reflected in consortium size and in project budget) on various performance dimensions of publicly funded collaborative research and development (R&D) projects. Recent trends in European research policy are founded on the premise that such scale economies do exist, hence the emphasis placed on the critical mass of R&D projects. We argue that large scale in collaborative R&D confers both positive and negative effects, and thus we hypothesize an inverse U‐shaped relation between scale and performance. We only find an inverse U‐shaped effect of consortium size on networking impacts and a U‐shaped effect of project budget on goal achievement. No other statistically significant relationships were found for the remaining performance dimensions (i.e. scientific outputs, technological outputs, and research capacity impacts). More generally, and consistent with recent work on the relation between quality and quantity in academic research, our analyses offer little evidence to support the idea that increasing scale generally improves collaborative R&D performance. It appears that the simple assumption ‘bigger is better’ in European Union‐financed collaborative R&D may need to be reconsidered.     

Social capital and creativity in R&D project teams

The existing research contributes to our understanding about the value of social capital in a wide range of social science disciplines; however, it does not well address the role of social capital in creativity for research and development (R&D) project teams in a given context. Using a sample of 54 R&D project teams in high-technology firms of Taiwan, we examined the impacts of social capital on creativity of R&D project teams from an intra-team perspective. Results of factor analysis revealed four factors extracted from the concept of social capital, namely social interaction, network ties, mutual trust, and shared goals. Findings suggested that social interaction and network ties had significant and positive impacts on creativity of R&D project teams, but mutual trust and shared goals did not. Managerial implications for managing social capital in R&D project teams are discussed.     

Mortgage Refinancing with Asymmetric Information

Information asymmetry exists between the lender and the borrower regarding the holding period of the mortgaged real estate; the lender does not know how long the borrower plans to own the house. This information asymmetry allows the cost of obtaining a mortgage to deviate from its value to the borrower. As a result, the exercise price of the option to refinance becomes the cost to the borrower of obtaining a new mortgage instead of the outstanding balance of the existing mortgage as used in previous models. The option to refinance is a sequential option; after the borrower refinances, a new option is obtained to refinance again in the future. A mortgage refinancing model is developed taking information asymmetry and sequential refinancing into account. The model is used to solve for (1) the value to the borrower of a callable mortgage and (2) the minimum interest rate differential between the contract rate of the existing mortgage and the market interest rate needed to justify refinancing.     

Optimal exploration investments under price and geological‐technical uncertainty: a real options model

This article develops a real options model for valuing natural resource exploration investments (e.g. oil or copper) when there is joint price and geological‐technical uncertainty. After a successful several‐stage exploration phase, there is a development investment and an extraction phase. All phases are optimized contingent on price and geological‐technical uncertainty.
Several real options are considered. There are flexible investment schedules for all exploration stages and a timing option for the development investment. Once the mine is developed, there are closure, opening and abandonment options for the extraction phase. Our model maintains a relatively simple valuation structure by collapsing price and geological‐technical uncertainty into a one‐factor model.
We apply the model to a copper exploration prospect and find that a significant fraction of total project value is due to the operational, development and exploration options available to project managers.     

The case for a real options approach to ex-ante cost-benefit analyses of agricultural research projects

The United States Agency for International Development (USAID), like many other development agencies and donors, increasingly emphasizes evidence-based programming. This requires assessments of project performance at all stages of implementation, comprising ex-ante impact assessment, monitoring and evaluation, and ex-post attribution of outcomes. Ex-ante impact assessment, in particular, involves performing Cost-Benefit Analysis (CBA) to determine the Expected Net Present Value (ENPV) of the project in question. Unfortunately, the traditional ENPV approach has proven inadequate for dealing with uncertainty in the timing of investments and flexibility in future decision making. This is especially relevant for Research and Development (R&D) projects which require several stages of product development and multiple rounds of testing prior to releasing final products. As a consequence, the real-options approach to CBA has increasingly been used to evaluate private sector R&D projects. This paper advocates for the adoption of the real options approach in the evaluation of public investments in agricultural research, and illustrates its practical utility with an assessment conducted by USAID to determine the economic viability of a proposed project to develop improved varieties of critical food security crops in Uganda.