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A real options game of alliance timing decisions in biopharmaceutical research and development
Abstract
In this article we examine the alliance timing trade-off facing both pharmaceutical and biotech firms in a stochastic and competitive environment. Specifically, we introduce a real options game (ROG), where a pharmaceutical company can choose between two competing biotech firms by sequentially offering a licensing deal early or late in the new drug development process. We find that, when the alliance raises the drug market value significantly, the agreement is signed late in the drug development process. This suggests that the postponement effect implied by the use of real options prevails over the biotech firms’ competition effect, which would instead play in favor of an early agreement for pre-emption reasons. When the alliance does not raise the drug market value significantly, the optimal timing depends on the level of royalties retained by the pharmaceutical company. In particular, an early agreement is signed in the presence of a low level of royalties. In this case, indeed, the competition effect becomes predominant because the pharmaceutical company can substantially reduce the upfront payment and thus the potential loss incurred if the biotech partner does not exercise her option to continue the new drug development process. We also show that the alliance timing outcomes of our real options game considerably differ from those obtained when both parties use the net present value (NPV) to assess their payoffs.
... According to the "Annual Survey of Enterprise Innovation" in China in 2022, 63.11% of large-sized enterprises 1 that carried out innovative activities also engaged in innovative cooperation. Among them, 39.3% of enterprises collaborated with universities or scientific research institutions (i.e., innovation institutions), which is a pretty common form of R&D cooperation (Morreale et al., 2017). For example, Chinese battery manufacturer Zhuoyue New Energy signed a $1 million low-cost zinc-manganese battery technology R&D contract with the University of Adelaide in Australia; Nanjing University and Nanjing Jingjie Biotechnology Co., Ltd. ...
... The contract design of R&D collaboration is a common and vital practical problem in many technology-intensive industries (Banerjee & Siebert, 2017;Crama et al., 2016;Morreale et al., 2017;Xiao & Xu, 2012). The advantages and incentives of R&D cooperation have been proven to be well-proven. ...
In an R&D cooperation with an innovation institution, the firm needs to design an efficient cooperation contract to enhance the cooperation efficiency under asymmetric information. Many governments encourage innovation through selective subsidies, whose review and supervision do disclose more information. However, the existing studies have ignored these impacts on designing R&D cooperation contracts. Thus, this paper constructs game models of R&D cooperation to obtain the optimal contract terms under different subsidy strategies and then further discusses the impacts of selective subsidy on R&D contract design.
... Unlike NPV, the real options theory (ROT) considers that management can react when it has more information about the uncertainties to be faced. The higher the investments and the more unlikely the chances of reversing them, the greater the usefulness of the real options (Choi et al., 2016;Perdomo et al., 2017;Morreale et al., 2017;Gazheli & Bergh, 2018). ...
... Created in 1973 by economists Fischer Black and Myron Scholes, the Black-Scholes model (BSM) became the most used methodology for assessing options and it fostered the development of the derivatives market (Del Giudice et al., 2015;Morreale et al., 2017;Bacelar et al., 2018). The idea behind BSM was a portfolio composed of two assets: the first was called the underlying asset, and the other was a risk-free asset. ...
Purpose-This work aims to analyze the assertiveness of net present value (NPV) and real options theory (ROT), at the moment of decision making for investments in renewable energy projects, considering the 244 winning projects in the auctions of reserve energy that occurred between 2011 and 2015. Design/methodology/approach-This is a quantitative study that used real data from 150 wind power and 94 photovoltaic projects available on ANEEL´s website. For data analysis, the confusion matrix, the area under the ROC Curve and the Kappa Index were used. Findings-It was concluded that NPV is more effective for recommendations to invest in projects with chances to be successful, while ROT is more accurate in suggesting against investing in projects with propensity for failure. It was also found that the degree of agreement between the two techniques is substantial and determined by the level of volatility of real options. Research limitations/implications-The limitations of this study refer to the difficulties of identifying the reasons that motivated failures in the projects. Originality/value-Theoretically, this work contributes to identify the characteristics that effectively differentiate ROT from NPV at the time of decision making. Empirically, this work contributes to doing an ex-post analysis of the projects.
... Diferentemente do VPL, para a TOR a administração é ativa, pois reage quando tem mais informações sobre os riscos a serem enfrentados. Quanto mais elevados forem os investimentos e improváveis a possibilidade de revertê-los, maiores são as utilidades das opções reais (Tubetov, 2013;Schwartz, 2013;Čulík, 2016;Choi, Kwak, & Yoo, 2016;Hernandez-Perdomo, Mun, & Rocco, 2017;Morreale, Robba, Nigro, & Roma, 2017;Gazheli, & Bergh, 2018). ...
... É oportuno destacar que o VPL está implícito na avaliação das opções reais, servindo de base para a sua valoração (Rocha, 2008). Essa assertiva é ratificada por Pivoriené (2017) Morreale et al., 2017;Bacelar et al., 2018), ao considerar que elas estavam sendo incorretamente precificadas e que os passivos corporativos poderiam ser comparados a um portfólio de opções de compra do tipo europeia (Black, & Scholes, 1973). Para isso, Black e Scholes formaram um portfólio composto pelo ativo subjacente e por um livre de risco, cujo fluxo de caixa era semelhante ao da opção que estivesse sendo avaliada, e que poderia ser solucionado por meio de equações diferenciais parciais (Schwartz, 2013). ...
Resumo Este estudo objetiva discutir a assertividade das técnicas de análise de investimento, bem como o grau de concordância entre as mesmas, com base em dados reais de projetos de energia de reserva ocorridos entre 2011 e 2015. Trata-se de um estudo quantitativo no qual foram utilizadas informações de 244 empreendimentos eólicos e fotovoltaicos contratados nos leilões da ANEEL. Utilizou-se a matriz de confusão para verificar a assertividade nas tomadas de decisões emanadas pelo valor presente líquido (VPL) e pela teoria das opções reais (TOR). O grau de concordância entre essas técnicas foi verificado por meio do Índice de Kappa. Concluiu-se que o VPL e a TOR são métodos complementares. O VPL é mais eficaz na previsão dos projetos rentáveis, enquanto a TOR é mais precisa na identificação dos não lucrativos. Constatou-se também que o grau de concordância entre as duas técnicas varia em uma escala entre regular e moderado.
... Our specific focus in this three-player bio-pharmaceutical game is on analyzing the effect that the availability of information has on the outcome of the game. For the purposes of the analysis we have adopted the general architecture of the model previously presented in [2] and constructed an initial three-player game-model simulation with Matlab to study the effect of different (amounts of) available information on the game by way of simulation. The constructed model allows many further developments to the initial model that include the addition of more players, the integration of expert judgment into the model structure and into the stochastic processes used in the simulation, and the study of the effect of decisionmaking biases. ...
... We assume that the biotech-companies consider and try to maximize their expected pay-offs, when deciding on whether to ally with the pharmaceutical company. The pay-offs are assumed to depend on the market value of the project under development that is expected, in vein with [2] to follow a geometric Brownian motion (GBM). The constructed Matlabmodel allows the easy testing of how other (stochastic) processes, including processes that include expert judgment, affect the results. ...
The practice of building corporate alliances to carry out bio-pharmaceutical research related to new drug development has become more widespread in recent years. Typically the reasons for alliances lie in the hunt for efficiency improvements and cutting costs in the research process and on securing a future product pipeline for the pharmaceutical companies involved.
In this research we are interested in analyzing the game situation in the context of bio-pharmaceutical research business and for this purpose we study the situation, where two biotech (research) companies exist on the markets and compete to sign an alliance with a pharmaceutical company – a three player game.
We expect our results to show how availability of information affects the decision to ally in the context of the bio-pharmaceutical research industry and to shed light on how
the use of different types of processes for modeling the valuation of the underlying research portfolio affect the same.
... These include the stability of trust between partners, government green subsidies provided to various stakeholders, the investment benefits for enterprises, the variability in managerial behavior and decisionmaking processes, as well as the consumption costs incurred by consumers [13][14][15]. In addition, several scholars have analyzed longitudinally how university-industry alliances or inter-enterprise alliances influence the decisions of key subjects through benefit and trust games [16][17][18][19]. These analyses sufficiently reflect the current societal objective of green economic development, which aims to leverage green technology innovation for multidisciplinary applications. ...
In the new media era, the green technology alliance with multi-participation has emerged as a powerful contributor to achieving the strategic goal of a green economy. Therefore, this paper constructs a stochastic evolutionary game model of green technology innovation led by the government under an uncertain environment and jointly promoted by enterprises, universities, and research institutes. Then, this study firstly explores the influence of different factors on evolutionary equilibrium and secondly discusses the role of main factors on the behavior strategies of each game subject. Furthermore, numerical simulation analysis using Matlab R2019a 9.6 will be used to prove the model’s validity. The research has shown (1) that media monitoring positively impacts the stability of the alliance and that product greenness can further accelerate alliance evolution when media monitoring is in place. When this factor is small, it will lead to the transformation of Industry-University-Research’s (IUR) optimal strategy into non-cooperation in the early stage. (2) The green degree of products positively affects the decision-making choice of the IUR, but it is not the case for the government. And the role of media supervision will further coordinate its influence and accelerate the evolution of alliances. (3) The enhancement of media monitoring capacity can encourage game subjects to evolve in a more beneficial way. In addition, the implementation of media supervision will help reduce the cost of government supervision and provide reputation benefits. The research fully accounts for the complexity and variability of the environment, and the results provide theoretical support and practical advice for the high-quality development of the green technology innovation alliance.
... The existing research shows that the investment in technological innovation has the characteristics of a long time span and phased investment, and the investment in technological innovation runs through the whole process of product R&D and formal production. Different investment strategies are the comprehensive reflection of managers' judgment on the future opportunity value and risk of the project [33]. In order to reasonably analyze the input strategies of enterprises participating in the industry-university-research alliance and non-alliance enterprises, this paper divides the technological innovation input of the two types of enterprises into three input stages: R&D (stage 1), production preparation (stage 2), and formal production (stage 3) based on the theory of technological innovation input. ...
According to their resource ownership and resource acquisition channels, this paper constructs an evolutionary game model of enterprises’ technological innovation investment channels considering managers’ expectations. This results in the strategy of non-alliance firms along the first channel having a significant impact on the evolutionary equilibrium. For non-alliance enterprises, the difference in the amount of investment in the R&D stage has the same impact on the probability of such enterprises following the first channel. The number of active managers, group capacity, the difference in managers’ expectations, and the difference in the amount of technological innovation investment in the R&D stage have significant differences in the possibility of choosing the first channel between the two types of enterprises. The research conclusions can provide valuable references for enterprises to make technological innovation decisions.
... New product investments are often accompanied by high risk, unpredictability, and irreversibility, so companies often introduce partners to alleviate risks and costs (Chesbrough & Schwartz, 2007;Kogut, 1991). Previous studies have focused on joint ventures (Banerjee et al., 2014;Cvitanić et al., 2011), venture capital (Ferreira & Pereira, 2021;Lukas et al., 2016), strategic alliances (Graf & Kimms, 2011;Morreale et al., 2017) and mergers and acquisitions (Lukas et al., 2019;Lukas & Welling, 2012). Chen (2012) made pioneering research in real options and supply chain. ...
... Pharmaceutical supply chains in the operations management literature typically span from drug manufacturing over the physical distribution until the point of dispensing the drug to the patient (Settanni et al., 2017). Recent operations management research around pharmaceutical supply chains addresses a board range of topics from coordinating multiple supply chain partners in the development of new drugs (Lütkemeyer et al., 2021;Taneri and De Meyer, 2017) over investment and licensing decisions into drugs under uncertainty (Morreale et al., 2017;Brandão et al., 2018) to the allocation of limited medical supplies such as ventilators, vaccines or test kits the during the Covid-19 pandemic (Yin et al., 2023;Thul and Powell, 2023). ...
Purpose
Disruptions and shortages of drugs have become severe problems in recent years, which has triggered strong media and public interest in the topic. However, little is known about the factors that can be associated with the increased frequency of shortages. In this paper, the authors analyze the drivers of drug shortages using empirical data for Germany, the fourth largest pharmaceutical market.
Design/methodology/approach
The authors use a dataset provided by the German Federal Institute for Drugs and Medical Devices (Bundesinstitut für Arzneimittel und Medizinprodukte [BfArM]) with 425 reported shortages for drug substances (DSs) in the 24-month period between May 2017 and April 2019 and enrich the data with information from additional sources. Using logistic and negative binomial regression models, the authors analyze the impact of (1) market characteristics, (2) drug substance characteristics and (3) regulatory characteristics on the likelihood of a shortage.
Findings
The authors find that factors like market concentration, patent situation, manufacturing processes or dosage form are significantly associated with the odds of a shortage. The authors discuss the implications of these findings to reduce the frequency and severity of shortages.
Originality/value
The authors contribute to the empirical research on drug shortages by analyzing the impact of market characteristics, DS characteristics and regulatory characteristics on the reported shortages. The authors’ analysis provides a starting point for better prioritizing efforts to strengthen drug supply as it is currently intensely discussed healthcare authorities.
... They use game theory to analyze several agents' strategies (generators) to increase their profits. Morreale et al., (2017) study the trade-offs of a strategic alliance between a pharmaceutical and a biotech firm considering the uncertainty and competitive environment. Azevedo (2014) talks about important details on mixing real options and games theory and reviews many option games model types; they show a summary of results of empirical evidence and suggest future research. ...
We know change is the only constant; new changes are ever emerging given the evolution in situations, types of engineering endeavors, requirements, etc. Thus, change management needs to be evolved. The world has recently faced a pandemic, COVID-19, which has presented projects with new and unique challenges. This has forced engineering managers to manage these changes and subsequently look at improvements to existing change management frameworks. The paper identifies some challenges engineering projects face, and the change management approaches used to overcome these during COVID-19. The paper has analyzed projects in the regions of Asia and Australia to derive an updated change management framework for use in unique situations such as pandemics. This paper presents the findings of a literature review focused on change management in enterprises during the COVID-19 pandemic. In addition, the paper identifies the drivers, contributors, and moderators associated with change response. It introduces a new holistic change framework, which will aid project managers in facilitating its implementation within their projects.
... In addition, we believe that a broader set of applications of the tools provided by game theory can be applied to research questions related to the abandonment option. Usually, papers applying both game theory and the real option to abandon a project study the interaction between two firms, not only in competitive duopolies such as the characteristics of competitive thresholds to exit present in Goto et al. (2008) and, depending on the degree of asymmetry or the debt-financing structure, in Murto et al. (2004) and Lambrecht (2001) respectively, but also concerning Stackelberg-like type of games in supply chains as demonstrated by Burnetas & Ritchken (2005) or R&D alliances (Morreale et al, 2017). Therefore, the literature could develop additional knowledge in this area by developing models with more complex market structures and/or a greater number of players. ...
... Stackelberg-like type of games in in supply chain as Burnetas & Ritchken (2005) or R&D alliances (Morreale et al, 2017). Therefore, the literature could advance by developing models with more complex market structures and/or a greater number of players. ...
We review the most relevant contributions to the abandonment option since the late 1960s. We begin by approaching the contributions to the literature before the emergence of the real options approach to capital investment decisions, and thereafter, under a consistent real options approach, highlighting the interactions between the option to abandon and other types of options. We then identify the methodologies adopted, and the business sectors/ types of investment projects where the abandonment option is more frequently studied. We also debate the strategic role of the abandonment solution in corporate divestitures and under a game-theoretical
approach. Finally, we present some concluding remarks and identify how certain gaps found in the literature may constitute opportunities for future research.
... Future research could also focus on incorporating tools in parallel or allowing the use of different methods for predicting demand. Finally, the efficiency of the decision support system could be increased by training machine learning models with larger datasets as well as using more complex models, which include, for instance, real options [52]. ...
Decision support systems (DSSs) have been traditionally identified as useful information technology tools in a variety of fields, including the context of cultural heritage. However, to the best of our knowledge, no prior study has developed a DSS framework that incorporates all the main decision areas simultaneously in the context of cultural heritage. We fill this gap by focusing on design-science research and specifically by developing a DSS framework whose features support all the main decision areas for the sustainable management of cultural assets in a comprehensive manner. The main decision-making areas considered in our study encompass demand management, segmentation and communication, pricing, space management, and services management. For these areas, we select appropriate decision-making supporting techniques and data management solutions. The development of our framework, in the form of a web-based system, results in an architectural solution that is able to satisfy critical requirements such as ease of use and response time. We present an application of the innovative DSS framework to a museum and discuss the main managerial implications and future improvements.
... More recently, the applications of RO are carried out in different areas: mining [32][33][34][35]; airport [36][37][38]; participation-public-private (PPP) [39,40]; agriculture [41][42][43]; and pharmaceutical [44][45][46][47][48][49]. However, in the aquaculture sector, research in this area is very scarce. ...
Aquaculture is an increasingly relevant sector in the exploitation of natural resources; therefore, it is appropriate to propose various models that include the fundamental variables for its economic-financial valuation from a business point of view. The objective of this paper is to analyze different models for the valuation of investment projects in a company in the aquaculture sector in order to conclude whether there is a model that represents a better valuation. Therefore, in this study, four valuation models have been applied, three classical models (net present value, internal rate of return, and payback) and a more recent model, real options (RO) for a company producing and marketing seaweed in Galicia (region located in the northwest of Spain). The results obtained, RO (€5,527,144.04) and net present value (€5,479,659.19), conclude that the RO model estimates a higher added value by taking into account in its calculations the flexibility given by the expansion option. Future lines of research include the application of valuation models that have been applied to companies belonging to the same sector in order to compare whether the results found are similar.
... Real option theory is well known for capitalizing on uncertainties and risks through strategic investments and contracts (Brouthers & Dikova, 2010;Buyukyoran & Gundes, 2018;Li et al., 2017;Morreale et al., 2017;Trigeorgis & Tsekrekos, 2018). Thus, option thinking can be used to proactively design and manage strategic investments (Attarzadeh et al., 2017;Galera & Soliño, 2010). ...
Escalation of commitment (EOC) is a common behavior among investors who receive negative feedback (NF) in public-private partnership (PPP) projects, and this behavior typically leads to sizable losses. Recognizing this, investors set a mental threshold and track investments for escalation. Once losses reach the threshold, investors will terminate the escalation behavior, namely, they will transfer projects to governments to obtain compensation or residual asset value. This paper analyzes the maximum amount of NF that investors can sustain based on a belief-adjustment model, followed by the analysis of the greatest loss degree. Then, a threshold model for EOC is constructed using real option thinking. Different from the usual judgment criteria of the traditional option method, the threshold is less than zero in the EOC scenario. The results show that the threshold correlates with the initial generative cognition, the sunk cost level, the degree of the government guarantee and investors’ behavioral preferences as well as with total investment and return on investment. These findings serve as a reference for governments to de-escalate investors’ commitment in PPP projects.
... empirical studies) into the significant effect of collaborative innovation between enterprises and universities (Al-Ashaab et al., 2011;Cui et al., 2020;Estrada et al., 2016;Fontana et al., 2006;Jeon et al., 2015). On the other hand, longitudinal analysis of how the alliances of university-industry or interfirm can shape the decision-making of key actors by the interest and trust game (Farazi et al., 2019;Morreale et al., 2017;Perkmann et al., 2011;Zhang et al., 2019). However, a paucity of literature investigates the collaborative impacts of governmentuniversity-industry influencing cleantech innovation in the green innovation ecosystem by a game-based approach. ...
Addressing the high research and development risk and negative externality of existing green technology alliances, the Chinese government is focusing on the green innovation ecosystem to facilitate the green collaborative innovation activities of the government-university-industry alliance. However, green innovation ecosystems often suffer from insufficient stability because of high costs, low profits, and long-term return on investment for cleantech innovation. The existing literature analyzed the impact of environmental regulation on cleantech innovation and the effect of innovation ecosystem on the function and role of multi-agent collaborative innovation. However, what are key factors affecting the stabilization of the green innovation ecosystem, and what the effects of environmental regulation policies on the multi-agent collaborative innovation in this ecosystem? To fill these gaps, unlike previous studies in the green innovation literature, this study employed game-based theory to reveal the game strategy changes during the green innovation process, and examine the evolutionary game mechanism of environmental regulation in university-industry ecological innovation alliances. On this basis, the case simulation method is used to change parameter values to simulate key factors that affect the stability of the green innovation ecosystem in multi-agent collaborative innovation. Research shows that there will be two final strategies for evolutionary game analysis: first, industry groups and university groups will adopt the collaborative innovation strategy; second. industry groups and university groups will adopt the betrayal alliance strategy. In the market mechanism, higher default costs and the distribution ratio of research and development costs and green innovation benefits are key factors effecting the stabilization of the green innovation ecosystem. However, the introduction of stricter environmental regulations can make up for shortcomings of the market failure. Specifically, the penalty costs and innovation subsidies that the government offers to industry groups and university groups have a positive correlation with the stabilization of the green innovation ecosystem.
... ROA has been used to value the money for mining projects with high price fluctuation and develop models to make the best use of the managerial flexibility . ROA has been used in the pharmaceutical industry to be compared with NPV for the investment value evaluation and to explore the optimal timing for drug development and the organizational optimization of pharmaceutical development (Morreale et al., 2017). Researchers, such as Trigeorgis and Reuer (2017) and Andalib et al. (2018), applied ROA to the business industry for strategy management, managerial behavior, and decision-making analysis. ...
... The real options approach has been applied to a wide range of situations. Among the main relevant ones, we can find the valuation of pharmacological projects [50,51], the disruption risk in the supply chain [52,53], or the investment timing and capacity choice for energy projects [54][55][56][57] and its storage [58]. Likewise, real options assessment plays a significant role in long-term projects, for example, the assessment of future strategic actions by companies in a setting subject to the impact of climate change [59][60][61]. ...
Internet of things is developing at a dizzying rate, and companies are forced to implement it in order to maintain their operational efficiency. The high flexibility inherent to these technologies makes it necessary to apply an appropriate measure, which properly assesses risks and rewards. Real options methodology is available as a tool which fits the conditions, both economic and strategic, under which investment in internet of things technologies is developed. The contribution of this paper is twofold. On the one hand, it offers an adequate tool to assess the strategic value of investment in internet of things technologies. On the other hand, it tries to raise awareness among managers of internet of things technologies because of their potential to contribute to economic and social progress. The results of the research described in this paper highlight the importance of taking action as quickly as possible if companies want to obtain the best possible performance. In order to enhance the understanding of internet of things technologies investment, this paper provides a methodology to assess the implementation of internet of things technologies by using the real options approach; in particular, the option to expand has been proposed for use in the decision-making process.
... Ball et al. (2015) used the ROA to establish a flexible decision model under agent uncertainty. Morreale et al. (2017) used game options to shape the timing of biopharmaceuticals and biotechnology alliances. Lee and Lin (2018) used the real option approach method to construct a three-stage management decision model. ...
The study considers the product life cycle in the stages of technological innovation, and focuses on how to evaluate the optimal investment strategy and the project value. It applies different product stages (three stages including production innovation, manufacture innovation, and business innovation) factors to different risks to build a technology innovation strategy model. This study of option premiums aims for the best strategy timing for each innovation stage. It shows that the variation of business cycle will affect the purchasing power under the uncertainty of Gross Domestic Product (GDP). In application, the compound binomial options for the manufacture innovation will only be considered after the execution of the production innovation, whereas the operation innovation will only be considered after the execution of the manufacture innovation. Thus, this paper constructs the dynamic investment sequential decision model, assesses the feasibility of an investment strategy, and makes a decision on the appropriate project value and option premiums for each stage under the possible change of GDP. Numerically, the result shows the equity value of the investment is greater than 0. Therefore, this paper recommends the case firm to invest in its innovation project known as one-time passwords. Sensitivity analysis shows when the risk-adjusted discounted rate r increases, the risk of the investment market increases accordingly, hence the equity value must also be higher in order to attract the case firm’s investment interest. Also, the average GDP growth rate u sensitivity analysis results in different phenomena. The equity value gradually decreases when the average GDP growth rate rises. When the average GDP growth rate u rises to a certain extent, however, its equity value is gradually growing. The study investigates the product life cycle innovation investment topic by using the compound binomial options method and therefore provide a more flexible strategy decision compared with other trend forecast criteria.
... Ball et al. (2015) used the ROA to establish a flexible decision model under agent uncertainty. Morreale et al. (2017) used game options to shape the timing of biopharmaceuticals and biotechnology alliances. Lee and Lin (2018) used the real option approach method to construct a three-stage management decision model. ...
The purpose of this paper is to evaluate the timing of innovative investment in technology product life cycles using a compound binomial option with management flexibility. Considering the business cycles changes in the macroeconomic will affect consumer purchasing power. The focus is how to evaluate the optimal investment strategy and the project value. It was applied to different product stages (three stages including production innovation, manufacture innovation, and operation innovation) and factored to different risks to build a technology innovation strategy model. An aim of this study is the options premium of the best strategy timing for each innovation stage. Its application of the compound binomial options for the manufacture innovation will only be considered after the execution of the production innovation. The same condition is applied to the operation innovation, which will only be considered after the execution of the manufacture innovation. Then, this paper constructs the dynamic investment sequential decision model, assesses the feasibility of an investment strategy, and makes a decision on the appropriate project value and options premium for each stage under the possible change of Gross Domestic Product (GDP). This paper investigates the product life cycle innovation investment topic by using the compound binomial options method and will provide a more flexible strategy decision compared with other trend forecast criteria.
... Pennings and Sereno (2011) also evaluated a pharmaceutical R&D venture where the technical uncertainty is modeled as a Poisson process and the firm has a series of compound options to abandon the development, and show that project uncertainties increase the option value. Morreale, Robba, Lo Nigro, and Roma (2017) examine the optimal timing problem from the point of view of a pharmaceutical firm who must decide when to offer a licensing deal to a biotech firm under an option game theoretical framework. Loch and Bode-Greuel (2001) show that growth options play a relevant role in the valuation of pharmaceutical products. ...
Multistage investment projects are subject to several sources of uncertainty, but also present significant embedded flexibilities both during and after the development process. An important characteristic of this class of projects is that as the firm incurs a cost and invests, it learns both about the difficulty of developing and implementing the project and also about market conditions. We develop a real options model where the firm continuously updates its prospects of timely completion and future market conditions. This information can then be used to optimally decide whether further investment is warranted or not, given the expected future revenues of the whole venture. We apply this model to a Research and Development (R&D) project in the pharmaceutical industry and value this investment opportunity as a compound contingent claim where the underlying asset is the value of the completed project, and find that the opportunities to abandon and to expand into related markets and applications once the original product is proven successful have a significant impact on the value of the project as a whole. This article differs from previous work in the field by capturing the potential upsides that may occur during product development through a novel quality model, and their effect on the expansion opportunities during the market phase. We use a simulation approach for the solution and show that for complex real options models, this method can be an effective way to value such projects while providing adequate precision compared to other models in the literature.
This paper presents a framework for valuing mergers and acquisitions by explicitly incorporating synergies and risk diversification based upon the real options approach. We examine the impact of stochastic profit flows on the timing and terms of mergers in the context of the synergy of the mergers. The paper also explores the analytical properties of the region where the merger is optimal. Numerical examples demonstrate how much more or less a mergers is likely to take place as parameters change.
This paper deals with the valuation of a project of Mexican Bioclon Institute, a firm producing antivenoms; this valuation comprises a R&D research portfolio of three antivenoms targeted to the US market. A compound option methodology is used. Bioclon Institute is a world leader in the production, research and development of fabotherapics; these products are manufactured using its own technology, recognized internationally; it is a large company of antivenoms globally and it is the only Mexican biotech company authorized by the US to conduct clinical trials. Real Options valuation constitutes an important analytical tool of limited use by managers and entrepreneurs in developing countries because they are not fully aware about this methodology and its benefits for strategic sequential project analysis.
We study the optimal firms' strategies in an R&D oligopoly environment for different types of cooperative agreements under fuzzy uncertainty about the effects of cooperation. We address the problem as an R&D fuzzy bargaining game, in which the firms' fuzzy payoffs are computed using the real exchange option methodology that takes into account the managerial flexibility embedded in the R&D opportunities. The main novelty of our contribution is the parametric description both of the intensity of the collaboration for each dimension involved in the agreement and of its fuzzy impact on the firms' payoffs. Interestingly, we show that the degree of imprecise knowledge has a significant impact on the strategic alliances adopted by firms.
In the pharmaceutical industry, personalized medicine is increasingly replacing the traditional blockbuster drug concept. Personalized medicine consists of a targeted drug that is only prescribed if a companion diagnostic test detects the corresponding biomarker. This concept promises improved treatments of various diseases. However, personalized medicine also presents pharmaceutical firms with new challenges resulting from interdependencies in the drug and diagnostic test development processes. Although pharmaceutical firms generally benefit from competition among diagnostic firms, the threat of substitutes from competitors could cause diagnostic firms to step back from new product development in the first place, leading to lost revenues for the pharmaceutical firm. We consider a pharmaceutical firm that may inform two competing differentiated diagnostic firms about a drug under development, such that these firms can develop a corresponding diagnostic test. We show which diagnostic firm the pharmaceutical firm should inform first and how granting early exclusivity to a single diagnostic firm can maximize pharmaceutical profits from personalized medicine.
Different types of government subsidies are used to promote green technology innovation in many countries. Driven by government subsidies and consumer green preference, it’s common for manufacturers (the marketer) to cooperate with research institutes (the innovator) in green technology research and development (R&D). Managing R&D collaboration under different government subsidies is a challenge for the marketer. Meanwhile, how can the government design the subsidy policies to achieve a better effect is also vital. Thus, a two-level three-player game is proposed. The upper layer is the government's decision on R&D subsidy or per-unit production subsidy, and the lower layer is a principal-agent model composed of a marketer and an innovator, who cooperate simply or deeply with a contract with or without milestone payment. We find that: (a) It is always advantageous for the marketer to offer a milestone payment after successful R&D; (b) Deep R&D cooperation is more profitable for the marketer; (c) R&D subsidy is better at stimulating the environmental performance of green technology cooperation.
Managers frequently make decisions under conditions of fundamental uncertainty due the stochastic nature of the outcomes and competitive rivalry. In this study, we experimentally test a theoretical model under fundamental uncertainty and competitive rivalry by designing a sequential interaction game between two players. The first mover can decide either to choose a sure outcome that assigns a risky outcome to the second mover or to pass the decision to the second mover. If the second player gets the chance to decide, she can choose between a sure outcome, conditioned by the assignment of a risky payoff to the first mover, or the sharing of the risky outcome with the first mover. We then introduce the following experimental treatments: (i) relegating second-mover participants to a purely passive role and substituting them with a random device (absence of strategic uncertainty – that is, when the source of uncertainty is a human subject); (ii) providing information about the behaviour of second-mover counterparts; and (iii) completely removing the second-mover participant.
We find that decision makers are sensitive to the presence or absence of strategic uncertainty; indeed, in the presence of strategic uncertainty, first movers more often diverge from the behaviour predicted by the model. Given our experimental results, the theoretical model needs to be revisited. The standard model of monetary payoff-maximizing agents should be substituted by one of decision makers who maximize a utility function which includes the psychological cost induced by strategic uncertainty.
This paper deals with the valuation of a project of Mexican Bioclon Institute, a firm producing antivenoms; this valuation comprises a R&D research portfolio of three antivenoms targeted to the US market. A compound option methodology is used. Bioclon Institute is a world leader in the production, research and development of fabotherapics; these products are manufactured using its own technology, recognized internationally; it is a large company of antivenoms globally and it is the only Mexican biotech company authorized by the US to conduct clinical trials. Real Options valuation constitutes an important analytical tool of limited use by managers and entrepreneurs in developing countries because they are not fully aware about this methodology and its benefits for strategic sequential project analysis.
Purpose
A lack of visibility into the manufacturer’s production cost information impedes a retailer’s ability to maximize her own profits, especially when market demand is uncertain. The purpose of this paper is to investigate the use of an option contract within a one-period two-echelon supply chain in the presence of asymmetric cost information.
Design/methodology/approach
Based on the principal-agent model, the retailer, acting as a Stackelberg leader, offers a menu of option contracts to mitigate the risk of uncertain demand and reveal asymmetric production cost information. The optimal contract in asymmetric and symmetric information scenarios is derived. Finally, the impact of production costs on the optimal contracts and the actors’ profits is explored by numerical experiments.
Findings
By comparing the optimal equilibrium solutions in two scenarios, the authors show that asymmetric cost information has a large impact on the optimal option contract and profits. In addition, information rent is affected by the type differential. The results prove that the level of information asymmetry plays a vital role in option contracts and profits.
Originality/value
Different from the existing literature on private demand information, this paper considers a supply chain with asymmetric cost information in the context of option contracts. Interestingly, not only the production cost but also the probability of a low production cost can affect the option strike price. In addition, from the perspective of the manufacturer, a high cost does not always bring a high information rent. These findings can provide some guidance to decision-makers.
Managers of biotechnology companies face great technological and market risks in making investment decisions. Traditional investment decision tools such as the discounted cash flow (DCF) approaches are often deemed insufficient in the face of the highly uncertain environment surrounding biotechnology projects. More recently, there is an increasing interest in real options approaches, which, in contrast to DCF, explicitly takes into account the managerial flexibility to respond to changing internal and external conditions during the course of the project. It is this flexibility that makes real options reasoning not only perceived to be superior for evaluating projects, but also for developing value-enhancing strategies. However, there is considerable confusion about when the real options approach might be applicable in practice, be it in a formal or an informal way. Based on insights derived from interviews with European biotechnology investors and managers, this study provides an overview of the potential benefits and limitations real options thinking has on evaluating and managing risky projects, particularly with respect to biotechnology companies.
The competitive landscape where pharmaceutical and biotechnology companies operate has changed radically due to a scientific/technological progress that has revolutionised the process by which drugs are developed. In fact, pharmaceutical industry more and more relies on advances in biochemistry and molecular biology. As a consequence, the number of partnerships between pharmaceutical and biotech firms has grown significantly. Research contributions addressing the biopharmaceutical alliances design have also focused on the optimal timing to sign a partnership. In this paper, we introduce and analyse the effect of competition in biotechnology industry by modelling the decisions of whether and when ally with a pharmaceutical company through a real options game. We find that the timing decisions depend on the level of the competition, synergies obtained through the alliance and contract terms offered by the pharmaceutical company as well. Also, we show that the first mover might not always pre-empt the follower in partnering with the pharmaceutical company.
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.
We argue that the greater the extent to which choice sets evolve as a consequence of firms' exploration activities, the less structured the firms' abandonment decisions become and, in turn, the less distinguishable a real option is from more generic notions of path dependence - a sequential stream of investment in and of itself does not constitute a real option. While organizational adaptations can extend the applicability of real options, they impose tradeoffs that may lead to the underutilization of discoveries made in the course of exploration.
The validity of the classic Black-Scholes option pricing formula depends on the capability of investors to follow a dynamic portfolio strategy in the stock that replicates the payoff structure to the option. The critical assumption required for such a strategy to be feasible, is that the underlying stock return dynamics can be described by a stochastic process with a continuous sample path. In this paper, an option pricing formula is derived for the more-general case when the underlying stock returns are generated by a mixture of both continuous and jump processes. The derived formula has most of the attractive features of the original Black-Scholes formula in that it does not depend on investor preferences or knowledge of the expected return on the underlying stock. Moreover, the same analysis applied to the options can be extended to the pricing of corporate liabilities.
Real options (RO) analysis has been of growing interest to the academic community as a promising approach to supporting investment decisions under uncertainty. In this article we examine an applied investment decision in the telecommunications industry to highlight the main benefits associated with using real options. The paper then discusses the theoretical issues raised by real options. Specifically, we examine two research streams to explain how real options contributes to a theoretical understanding of strategic management, and to better understand the gap between theory and practice of real options. Finally, we lay out an agenda for future research.
Biotechnology companies rely heavily on alliances with pharmaceutical companies to finance their research and development expenditures, and pharmaceutical firms rely heavily on alliances to supplement their internal research and development. Previous studies suggest that asymmetric information may lead to inefficient contracting. We examine the determinants of biotech-pharmaceutical deal prices to determine whether the market for deals between biotech and pharmaceutical companies functions as a well-informed market or whether it is characterized by asymmetric information. We find that inexperienced biotech companies receive substantially discounted payments when signing their first deal. Drugs that are jointly developed are more likely to advance in clinical trials than drugs that are developed by a single company, so the first-deal discount is not consistent with the post-deal performance of these drugs. We also find that biotech companies that sign deals receive substantially higher valuations from venture capitalists and from the public equity market, which implies that the discounts are rational; a biotechnology company that is developing its first product benefits from forming an alliance with a pharmaceutical company because it sends a positive signal to prospective investors.
By mixing concepts from both game theoretic analysis and real options theory, an investment decision in a competitive market can be seen as a “game” between firms, as firms implicitly take into account other firms’ reactions to their own investment actions. We review two decades of real option game models, suggesting which critical problems have been “solved” by considering game theory, and which significant problems have not been yet adequately addressed. We provide some insights on the plausible empirical applications, or shortfalls in applications to date, and suggest some promising avenues for future research.
Research and development (R&D) collaborations, common in high-tech industries, are challenging to manage because of technical and market risks as well as incentive problems. We investigate how control rights, options, payment terms, and timing allow the innovator to capture maximum value from its R&D collaborations with a marketer. Our study reveals a counterintuitive result; the innovator may, under certain conditions, prefer to grant launch control rights or buyout options to the marketer despite the fact that both terms restrict its downstream actions. We demonstrate that a menu of contracts is not necessary to address the adverse selection problem because the menu can be replicated by a single option contract. We show that timing, through renegotiation or delayed contracting, as well as the careful allocation of control rights and options can have a significant influence on the value of collaborative R&D. We provide recommendations on the optimal contract structure and timing based on two project characteristics, novelty of the R&D process and market-potential variability.
This paper was accepted by David Hsu, entrepreneurship and innovation.
Founders and funders of medical and life science companies must often determine the value of particular technologies and establish appropriate royalty rates to charge for their use. This article will describe the three basic methods used to determine the value of intangible assets; summarize the 25% rule and the 5% rule for setting royalty rates; and identify the key business factors that must be considered in any final determination of an appropriate royalty.
The level of deal making in the pharmaceutical industry was negatively affected by the lack of venture funding invoked by the global economic downturn of 2008/2009. Fewer deals were concluded in 2009 as pharmaceutical companies forced to restructure and reduce their headcounts were reticent to part with their cash except for the most promising and innovative products and technologies. Even when they did sign deals, they were not willing to take on much risk.
Drugs available in the market today, selected several years ago under very uncertain future scenario, have experienced a long and expensive process of research and development carried out following both a closed and an open innovation path. To support this critical selection process, we propose a Decision Support System, able to choose among different candidates the most promising drugs along their best development path. The Decision Support System, based on a real options portfolio optimization model, mapping tools, and what-if rules as well, has been applied to a numerical example available in literature, and the research findings show interesting managerial and academic implications. Copyright © 2015 John Wiley & Sons, Ltd.
We study three contractual arrangements—co-development, licensing, and co-development with opt-out options—for the joint development of new products between a small and financially constrained innovator firm and a large technology company, as in the case of a biotech innovator and a major pharma company. We formulate our arguments in the context of a two-stage model, characterized by technical risk and stochastically changing cost and revenue projections. The model captures the main disadvantages of traditional co-development and licensing arrangements: in co-development the small firm runs a risk of running out of capital as future costs rise, while licensing for milestone and royalty (M&R) payments, which eliminates the latter risk, introduces inefficiency, as profitable projects might be abandoned. Counter to intuition we show that the biotech's payoff in a licensing contract is not monotonically increasing in the M&R terms. We also show that an option clause in a co-development contract that gives the small firm the right but not the obligation to opt out of co-development and into a pre-agreed licensing arrangement avoids the problems associated with fully committed co-development or licensing: the probability that the small firm will run out of capital is greatly reduced or completely eliminated and profitable projects are never abandoned.
By mixing concepts from both game theoretic analysis and real options theory, an investment decision in a competitive market can be seen as a “game” between firms, as firms implicitly take into account other firms’ reactions to their own investment actions. We review two decades of real option game models, suggesting which critical problems have been “solved” by considering game theory, and which significant problems have not been yet adequately addressed. We provide some insights on the plausible empirical applications, or shortfalls in applications to date, and suggest some promising avenues for future research.
We apply real options modeling to a common pharmaceutical industry licensing arrangement to take into account various uncertainties and the flexibility value. The managerial flexibility is limited to the project abandonment option. We extend previous work by incorporating the phases required to bring the project from patent approval to market. We also incorporate a deterministic variable into the cash flow process that provides a realistic product lifecycle. We focus on the allocation of project value between licensor and licensee, i.e., the so-called “profit split” ratio (PSR) because it is commonly used in practice to negotiate terms. We find: (1) Ignoring the managerial flexibility in valuation may cause the licensee to either forego an acceptable deal or enter into an inferior deal. (2) The magnitude of project profitability as well as cost and sales uncertainty affects the licensor's bargaining power over compensation for granting abandonment flexibility to the licensee. (3) Managers must exercise care when estimating sales volatility because the flexibility value is more sensitive to sales volatility than it is to cost volatility. (4) Failure to incorporate the product lifecycle will produce suboptimal capital investment decisions.
This paper presents a valuation approach for merger and acquisition (M&A) deals employing contingent earnouts. It is argued that these transactions have option-like features, and the paper uses a game-theoretic option approach to model the value of such claims. More specifically, the paper examines the impact of uncertainty on the optimal timing of M&A using earnouts, and it also investigates the impact of uncertainty on the terms of the earnout. Optimal earnout and initial payment combinations are endogenously derived from the model, and testable hypotheses are developed. The theoretical contribution of this paper is a dynamic decision-making model of the invest-to-learn option generated upon investment in an acquisition. The paper also offers practical implications for the design of acquisitions employing earnouts.
Presents a real options and game-theoretic approach to corporate investment strategy under competition. Analysis of aspects of competition in a microeconomic framework; Forecasting of operating cash inflows based on economic rents or excess profits; Implications of results obtained.
Despite its major advances, finance theory has had scant impact on strategic planning. Strategic planning needs finance and should learn to apply finance theory correctly. However, finance theory must be extended in order to reconcile financial and strategic analysis.
The valuation of multi-staged pharmaceutical R&D can be interpreted as a chain of real options. In valuing these compound option models, a crucial problem is how to deal with the different types of risk. Previous models, such as Cassimon et al. (2004), offer a closed-form solution for the valuation of a new drug development using a generalized n-fold compound option model, but implicitly bundle both commercial and technical risk in one risk measure. We extend this model by explicitly incorporating technical risk, while still preserving the closed-form solution of the model. As such, this extended model is better suited to handle real-life valuation cases in the pharmaceutical industry. We document the theoretical model with a real-life project of a major pharmaceutical multinational.
This paper develops a model for assessing options in joint ventures. The model is used specifically to examine the option to acquire or divest a joint venture, both in the case where the acquisition/divestiture price is specified ex ante in the initial contract and in the case where the price is to be negotiated ex post. The results derived from the model show how the value of the option and each partner's pay-off from the venture vary with the structure of the option and how the presence of the option may affect the structuring of the joint venture. The main theoretical insights are stated in 12 potentially testable propositions, and possible ways to operationalize some of the propositions for empirical testing are also explored. Copyright © 2000 John Wiley & Sons, Ltd.
Growth in the internationalization of R&D activity has increased in concert with the increasing use of strategic alliances. Typically, to date MNEs have preferred to internalize their R&D activities by setting up wholly owned subsidiaries or by acquiring a suitable target. By considering the sequential nature of market entry and the contingency for subsequent reorientation following an initial commitment for research collaboration, we use an option framework to derive a value for the overall flexibility. In particular, we present critical thresholds for timing and termination strategy selection and provide a novel perspective on existing empirical results in the domain of joint ventures. The results show that technolo-gical uncertainty promotes the formation of joint ventures. Moreover, we find an ambiguous effect of technological uncertainty on the expected duration of joint ventures. While the model also helps to refine the impact of different types of innovation on longevity of the JV it provides a number of new testable predictions. Keywords international joint venture, research and development, foreign direct investment, merger and acquisition, divestment, real options, Levy process.
This paper presents a stochastic optimization model (OptFolio) of pharmaceutical research and development (R&D) portfolio management using a real options approach for making optimal project selection decisions. A method is developed to model new product development as a series of continuation/abandonment options, deciding at each stage in pharmaceutical R&D whether to proceed further or stop development. Multistage stochastic programming is utilized to model the flexibility afforded by the abandonment option. The resulting mixed-integer linear program-ming formulation is applied to a case study involving the selection of the optimal product portfolio from a set of 20 candidate drugs at different stages in the developmental pipeline over a planning horizon of 6 years. This proposed framework provides a road map for future decisions by tracking the decision of abandonment over time and calculating the minimum market value above which development is continued under changing resource constraints and estimated market and technical uncertainty. Results indicate that the riskier the project is, the larger the minimum market value required for continuing testing in future stages. Consequently, the value of the abandonment option increases with rising market uncertainty or decreased probability of clinical trial success. In addition, a framework for incorporating additional managerial choices to the OptFolio model is discussed.
The real options approach is frequently advocated as an ap-proach that offers a positive and radical reassessment of the value of risk and exploration. We examine a recent case where Merck used the real options approach to justify an investment in an R&D project. This case is used to highlight some of the problems associated with using real options. We note that the assumptions incorporated in most standard option valuation models can conflict with the conclusions reached by strategic analysis. As a result, users of real options models should un-derstand the quantitative aspects of these models, and may often need to create a customized model for each situation. The dif-ficulty of developing customized models may explain, in part, the limited use of the real options approach in strategic analysis.
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.
In today's intensely competitive business environment, pharmaceutical companies are augmenting their product pipelines by both developing drugs on their own and in-licensing proprietary compounds or drug discovery–related technologies from smaller biotechnology companies. In this work, the OptFolio model of pharmaceutical R&D portfolio management is extended to evaluate partnership opportunities as real options and determine the optimal timing and investment policy for proposed alliances in the face of technological and market uncertainties and budgetary restrictions. Licensing deals are modeled within a decision tree as a series of continuation/abandonment options for the pharmaceutical company, deciding at each stage of R&D whether to make a predetermined milestone payment to continue the alliance or terminate the alliance because of unfavorable market conditions and/or internal resource limitations. Results indicate that early stage alliances become more valuable as market uncertainty and the ability of pharmaceutical companies to enhance the value of the licensed drug increase because of the ability to control downside risk by the abandonment option. © 2004 American Institute of Chemical Engineers AIChE J, 51: 198–209, 2005
We find the optimal time for entering a joint venture by two firms, and the optimal linear contract for sharing the profits. We consider risk-sharing, timing-incentive and asymmetric decisions contract designs. If the firms are risk-neutral and the cash payments are allowed, all three designs are equivalent. With risk aversion, the optimal contract parameters may vary significantly across the three designs and across varying levels of risk aversion. We also analyze a dataset of joint biomedical ventures, in which, in agreement with our theoretical predictions, both royalty and cash payments are mostly increasing in the smaller firm's experience, and the time of entry happens sooner for more experienced small firms.
This article demonstrates how to use strategic options and games to quantify the option value of technology investments. Research and product development in electronics, capacity expansion in telecommunications or strategic acquisitions to enter new markets are examples of strategic investments that are difficult to analyse based on standard discounted cash flow approaches. Yet these decisions determine a firm's competitive success in a dynamic technological and competitive landscape. How much is such a strategic option worth? How does one analyse strategic options in a dynamic, competitive environment? We describe basic principles for analysing competitive strategies under uncertainty by incorporating game theory in real options analysis. We show how executives can analyse high-stakes multi-stage investment decisions under uncertainty, both under a proprietary setting and under different kinds of competitive structures. The analysis can apply in the last stage of commercialisation or in the innovation/R&D stage. Our proposed valuation of competitive investment strategies can help answer strategic questions such as: When should an innovator take a tough stance to pre-empt market share and force its rival to retreat, and when should it take an accommodating stance to avoid a retaliation and intensified competition? When should a firm co-operate (e.g. via joint R&D ventures) and when should it choose head-on competition (e.g. innovation races)? Step-by-step illustrative analyses provide guidelines that practitioners can adapt in realistic settings.
The goal of this paper is to understand the factors explaining differential growth in biotechnology firms. It aims also to add some caution to the generalized opinion according to which alliances are the key factor behind new firm performance. The theoretical framework is based on competence, and evolutionary theories of the firm. These approaches underline the fact that within similar industries and technologies firms display clear and persistent variety in performance. Some 60 dedicated biotechnology firms (DBFs) were interviewed across Canada; half of them experienced rapid growth. A few variables, including alliances, explained much of the fast growth.
This paper illustrates the use of real options valuation and game theory principles to analyze prototypical investment opportunities involving important competitive/strategic decisions under uncertainty. It uses examples from innovation cases, alliances and acquisitions to discuss strategic and competitive aspects, relevant in a range of industries like consumer electronics and telecom. It particularly focuses on whether it is optimal to compete independently or coordinate/collaborate via strategic alliances.
High-technology companies that discover new technological opportunities face two critical decisions: whether and when to collaborate in exploiting these opportunities. Prior research has examined factors such as transaction costs that determine whether firms decide to collaborate. In this study, we aim to understand when firms collaborate in exploiting opportunities. To this end we study the history of 86 biopharmaceutical product-development projects. We find that factors that reduce articulation and appropriation uncertainties in these projects—patent protection, high R&D intensity of the discoverer, partners’ prior collaboration experience, and support infrastructures in the industry—can speed up collaboration. Interestingly, project-specific factors do not seem to affect timing.
Real options analysis was often recommended as an emerging valuation technique for high-risk investment projects. Former inter-sectoral surveys have drawn an ambivalent picture of real options usage in general. In addition, there is a lack of sector-specific investigations. In the following article the results of an in-depth analysis of collected empirical data regarding the application of this new tool in the pharmaceutical sector is presented by capturing the internal view from the pharmaceutical companies themselves and the external view from the health care departments of financial service firms. R&D stage specific modi of application, reasons for reluctance in the employment of real options and their assumed future prospects are elucidated.
We argue that incumbents may be in a position to adapt to radical technological change via interfirm cooperation with new entrants when the incumbents have complementary assets within their firm boundaries that are critical to commercializing the new technology. We study 889 strategic alliances of pharmaceutical companies with new biotechnology firms. We find that an incumbent’s alliances with providers of the new technology are positively associated with the incumbent’s new product development and, in turn, new product development is positively associated with firm performance. At the industry-level, we show that incumbents exhibit a preference towards alliances that leverage complementary assets (exploitation alliances) over alliances that focus on building new technological competencies (exploration alliances). In addition, the cooperation between incumbents and new entrants may contribute to an improvement in incumbent industry performance.
We present a modeling framework for the analysis of investments in an oligopoly market for a homogenous commodity. The demand evolves stochastically and the firms carry out investment projects in order to adjust their production cost functions or production capacities. The model is formulated as a discrete time state-space game where the firms use feedback strategies. The firms are assumed to move sequentially to ensure a unique Markov-perfect Nash equilibrium. Once the equilibrium has been solved, Monte Carlo simulation is used to form probability distributions for the firms' cash flow patterns and accomplished investments. Such information can be used to value firms operating in an oligopoly market. An example of the model is given in a duopoly market. The example illustrates the trade-off between the value of flexibility and economies of scale under competitive interaction.
This study sets up a compound option approach for evaluating pharmaceutical R&D investment projects in the presence of technical and economic uncertainties. Technical uncertainty is modeled as a Poisson jump that allows for failure and thus abandonment of the drug development. Economic uncertainty is modeled as a standard diffusion process which incorporates both up-and downward shocks. Practical application of this method is emphasized through a case analysis. We show that both uncertainties have a positive impact on the R&D option value. Moreover, from the sensitivity analysis, we find that the sensitivity of the option with respect to economic uncertainty and market introduction cost decreases when technical uncertainty increases.
Investment is a central theme in economics, finance, and operational research. Traditionally, the focus of analysis has been either on assessing the value of flexibility (investment under uncertainty) or on describing commitment effects in competitive settings (industrial organization). Research contributions addressing the intersection of investment under uncertainty and industrial organization have become numerous in recent years. In this paper, we provide an overview aimed at categorizing and relating these research streams. We highlight managerial insights concerning the nature of competitive advantage (first- versus second-mover advantage), the manner in which information is revealed, firm heterogeneity, capital increment size, and the number of competing firms.
If options are correctly priced in the market, it should not be possible to make sure profits by creating portfolios of long and short positions in options and their underlying stocks. Using this principle, a theoretical valuation formula for options is derived. Since almost all corporate liabilities can be viewed as combinations of options, the formula and the analysis that led to it are also applicable to corporate liabilities such as common stock, corporate bonds, and warrants. In particular, the formula can be used to derive the discount that should be applied to a corporate bond because of the possibility of default.
Clinical Pharmacology & Therapeutics (2001) 69, 297–307; doi: 10.1067/mcp.2001.115446
A lesser known valuation approach for biopharmaceutical products offers project managers and out-licensing biotech companies an edge in budget and license negotiations.
This paper introduces the Asset and Liability Management (ALM) compound option model. The model builds on the observation that the public sector net worth in a multi-period setting corresponds to the value of an option on an option on total government assets. Hence, the ALM compound option model is better suited for analyzing and evaluating the risk profile of public debt than existing one-period models, and is especially useful for analyzing the soundness of exit strategies from the large fiscal expansions undertaken by G-20 countries in the wake of the recent financial crisis. As an illustration, the model is used to analyze the risk profile and sustainability of Australia's public debt under different policies.
We value real (investment) options when the underlying asset follows a mixed jump-diffusion process involving various types (sources) of rare events (jumps). These jumps are assumed independent of each other, with each type having a log-normally distributed jump size and a random (Poisson-distributed) arrival time. They may represent uncertainties about the arrival and impact (on the underlying investment) of new information concerning technological innovation, competition, political risk, regulatory effects and other sources. An analytic solution is presented for European claims (call or put options) with multiple sources of jumps. A discrete-time (Markov-chain) methodology (implemented within a finite-difference scheme) is proposed for the valuation of American as well as European options. The approach is also applicable to financial options with multiple types of rare events. The approach is illustrated through valuing complex real options with compound features involving interactions between optimal investment and subsequent operating decisions. Specifically, we value a growth option and an extension option.
This paper clarifies how uncertainty affects irreversible investment in a competitive market equilibrium. With free entry, irreversibility affects the distribution of future prices, and thereby creates an opportunity cost of investing now rather than waiting. As with an imperfectly competitive firm, uncertainty can also increase the value of a marginal unit of capital. I show that with an infinite horizon, the opportunity cost is larger than this increase in value, so that uncertainty reduces investment.
Real Options: Dos and don'ts
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Making a good (fast) start in multi--stage R&D projects
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Lycera announces research collaboration with Merck to discover, develop and commercialize drugs for autoimmune diseases
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