Influential Article Review - A Binomial Compound Option Approach to Modeling Sequential R&D Investments

This paper examines research and development. We present insights from a highly influential paper. Here are the highlights from this paper: In this paper, we propose a binomial approach to modeling sequential R&D investments. More specifically, we present a compound real options approach, simplifying the existing valuation methodology. Based upon the same set of assumptions as prior models, we show that the number of computational steps for valuing any compound option can be reduced to a single step. We demonstrate the applicability of our approach using the real-world example of valuing a new drug application. Overall, our work provides a heuristic framework for fostering the adoption of binomial compound option valuation techniques in R&D management. For our overseas readers, we then present the insights from this paper in Spanish, French, Portuguese, and German

Valuation of Biotechnological Research: A Real Options Application for a Mexican Company

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.

European Option Based R&D Investment Decision Making under Uncertainties

This paper establishes the payoff models of the European option for research and development (R&D) projects with two enterprises in a research joint venture (RJV). The models are used to assess the timing and payoffs of the R&D project investment under quantified uncertainties. After the option game, the two enterprises can make optimal investment decision for the R&D project investment in the RJV

Risky choices in strategic environments: An experimental investigation of a real options game

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. (C) 2019 Published by Elsevier B.V

Management Perceptions of the Value of Artificial Intelligence in Drug Discovery and Development

The challenge of bringing new drugs to market through discovery and development is nothing short of daunting in the biopharmaceutical industry. The process is very expensive, very risky and carries a high failure rate, however, it is necessary to sustain the biopharmaceutical industry in bringing new medications to market. In fact, much of the recent literature supports the position that the biopharmaceutical industry faces a more difficult task than most industries in the development of new products, given the escalating cost of research and development, the high regulatory hurdles, and a declining success rate in the discovery of new drug compounds. (Lo, 2021) In addition, the expense of drug discovery and development has a direct impact on the price of medications once they reach the market. Improvements in the drug discovery and development process, therefore, are critical to the long-term success of the biopharmaceutical industry and its ability to find new medications to improve the lives of patients. Artificial Intelligence has long been touted as a route to more efficient, less costly, and more predictable outcomes in the drug discovery and development process. Yet, despite the promise of this technology, there has been little evidence to support that drug discovery and development has become more efficient or less expensive in recent years. In addition, the biopharmaceutical industry has not fully embraced or embedded AI as a core strength in bringing new drugs to market, despite its potential to lower costs, time needed, and manpower required to discover new medicines. This research explores perceived value of AI within the industry as contributing factor to the relatively low adoption of this technology. The specific focus in this research is the view that executive biopharmaceutical managers, tasked with the allocation of limited resources, hold regarding the use, value, and applicability of Artificial Intelligence as a tool within drug discovery and development

Three Essays on Investor Reaction to Strategic Alliance Announcements

ABSTRACT ESSAY 1 RAPID OVER-REACTION: PERCEIVED VALUE CREATION VIA ALLIANCE ANNOUNCEMENTS The management literature has widely acknowledged the importance of studying and understanding the determinants of the market\u27s reactions to the announcements of strategic alliances. With a focus on dyadic alliances, I ask what types of information signaled to the market by the alliance announcement influence the investors\u27 perception of value. I hypothesize that the type of technical expertise, relationship expertise, and market expertise of each alliance partner, expressed as either explorative or exploitative, sends decodable signals to the investors, which in turn influences their reaction to the new alliance announcement. Using a sample of 927 alliances extracted from a unique biopharmaceutical dataset, I proxied investors\u27 reaction to the alliance announcements by calculating the cumulative abnormal return during a three-day window around the alliance announcement. I found that while technical expertise does not appear to be a signal that investors consider when valuing firms involved in a new alliance, both relationship expertise and market expertise showed a statistically significant influence on the investors\u27 perception of value. ABSTRACT ESSAY 2: REDUCING INVESTOR ANXIETY VIA ALLIANCE PARTNER SELECTION The management literature has recognized strategic alliances as an organizational form that has the potential to reduce uncertainty. One important step for alliances in order to achieve a reduction in uncertainty is selecting the right partner, one that enables the alliance to effectively address the specific type of uncertainty it faces. In this study, I specifically address the question of whether the perceived uncertainty of investors at the time of the alliance announcement is influenced by whether the skills and expertise of the two alliance partners are similar or complementary (diverse). I suggest that the level of technical expertise, expressed as either explorative or exploitative and interpreted as either similar or complementary, sends a signal to the investors, which in turn will impact their perception of uncertainty. In addition, I study whether this relationship is moderated by the level of exogenous uncertainty faced by the alliance. Using a sample of 927 alliances extracted from a unique biopharmaceutical dataset, I found that exogenous uncertainty in fact moderates the relationship between partner similarity/ complementarity and investors\u27 perception of uncertainty. ABSTRACT ESSAY 3: SPILLOVER EFFECTS IN ALLIANCE RELATIONSHIPS Entering multiple simultaneous alliances is a common practice, especially in R&D intense industries. While this strategy may enhance the possibility of success by attempting to simultaneously unlock possible synergistic effects in multiple alliances, it also exposes the alliance partners to spillover effects created by their partners\u27 alliances. In this study I will examine how one specific action of one partner, to enter a new alliance, affects the initial alliance partner. Specifically, given that firm A and firm B are in an existing alliance, how will the market react to the information that firm A has entered into a new alliance with firm C, and how will the market reaction affect firm B (the initial alliance partner)? I develop and test two sets of competing hypotheses using a unique biopharmaceutical dataset and find that the market reacts favorably to the new alliance as measured by the change in value of firm B\u27s stock price. My goal is to contribute to the literature by testing how the signals sent by the alliance to the market affect the initial alliance partner and thus if investors monitor and react to post-alliance events

Market entry through success based milestone payments

Diese Arbeit hat als Hauptziel ein Bewertungsmodell für eine pharmazeutische Investition nach dem Ansatz der Realoptionen zu formulieren und zu implementieren. Nach einer Diskussion verschiedener Bewertungsmodelle wird eine Least-Squares Monte Carlo Simulation nach Schwartz (2004) vorgestellt und implementiert. Als Fallstudie dient die erfolgsbedingte Kooperation zwischen dem Pharmaunternehmen GlaxoSmithKline und dem Biotechunternehmen Apeiron. Die Daten wurden durch Interviews mit dem Management von GlaxoSmithKline und Apeiron erhoben und ausgewertet. Nach der Implementierung des einstufigen Modells wurden die Ergebnisse erstellt und mit GlaxoSmithKline und Apeiron besprochen. In der Folge wurden die Ziele für ein erweitertes Bewertungssystems festgelegt. Dieses erweiterte Modell betrachtet die Forschungs- und Entwicklungsphasen als getrennte Stufen. Es bildet daher die unterschiedlichen Kostenstrukturen und Ausfallswahrscheinlichkeiten zwischen den Phasen realistischer ab. Diese Magisterarbeit ist wie folgt aufgebaut: Nach der Einleitung gibt das zweite Kapitel einen Überblick über die Realoptionen Theorie beginnend mit allgemeinen Charakteristika von Finanzoptionen. Das dritte Kapitel gibt einen Überblick über die Struktur des pharmazeutischen Forschungs- und Entwicklungsprozesses und diskutiert verschiedene Methoden zur Schätzung von Kosten und Dauer. Das vierte Kapitel beschreibt die Monte Carlo-Simulation im Allgemeinen und entwickelt das Least Squares Monte-Carlo Modell für den einstufigen Prozess. Darüber hinaus wird erläutert warum der Least-Squares Monte Carlo Ansatz gewählt wurde und dass dieser zu verlässlicheren Ergebnissen als die Net-Present Value Analyse führt. In Kapitel fünf wird die Kooperation zwischen GlaxoSmithKline und Apeiron vorgestellt. Als Basis für dieses Kapitel dienten vor allem Interviews mit dem Management von GlaxoSmithKline Austria und Apeiron. In Kapitel sechs werden die Ergebnisse des einstufigen Modells für die erfolgsbedingte Kooperation zwischen GlaxoSmithKline und Apeiron dargelegt und anhand von Konvergenz- und Sensitivitätsanalysen diskutiert. Des Weiteren wird die Implementierung von verschiedenen Basisfunktionen wie Legendre oder Chebyshev Polynom erörtert. In Kapitel sieben wird die Erweiterung des Modells von Schwartz (2004) in ein mehrstufiges Modell vorgestellt. Die Resultate aus dem mehrstufigen Modell werden mit den Ergebnissen des einstufigen Modells verglichen. Die Ergebnisse, welche sowohl durch das einstufige als auch durch das mehrstufige Model erreicht werden, stellen bei entsprechender Datenlage in jedem Fall eine Verbesserung zur gängigen Net-Present Value Analyse dar.The main objective of this work is to formulate, implement and evaluate a pharmaceutical investment following the real options approach. After a presentation of different models the Least-Squares Monte Carlo model by Schwartz (2004) is presented and implemented. The input data for the model is based on the case of a milestone payments agreement between GlaxoSmithKline and the biotech company Apeiron. The data was collected and validated through interviews with the management of GlaxoSmithKline and Apeiron. After the implementation of the single-stage model, the results were again discussed with GlaxoSmithKline and Apeiron and the objectives for an extension of the model were set. The extended model considers the research and development phases as separate stages with different cost structures and probabilities of failure. This thesis is structured as follows: After the introduction the second chapter gives an overview of the real options theory, starting with general characteristics of financial options. The third chapter provides an overview of the structure of the pharmaceutical research and development process and discusses various methods for estimating cost and duration. The fourth chapter describes the Monte Carlo simulation in general and develops the Least Squares Monte-Carlo model for the single-stage process. In addition, explanations as to why the Least-Squares Monte Carlo approach was chosen over other approaches and why it generates better results than the standard net present value analysis are presented. Before the results of the single-stage model are presented in chapter six, the milestone payments agreement between GlaxoSmithKline and Apeiron is presented in chapter five. In chapter six the results of the single-stage model for the milestone payments agreement between GlaxoSmithKline and Apeiron are presented and discussed in terms of convergence and sensitivity analysis. Furthermore as in chapter seven, the implementation of various basis functions such as Legendre or Chebyshev polynomials is discussed. Lastly, in chapter seven the extension of the model of Schwartz (2004) in a multi-stage model is introduced and the achieved results are compared with the results obtained from the single-stage model in chapter six. The results, which are achieved by both the single-stage and the multi-stage model, display an improvement on net present value analysis if the underlying data is suitable

One Lab, Two Firms, Many Possibilities: on R&D outsourcing in the biopharmaceutical industry

We draw from documented characteristics of the biopharmaceutical industry to construct a model where two firms can choose to outsource R&D to an external unit, and/or engage in internal R&D, before competing in a final market. We investigate the tension between internal and outsourced operations, the distribution of profits among market participants, and the incentives to coordinate outsourcing activities or to integrate R&D and production. Consistent with the empirical evidence, we find that: (i) internal and external operations are neither substitutes nor complements in general, as each firm’s in-house effort level can be reduced or stimulated by the external unit’s activities, depending on the nature of R&D returns; (ii) an aggregate measure of technological externalities drives the distribution of industry profits, with higher returns to the external unit for development (clinical trials) than for research (drug discovery); (iii) in the latter case, the delinkage of investment incentives from industry value, and the vulnerability of investors’ returns to negative shocks, both suggest the abandonment of projects with economic and medical value as a likely consequence of outsourcing; (iv) upstream entry is stimulated by the long-run perspective for founders of a research biotech, more than of a clinical services unit, to extract – or reappropriate – industry profits by selling the equity to a client firm

One Lab, Two Firms, Many Possibilities: on R&D outsourcing in the biopharmaceutical industry

We draw from documented characteristics of the biopharmaceutical industry to construct a model where two firms can choose to outsource R&D to an external unit, and/or engage in internal R&D, before competing in a final market. We investigate the tension between outsourced and internal operations, the distribution of profits among market participants, and the incentives to coordinate outsourcing activities, or to integrate R&D and production. Consistent with the empirical evidence, we find that: (1) each firm’s internal R&D activity is monotonic in the technology received from the external unit, and the sign of the relationship does not depend on the technology received or generated by the competitor; (2) a measure of direct and indirect technological externalities drives the distribution of industry profits, with lower returns to an external unit involved in research (drug discovery) than in development (clinical trials); (3) upstream entry is stimulated by the long-term perspective for the external unit’s owners to earn a larger share of industry profits by selling out assets to a client firm than by running operations. However, in the case of early-stage research, the delinkage of investment incentives from industry value, and the vulnerability of investors’ returns to negative shocks, both suggest the abandonment of projects with economic and medical value as a likely consequence of R&D outsourcing

Risks in new product development (NPD) projects

New product development (NPD) is vulnerable to a wide variety of risks arising from within the firm or from the external environment. Existing categorizations of NPD project risks are partial or ill-defined and consequently there is no clear consensus among researchers and practitioners about what constitute NPD project risks. To address this gap, this thesis deploys a systematic literature methodology to inductively develop a comprehensive risk taxonomy from a review of 124 empirical studies. This taxonomy is then empirically validated through a survey capturing data from 263 NPD projects conducted by UK firms. The thesis further investigated the moderating effect of NPD project type (incremental or radical), firm size (SMEs and large firms) and industry sectors on the proposed risk taxonomy. Variation in the perceptions of NPD risk by different members of the team was explored as well. The findings revealed that the principal risk factors affecting NPD projects are technological rapidity risk, supply chain risk, lack of funding and resource risk. The risk profile of radical NPD projects differed to that of incremental projects. SMEs were more vulnerable to NPD project risks than large firms. Most risks influenced NPD projects equally across industrial sectors. Members of NPD project teams from different backgrounds or with different roles perceived risks differently. The proposed taxonomy and its subsequent empirical validation provides a comprehensive and robust taxonomy for identifying and managing risks associated with different types of NPD project conducted by firms of varying sizes from different industrial sectors