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

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

An R&D Investment Game under Uncertainty in Real Option Analysis

One of the problems of using the financial options methodology to analyse investment decisions is that strategic considerations become extremely important. So, the theory of real option games combines two successful theories, namely real options and game theory. The investment opportunity and the value of flexibility can be valued as a real option while the competition can be analyzed with game theory. In our model we develop an interaction between two firms that invest in R&D. The firm that invests first, defined as the Leader, acquires a first mover advantage that we assume as a higher share of market. But, several R&D investments present positive externalities and so, the option exercise by the Leader generates an “Information-Revelation”, that benefits the Follower.     

Real options approach to evaluating genetic algorithms

The real options technique has emerged as an evaluation tool for investment under uncertainty. It explicitly recognizes future decisions, and the exercise strategy is based on the optimal decisions in future periods. This paper employs the optimal stopping policy derived from real options approach to analyze and evaluate genetic algorithms, specifically for the new branches namely Estimation of Distribution Algorithms (EDAs). As an example, we focus on their simple class called univariate EDAs, which include the population-based incremental learning (PBIL), the univariate marginal distribution algorithm (UMDA), and the compact genetic algorithm (cGA). Although these algorithms are classified in the same class, the characteristics of their optimal stopping policy are different. These observations are useful in answering the question “which algorithm is suitable for a particular problem”. The results from the simulations indicate that the option values can be used as a quantitative measurement for comparing algorithms.     

Effects of time-inconsistent preferences on information technology infrastructure investments with growth options

Increasing information technology (IT) infrastructure spending and the capability of such projects to provide a platform for a firm to realize value from IT marks their importance. Effective management of IT infrastructure investments includes identification of embedded growth options in the infrastructure, and exercising them in a timely manner. Extant research has recognized that while managers could use real options thinking in IT investment management, managerial bias could affect the timing of option exercise and their realized value. We analyze the effect of time-inconsistent preferences of present-biased managers on the exercise time of real growth options and the realized value using a discrete time option valuation model. The results show that present-biased managers are more likely to exercise options early when the net payoffs are low, the option payoffs have high volatility, and the risk free discount rate is small. In addition, present biased managers are more likely to exercise a growth option early in its life when the project is performing well. We provide implications for practice and IT governance.     

Real options analysis of the timing of IS investment decisions

Many information systems (IS) investments belong to a class of capital budgeting problem where there is an option: the investment may be made straight away or delayed for some period. A real options analysis could allow decision-makers to add value to these investment decisions by providing a framework that explicitly recognises uncertainty. This paper uses options pricing theory to determine the optimal timing of IS investments and to explore the effect of different investment review cycles. The findings provide support for the common industry practice of demanding short payback periods for IS investments.     

Analysis of the SSAP Method for the Numerical Valuation of High-Dimensional Multivariate American Securities

We consider a new numerical method developed by Barraquand and Martineau for the pricing of American securities where the payoff depends on several sources of uncertainty. This method utilizes Monte Carlo simulation and is referred to as Stratified State Aggregation along the Payoff (SSAP). Since there are no other methods that so effectively reduce the dimensionality of high-dimensional problems, the SSAP method has generated significant interest. Numerical results are presented showing that, if a sufficiently large number of time steps are used, in the cases of the two-dimensional maximum and minimum options, SSAP typically prices to within 6 cents of the true price. However, we show that if the security depends on two or more sources of uncertainty, then the price obtained by the SSAP method will not, in general, converge to the correct theoretical price, due in large part to incorrect exercise decisions being made. We analyze the exercise regions in the cases of the two-dimensional maximum and minimum options and show how SSAP makes incorrect exercise decisions. Suggestions for improving SSAP pricing accuracy by choosing a partition other than the payoff are discussed. Received October 3, 1997; revised February 10, 1998.     

Consumption Utility-Based Pricing and Timing of the Option to Invest with Partial Information

This paper extends real options theory to consider the situation where the mean appreciation rate of the value of an irreversible investment project is not observable and governed by an Ornstein–Uhlenbeck process. Our main purpose is to analyze the impact of the uncertainty of the mean appreciation rate on the pricing and investment timing of the option to invest under incomplete markets with partial information. We assume that an investor aims to maximize expected discounted utility of lifetime consumption. Based on consumption utility indifference pricing method, stochastic control and filtering theory, we obtain under CARA utility the implied values and the optimal investment thresholds of the option to invest, which are determined by a semi-closed-form solution to a free-boundary partial differential equation (PDE) problem. The solution is independent of the utility time-discount rate. We provide numerical results by finite difference methods and compare the results with those under a fully observable case. Numerical calculations show that partial information leads to a significant loss of the implied value of the option to invest. This loss, called implied information value, IIV increases quickly with the uncertainty of the mean appreciation rate. A high volatility of project values might decrease the IIV, as well as the implied value of the option.     

IDEA:一种基于P2P借贷网络的投资决策分析算法

互联网金融P2P借贷平台上存在着较大的贷款投资风险,为协助投资人获得更佳的贷款收益,本文综合考虑贷款坏账风险、流标风险、利率和投资人风险偏好等要素,提出投资决策算法IDEA（Investment DEcision Analysis）：构建投资人-贷款网络,充分利用网络中的贷款投资行为信息来度量贷款坏账风险,利用图半监督学习方法度量贷款流标风险,为投资决策提供依据. 在真实数据集上的实验结果表明,相对于现有算法,我们的算法不仅可以取得更佳的投资收益,而且能够协助具有不同风险偏好的投资人进行投资决策.     

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

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

A simulation approach to real estate investment analysis

This paper describes a Monte Carlo simulation approach to the analysis of real estate investments under uncertainty. The procedure suggested in the paper is based upon the use of computer simulation to provide realistic estimates of the internal rate of return on a real estate investment and the risk of that investment. The mathematical structure and development of the computerized real estate investment analysis model are described in detail, and computational results from the model's application are presented and discussed.     

An Evolutionary Model of Endogenous Business Cycles

In this paper, we present an evolutionary model of industry dynamics yielding endogenous business cycles with ‘Keynesian’ features. The model describes an economy composed of firms and consumers/workers. Firms belong to two industries. The first one performs R&D and produces heterogeneous machine tools. Firms in the second industry invest in new machines and produce a homogenous consumption good. Consumers sell their labor and fully consume their income. In line with the empirical literature on investment patterns, we assume that the investment decisions by firms are lumpy and constrained by their financial structures. Moreover, drawing from behavioral theories of the firm, we assume boundedly rational expectation formation. Simulation results show that the model is able to deliver self-sustaining patterns of growth characterized by the presence of endogenous business cycles. The model can also replicate the most important stylized facts concerning micro- and macro-economic dynamics. Indeed, we find that investment is more volatile than GDP; consumption is less volatile than GDP; investment, consumption and change in stocks are procyclical and coincident variables; employment is procyclical; unemployment rate is anticyclical; firm size distributions are skewed but depart from log-normality; firm growth distributions are tent-shaped. JEL Classifications: C15, C22, C49, E17, E22, E32.     

The Impact of Short-Sale Constraints on Asset Allocation Strategies via the Backward Markov Chain Approximation Method

This paper considers an asset allocation strategy over a finite period under investment uncertainty and short-sale constraints as a continuous-time stochastic control problem. Investment uncertainty is characterised by a stochastic interest rate and inflation risk. If there are no short-sale constraints, the optimal asset allocation strategy can be obtained analytically. We consider several kinds of short-sale constraints and employ the backward Markov chain approximation method to explore the impact of short-sale constraints on asset allocation decisions. Our results show that the short-sale constraints do indeed have a significant impact on these decisions.     

A new multi-period investment strategies method based on evolutionary algorithms

This work introduces a new algorithmic trading method based on evolutionary algorithms and portfolio theory. The limitations of traditional portfolio theory are overcome using a multi-period definition of the problem. The model allows the inclusion of dynamic restrictions like transaction costs, portfolio unbalance, and inflation. A Monte Carlo method is proposed to handle these types of restrictions. The investment strategies method is introduced to make trading decisions based on the investor’s preference and the current state of the market. Preference is determined using heuristics instead of theoretical utility functions. The method was tested using real data from the Mexican market. The method was compared against buy-and-holds and single-period portfolios for metrics like the maximum loss, expected return, risk, the Sharpe’s ratio, and others. The results indicate investment strategies perform trading with less risk than other methods. Single-period methods attained the lowest performance in the experiments due to their high transaction costs. The conclusion was investment decisions that are improved when information providing from many different sources is considered. Also, profitable decisions are the result of a careful balance between action (transaction) and inaction (buy-and-hold).

     

Optimal strategies for government securities portfolio management taking into account propensity to risk

One of the fundamental propositions of economic theory is that of the rational behavior of people making decisions during economic activity. Investigations by Kahneman and Smith, Nobel Prize winners in 2002, showed that the majority of people do not behave in the way expected by economists; they behave illogically when encountering uncertainty in choice. In this work, an attempt is made to evaluate the character of attitudes to risk toward investment in different countries—the national propensity to risk. Using Markowitz’s portfolio theory and modern methods of risk assessment CVaR and CDaR, the characteristics of real and optimal portfolios of government securities are analyzed.     

The valuation of RFID investment using fuzzy real option

Net present value (NPV) and return on investment (ROI) are commonly used to evaluate investment in new technologies. Sometimes, however, measuring the value of investment in new IT becomes very difficult due to its wide scope of application coupled with embedded options in its adoption. Therefore, comprehensive but easily understandable methodologies are needed to solve the complicated problems resulting from the complexity of new technologies. This paper employs a real option analysis to evaluate RFID adoption in the supply chain. Real options analysis should be a better way to evaluate a disruptive technology like RFID. However, the pure (probabilistic) real option rule characterizes the present value of expected cash flows and the expected costs by a single number, which is not realistic in many cases. To solve the problem, this paper considers the real option rule in a more realistic setting, namely, when the present values of expected cash flows and expected costs are estimated by trapezoidal fuzzy numbers. Specifically, it drew out their means and variance and presented a method of calculating fuzzy real options through numerical value examples of RFID investment assuming the current value of expected cash flow and investment costs using trapezoid fuzzy number fuzzy real options. Since advanced information technology such as RFID has very high risk and options such as change, extension, delay and withdrawal, etc., investment valuation using the real options technique should be done, and in the process, in a more realistic and practical approach, the fuzzy real options model presented in this study is judged to be useful.     

Multistage portfolio optimization with stocks and options

We develop a multistage portfolio optimization model that utilizes options for mitigating market risk in a dynamic setting. Due to the key role of scenarios in the quality of investment decisions, a new scenario generation method is proposed that characterizes the dynamic behavior of asset returns. This methodology takes the dependence structure of different asset returns into account, and also considers serial correlations of each of the asset returns. Moreover, it preserves marginal distributions of asset returns. Also, it precludes arbitrage opportunities. To investigate the role of options, we implement the scenario generation method on a set of stocks selected from the New York Stock Exchange. Results show the high performance of the proposed scenario generation method. Afterwards, the generated set of scenarios is used as the uncertainty set for the multistage portfolio optimization model. Static and dynamic assessments are used for measuring the performance of options in mitigating market risks and generating additional returns. Finally, backtesting simulations are used for assessing different trading strategies of options.     

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

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

A decision support system for evaluating intangible investments

In this research project we have connected the procedural and substantive decision support by means of modern information technology. We have showed how the semistructured strategic decisions concerning intangible investments can be supported effectively by relating appropriate analysis methods to the different phases of the investment process. Because for the intangible investments there is no well-defined solution procedure available, a variety of methods must be integrated to support the solution process.

As a demonstrative example we showed how the intangible investments in logistic systems can be managed. It was highlighted that the logistics systems are complex, cross-functional systems that affect all major functions or departments within the firm beginning from the raw material deliveries and ending with the deliveries of finished goods. Investments in the logistics systems have corporatewide, cross-functional effects, and the investment decisions are strategic decisions. Consequently, the investments in the logistics systems should be strategically justified, not only cost-justified. The cross-functional effects imply that the investment analysis concerning the whole logistics system must be fulfilled under multiple, diversified criteria. We can not, however, ignore the financial, cost and revenue oriented, traditional investment criteria. They just have to be used in a new, flexible way in relation to the goals and objectives of the firm.

Strategic planning is a managerial area where semistructured problems frequently exist. One of the key problems faced in strategic management is the issue of investment decisions. Investments in information systems, logistic systems, flexible production systems, corporate image, etc, are examples of complex investment problems that are difficult to quantify and analyze with traditional techniques. Modern computer technology can enrich the analysis and improve the quality of strategic decision making.     

Simulation–optimization mechanism for expansion strategy using real option theory

A right expansion strategy can bring a company more market shares and profits, and hence increase shareholders’ equities. However, limited financial resources and various uncertainties require business practitioners to achieve their goals while controlling the risks incurred at an acceptable level. Therefore, justification of expansion investments is an important and complex topic in industry. The traditional investment analysis tools such as net present value (NPV) often tend to undervalue investment decisions. We formulate the expansion investments using real options, and develop a financial model to assess the option value. Monte Carlo simulation is considered a good way to estimate the value of the option. This valuation gives decision makers a way to choose the appropriate expansion strategy based on an integrated view of the market dynamics, but optimization is still a difficult problem to resolve. This paper presents a model of optimization under uncertainty combining system simulation with GA-based optimization to resolve the expansion problem. An industry case is used to demonstrate the application of real options to value expansion investment by using simulation–optimization. This approach also provides some new insights for the real options theory.     

Garbage‐Can Decision Making and the Accommodation of Uncertainty in New Drug Development Work

Innovation processes are sometimes described as a series of sequential activities, smoothly transforming into one another. However, in real‐life settings, innovation work is characterized by uncertainty, risk taking, politics and time pressure, and consequently much decision making in innovation work deviates from such rationalist models. Instead, decisions are made in the form of garbage‐can decision making, demonstrating a variety of non‐linear elements. Such characteristics are especially pronounced in industries based on science‐based innovation, operating under genuine uncertainty. This article reports a study of the clinical trial work in a major multinational pharmaceutical company and suggests that decision making includes at least four coping strategies for dealing with non‐linear and migrating decision‐making processes.