Strategic planning: valuing and managing portfolios of real options

This article presents a value-based strategic planning framework suitable for valuing and managing portfolios of corporate real options. The proposed framework combines insights from strategic management theory with novel quantitative valuation tools from finance. Strategic planning is viewed as a process of actively developing and managing portfolios of corporate real options in the context of competitive interactions. As such, the expanded valuation framework recognizes that future growth opportunity value deriving from the firm's resources and capabilities must explicitly account for uncertainty, adaptability, and competitive responsiveness. The resulting expanded valuation framework is able to capture the value of the adaptive resources and capabilities that enable a firm to adapt and re-deploy assets, develop and exploit synergies, and gain competitive advantage via time-to-market and first- or second-mover advantages. We show how two basic metrics in this value-based framework, current profitability of assets in place and future growth option value, can be obtained from financial market data and how they can be used in active portfolio planning.     

EXPLOITING UNCERTAINTY—INVESTMENT OPPORTUNITIES AS REAL OPTIONS: A NEW WAY OF THINKING IN ENGINEERING ECONOMICS

A new trend in corporate planning is to exploit uncertainty by taking investment opportunities as real options. This options approach is to complement the conventional net present value (NPV) criterion in evaluating risky investments. In this paper, we take a broad look at the real options approach to various engineering economic decision problems, laying out how it provides an immediate and important perspective on value creation in an uncertain world. Unlike financial options, real options analysisdeals with investments in real assets, which is one of the primary interest areas in engineering economics. For that reason, we believe that any advancement in the real options decision framework will benefit the field of engineering economics.     

Expansion planning for transmission network under demand uncertainty: A real options framework

In recent years, there has been much expectation that transmission expansion planning should address ever increasing demands for transmission services under significant and complex economic and regulatory uncertainties. In this article, toward meeting the aforementioned expectation, we develop and analyze a real options framework that provides the valuation of a transmission owner's option to expand in his or her network. What distinguishes our framework from the extant literature is that the evolution of the demand follows a geometric Brownian motion process, it explicitly accounts for the physical flow of the electric power economically manifested as the locational marginal prices, and it shows how the values of the expansion options can be determined in the transmission network. Furthermore, our framework shows how to value an option to expedite or delay can be determined given that a specific expansion is planned. An extensive numerical example is presented to illustrate the key features of our framework.     

Strategic NPV: Real Options and Strategic Games under Different Information Structures

Research summary : Among the most difficult firm strategic choices is the trade‐off between making a long‐term commitment or holding off on investment in the face of uncertainty. To operationalize strategic management theory under demand, technological and competitive uncertainty, we develop a Strategic Net Present Value (NPV) framework that integrates real options and game theory to quantify value components and interactions at the interface between NPV, real options, and strategic games. Our approach results in new propositions clarifying the way learning‐experience conditions, technological uncertainty, and proprietary information interact to tilt the balance in the interplay between wait‐and‐see flexibility and strategic commitment. As such, Strategic NPV adds to our understanding of the conditions where NPV, real options, or strategic thinking are more relevant. Managerial summary : This study develops and elucidates implementation of a new valuation construct, “Strategic Net Present Value (NPV),” that integrates real options and game theory to more accurately portray strategic decisions underlying management theory. Among the most difficult firm strategic choices in capital intensive industries, such as energy, mining, chip manufacturing, and infrastructure development, is the trade‐off between making a long‐term commitment or holding off on investment in the face of demand, technological, and competitive uncertainties. The study provides new insights on the way various conditions, such as learning‐experience effects, technological uncertainty, and proprietary information, interact to tilt the balance in the interplay between commitment and wait‐and‐see flexibility. As such, Strategic NPV adds to our understanding of when NPV, real options, or strategic thinking matter more critically for decision making. Copyright © 2017 John Wiley & Sons, Ltd.     

Real Option Pricing and Bounds in Incomplete Markets

This article prices a real option and constructs narrow bounds around the value of real options embedded in capital budgeting decisions by applying the minimax deviations approach to real options in incomplete markets. While it is straightforward to obtain the unique value of a real option with hyperbolic absolute risk aversion (HARA) utility functions, the parameters of risk aversion are often subject to misspecification and raise concerns for practical uses. Recognizing that investors allow deviation from parameter values related to a benchmark pricing kernel, we derive narrow bounds on a real option price. Comparison with the approaches in the literature clarifies advantages of the minimax bounds: simple, consistent, and efficient.     

Non‐additivity in portfolios of exploration activities: a real options‐based analysis of equity alliances in biotechnology

Firms invest in exploration‐oriented activities to seek competitive advantage and in response to changing environments. Real options formulations represent an emerging strand of thinking on such investments. In this paper we begin with the observation that firms often simultaneously invest in multiple exploration projects. We identify two sources of potential interactions among these real options investments. First, we investigate the effects of correlations between the outcomes in different options. Second, we analyze the effects of investments that are fungible across project options. We show that under different conditions multiple options can be sub‐additive (due to redundancies in outcomes) or super‐additive (due to fungible inputs). We test the implications of our model with data from the biotech industry and find supporting evidence. Our model and results have some interesting implications for the exploration literature and real options lens. Copyright © 2004 John Wiley & Sons, Ltd.     

A Range-Based Finite Difference Approach to Valuing Options in Markov Switching Regimes*

Recognizing the inflexibility inherent in standard capital budgeting analysis, recent research has provided new insights using a real options framework. This paper uses the explicit finite difference approach to value real options. However, instead of assuming a constant return and volatility term, we assume that these variables are sensitive to changes in the economic environment. Accordingly, we adapt our approach to incorporate a Markov switching feature. Further, we recognize that some of the modeling assumptions can be violated in a practical application. Therefore, we recommend using range based estimates of the real option value, as opposed to a point estimate.     

商业地产投资的实物期权特性及应用框架研究

商业地产开发投资中存在投资的不可逆性、外部环境的不确定性和决策的灵活性，因而具有实物期权特性。从确定要解决的问题、分析不确定性的来源、鉴别关键的不确定性因素、识别实物期权类型、构建期权定价模型、计算项目价值、检查计算结果和重新设计8个方面，构建了商业地产投资决策的实物期权分析框架。     

Managing commitments and flexibility by real options

In the past, transaction cost economics (TCE) literature has largely stressed the benefits of contractual commitments in relationships. TCE traditionally recommends safeguarding specific assets against holdup, thus reducing behavioral uncertainty. In contrast, the reverse side of the coin has been disregarded for a long time. Firms may lose some of their flexibility and thus the opportunity to benefit from future emerging business activities due to prior contractual commitments. Flexibility becomes especially important in dynamic environments where there is rapid technological change. To manage the emergent trade-off between contractual commitments and flexibility in dynamic markets, the authors propose a real options approach. The value of a firm's flexibility to switch to a new trading partner is represented by a real switching option. It can be shown how contractual commitments directly affect the value of the switching option. To clarify, the authors numerically analyze the respective trade-off and derive implications for an optimal choice of contractual commitments.     

DIVERSIFICATION AS REAL OPTIONS AND THE IMPLICATIONS ON FIRM-SPECIFIC RISK AND PERFORMANCE

The management literature posits that firms can create value through diversification. In contrast, the established finance literature concludes that diversified firms destroy value.

This paper suggests a way to reconcile these two warring camps by articulating a new theory of the way in which diversification can add value not by increasing performance, but by reducing risk in ways mat investors cannot replicate.

Specifically, diversification, understood dynamically, provides a way for companies competing in especially turbulent industries to hedge the competitive risk attendant to “convergence” phenomena. That is, in industries where the optimal operating scope of a firm is in question because the promise of convergence cannot be exploited using market-mediating mechanisms, firms will “over diversify” as a hedge against uncertain future reconfigurations of industry boundaries.

In other words, these firms diversify as a way to create “real options” on future integration. These options create a form of “strategy insurance” that investors can not recreate with a portfolio of focused firms: investments in two separate, focused firms do not create an option on a single firm that encompasses the activities of those two companies. As uncertainty spawned of convergence begins to fade, strategically-hedged firms will re-focus their operations by exercising or abandoning their options on integration in a manner appropriate to the demands of their newly-defined marketplaces.

Option-creating diversification has potentially profound implications on operating performance and risk profile. Most significantly, options-based diversification is asserted to cause the operating performance of such firms to fall as they diversify and increase as they focus, in keeping with the observations of the finance literature. Yet the firms will have created value for shareholders by compensating for significant strategic risks that investors cannot otherwise hedge.     

Comparative statics for real options on oil: What stylized facts?

An important application in the real options literature has been in investments in the oil sector. Two commonly applied “stylized facts” in such applications are tested here. One is that the correlation of the returns on oil and the stock market is positive; the other that it is invariant to changes in oil price volatility. Both are rejected in data for 1993–2008 for crude oil and Standard & Poor's 500 stock market index. Based on real options theory, consequences are pointed out. The widespread idea that higher volatility leads to increased value and postponed investment is not necessarily valid.     

Valuation of Learning Options in Software Development Under Private and Market Risk

Commercial software development is an inherently uncertain activity. Private risk is high, schedule and cost overruns are common, and market success is elusive. Such circumstances call for a disciplined project evaluation approach. This paper addresses the use of market and earned value management data in assessing the economic value of commercial software development projects that are simultaneously subject to schedule, development cost, and market risk. The assessment is based on real options analysis, a financial valuation technique that can tackle dynamic investment decisions under uncertainty. The paper demonstrates the application of real options analysis to a development scenario that consists of two consecutive stages: a mandatory prototyping stage and an optional full-development stage. The full-development stage is undertaken only if the prototype is successful and the market outlook is sufficiently positive at the end of the prototyping stage, thus giving the full-development stage the flavor of an option. The project's staged design increases its value. Real options analyses capture the extra value due to optionality.     

Making real options work for practitioners: a generic model for valuing R&D projects

We propose a generic valuation framework for the appraisal of R&D projects based on real option theory. The added value of this approach is the presentation of a model that was implemented in a manner that allows corporate decision makers to use real options in an intuitive and standardized way. The project valuation procedure is separated into three main phases: project modeling, data and input collection, and result generation and analysis. The project model represents the structure of the real world R&D project with its investments, expected results, and decisions that need to be taken conditionally on the outcomes of research activities. The project model is represented in the form of a decision tree, where different research results or taken decisions lead to new branches. In this way, every possible situation the project can pass through can be represented. Uncertainties are separated into market uncertainties (e.g., market prices) and project specific, private uncertainties (e.g. uncertainty of research results). For both uncertainties, event trees are constructed which are then combined and merged with the above mentioned decision tree in order to represent the value evolution of the R&D project under given decisions and uncertainties. For every possible state of the project the real option value is calculated. By creating multidimensional trees, a multitude of decision steps and various kinds of real options (e.g., continue, expansion, switch, abandonment) can be modeled. The calculation complexity for the decision trees is given. From the tree structure we can calculate the real option value of starting an R&D project, i.e., the value of undertaking the first investment and thus acquiring the subsequent decision opportunities given by the completion of the first research effort. Furthermore, the optimal exercise strategy is derived from the decision tree. The exercise strategy gives the manager the possibility to have an a priori overview of where an R&D project may lead to, which decisions need to be taken in which circumstances, and when the project needs to be stopped in order not to generate losses. In an in‐depth case study we use an illustrative R&D project to set up and discuss the three phases of project modeling in the real options framework: building the multidimensional decision tree, input generation, and calculation of the real option value as well as the optimal strategy for the R&D project.     

Multiple point competition

Situations where firms compete against each other simultaneously in several markets abound in real life. However, there is very little conceptual or theoretical literature on multiple point competition. This paper offers a first attempt at developing a conceptual framework for analysing and understanding situations involving multiple point competition. Several examples are discussed to provide insights into the options available to the competitors and the equilibrium outcomes of such competition.     

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

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

Project valuation in the pharmaceutical industry: a comparison of least‐squares Monte Carlo real option valuation and conventional approaches

Least‐squares Monte Carlo simulation (LSM) is a promising new technique for valuing real options that has received little or no attention in the pharmaceutical industry. This study demonstrates that LSM can handle complex valuation situations with multiple uncertainties and compounded American‐type options. The limited application of real option valuation (ROV) in the pharmaceutical industry is remarkable, given the importance of accurate project valuation in an industry that requires large investments in high‐risk projects with long pay‐back periods, which is furthermore suffering from ever‐increasing development costs and shrinking profit margins. The LSM model developed in this study is constructed as an extension of a discounted cash flow model that should be familiar to economists active in the pharmaceutical industry. A number of pharmaceutical projects have been evaluated using LSM ROV, binominal real option valuation and expected net present value techniques. The different results yielded by these methods are explained in terms of differences in risking assumptions and ability to capture the value of flexibility. The analysis provides a framework to introduce the basic concepts of real option pricing to a non‐specialist audience. The LSM model illustrates the potential for real‐life commercial assessment as the versatility of the technique allows for an easy customisation to specific business problems.     

Integrating Real Options with Managerial Cash Flow Estimates

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

Toward a behavioral theory of real options: Noisy signals,bias, and learning

Research Summary: We develop a behavioral theory of real options that relaxes the informational and behavioral assumptions underlying applications of financial options theory to real assets. To do so, we augment real option theory's focus on uncertain future asset values (prospective uncertainty) with feedback learning theory that considers uncertain current asset values (contemporaneous uncertainty). This enables us to incorporate behavioral bias in the feedback learning process underlying the option execution/termination decision. The resulting computational model suggests that firms that inappropriately account for contemporaneous uncertainty and are subject to learning biases may experience substantial downside risk in undertaking real options. Moreover, contrary to the standard option result, greater uncertainty may decrease option value, making commitment to an investment path more effective than remaining flexible. Managerial Summary: Executives recognize the need to make uncertain investments to grow their business while mitigating downside risk. The analogy between financial options and real corporate investments provides an appealing method to consider the practical challenge of such investment decisions. Unfortunately, the “real options” analogy seems to break down in practice. We identify how a second form of uncertainty confounds real options intuition, leading managers to overestimate the value of uncertain investments. We present a behavioral real options model that accounts for both forms of uncertainty and suggest how uncertainty interacts with behavioral bias in the option execution/termination decision. Our model facilitates assessment of the conditions under which investments in uncertain opportunities are usefully considered as real options, and provides a means to evaluate their attractiveness.     

Irreversibility, sunk costs and investment under incomplete information

Despite its importance to economic growth, the investment behavior of firms remains poorly understood. Existing models ignore irreversibility and the opportunity to wait for new information. Even if some recent literature accounts for these two characteristics, these models ignore information costs. This paper presents a framework for the valuation of investment opportunities accounting for information costs regarding the project cash‐flows.
We develop some basic models of irreversible investment to illustrate the option‐like characteristics of investment opportunities under incomplete information. We show how optimal investment rules can be obtained using option pricing theory under incomplete information. It is possible to value real options and investment decisions using our approach in a context of incomplete information. Simulations are provided to illustrate our main results.