منابع، گزینه های واقعی و استراتژی شرکت
|کد مقاله||سال انتشار||مقاله انگلیسی||ترجمه فارسی||تعداد کلمات|
|12416||2002||24 صفحه PDF||سفارش دهید||محاسبه نشده|
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Journal of Financial Economics, Volume 63, Issue 2, February 2002, Pages 211–234
The types of investments a firm undertakes will depend in part on what it expects the outcome of those investments to reveal about its skills, capabilities, and assets (i.e., its resources). We predict that a firm will specialize when young, then experiment in a new line of business for some time, and then either expand into a large, multisegment business or focus and scale up its specialized business. We derive several empirical implications for firm valuations and the reaction of stock prices to news about firm prospects. We also offer a novel explanation for the well-documented “diversification” discount.
A firm's investment strategy is determined by leveraging the capabilities, skills, and assets (i.e., resources) that are the source of its competitive advantage (Penrose, 1959; Wernerfelt, 1984). However, a firm might be uncertain about the degree to which its resources will generate economic rents. One way that firms learn about their resources is by undertaking investments and observing their outcomes (Jovanovic, 1982). The realizations of various performance measures, such as cash flows, revenues, and growth in market share, provide signals about the level of the firm's resources relevant for the success of their investments. These signals are valuable for guiding future investment decisions. Thus, when making investment decisions, firms will optimally consider both the stand-alone cash flows and the value of the information they expect to learn (Easley and Keifer, 1988). Resources can be of many types and can also differ in their degree of specificity (Montgomery and Wernerfelt, 1988). For example, R&D expertise might be valuable in only a small number of businesses, while more general resources, such as an efficient distribution system, can be leveraged in many different businesses.1 It can thus be important and useful for firms to experiment with new lines of business to help disentangle whether specific resources or general resources are responsible for their success. Such experimentation allows firms to focus on those (current and future) investments and business opportunities that best exploit their resources. In Section 2, we develop a simple, discrete-state, discrete-time model to formalize these ideas. We consider a risk-neutral firm that must choose among numerous investment opportunities (projects). The net cash flows from any project depend on the firm's general resources, applicable to all projects, as well as on the firm's specific resources pertaining only to that project. We assume that the firm can scale up its investment in any project at any time and that this scaled-up investment is irreversible. The key feature of our model is that while the firm is uncertain about its general and specific resources, it can learn about them by observing the outcomes of its investments. We further suppose that the firm has prior beliefs that it has a valuable specific resource applicable to a particular project. If the firm undertakes this specialized project, it learns about the sum of its general and specific resources, but not about each component separately. If the firm undertakes multiple projects, however, it can obtain a better signal about its general resources. We predict that firms will follow a life cycle which begins with undertaking the specialized project, then experimenting with a new line of business to learn about its resources, then either expanding into a large, multisegment business or focusing and scaling up its specialized business.2 We show that similar investment opportunities can be valued differently when firms differ in their resource base and current life-cycle stage (which impacts the value of learning). We also predict that firms can dramatically increase the level and intensity of investment in a specialized line of business after failing in an unrelated line of business. In Section 3, we extend our model to continuous distributions and continuous time to derive a richer set of implications. For example, firms that can observe performance measures with less noise will learn about their resources faster, which allows them to improve future investment decisions. A reasonable proxy for the degree of noise about resources is the correlation across time of the firm's earnings or cash flows. Consequently, we predict that firms with more highly correlated earnings (cash flows) across time will have higher market valuations. Furthermore, we predict that young firms will be more valuable than older firms with the same expected level of resources because younger firms have more to learn about their resources and therefore have more valuable real options. This implication of our model could potentially explain the “diversification” discount—the well-documented empirical result that the market value of firms operating in several business segments appears to be less than the sum of the market values of single-segment firms operating in corresponding businesses (Lang and Stulz, 1994; Berger and Ofek, 1995). Finally, we also provide empirical implications for stock price reactions to news about firm prospects. For example, we show that the announcement effects of positive and negative earnings news on a firm's stock price are asymmetric and depend on the firm's resource base, future investment opportunities, and current life-cycle stage. There is a considerable literature on the effects of learning by firms. Arrow (1962) is the seminal work on the economic implications of learning-by-doing. In Jovanovic and MacDonald (1994a), firms improve their knowhow both by producing new knowledge (innovation) and by learning from others (imitation). Numerous papers explore learning via experimentation. In one strand of this literature, firms learn about some aspect of their external environment (e.g., Prescott, 1972; Grossman et al., 1977; Zeira, 1987; Rob, 1991; Berk et al., 1999; Ryan and Lippman, 2000). Our work, however, is most closely related to the strand of literature in which experimentation allows the firm to learn about its own characteristics. The classic work in this area is Jovanovic (1982), in which a firm learns about its costs as it operates in the industry. As a low-cost firm learns of its advantage, it optimally scales up production. The dynamics of this learning process yield numerous interesting implications. For example, smaller firms are predicted to have higher and more variable growth rates because they learn more than larger, more mature firms. Moreover, entry and exit from an industry occurs as efficient firms grow and survive while inefficient firms decline and fail. Hopenhayn (1992) extends this analysis by introducing a concept of stationary equilibrium in a competitive industry to account for entry, exit, and heterogeneity in the size and growth rate of firms. Our work differs from Jovanovic's in two important respects. First, in our framework, firms experiment with new projects to learn to what degree their resources can be exploited in different lines of business.3 Second, we derive many novel implications for firm valuations, return volatilities, and the reaction of stock prices to news about firm prospects.
نتیجه گیری انگلیسی
Firms learn about their resources by undertaking real investments and observing their outcomes. This has profound implications for corporate strategy and investment policies because when valuing potential investments, firms will consider both the stand-alone cash flows and the value of the information they expect to learn about the different types of resources they possess. Since firms can differ in what they know about their resources as well as in the types of real investments available, they will differentially value the information generated by the outcomes of their investments, and thus similar investment opportunities can be valued differently by firms. We develop a model in which young firms specialize because, absent business opportunities in which they have a special skill, it might not be worthwhile for them to undertake an investment even after considering the value of information. The presence of some specialized skill, however, can make the investment worthwhile. If the firm is successful at the specialized business consistently for some period, it will learn that its total resources are high and it might have a reason to believe that its general resources are high as well. The firm now has an incentive to learn whether it is high specific resources or high general resources that are responsible for its success. This can induce the firm to undertake an investment in which it has no special skills but whose success depends largely on general skills. In other words, the firm has an incentive to undertake a general project so that it can learn whether its resources are principally specific or principally general. If the firm learns that it has high general skills, it can then leverage its resources into many different businesses and build a large multisegment business. If, on the other hand, the firm learns that its general skills are unusually low, it might focus and scale up its specialized business instead. Our central arguments thus allow us to predict a life cycle for firms: firms specialize when young, then experiment in a different line of business for some time, and then either expand into large multisegment firms or focus and scale up their specialized business. We derive several empirical predictions that rely heavily on our real options framework. We clarify the intuition in many real options models that predict that higher volatility is associated with higher valuations. We show that this intuition is incorrect if higher volatility is a result of noise in observing the relevant signals about firm performance. It is the volatility of resource uncertainty that leads to higher valuations. In particular, we show that young firms that have much to learn about their resources will have higher valuations than mature firms with the same expected level of resources but less uncertainty about them. We suggest that this is a possible explanation for the “diversification” discount discussed in the literature. Furthermore, young firms will also have higher asset return volatility. Our model also allows us to predict that among firms that expand, those that expand early have higher valuations. Evidence consistent with this appears in the literature on industry life cycle. In order to distinguish empirically our explanation from the alternatives suggested in the literature, we examine implications regarding stock price reactions. In our real options framework, the announcement effects of positive and negative news are asymmetric: a positive earnings surprise has a larger (positive) announcement effect than an equivalent negative earnings surprise. We further predict that firms whose market values are more responsive to earnings surprises (when they are experimenting with general projects) are more likely to expand into multisegment businesses. Conversely, firms whose market values are less responsive to earnings surprises are more likely to focus and scale up their specialized businesses. We believe that incorporating the role of learning using a real options framework, as we have done in this paper, not only provides many useful insights but also has the potential to explain many empirically observed phenomena about corporate investment strategies and firm valuations.