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Real Options Framework (real + option_framework)

Distribution by Scientific Domains
Business, Economics, Finance and Accounting40%

Selected Abstracts

A Learning Real Options Framework with Application to Process Design and Capacity Planning

PRODUCTION AND OPERATIONS MANAGEMENT, Issue 1 2005
Luke T. Miller
This paper studies the impact of learning on a multi-staged investment scenario. In contrast to other models in the real options literature in which learning is viewed as a passive consequence of the delay period, this paper quantifies information acquisition by merging statistical decision theory with the real options framework. In this context, real option attributes are discussed from a Bayesian perspective, thresholds are identified for improved decision-making, and information's impact on downstream decision-making is discussed. Using real data provided by a firm in the aerospace maintenance, repair, and overhaul industry, the methodology is used to guide a multi-phased irreversible investment decision involving process design and capacity planning. [source]

Real Options, (Dis)Investment Decision-Making and Accounting Measures of Performance

JOURNAL OF BUSINESS FINANCE & ACCOUNTING, Issue 3-4 2000
Andrew W. Stark
This paper suggests that a residual income-type measure of performance can be designed which supports optimal investment and disinvestment decision-making in a real options framework involving the options to wait before investing and to abandon. The measure has a number of advantages and disadvantages. Nonetheless, the balance of advantage versus disadvantage for the proposed measure must be set against the inadequacies of other competing measures of performance and associated organisational designs. Even if the measure of performance suggested is not regarded as practically useful, it has another general advantage , it can be used as a benchmark against which to evaluate other performance measures with regard to their support of optimal investment and disinvestment decision-making in a real options framework. [source]

Market risk and process uncertainty in production operations

NAVAL RESEARCH LOGISTICS: AN INTERNATIONAL JOURNAL, Issue 7 2006
Bardia Kamrad
Abstract By adopting a real options framework we develop a production control model that jointly incorporates process and market uncertainties. In this model, process uncertainty is defined by random fluctuations in the outputs' yield and market risk through demand uncertainty for the output. In our approach, production outputs represent commodities or items for which financial contracts do not trade. Outputs are also functionally linked to the level of input inventories. To extend the model's applicability to a wide range of production industries, inputs are modeled to reflect either renewable or partially renewable or non-renewable resources. Given this setting, techniques of stochastic control theory are employed to obtain value maximizing production policies in a constrained capacity environment. The rate of production is modeled as an adapted positive real-valued process and analogously evaluated as a sequence of complex real options. Since optimal adjustments to the rate of production also functionally depend on the outputs' yield, we optimally establish "trigger boundaries" justifying controlled variations to the rate of production over time. In this context, we provide closed form analytic results and demonstrate their robustness with respect to the stochastic (including mean reverting) processes considered. Using these results, we also demonstrate that the value (net of holding costs) accrued to the producer from having an inventory of the output is equivalent to the producer's reservation price to operationally curb its process yield. These generalizations extend the scope of model applicability and provide a basis for applying the real options methodology in the operations arena. The model is explored numerically using a stylized example that allows for both output and demand uncertainty and achieves greater realism by incorporating an element of smoothing into the sequence of production decisions. © 2006 Wiley Periodicals, Inc. Naval Research Logistics, 2006 [source]

A Learning Real Options Framework with Application to Process Design and Capacity Planning

PRODUCTION AND OPERATIONS MANAGEMENT, Issue 1 2005
Luke T. Miller
This paper studies the impact of learning on a multi-staged investment scenario. In contrast to other models in the real options literature in which learning is viewed as a passive consequence of the delay period, this paper quantifies information acquisition by merging statistical decision theory with the real options framework. In this context, real option attributes are discussed from a Bayesian perspective, thresholds are identified for improved decision-making, and information's impact on downstream decision-making is discussed. Using real data provided by a firm in the aerospace maintenance, repair, and overhaul industry, the methodology is used to guide a multi-phased irreversible investment decision involving process design and capacity planning. [source]

Corporate Investment and Asset Price Dynamics: Implications for SEO Event Studies and Long-Run Performance

THE JOURNAL OF FINANCE, Issue 3 2006
MURRAY CARLSON
ABSTRACT We present a rational theory of SEOs that explains a pre-issuance price run-up, a negative announcement effect, and long-run post-issuance underperformance. When SEOs finance investment in a real options framework, expected returns decrease endogenously because growth options are converted into assets in place. Regardless of their risk, the new assets are less risky than the options they replace. Although both size and book-to-market effects are present, standard matching procedures fail to fully capture the dynamics of risk and expected return. We calibrate the model and show that it closely matches the primary features of SEO return dynamics. [source]