Search results1 – 3 of 3
Option models have provided insight into the value of flexibility to switch from one state to another (such as switching a mine or refinery from operating to closed…
Option models have provided insight into the value of flexibility to switch from one state to another (such as switching a mine or refinery from operating to closed status). More complex flexible processes offer multiple possibilities for switching states. A fabrication facility, for example, may offer options to shift from the current status to any of several alternatives (reflecting reconfiguration of basic facilities to accommodate different operating processes with different outputs). New algorithms enable practical application of complex option pricing models to flexible facilities, improving analysts’ ability to draw sound conclusions about the effects of flexibility and innovativeness on share value. Such models also apply for options with information items as the underlying assets. Information organizations such as oil exploration and development companies may include options to shift from the current capability to any of several alternatives reflecting added abilities to handle new information sources or apply the organization’s talents in new ways. In the case of either physical or information processing, careful attention to estimating the matrix of correlations among the values of potential alternative states allows explicit integration of financial analysis and strategic analysis – especially the influence of substitutes and the anticipated reactions of competitors, suppliers, and potential new entrants.
The purpose of this paper is to examine the nature of the relationship between business risk and financial leverage. While past theoretical and empirical studies on this…
The purpose of this paper is to examine the nature of the relationship between business risk and financial leverage. While past theoretical and empirical studies on this topic use similar variables, overall, their findings are inconclusive. In this paper, the author contends this is partially due to inappropriate proxies for business risk that are commonly used in these research papers. To correct for this misspecification, this paper proposes an alternative proxy for business risk that is isolated from the effects of financial leverage.
Past research on the relationship between business risk and financial leverage uses some variations in a firm’s operating cash flow as a proxy for business risk. This proxy cannot solely reflect business risk and may very well be affected by the level of financial leverage, especially for financially distressed firms. This paper proposes an alternative proxy for business risk that is isolated from the effects of financial leverage. This proxy is the cost of capital of an all-equity firm. The theoretical model developed in this paper is based on deriving the optimum level of debt as a function of business risk in the context of the Modigliani and Miller Proposition II model.
The findings show a positive linkage between business risk and financial leverage. This relationship is robust to the various forms the cost of financial distress function may take.
The mixed findings in past research papers regarding the relationship between business risk and financial leverage are mainly due to “inappropriate” measures of business risk that do not only reflect one firm attribute and are contaminated with other factors mainly financial leverage. As such, since the variable of interest is misspecified, the outcome of these studies cannot be credible. This paper attempts to correct for such misspecification by proposing a proxy that only reflects business risk. In addition, the proposed model is based on the widely acceptable Modigliani and Miller static theory of capital structure.
Analysis of Information Options offers new tools for evaluating investments in research, mineral exploration, logistics, energy transmission, and other information…
Analysis of Information Options offers new tools for evaluating investments in research, mineral exploration, logistics, energy transmission, and other information operations. With Information Options, the underlying assets are information assets and the rules governing exercise are based on the realities of the information realm (infosphere). Information Options can be modeled as options to “purchase” information assets by paying the cost of the information operations involved. Information Options arise at several stages of value creation. The initial stage involves observation of physical phenomena with accompanying data capture. The next refinement is to organize the data into structured databases. Then bits of information are selected from storage and synthesized into an information product (such as a management report). Next, the information product is presented to the user via an efficient interface that does not require the user to be a field expert. Information Options are similar in concept to real options but substantially different in their details, since real options have physical objects as the underlying assets and the rules governing exercise are based on the realities of the physical world. Also, while exercising a financial option typically kills the option, Information Options may include multiple exercises. Information Options may involve high volatility or jump processes as well, further enhancing their value. This chapter extends several important real option applications into the information realm, including jump process models and models for valuing options to synthesize any of n information items into any of m output assets.