Narrow Results

In this paper, we study the financing of high uncertainty projects. High uncertainty is defined as the lack of knowledge of whether growth options exist. In this paper, we will describe this uncertainty by a probability distribution which describes the arrival of a growth option at a deterministic time. Once the option arrives, an additional uncertainty exists since it is not certain that it is profitable to exercise it. We value the corporate securities with contingent claims valuation both for a whole equity-financed firm and a debt-equity-financed firm. Unlike traditional capital structure models, we find nonconvex value functions for the firm vis-a-vis the debt coupon under specific parametrizations. High and low leverage can yield similar firm value maximizing policies.

This article advocates the multiple benefits of applying probabilistic approaches to capital budgeting through enriching the deterministic framework with a stochastic modeling of main impacting inputs (including a methodology for selecting the most important inputs to be modeled stochastically). The essential limitations of the deterministic capital budgeting methodology are presented: behavioral biases (optimism, asymmetric probability distribution, etc.), incomplete view of the risk return profile, neglecting real options (be it to evaluate a project or to reshape it in a value creation perspective), portfolio diversification impact, etc. Through some selected examples, we illustrate how each of these limitations can be mitigated thanks to probabilistic approaches leading to a better decision-making process and ultimately more value creation.

The primary purpose of this paper is to discuss how to use the active and interdisciplinary approaches to teach corporate finance. First, I describe the content and structure of the book entitled Corporate Finance and Strategy: An Active Learning Approach [Lee, CF, AC Lee, JC Lee and M Lee (2022). World Scientific]. Second, I discuss how the interdisciplinary approach is used to integrate corporate finance and strategy with other subjects. Third, I discussed how I require students to write three projects to make this course become active instead of passive to learn corporate finance. Finally, I discuss how students can benefit from active and interdisciplinary approach to learn finance.

The information needed for capital budgeting is generally not known with certainty. The sources of uncertainty may be the net cash inflows, the life of the project, or the discount rate. We propose a capital budgeting model under uncertainty environment in which the concept of probability is employed in describing fuzzy events and cash flow information can be specified as a special type of fuzzy numbers. The present worth of each fuzzy project cash flow can be subsequently estimated. At the same time, to select fuzzy projects and determine the optimal decision time under limited capital budget, we offer an example to analyze the results of the capital budgeting problem under uncertainty using a fuzzy real option valuation.

Over the past 30 years, multinational firms’ investment grew four times faster than worldwide GDP. Yet the evidence on whether global diversification is valuable is inconclusive. This paper uses detailed foreign direct investment (FDI) data for 251 UK multinational firms and 4,676 subsidiaries for the period 1999–2005 to show that multinational firms exhibit, on average, a global diversification premium. I investigate this result and show that the premium is positively related to “winner-picking” transfers in internal capital markets, and more so for better-governed firms. The findings help explain why multinational firms’ investment and global diversification have significantly increased over the past three decades.

In this chapter, I develop a model that measures the effects of political risk on the outcome of a foreign direct investment project as the value of an insurance policy that reimburses all losses resulting from the political event or events in question. The evolutionary process of political risk is explicitly defined and includes a stochastic element as well as the timing of the political events that trigger losses. All parameters can be estimated from current data, which eliminates the difficulty of forecasting political events far into the future. Valued in this way, political risk enters the capital budgeting process directly as a cost. Finally, I simulate the model’s use for the case of expropriation and the case of a series of losses.

In this chapter, we develop and implement a model for evaluating the present value of expected future losses of a foreign direct investment (FDI) due to the political risk associated with currency crises. The model we develop for valuing dependent, multivariate currency risk follows Clark (1997) and Clark and Tunaru (2003) that model political risk as the value of a hypothetical insurance policy that pays any and all losses generated by a crisis event. The model incorporates the fact that loss causing events surrounding these crises, such as interest rate hikes, devaluation, currency controls, multiple exchange rates, import tariffs, credit restrictions, resource rationing, etc., are multivariate with different arrival rates and often mutually dependent. It also incorporates the fact that the size of a loss when an event occurs can vary depending on the economic, social and political conditions when it occurs. Finally, it provides for an updating mechanism whereby expectations of loss causing events can be revised to take into account the most recent information.

In this chapter, we extend the usage of CAPM to the problem of estimating the cost of capital in funding risky projects. This estimation involves the estimation of betas, which we had shown in the last chapter, as well as the estimation of market risk premium. The latter is a bit more tricky and sometimes requires auxiliary regressions involving constraining the intercept to be zero. The latter is the same as regression through the origin. Although constrained regression is more general and can apply to the constraint of any sets of coefficients in a linear regression equation, we consider only the case of regression through the origin here.