Credit Spreads under Certainty: An Approach to Capital Budgeting

Abstract

In finance, leverage exists because capital expenditures represent, like tax expenses and retained earnings, a part of income rationed from shareholders. A credit spread is the yield difference (in percentage points) between investment decisions for one or more periods. Leverage is based on a chosen credit spread. However, when management maintains a budget, it keeps track of operating cash flows, and how much is borrowed. Essentially, it measures free cash flows as a restriction indicator. When these inflows and expenditures converge toward the firm's objective, there is no rational expectation of significant earnings fluctuations. Thus, spending control holds with an expected yield, which eliminates the uncertainty regarding variability.

The main difference between the economic theory of investment and capital rationing is the independence criteria. Recent studies under certainty still focus on the adjusted net present value method and Tobin's q for marginal cost of capital. In praxis, capital budgeting problems come from within corporate policy expenditure limits, business, and/or market risk. Firm financing and investment policies come to deal with projects yield-risk balancing.

In terms of discrete and linear optimization, projects are mutually exclusive (or independent if any) and contingent (or independent if all), respectively. How many of each independent, contingent, or mutually exclusive project should be accepted, undertaken, or selected, respectively? It all started back in the year 1951 at the University of Chicago with a review of a new theory of statistical decision.

Other investment problems continue to bubble until the 1980s, when operations research started to work with management teams. Today, operations management recognizes operations research through management science. From binary, multi-dimensional, to non-linear integer programing and fuzzy cash flows, mainstream literature focuses on flexibility. And recent studies on uncertainty work on multiple optimization objectives.