In recent financial literature a large volume of the articles dealt with asset leasing. This author and his colleagues [6] and others [7] developed the conditions under which asset leasing cannot increase the overall firm's value over normal debt leverage. Many others [2, 3, 11] analyzed the “lease-buy” decision using a variety of models and assumptions. None, however, considered the effect of asset leasing on the firm's capitalization rate. While asset leasing per se would not affect the firm's unlevered cost of capital, it should affect its estimation. This paper developed the adjustment factor to obtain the firm's corresponding unlevered cost of capital with leasing leverage. Basically, Modigliani and Miller's methodology [9] was adjusted for the different tax situation with asset leasing. The effective benefit of leasing on the firm's average cost of funds was shown to be not nearly as effective as an equivalent amount of ordinary debt.

Recent financial literature has discussed how a creditor should determine its investigation and extension policy. Mehta [8, 9] has developed a sequential process for credit extension, and others [1, 2, 4, 7, 10, 12, 14] have used credit-scoring functions to develop decisions rules. Instead of discussing the use of a particular system or the development of a new system, this paper shifts the focus to selection of the best of alternative systems. Different creditors face different profit-loss ratios on loans, business volume, and prior probabilities of good and bad customers. Furthermore, since the alternative systems have different initial costs, effectiveness, and investigation costs per application, no one system is optimal for all creditors. Finally, any credit-scoring alternative declines in effectiveness over time. Measurement of the overall effectiveness of a system requires that the optimal time between updating the system be known.

Michael S. Long, Illeen B. Malitz, Stephan E. Sefcik, An Empirical Examination of Dividend Policy Following Debt Issues, The Journal of Financial and Quantitative Analysis, Vol. 29, No. 1 (Mar., 1994), pp. 131-144