The factors responsible for Latin American economic “dependency” have long been debated by economic historians. In this article, Professor Guy considers the example of Argentine industrialization between 1870 and 1940. Argentine reliance upon foreign capital, she concludes, was due much more to local Argentine institutions — the commercial law, the stock market, and the government inspection bureau — than to any pressures from abroad. She adds that, though dependency theory has its limits, by focusing attention on local institutions, it remains a valuable tool for understanding Third World development.

This paper develops a normative model to analyze the hedging and fee-pricing decisions of a financial institution supplying fixed rate loan commitments to its customers. In supplying fixed rate loan commitments, the financial institution (hereafter referred to as “bank”) is assumed to act as an agent that transforms commitment risk through the use of financial futures contracts. While most previous loan commitment models have analyzed the interest rate (or price) risk the bank faces in supplying fixed rate loan commitments, they either have ignored or assumed away the loan take-down (or quantity) risk.

In a recent study, Levy and Markowitz [15] demonstrate that, at least for some utility functions, expected utility can be approximated by a judiciously chosen function defined over mean and variance. In addition to resurrecting mean-variance analysis from the limbo into which it was placed by the criticisms of Borch [10] and others, the analysis by Levy and Markowitz yields a more direct approach to portfolio analysis than that provided by the current empirical literature. The current portfolio literature is concerned with notions of efficient sets and systematic risk rather than with utility functions and mean-variance. While much has been gained from a utility-free methodology, it is ultimately predicated upon a separation theorem and, hence, an environment with zero transactions costs. But security markets are not costless and the separation theorem may not hold. In that event, a utility-dependent approach to portfolio analysis could potentially lead to more powerful results especially if such an approach could be empirically implemented.

We have proposed two additions to the theory of leverage optimization by firms operating in a competitively structured industry.

First, whether the capital market is or is not subject to leverage-related imperfections, the force of entry will cause the long-run relationship between profits, π, and leverage, γ, to be given by the equation

(17) π(γ) = 0.

Even if it is possible in the short run for firms to add positive increments to profits by increasing γ beyond its current level, competitive pressures will erode these increments completely.

Second, if the capital market is subject to leverage-related imperfections, firms will choose smaller leverage ratios than they would in the absence of these imperfections. If, in addition, the industry is subject to the force of entry, optimal leverage ratios will be even smaller. Put another way, each firm's leverage ratio in the long run will reflect not just the impact of imperfections on capital costs, but also the impact of entry on product price, output, and operating profits.

In the world of practical finance, the management of working capital—cash, marketable securities, receivables, and inventories is, perhaps, the most pressing and most frequently encountered problem for financial managers. Yet, in the realm of theoretical finance, and even at the level of “textbook” finance, working capital is given minimal attention.

Financial analysts have been intrigued by bond ratings since John Moody first started publishing them in 1909. Bond ratings are assigned by three agencies (Moody's, Standard and Poor's (S&P), and Fitch); these ratings are widely publicized and are, therefore, critically important. A bond's rating affects investors' purchase decisions and, consequently, the issuing firm's cost of debt and, indirectly, its cost of equity.

Several studies (see [7], [15], [9], and [16]) have attempted to ascertain the empirical relationships that exist between the financing and investment decisions of firms. The primary impetus for these efforts was provided by Modigliani and Miller (MM) [20], [21] when they demonstrated that under the assumption of perfect capital markets, the optimal investment decisions of a firm are separable from its financing decisions. In the presence of market imperfections, interdependencies between these decisions that violate the basic MM propositions may exist. Therefore, the studies referred to above have essentially focused upon determining whether market imperfections have been of sufficient magnitude to lead to joint determination of investment and financing decisions. Though the original MM propositions were introduced more than two decades ago, the nature of the empirical relationship between investment and financing decisions remains a controversial issue. The fact that this matter is still unresolved is not attributable to a lack of attention, for the original propositions have been examined in the presence of taxes, bankruptcy costs, and agency costs with no clear consensus emerging.

In deciding among these estimators for a particular application, gains in efficiency must be weighed against additional computational effort. On these grounds, there would seem to be good reason in most circumstances for preferring the wrap-around overlap estimator to the simple unbiased one. The gain in efficiency is achieved at a small cost in additional computation, and, in addition, no data need be discarded if the sample size (T) does not happen to be an integral multiple of N. In going beyond the wrap-around estimator to the combinatorial one, however, the decision is less clear. The gain in efficiency is relatively small and the number of return relatives to be computed may become quite large. For example, with T=30 and N=10, the last parameter set for which computations are made in Table 1, evaluation of the combinatorial estimator requires computation of (= approximately 3×108) ten-period return relatives. In many applications, this additional effort is probably not worthwhile. Significantly, the ordinary overlap estimator seems to fare worst, probably due to its asymmetric use of the data, and, therefore, should not be used.

It also should be noted that when relatively large amounts of data are available, the simple unbiased estimator will in most cases perform quite adequately. A common application in financial research involves computing annual stock returns from monthly data. Representative relative efficiency computations using μ = 1.01, σ = .05, and N = 12 are given in panel B of Table 1 for T = 60, 120, and 360 months. The incremental improvement of the wrap-around estimator relative to the simple one is quite small. Figures for the combinatorial estimator are not presented because the number of permutations would be prohibitively large.