James H. Vander Weide

Entry auctions and strategic behavior under cross-market price constraints

Abstract We examine how universal service provisions and price restrictions across markets impact strategic entry and pricing. We develop a simple multi-market model with an oligopolistic (profitable) urban market and entry auctions for (unprofitable) rural service. Cross-market price restrictions induce a firm operating in both markets to become a ‘softer’ competitor, thus placing the firm at a strategic disadvantage. When we account for entry incentives and strategic bidding, the downstream strategic disadvantage becomes advantageous, leading to higher prices and profits. Price restrictions may also put outside firms, even relatively inefficient ones, at a strategic advantage.

A Monte Carlo Investigation of Characteristics of Optimal Geometric Mean Portfolios

We began this paper by posing four questions about the characteristics of optimal geometric mean portfolios. Tentative answers to these questions were obtained from an examination of the solutions to a relatively large number of geometric mean portfolio problems generated from a Monte Carlo simulation of ex-ante security return data. The results of this examination can be summarized as follows. First, the number of risky securities contained in an optimal geometric mean portfolio depends on ones expectations concerning future market conditions and on the conditions under which borrowing is permitted. If all capital must be invested, the investor who believes the market will fall should invest in just one security, he who believes the market will remain unchanged should diversify among two securities, while he who believes the market will rise should diversify among four to seven securities. In the event that some capital must be withheld from investment, the investor who believes the market will rise and who can borrow on reasonable terms will again diversify among four to seven securities, choosing the same securities in the same relative proportions as in the preceding situation. Should the investor be permitted neither to borrow nor to invest all his capital, his best portfolio consists of two to four securities, assuming he expects the market to rise.

Principles for Lifetime Portfolio Selection: Lessons from Portfolio Theory

Portfolio theory is concerned with developing general principles and practical models for making sound lifetime portfolio decisions. Much of the current research on portfolio theory emanates from the path-breaking mean–variance portfolio model of Nobel Laureate Harry Markowitz. Although the mean–variance model continues to be the most widely used portfolio model in financial practice, economists have devoted considerable effort to research on two additional models of portfolio behavior, the geometric mean model, and the lifetime consumption–investment model. These models are also useful to investors because they offer significant additional insights into optimal portfolio behavior. The purpose of this chapter is to review the major findings of the research literature on the mean–variance model, the geometric mean model, and the lifetime consumption–investment model, and, on the basis of this review, to develop a set of practical guidelines for making lifetime portfolio decisions.

A note on the optimal investment policy of the regulated firm

ConclusionsWe have demonstrated that, contrary to the spirit of much of the thinking based on the work of Averch and Johnson, it need not be true that rate-of-return regulation entices a firm to over-invest in productive capital. For a simple dynamic model featuring an imperfect capital market, we find that it may be optimal for the regulated firm to invest at every instant of time at a rate less than its unregulated counterpart. Our findings demonstrate once again the strong dependence on model specifications of the conclusions one draws in the study of regulatory problems.

A decision analysis approach to the computer lease-purchase decision

Abstract The lease-purchase decision is analyzed with the use of decision analysis. The formulation proposed captures both the stochastic and sequential nature of this decision, and should provide a substantial improvement over a simple present value approach.