Ian R. C. Eggleton, [Discussion of Patterns, Prototypes, and Predictions: An Exploratory Study]: A Reply, Journal of Accounting Research, Vol. 14, Studies on Human Information Processing in Accounting (1976), pp. 145-158

Prediction, Decision making, Sequential patters, Time-series

The rising cost of funds internationally is forcing multinational corporations to pay more attention to effective cash management on a global basis. However, the available literature is preoccupied with cash management in only one currency. This is a serious oversight given the heavy involvement of U.S. firms overseas. In 1970, for example, the ratio of foreign source earnings plus income from abroad (royalties, fees, service charges) to total U.S. corporate after-tax profits was over 25 percent [14]. If export and import activities were included, this statistic would be more impressive yet.

There is still a great deal of doubt whether we can avoid a capital shortage as economic recovery proceeds. In the near term, one sign of an impending capital shortage will be the appearance of bottlenecks in the industrial sector of our economy. Presently the data on capacity and its utilization are seriously defective.The Federal Reserve Board, in order to remedy the deficiency of the data, is improving its series on utilization rates. The new series in general will show that we have substantially less unused capacity than indicated by the old series.My preliminary reading of the improved data, ne ertheless, is that we need not be greatly worried about major bottlenecks well into 1977.Thereafter, the pace of recovery will be a critical factor. If the economy expands very rapidly, we may not have time to put in place enough capacity to avoid shortages. A moderately paced recovery will give us more time to produce the plant and equipment.

Journal Article A Theory of Securities Markets Under Uncertainty Get access Bruce C. Dieffenbach Bruce C. Dieffenbach University of Pennsylvania Search for other works by this author on: Oxford Academic Google Scholar The Review of Economic Studies, Volume 43, Issue 2, June 1976, Pages 317–327, https://doi.org/10.2307/2297327 Published: 01 June 1976

The validity of the classic Black-Scholes option pricing formula depends on the capability of investors to follow a dynamic portfolio strategy in the stock that replicates the payoff structure to the option. The critical assumption required for such a strategy to be feasible, is that the underlying stock return dynamics can be described by a stochastic process with a continuous sample path. In this paper, an option pricing formula is derived for the more-general case when the underlying stock returns are generated by a mixture of both continuous and jump processes. The derived formula has most of the attractive features of the original Black-Scholes formula in that it does not depend on investor preferences or knowledge of the expected return on the underlying stock. Moreover, the same analysis applied to the options can be extended to the pricing of corporate liabilities.

Journal Article Unbounded Utility Functions in Expected Utility Theory Get access Peter C. Fishburn Peter C. Fishburn Pennsylvania State University Search for other works by this author on: Oxford Academic Google Scholar The Quarterly Journal of Economics, Volume 90, Issue 1, February 1976, Pages 163–168, https://doi.org/10.2307/1886093 Published: 01 February 1976

An approximate solution, based on the method of dynamic programming, is provided for the optimal control of a system of nonlinear structural equations in econometrics with unknown parameters using a quadratic loss function. It generalizes the methods previously proposed by the author for the control of a nonlinear econometric model with constant parameters and of a linear econometric model with uncertain parameters. It is an improvement over the method of certainty equivalence which replaces the unknown parameters by their mathematical expectations and utilizes the solution for the resulting model. Since the solution is given in the form of feedback control equations, many of the useful concepts and techniques developed in the theory of optimal feedback control for linear systems are now applicable to the control of nonlinear systems using the method proposed, including the calculation of the expected loss of the system under control by analytical rather than Monte Carlo techniques. IN THIS PAPER, I present an approximate solution to the optimal control of a system of nonlinear structural equations using a quadratic welfare loss function when the parameters of the system are unknown. This is a generalization of ths solution given in Chapter 12 of Chow [2] for the control of nonlinear econometric systems with known parameters. It is also a generalization of the solution given in Chow [1] for the control of linear econometric systems with unknown parameters. The method of dynamic programming is applied to solve an optimal control problem involving a nonlinear econometric system with unknown parameters. As it turns out, the solution amounts to linearizing the nonlinear model about some nearly optimal control solution path and then applying a method for controlling the resulting linear model with uncertain parameters. This paper advances the state of the art in the control of nonlinear econometric systems as it improves upon the certainty-equivalence solution which is obtained by replacing the random parameters in a system by their mathematical expectations. It provides for a set of numerical feedback control equations based on a system of nonlinear structural equations in econometrics. It will show that many useful analytical concepts and tools developed in the theory of control of linear systems are indeed applicable to the control of nonlinear systems. Furthermore, in the derivation of an approximate solution using the method of dynamic programming, it will indicate precisely where the approximation takes place and why an exact solution is difficult to achieve. In Section 2, we set up the control problem and provide an exact solution to the optimal control problem for the last period. In Section 3, we give an approximate solution to the multiperiod control problem using dynamic programming. In Section 4, the mathematical expectations required in the solution of Section 3