Using 4GL Tools in Project-Oriented Courses

The widespread availability of fourth generation languages (4GL\u27s) and CASE (computer-aided software engineering) tools has presented students with the opportunity to design and implement real-world systems as class projects within the span of a one-semester course. The use of these tools during the systems development life cycle is discussed based on experiences with a microcomputer-based system which was developed by students in an upper division MIS class. The methodology used for directing student projects and some particular problems faced when using 4GL\u27s as development tools are addressed

A Strategy for Integrating Artificial Intelligence Technology into a Graduate Business Curriculum

There is much evidence that artificial intelligence technology is beginning to emerge from the research lab and move into business computer-based systems. Applications of artificial intelligence in business in the areas of finance, manufacturing, and software development and data management, are increasing. Since graduate programs in business attempt to provide students with background in, and experience with computer-based modeling, it is important that universities anticipate and plan for the integration of artificial intelligence technology into the Master\u27s degree in Business Administration (MBA) program. The purpose of this paper is two-fold. First, a framework for integrating artificial intelligence applications and methodology into the curriculum of a graduate business program is presented. Second, an implementation strategy is discussed and detailed examples are given

A Neural Network Model for Estimating Option Prices

A neural network model that processes financial input data is developed to estimate the market price of options at closing. The network\u27s ability to estimate closing prices is compared to the Black-Scholes model, the most widely used model for the pricing of options. Comparisons reveal that the mean squared error for the neural network is less than that of the Black-Scholes model in about half of the cases examined. The differences and similarities in the two modeling approaches are discussed. The neural network, which uses the same financial data as the Black-Scholes model, requires no distribution assumptions and learns the relationships between the financial input data and the option price from the historical data. The option-valuation equilibrium model of Black-Scholes determines option prices under the assumptions that prices follow a continuous time path and that the instantaneous volatility is nonstochastic

Do-Ahead Replaces Run-Time: A Neural Network Forecasts Options Volatility

In this paper, we compare three methods of estimating the volatility of daily SBP 100 Index for options. The implied volatility, calculated via the Black-Scholes model, is currently the most popular method of estimating volatility and is used by traders in the pricing of options. Historical volatility has been used to predict the implied volatility, but the estimates are poor predictors. A neural network for predicting volatility is shown to be far superior to the historical method

Economic Planning and Uncertainty in Renewable Resources

This work is intended to be a first step in analyzing optimal harvesting policies under the assumption of uncertainty about the time of extinction. In general, we find the expected result that a higher hazard rate results in more vigorous harvesting. When we allow the hazard rate to depend only on time, it is shown that changing beliefs about the survival rates of the resource may account for the nonmonotonic behavior which is often observed

Beating the Best: A Neural Network Challenges the Black-Scholes Formula

A neural network model which processes financial input data is presented to estimate the market price of options. The network\u27s ability to estimate option prices is compared to estimates generated by the Black-Scholes model, a traditional financial model. Comparisons reveal that the neural network outperforms the Black-Scholes model in about half of the cases examined. While the two modeling approaches differ fundamentally in their methodology for determining option prices, some common results emerge. While the neural network performs better than Black-Scholes on prices out-of-the-money, estimations near the expiration data are accurate for both

An Empirical Study of the Use of Business Expert Systems

The evolution of computers from computational tools to “thinking machines” is causing businesses to evaluate their views of the computer\u27s role. The inevitable availability of smart computers leads to questions of how and when fifth generation hardware and software will be integrated into corporate culture. Here, we present the results of a survey given to information systems managers to determine the extent of expert systems development by data processing departments and expert systems usage in organizations. The attitudes of management toward the future of expert systems are also discussed using the survey data. It was discovered that, while computer managers are receptive toward this new tool, most have no definite plans to develop expert systems in the near future. These results seem to be in conflict with other evidence about the growing numbers of expert systems in business applications. One explanation is that this new technology is part of the continuing “grass roots” movement of end-user computing