64 research outputs found
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
Predicting bankruptcy using neural networks and other classification methods: The influence of variable selection techniques on model accuracy
The factors influencing the decision to list on Abu Dhabi securities exchange
The Abu Dhabi Securities Exchange is established to fund corporates, investments and economic growth. However, many companies operating in Abu Dhabi do not take the opportunity and list in the market. In this paper we survey a sample 145 chief executive officers and deputies of the CEO’s in order to explain why firms refrain from going public and float their equity in the market. Our findings indicate that the poor quality of the Abu Dhabi equity market in terms of its inefficiency and inadequate liquidity plays a crucial role in discouraging firms to list in the market. Moreover, management do not list in order to avoid dilution of ownership as well as to retain control of the company. Finally, we find that knowledgeable managers in big companies are more likely to list in the market particularly when they operate in a competitive industry
Study of Discrete Choice Models and Adaptive Neuro-Fuzzy Inference System in the Prediction of Economic Crisis Periods in USA
Neural Networks in Bankruptcy Prediction - A Comparative Study on the Basis of the First Hungarian Bankruptcy Model
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
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