Risk Scenes of Managerial Decision-Making with Incomplete Information: An Assessment in Forecasting Models Based on Statistical and Neural Networks Approach

Abstract

The paper is concerned with measuring of risks in managerial decision-making. It builds upon the uncertainty of economic information, which is converted into the concept of risk expressed in terms of probability and using confidence intervals and standard deviations of the predicted quantities. The paper explains the relation of a degree of risk expressed by the classical information measure, bit, by the concept of confidence intervals, or possibly by the standard deviation. Forecasting models are applied which are based on a statistical theory and a neural approach. The aim is also to examine whether potentially highly non-linear neural network models outperforms the advanced statistical methods and better reduce risk in managerial decision-making, or they yield competitive results. A method for finding the forecasting horizon within which the risk is minimal is also presented.

Keywords

• Confidence interval; Entropy; Prediction models; Neural networks; ARIMA/ARCH models; Managerial decision; Risk assessment