Forecasting stock exchange using soft computing techniques

Abstract

The financial industry is becoming more and more dependent on advanced computer technologies in order to maintain competitiveness in a global economy. Fuzzy logic represents an exciting technology with a wide scope for potential applications. There is a growing interest both in the field of fuzzy logic computing and in the financial world in explaining the use of fuzzy logic to forecast the future changes in prices of stocks, exchange rates, commodities, and other financial time series. Fuzzy algorithms are intensively used for the identification of dynamic models, combining both numerical and heuristic knowledge. Fuzzy logic provides a remarkably simple way to draw definite conclusions from vague, ambiguous or imprecise information. In this paper, we are investigating the ability of Fuzzy logic (FL) to tackle the financial time series forecasting problems. Experimental results on set of applications indicated that fuzzy logic can effectively solve these types of problems. In order to examine the effectiveness of fuzzy logic applied to forecasting, the comparison with Artificial Neural Networks (ANNs) is performed.