Soft Computing in Finance

The stock market is very attractive due to high expected profit. On the other hand it is very risky. This creates a need for intelligent stock-trading systems that are intended to help the investors make realistic prediction for taking optimal decisions. Conventional approaches address Regression and Time Series Analysis methods for stock market prediction [5,14]. These methods do not give expected results in situations when the data are influenced by subjective factors such as psychological, macro-economical, or political issues. Also we cannot ignore those factors at all, because technical indexes only are not capable of proper description of a complicated real-world environment. An effective stock trading system must use both qualitative and quantitative factors.