This paper addresses the problem of clustering in large sets discussed in the context of financial time series. The goal is to divide stock market trading rules into several classes so that all the trading rules within the same class lead to similar trading decisions in the same stock market conditions. It is achieved using Kohonen self-organizing maps and the K-means algorithm. Several validity indices are used to validate and assess the clustering. Experiments were carried out on 350 stock market trading rules observed over a period of 1300 time instants.
This paper addresses the problem of stock market data prediction. It discusses the abilities of neural networks to learn and to forecast price quotations as well as proposes a neural approach to the future stock price prediction and detection of high increases or high decreases in stock prices. In order to validate the approach, a large number of experiments were performed on real-life data from the Warsaw Stock Exchange.