| Abstract [eng] |
SUMMARY This is a survey on the application of auto associative neural networks and principal component analysis in clustering stocks. Main principles of these two methods are presented, reviewing the current usage of AANN and PCA and future outlook. An experiment is being carried out by building two stock portfolios using PCA. The portfolios are being monitored within one year. The main goal of the survey is to estimate the abilities of application of auto-associative neural networks stock forecasting in the US stock market. In order to reach the goal, the following tasks have been set: • To analyze the probability of general market prediction; analyze fundamental and technical factors, select the most suitable ones for further investigation. • To consider different implementations of artificial neural networks, select the most suitable ones for stock market forecasting • To compare various stock forecasting software solutions based on neural networks or different intelligent systems. • According to the chosen methods and software, perform the historical stock data analysis, build investment portfolios. • To analyze the performance of portfolios on the time basis, compare the efficiency level of different methods applied. The US stock market has been selected as the most popular market with the highest efficiency of economical laws. A set of 8 fundamental keys has been selected for the further investigation. The PCA and the AANN have been selected to compare the efficiency of different intelligent systems application on stock market. PCA based portfolios were built using “PickStock” software. “Statistica” application and own created “autoassociative_ANN” script was used in order to separate respectively PCA and ANN based clusters of stocks. All portfolios created by “PickStock” were highly profitable, while stocks in the clusters built by “Statistica” were not diverging, and “autoassociative_ANN” was not able to extract any apparent cluster. This leads to conclusion, that AANN is still promising way to for stocks clustering and forecasting, however it requires further investigation based on technical factors. |
