Selected Methods of the Prediction of PX Index Trend Reversal

The paper is concerned with the use of several methods that can be useful from the point of view of trend reversal in financial time series. These methods are demonstrated on PX index time series during 2002-2009. The research itself is subdivided into four parts corresponding to individual analytical methods used. The first group contains the use of moving EGARCH(1,1) model to daily relative returns of PX index. The results obtained are indicative of the importance of negative parameter values, which can be considered as precursors of the trend reversal. The second group contains different moving characteristics that are able to signalize regime changes in certain time intervals. Particularly, the information related to intraday price variations proved to be useful. Third, selected price indicators from technical analysis were employed. Among them, Simple Moving Averages, Bollinger Bands, Relative Strength Index and Stochastic led to acceptable predictions. Last, the predictive ability of Artificial Neural Networks was tested with respect to different network structure and number of delayed values of explanatory variable. The results obtained here are promising, but further research in this direction is necessary.