Tracing of stock market long term trend by information efficiency measures

The goal of the article is to research financial market efficiency and to recognize major reversal points of long-term trend of stock market index, which could indicate forthcoming crisis or market raise periods. We suggest computational model of financial time series analysis, which combines several approaches of soft computing, including information efficiency evaluation methods (Shannon's entropy, Hurst exponent), neural networks and sensitivity analysis. The model aims to derive the aggregated measure for evaluating efficiency of the financial market and to find its interrelationships with the reversal of long-term trend. The radial basis function neural network was designed for forecasting moments of cardinal changes in stock market behavior, expressed by its entropy values derived from the symbolized time series of stock market index. The performance of neural network model is explored by applying sensitivity analysis and resulted in selecting smoothing parameters of the input variables. The experimental research investigates behavior of the long-term trend of the three emerging financial markets within Nasdaq OMX Baltic stock exchange. Introduction of information efficiency measures improves ability of the model to recognize the approaching reversal of long-term trend from temporary market “nervousness” and can be useful for calibrating stock trading strategy.