Predicting financial markets and trends is an important issue, either on the microeconomic level for enterprise managers, investors, creditors and supervisors, or on the macroeconomic level. Complexity of financial decisions has increased rapidly, thus highlighting the importance of developing and implementing sophisticated and efficient quantitative analysis techniques for supporting and aiding financial decision making. Rough Set Theory RST is an emerging Automatic Target Recognition (ATR) methodology for determining features and then classifiers from a training data set. RST guarantees that once the training data has been labeled all possible classifiers (based on that labeling) will be generated. Different investors groups present different investment behavior and of course their trades may relate to asset prices in different manners. This study examines whether some investors groups are associated with concurrent or subsequent market-wide price movements through the market trend using rough set theory as one of an artificial intelligence techniques, and comparing the result with that of applying econometric analysis. The experimental results of this study prove on the following (1) the ability of the rough set approach to discover facts hidden in data which represent the market trend. (2) The efficiency of the suggested classifying method to manage the discretization reaching to the general market trend. (3) Using the generalization to interpret the extracting trading rules which represent the market trend to proof with self evident on general known trading rules such as in case of selling the price is downward trend in the market and vice versa.