Prediction of Stock Market Index ChangesSystems for inducing concept descriptions from examples are valuable tools for assisting in the task of knowledge acquisition for expert systems. In this research three machine learning techniques are applied to the problem of predicting the daily changes in the index of Istanbul Stock Market, given the price changes in other investment instruments such as foreign currencies and gold, also changes in the interest rates of government bonds and bank certificate of deposit accounts. The techniques used are Instance-Based Learning (IBL), Nested-Generalized Exemplars} (NGE), and Neural Networks (NN). These techniques are applied to the actual data comprising the values between January 1991 and July 1992. The most important characteristic of this data is the large amount of noise inherent in its domain. In this paper we compare these three learning techniques in terms of efficiency, ability to cope with noisy data, and human friendliness of the learned concepts.