CART decision tree

The especial importance of capital market in countries is undeniable in economic development via effective capital conduct and optimum resources allocation. Investment in capital market requires decision making in new stock exchanges, and accessing information in the case of future status of capital market. Undoubtedly, nowadays most part of capital is exchanged via stock exchange all around the world. National economies are extremely affected by the performance of stock market, high talent and unknown factors affecting stock market, and this causes unreliability in investment. It is clear that unreliable assets are inappropriate and in other side, for those investors who select stock market as a place to invest this asset is inevitable; thus, naturally all investors struggle to reduce unreliability. The present study compares four different models of predicting stock price, namely, Perceptron network, Fuzzy neural network, CART, Decision tree, and Support vector regression in Iran stock market during 2008 - 2012. Research sample includes 81 firms listed on the Tehran Stock Exchange (TSE). The findings compared in the case of five indicates show that for predicting stock price, using CART decision tree, has lower error than other ones. JEL Classifications: C10, C13, C18