Stock price forecasting has been in the center of interest of the stock market analysts and of the research community during the last four decades. A number of important studies have been con-ducted dealing primarily with the forecasting effectiveness of various models and methods; however, short-term stock price forecasting has not been sufficiently analyzed, thus crea-ting a need for comparative studies between discriminant standard and advanced techniques. The objectives and the scope of this paper emanates from the above remarks. An integrated computational forecasting system is developed, encompassing representa-tive techniques (multiple regression, exponential smoothing, neural net-works and Adaptive Network based Fuzzy Inference System -ANFIS), and a practically exhaustive comparative stu-dy of the performance and behavior of these techniques as well as the role of their core parameters in short-term stock price forecasting, is conducted. Interesting conclusions about the fore-casting abilities of the tested methodo-logies are drawn, while the impact of the models' parameters on the methods' behavior and the feasibility for accurate stock price predictions are evaluated.