This paper deals with the application of a well-known neural network technique, multi-layer back-propagation (BP) neural network, in financial data mining. A modified neural network forecasting model is presented, and an intelligent mining system is developed. The system can forecast the buying and selling signs according to the prediction of future trends to stock market, and provide decision-...

Fuzzy logic has been widely used in machinery, robotics, and industrial engineering. This chapter introduces the use of fuzzy logic for the needs of financial management. The process of globalization has led to the emergence of a complex network of relationships in the business environment. In a free market economy, this means increased complexity and uncertainty of factors affecting the financ...

This paper focuses on the optimistic and confirmation biases of experts with respect to major IT investments and their interaction with financial analysts' competencies in finance and information technology. We used an experimental design that involved asking subjects to predict the financial market's reaction to major IT investment announcements. Drawing on the literature on optimistic biases,...

The Sierpinski triangle is a fractal that is commonly used due to some of its characteristics and features. The Forex financial market is among the places wherein this trianglechr('39')s characteristics are effective in forecasting the prices and their direction changes for the selection of the proper trading strategy and risk reduction. This study presents a novel approach to the Sierpinski tr...

the purpose of resent research is to analysis and compares performance evaluation models of selected investment companies in tehran stock exchange market in the field of their portfolio management. the duration of research was between years 2009-2014. statistical society the research is consisting of all active investment companies in in tehran stock exchange market which were 30 companies. vol...

This research examines and analyses the use of Neural Network Regression (NNR) models in foreign exchange (FX) forecasting and trading models. The NNR models are benchmarked against traditional forecasting techniques to ascertain their potential added value as a forecasting and quantitative trading tool. In addition to evaluating the various models using traditional forecasting accuracy measure...

AI has long been applied to the problem of predicting financial markets. While AI researchers see financial forecasting as a fascinating challenge, predicting markets has powerful implications for financial economics – in particular the study of market efficiency. Recently economists have turned to AI for tools, using genetic algorithms to build trading strategies, and exploring the returns tho...