Neural network as a forecasting tool for financial decision-making

For the last decade, machine learning techniques have been applied to financial tasks such as portfolio management, risk assessment and stock market prediction. Among these techniques artificial neural network as a machine learning algorithm is the most widely used model. In stock market environment, multi layer perceptron with backpropagation model is dominant among others in stock market prediction. This study examines the forecasting power of multi layer perceptron models for predictingthe direction of ISE 100 daily index value. The results show that multi layer perceptron has a promising power in predicting the stock market trend. However, it also shows that selection of input variables is dominant factor in stock market prediction to obtain accurate results.