Decision support system for investing in stock market by using OAA-Neural Network

Abstract

In stock market, successful investors can earn maximum profits depended on a stock selection and a suitable time on trading. Generally, investors use two statistical techniques for making a decision, which are the fundamental analysis and the technical analysis. Recently, machine learning models which are a part of artificial intelligence, has been applied to enhance investors for investment. A number of machine learning models have been investigated for stock prediction such as Genetic Algorithms (GAs), Support Vector Machines (SVMs) and Neural Network (NN). In this paper, several multiclass classification techniques using neural networks are investigated. The multi-binary classification experiments using One-Against-One (OAO) and One-Against-All (OAA) techniques are tested and they are compared with the traditional neural network. Furthermore, an alternative data preparation and a data selection process are proposed. The experimental results show that the multi-binary classification using OAA technique outperforms other techniques. It can provide the return on investment greater than the traditional analysis techniques.