文章基本信息

标题：An Integrated Approach to Predictive Analytics for Stock Indexes and Commodity Trading Using Computational Intelligence
本地全文：下载
作者：Chandra J ; Nachamai.M ; Anitha S Pillai 等
期刊名称：The World of Computer Science and Information Technology Journal
印刷版ISSN：2221-0741
出版年度：2015
卷号：5
期号：8
页码：7
语种：English
出版社：WCSIT Publishing
摘要：Investment prediction is a method to decide the future values of stock indexes and commodity exchanges or trading of financial services. The aim of the model is to perform optimized prediction on commodities and stock market indexes. The investment prediction is an important task for an investor to maximize his or her return on investment. The purpose of the paper is to propose an optimized model using computational intelligence and it is a step by step method that follows an integrated approach which can solve several complex problems in predictive analytics. The integrated approach in this paper uses genetic algorithm, pearson’s correlation coefficient and multilayer perceptron adaline feed forward neural network to predict the next business day high values of stock indexes and commodities trading. As an integrated method, the model uses genetic algorithm as first step to check the data optimization, since the data is considered as an important element in data analytics. The optimized data is extracted using correlation coefficient and the classifier prediction is done with multilayer perceptron adaline feed forward neural network for making the prediction. The proposed model was implemented continuously on three months data to evaluate the performance and to check the accuracy on the NSEindia. The predicted values were checked against the next business day of original values, the predicted result is very close to the original values. The model is evaluated with the statistical parameter MRE, MMRE and the accuracy rate. In comparison with other existing methods, the current method outperforms other testing patterns. Keywords- Predictive Analytics (PA); Computational Intelligence (CI); Genetic Algorithm (GA); Pearson’s Correlation Coefficient(PCC); Multi Layer Perceptron (MLP); Adaptive Linear Element (ADALINE); Neural Network (NN).