Artificial Neural Network, Index Prediction
The project studies the predicting ability of an artificial neural network which employs the back propagate training algorithm. In the literature, the back propagate neural network has been widely applied to financial industry for solving different problems. This project focuses on Shanghai Composite Index movement using the BPN algorithm. The experiment finding shows that the BPN algorithm is able to capture the index future movement. Hence, in the future it can be used to develop trading rules based on CCFEA SETS database for contributing a heterogeneous decision making environment for CCFEA Artificial Stock Market.
Over the past few decades, artificial neural networks (ANN) has been widely applied in different industries, for examples, the applications of ANN in medicine, business, economics and finance. Especially when the financial industry becomes more and more dependent on advanced computer science technologies in order to maintain competitiveness in a global economic environment, neural network starts to represent an exciting and reliable technology in many financial applications, e.g., financial conditions analysis, business failure prediction, debt risk assessment, security market applications and financial forecasting. The successful applications of ANN are because the neural network has been motivated right from its inception by the recognition that human brain computers in an entirely different way from traditional digital computer and traditional mathematics computation. Human brain is a highly complex, nonlinear, and parallel information-processing system. The brain has the capability to organize its structural constituents, known as neurons which perform certain computations many times faster than digital computer in existence today. Using this important feature of neurons, researchers construct a paradigm which is composed of a large number of highly interconnected processing neurones network in order to solve some complex problems. In this project we explore ANN's ability with the BP algorithm to predict future stock index movement.
In this project applies the neural network with back propagation algorithm to study the prediction problem of stock market index movement. Although the simple three-layer neural network cannot output the in-sample market index prices precisely, it is able to capture the out-of-sample market index movement. To enhance the output precision of the neural network, increasing the layer numbers of the hidden layer in the network is a solution, and its extension is considered in the future work.