Comparisons of Stock Rates Prediction Accuracy Using Different Technical Indicators with Backpropagation Neural Network and Genetic Algorithm Based Backpropagation Neural Network

A challenging and daunting task is to find out which is more effective and accurate method for stock rate prediction so that a buy or sell signal can be generated for given stocks. This paper presents a number of technical indicators, back propagation neural network and genetic based backpropagation neural network to predict the stock price of the day. Stock rate prediction accuracy of different technical indicators, backpropagation neural network and genetic algorithm based backpropagation neural network has been compared. The results showed that the genetic algorithm based backpropagation neural network predict stock price more accurately as compared to other techniques.

1. Introduction

Prediction of financial markets has long been an attraction in the minds of equity investors. Technical Analysis [1] provides a framework for studying investor behavior, and generally focuses only on price and volume data. Technical Analysis using this approach has short-term investment horizons, and access to only price and exchange data. With the advent of powerful computers much attention has been focused on this field. Equity market prices depend on many influences. Key factors that influence future equity prices can be broadly divided into quantitative and qualitative types. Primary quantitative factors include open rate, high rate, low rate, close rate and volume for individual equities. Qualitative factors include socio-economic, political, international, regional and performance factors to name but a few. The aim of this paper is to compare different popular technical indicators, backpropagation neural network, genetic algorithm based back propagation neural network to find technique that can predict stock price more accurately as compared to other techniques. Preliminary research performed on Indian National Stock Exchange market has suggested that the inputs to the system may be taken as: previous day's closing rate for moving average and MACD, previous day's closing rate and volume of last trading day for backpropagation neural network and genetic algorithm based backpropagation neural network. After the inputs have been determined, the data have been gathered of Tata Power stock for the period of 01-Jan-2004 to 29-Dec-2006 for training backpropagation neural network and genetic algorithm based backpropagation neural network. For testing purpose we have used testing data of Tatapower Stock for the period of 02-Jan-2007 to 30-Mar-2007. Training and testing is performed using two network architectures. 1).

One Hidden Layer BPN Network

2).

One Hidden Layer GA-BPN Network