# Stock Prices Prediction Using Artificial Neural Networks

**Topics:**Stock market, Stock, Artificial neural network

**Pages:**20 (7213 words)

**Published:**October 27, 2009

Ajay Kamat

Flat 2, Jaysagar 2, Navy Colony

Liberty Garden, Malad west, Mumbai – 400064

+919833796261

ajay1185@gmail.com

ABSTRACT

The aim of this research paper is to facilitate prediction of the closing price of a particular stock for a given day. A thorough analysis of the existing models for stock market behavior and different techniques to predict stock prices was carried out. These included the renowned Efficient Market Hypothesis and its rival, the Chaos Theory. It was found that the Chaos Theory is the best model for modeling the behavior of a stock market. Chaos is a nonlinear process which appears to be random, i.e. there is an order-disorder relation between the various parameters affecting the process. Chaos theory is an attempt to show that order does exist in apparent randomness, and can be expressed mathematically. The problem domain required a model which could deal with uncertain, fuzzy, or insufficient data which fluctuate rapidly in very short periods of time. Hence an Artificial Intelligence approach was selected which could adapt to dynamic systems like the stock market. The model had to make systematic use of hints in the learning-from-examples approach. Artificial Neural Networks represent a general class of non-linear models that has been successfully applied to a variety of problems, with special emphasis on prediction of a time series. The ability of Neural Networks to effectively map non-linear relationships in input data proved to be a useful characteristic.

With this in mind, there was an attempt to study similar systems that have been practically and successfully implemented elsewhere, albeit on a much larger scale. These included the models developed for the Tokyo Stock Exchange and the Johannesburg Stock Exchange. The former adopted a clustering approach using Self-Organizing Maps based on the Kohonen Model, while the latter implemented the system using a Multi-Layer Feedforward Network using the Error-Backpropagation training algorithm. A Multi-Layer Perceptron was selected to implement the system and used the method of gradient descent to train the network.

The credibility of the Chaos Theory is proved if the Neural Network can outperform the market by consistently predicting stock prices. Thus two tasks was accomplished simultaneously; that of validating the Chaos theory and establishing a possibility to build sophisticated large-scale implementations that would prove to be cutting-edge for the investors in the long run.

1.INTRODUCTION

From the beginning of time it has been man’s common goal to make his life easier. The prevailing notion in society is that wealth brings comfort and luxury, so it is not surprising that there has been so much work done on ways to predict the markets. Various technical, fundamental, and statistical indicators have been proposed and used with varying results. However, no one technique or combination of techniques has been successful enough to consistently "beat the market". With the development of neural networks, researchers and investors are hoping that the market mysteries can be unraveled. Forecasting values of an asset gives, besides straightforward profit opportunities, indications to compute various interesting quantities such as the price of derivatives (complex financial products) or the probability for an adverse mode which is the essential information when assessing and managing the risk associated with a portfolio investment. Forecasting the price of a certain asset (stock index, foreign currency, etc.) on the ground of available historical data corresponds to the well known problem in science and engineering of time series prediction. While many time series may be approximated with a high degree of confidence, financial time series are found among the most difficult to be analyzed and predicted. 1.1Basics of the stock market

Stock is a share in the ownership of...

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