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Using Neural Networks to Forecast Stock Market Prices


A paper from the Department of Computer Science, University of Manitoba, is a survey on the application of neural networks in forecasting stock market prices. With their ability to discover patterns in nonlinear and chaotic systems, neural networks offer the ability to predict market directions more accurately than current techniques. Common market analysis techniques such as technical analysis, fundamental analysis, and regression are discussed and compared with neural network performance. Also, the Efficient Market Hypothesis (EMH) is presented and contrasted with chaos theory and neural networks. This paper refutes the EMH based on previous neural network work. Finally, future directions for applying neural networks to the financial markets are discussed.

From the beginning of time it has been man’s common goal to make his life easier. The prevailing notionin society is that wealth brings comfort and luxury, so it is not surprising that there has been so much workdone on ways to predict the markets. Various technical, fundamental, and statistical indicators have beenproposed and used with varying results. However, no one technique or combination of techniques has beensuccessful enough to consistently "beat the market". With the development of neural networks, researchersand investors are hoping that the market mysteries can be unraveled. This paper is a survey of currentmarket forecasting techniques with an emphasis on why they are insufficient and how neural networks havebeen used to improve upon them.The paper is organized as follows.

Section 2 provides the motivation for predicting stock market prices.

Section 3 covers current analytical and computer methods used to forecast stock market prices.

The majority of the work, in Section 4, details how neural networks have been designed to outperform current techniques.

Several example systems are discussed with a comparison of their performance with other techniques. The paper concludes with comments on possible future work in the area and some conclusions.

There are several motivations for trying to predict stock market prices. The most basic of these is financial gain. Any system that can consistently pick winners and losers in the dynamic market place would make the owner of the system very wealthy. Thus, many individuals including researchers, investment professionals,and average investors are continually looking for this superior system which will yield them high returns.

There is a second motivation in the research and financial communities. It has been proposed in the Efficient Market Hypothesis (EMH) that markets are efficient in that opportunities for profit are discovered so quickly that they cease to be opportunities. The EMH effectively states that no system can continually beat the market because if this system becomes public, everyone will use it, thus negating its potential gain. There has been an ongoing debate about the validity of the EMH, and some researchers attempted to use neural networks to validate their claims. There has been no consensus on the EMH’s validity, but many market observers tend to believe in its weaker forms, and thus are often unwilling to share proprietary investment systems.

Neural networks are used to predict stockmarket prices because they are able to learn nonlinear mappings between inputs and outputs. Contrary to the EMH, several researchers claim the stock market and other complex systems exhibit chaos. Chaos is a nonlinear deterministic process which only appears random because it can not be easily expressed. With the neural networks’ ability to learn nonlinear, chaotic systems, it may be possible to outperform traditional analysis and other computer-based methods.

In addition to stock market prediction, neural networks have been trained to perform a variety of financial related tasks. There are experimental and commercial systems used for tracking commodity markets and futures, foreign exchange trading, financial planning, company stability, and bankruptcy prediction.

Ramon Lawrence
December 12, 1997
Department of Computer Science
University of Manitoba
http://www.cs.uiowa.edu/~rlawrenc/research/Papers/nn.pdf


 

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