Using Neural Networks To Forecast Stock Markets

7649 words, 31 pages

Intro Sample...


2 Motivation
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 stock market 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 track View More »

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