V.11:9 (355-359): Neural Network Development For Financial Forecasting by Lou Mendelsohn

V.11:9 (355-359): Neural Network Development For Financial Forecasting by Lou Mendelsohn
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Neural Network Development For Financial Forecasting by Lou Mendelsohn

Extensive research has been conducted about the application of neural networks to financial forecasting in today's globalized trading environment. What makes this particular use of artificial intelligence so attractive to financial analysts and traders? Here, Lou Mendelsohn of Mendelsohn Enterprises highlights some of those issues and establishes goals for training neural networks.

With the advancements being made in computer and telecommunication technologies today, the world's major economies and financial markets are becoming more and more globalized. AS this trend accelerates, financial markets are becoming more and more interrelated and fundamental factors will become increasingly critical to financial market analysis. In the global marketplace, the prevailing method of technical analysis in which a single market is modeled through historical simulation and backtesting of its own past price behavior is rapidly losing its competitive advantage as institutions and individual traders both are increasingly applying artificial intelligence (AI) technologies to financial forecasting. Recent research shows that this nonlinear domain can be modeled more accurately with these technologies than with the linear statistical and single-market methods that have been the mainstay of technical analysis throughout the past decade.

It is because of these factors that the field of Al merits a closer look. The result of these new demands is the emergence of a new analytical method that merges technical and fundamental analysis with the more recent emphasis on intermarket analysis. This combined analytical method is known as synergistic market analysis, or synergistic analysis. This new method of analysis, utilizing artificial intelligence tools, synthesizes technical, intermarket and fundamental data within an analytical framework, resulting in better forecasting capabilities and earlier identification of trend changes and allowing traders to profit from market inefficiencies in the global markets of the l990s.

Such tools as neural networks, expert and knowledge-based systems, machine learning, fuzzy logic, wavelets, chaos theory and genetic algorithms are being applied across industries. In the same vein, neural networks may be applied to financial forecasting because neural nets have been shown to be technologically powerful and flexible, ideally suited to performing synergistic analysis.


Artificial neural networks are models based on the workings of the human brain, utilizing a distributed processing approach to computation. Neural nets are capable of solving a wide range of problems by "learning" a mathematical model for the problem; the model can then be used to map input data to output data. Anything that can be represented as a number can be fed into a neural network. Technical indicators and fundamental and price data related to a single target market, as well as intermarket data affecting the target market, can all be fed into a single neural net and used to predict price and trend directions for the target market.

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