Artificial neural networks are sophisticated computer systems that mimic the working of the human brain. Often referred to as ?artificial intelligence?, neural networks can perform various functions of the human brain and have cognitive abilities?that is, the ability to learn, which most computer programs do not. Deep Blue, the famous IBM computer that played chess against Garry Kasparov, and Deep Insight, an expert system that can recognise profitable trading patterns in the stock market, are examples of neural network systems that can perform functions and also learn as they go along.
Neural networks can be trained to perform such functions as speech and handwriting recognition, credit card fraud protection, establishing credit risk limits, loan application processing, and analysis of market research data. Neural networks are also employed in monitoring power plant systems, automatic language translation, text-to-speech conversion, bomb detection, prediction of traffic accidents, medical diagnostics and aircraft radars. In fact, neural networks and Kalman filters are used wherever an unknown state of a dynamic system, which is obscured by randomness or noise, has to be estimated.
The application of artificial neural networks to trading the financial markets is an interesting practical use of this technology. Just as traders are able to recognise patterns of arbitrage opportunities based on their trading experience, artificial neural networks can be trained to identify profitable trading opportunities. This technology has a distinctive edge over traders in that they are able to completely isolate human emotions such as greed and fear from the decision-making process.
An experienced trader can easily identify whether a particular market is ?bid? or ?offered? by looking at a computer screen blinking with price updates. However, traders need coffee breaks to escape from the stress of such highly focused work. Artificial neural networks can perform the same task 24/7, and free of emotions, too. One of the commandments of trading is that, ?If you have a position, forget your emotion?. This is easy to remember, but in practice most traders develop emotional attachment to their positions or their models, even after the markets have proven them wrong.
The saying, ?The markets may not be smart, but they are always right?, is another principle taught to all traders. In the book, When Genius Failed: The Rise and Fall of Long Term Capital Management, there is an anecdote about Lawrence Hillibrand, who insisted that his trading models were right even after he had raked up trading losses of a billion dollars. If smart people had infinite capital, then they may eventually turn out to be always right. However, in its absence, trading can be a humbling experience even for the most experienced. Artificial neural networks can provide an alternative to the human trader and make trading easier, more efficient and more profitable.
The construction of artificial neural networks is very similar to that of the human brain. They have multiple processors consisting of simple processing elements (the artificial neurons) that are connected to one another. In the human brain, there are about 100 billion neurons connected to each other by synapses, an electrochemical contact. Each neuron can be connected with up to 200,000 other neurons, though typically each neuron connects with only 1,000 to 10,000 other neurons. Yet, even the most complex artificial neural network does not have so many artificial neurons connected with each other.
In their report titled, ?Artificial Neural Networks Technology? prepared for the Rome Laboratory, Anderson and McNeil articulate how artificial neural networks can reproduce many of the functions of the human brain by simulating the neural architecture in either computer hardware or software.
There are three layers in any artificial neural network?the input layer, the hidden layer and the output layer. The input later corresponds to the dendrites of human neurons, which accept inputs from other neurons. The hidden layer corresponds to the soma, which processes the inputs, and the output layer corresponds to the axon in human neurons, which convert the processed inputs into output. The number of nodes in the input and output layers are fixed while the hidden layer has a variable number of nodes.
In a trading system based on neural networks, the outputlayer would typically have three nodes?one each for ?buy?, ?sell? or ?hold? signals.
In the human brain, neurons are clustered together in 3-D space. Creating a similar cluster of artificial neural networks is considered an art form because there are numerous ways in which the artificial neurons can be put together. The clustering of human neurons appears to take place without any limitations or restrictions. However, integrated circuits and microprocessors do limit interconnection. Artificial neural networks, thus, are limited by the physical constraints of silicon chips.
(To be continued)
?Rajat Bhatia is founder & CEO, Neural Capital LLC, of Florida, USA. Email: rajat@neuraltrader.com