The advantage of ANNs is that they are flexible. They will, if correctly trained, learn to classify any pattern in the training library correctly: if they are given a pattern on which they have been previously trained, they will produce the correct output. However, they will also produce the correct output if they are given a pattern similar to one they have seen during training. They will therefore classify patterns that they have never seen before, based on the closest matching training pattern.
Whereas conventional computer programs are written specifically and involve specific rules given by the human programmer, ANNs are trained by example. They learn to associate patterns on their inputs with corresponding patterns on their outputs. Training the neural net involves giving it a sample from the library of known input patterns and the corresponding desired output. Then the neural network is told to adapt its connections. This is repeated with other input-output pattern pairs. Typically, the set of input-output pairs used for training the net has to be presented repeatedly many times, and so neural networks can often take several hours to train. Once the neural network has been trained, you can present an input pattern to it and it will produce the corresponding output pattern.
How do neural networks work?
Whereas conventional computer programs are written specifically and involve specific rules given by the human programmer, ANNs are trained by example. They learn to associate patterns on their inputs with corresponding patterns on their outputs. Training the neural net involves giving it a sample from the library of known input patterns and the corresponding desired output. Then the neural network is told to adapt its connections. This is repeated with other input-output pattern pairs. Typically, the set of input-output pairs used for training the net has to be presented repeatedly many times, and so neural networks can often take several hours to train. Once the neural network has been trained, you can present an input pattern to it and it will produce the corresponding output pattern.
Neural Network Architectures
An ANN consists of a number of neurons simulated in a computer with connections between them. Each of these connections has a certain strength, or “weight”, indicating how strong the connection is. A connection with a large weight is efficient at passing on a signal from one neuron to another. The information and expertise that ANNs possess is coded in these strengths between neurons.
Simulated neurons can be connected in various patterns, termed “architectures”. Although complex architectures exist (for instance in Cell Assemblies, which model closely structures in the human brain), the simplest architecture, the Multi-Layer Perceptron, is probably the most common. An MLP consists of neurons in distinct layers, each of which is connected to the next layer along. Signals can pass only one way through the MLP, from the inputs of the first layer to the outputs of the last one.
What are Neural Networks?
Artificial Neural Networks (ANN) are simulations on a computer of groups of brain cells (neurons) configured in such a way that they perform a useful task. They are inspired by real neurons, which are simple switching elements – they gather electrochemical energy from their inputs, then either pass it on or block it. This means that individual neurons have little more intelligence than a light switch, and yet, but putting countless trillions of these cells together and setting up the correct connections between them, we get human intelligence and even (in a process that scientists haven’t even begun to understand) consciousness!
Technical Analysis does have its critics – those economists who say it is nothing more than a pseudoscience, one that has no basis in fact. Investors such as Warren Buffett and Peter Lynch say that there is precious little evidence that Technical Analysis works. However, various academic studies have shown that technical analysis, particularly when carried out by neural networks, can produce statistically significant returns, and many investors have made fortunes through the application of its principles. It is therefore unfair to dismiss it as mere nonsense, and one ignores it at one’s peril!
One of the tenets of Technical Analysis is that prices follow trends, either upwards, downwards or sideways (when the price is generally holding steady – a “flat” price). If investors spot an upward trend in a share, they may well try to jump on the band-wagon and buy that share, hoping to benefit from its rise, and thereby driving its price up further. Similarly, a downward trend may cause investors to dump shares on the market, causing a further fall in the price. This can cause the price to “zig-zag” – rise and fall several times in line with investor confidence.
Technical analysis is often compared to Fundamental Analysis, which also tries to predict share price but on the basis of company assets, the general health of the company and other factors such as the amount of competition in the market that a company faces. Technical analysis, on the other hand, is concerned only with the behaviour of the market: The market’s opinion of a particular company is more important than that company’s underlying worth. The theory is that all the information about the company that you need to predict its share price is already encoded in that price in some way.
Technical analysis covers two areas of the stock market, its sentiment (sometimes termed its “psych”) and the analysis of supply and demand. The sentiment of the market is the general feeling of investors. This is rather intangible, but does show itself in overall share movements. For instance, if investors are feeling generally optimistic (as in a bull market), share prices will tend to rise. The supply and demand aspect of the stock market is a measure of how much money there is available to invest in shares: If an investor has little spare money, for example, this restricts his or her ability to invest in shares and drive the market higher.
Any stock market produces vast amounts of data on a daily basis, far too much for anyone to cope with in its raw form. You only have to look at the share price pages in the Financial Times to get some idea of the quantities of figures that make up just one day’s trading. To make any sense of this deluge, we must apply various statistical measures that turn the raw data into meaningful information. There are several measures in common use, and different technical analysts tend to prefer different measures. These measures all have one thing in common – they reduce the amount of noise present on the data and help to reveal any underlying pattern.