GESTIÓN DE RECURSOS HUMANOS

Neural networks are loosely based on the human brain but are more sim- ilar to standard regression analysis than to neurons and synapses. Neural networks are much more powerful than regression analysis and can be programmed for many complex relationships and patterns that standard statistical methods can not. Their effectiveness in pattern recognition makes them ideal for developing trading systems.

Neural networks “learn” by using examples. They are given a set of input data and the correct answers for each case. Using these examples, a neural network will learn to develop a formula or model for solving a given problem.

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Let us now discuss how a simple neural network works. Artificial neural networks, like the human brain, are composed of neurons and synapses. Neurons are the processing elements of the brain, and synapses connect them. In our computer-simulated neural networks, neurons, also called nodes, are simply elements that add together input values multi- plied by the coefficients assigned to them. We call these coefficients weights. After we have added together these values, we take this total and apply a decision function. A decision function translates the total pro- duced by the node into a value used to solve the problem.

For example, a decision function could decide to buy when the sum is greater than 5 and sell when it is less than or equal to 5. Figure 11.1 shows a simple example of a neural network.

The rows of one or more nodes are called layers. The first row of nodes is the input layer, and the last row is the output layer. When only these simple neurons are connected, we call it a two-layer perceptron.

During the early Bernard two-layer perceptrons to solve many real-world problems-for example, short-range weather forecasts. even developed a weather forecasting neural network that was able to perform as well as the National Weather Service.

How do we get the value of the weights used to solve a problem? Be- fore a neural network starts learning how to solve a problem, each weight

R a w

Then we apply the decision function

FIGURE 11 A simple two-layer

An Overview of Advanced Technologies 151

is set to a random value. We call this process “initializing the weights.” Once we have initialized the weights, we can adjust them during the learning process.

A perceptron neural network learns by repeatedly producing an an- swer for each case, using the current value of its weights and compar- ing that value to the correct answer. It then adjusts the weights to try to better learn the complete set of data. We call this process “supervised learning.”

With simple two-layer perceptrons, the method or algorithm used to adjust these weights could not solve a very important type of problem.

In 1969, Minsky and in a book entitled proved that a simple perceptron with two layers could not solve “non-linearly separable problems” such as “Exclusive OR.” An example of an Exclusive OR problem is: You can go to the store or to see a movie, but you cannot do both.

This flaw in two-layer perceptron neural networks killed funding for neural network research until the mid-1980s. Many researchers still con- tinued working on neural networks, but, without funding, progress was slow.

In 1974, Dr. Paul Werbos developed a method for using a three-layer neural network to solve nonlinearly separable problems such as Exclusive OR. Rumelhart popularized a similar method and started the neural ex- plosion in the mid-1980s. This method, called “backpropagation,” is the most widely used neural network algorithm today.

Let’s see how this method differs from two-layer perceptrons. Figure 11.2 simple backpropagation neural network. The sec- ond row of nodes is called the hidden layer. The first and third layers are called the input layer (inputs) and the output layer (outputs), respectively. A backpropagation neural network will have one or more hidden layers. There are two major differences between a backpropagation neural net- work and a simple two-layer perceptron. The first difference is that the decision functions must now be more complex and nonlinear. The second difference is in how they learn. In general, a backpropagation neural

learns in the same way as the two-layer perceptron. The main dif- ference is that, because of the hidden layer(s), we must use advanced mathematics to calculate the weight adjustments during learning.

The classic backpropagation algorithm learns slowly and could take thousands through the data to learn a given problem. This is

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This diagram shows a simple neural network’s processing elements and connections.

FIGURE 11.2 A simple three-layer neural network.

why each neural network product has its own proprietary version of a backpropagation-like algorithm.

When you develop a solution to a problem using neural networks, you must preprocess your data before showing it to the neural network. Pre- processing is a method of applying to the data transforms that make the relationships more obvious to the neural network. An example of this pro- cess would be using the difference between historical prices and the mov- ing average over the past 30 days. The goal is to allow the neural network to easily see the relationships that a human expert would see when solv- ing the problem.

We will be discussing preprocessing and how to use neural networks as part of market timing systems in the next few chapters.

Let’s now discuss how you can start using neural networks success- fully. The first topic is the place for neural networks in developing mar- ket timing solutions. The second is the methodology required to use neural networks successfully in these applications.

Neural networks are not magic. They should be viewed as a tool for de- veloping a new class of powerful leading indicators that can integrate many different forms of analysis. Neural networks work best when used as part of a larger solution.

A neural network can be used to predict an indicator, such as a per- cent change, bars into the future; for example, the percent change of the 5 weeks into the future. The values predicted by the neural

network can be used just like any other indicator to build trading systems. Neural networks’ predictions don’t need to have high correlation with fu- ture price action; a correlation of or can produce huge returns.