Machine learning has proved to improve efficiency significantly, and there are many jobs which have been replaced by smarter and faster machines using artificial intelligence or machine learning. The stock markets are no exceptions to this. Today, there are several Machine Learning algorithms running in the live markets. These algorithms often provide greater returns than other alternate algorithms or sometimes even higher than experienced traders. In this article, I will talk about the concepts involved in a neural network and how it can be applied to predict stock prices in the live markets. Let us start by understanding what a neuron is.
This is the neuron that you must be familiar with, well if you aren’t you should now be grateful that you can understand this because there are billions of neurons in your brain. There are three components to a neuron, the dendrites, the axon and the main body of the neuron. The dendrites are the receivers of the signal and the axon is the transmitter. Alone, a neuron is not of much use, but when it is connected to other neurons, it does several complicated computations and helps operate the most complicated machine on our planet, the human body.
A computer neuron is built in a similar manner, as shown in the diagram. There are inputs to the neuron marked with yellow circles, and the neuron emits an output signal after some computation. The input layer resembles the dendrites of the neuron and the output signal is the axon. Each input signal is assigned a weight, wi. This weight is multiplied by the input value and the neuron stores the weighted sum of all the input variables. These weights are computed in the training phase of the neural network through concepts called gradient descent and back propagation, we will cover these topics in our subsequent blog posts on Neural Networks. An activation function is then applied to the weighted sum, which results in the output signal of the neuron. The input signals are generated by other neurons, i.e, the output of other neurons, and the network is built to make predictions/computations in this manner. This is the basic idea of a neural network. We will look at each of these concepts in more detail in this article.
Working of Neural Networks
We will look at an example to understand the working of neural networks. The input layer consists of the parameters that will help us arrive at an output value or make a prediction. Our brains essentially have five basic input parameters, which are our senses to touch, hear, see, smell and taste. The neurons in our brain create more complicated parameters such as emotions and feelings, from these basic input parameters. And our emotions and feelings, make us act or take decisions which is basically the output of the neural network of our brains. Therefore, there are two layers of computations in this case before making a decision.