Neural Network

What is a neural network?

A neural network is a series of algorithms that try to recognize the underlying relationships in a data set through a process that mimics the functioning of the human brain. In this sense, neural networks refer to neural systems, of an organic or artificial nature. Neural networks can adapt to changes in inputs; so that the network generates the best possible result without having to rethink the exit criteria. The concept of neural networks, which has its roots in artificial intelligence, is rapidly gaining popularity in the development of commercial systems.

Basics of neural networks

Neural networks in the financial world help develop processes such as forecasting time series, algorithmic trading, securities classification, credit risk modeling and building proprietary and derivative indicators of price.

A neural network works in a similar way to the neural network in the human brain. A “neuron” in a neural network is a mathematical function that collects and classifies information according to a specific architecture. The network strongly resembles statistical methods such as curve fitting and regression analysis.

A neural network contains layers of interconnected nodes. Each node is a perceptron and is similar to a multiple linear regression. The perceptron feeds the signal produced by a multiple linear regression in an activation function which can be nonlinear.

In a multilayer perceptron (MLP), the perceptrons are arranged in interconnected layers. The input layer collects the input models. The output layer has classifications or output signals to which input patterns can correspond. For example, models can include a list of quantities of technical indicators for a security; the potential outputs could be “buy”, “keep” or “sell”.

The hidden layers refine the input weights until the margin of error of the neural network is minimal. It is assumed that the hidden layers extrapolate the salient features in the input data which have a predictive power concerning the outputs. This describes feature extraction, which accomplishes a utility similar to statistical techniques such as the analysis of main components.

Key points to remember

  • Neural networks are a series of algorithms that mimic the operations of a human brain to recognize relationships between large amounts of data.
  • They are used in a variety of applications in financial services, from forecasting and marketing research to fraud detection and risk assessment.
  • The use of neural networks for forecasting stock prices varies.

Application of neural networks

Neural networks are widely used, with applications for financial operations, business planning, trading, business analysis and product maintenance. Neural networks have also been widely adopted in commercial applications such as forecasting and marketing research solutions, fraud detection and risk assessment.

A neural network evaluates price data and uncovers opportunities to make business decisions based on the analysis of the data. Networks can distinguish subtle non-linear interdependencies and patterns that other methods of technical analysis cannot. According to research, the accuracy of neural networks in forecasting share prices differs. Some models predict correct stock prices 50-60% of the time while others are accurate in 70% of the time. Some have argued that a 10% improvement in efficiency is all that an investor can ask for from a neural network.

There will always be datasets and task classes that will be better analyzed using previously developed algorithms. It’s not so much the algorithm that counts; it is the well-prepared input data on the target indicator that ultimately determines the level of success of a neural network.

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