A neural network is a series of algorithms that endeavours to recognize underlying relationships in a set of data through a process that mimics the way the human brain operates. In this sense, neural networks refer to systems of neurons, either organic or artificial in nature. Neural networks can adapt to changing input; so the network generates the best possible result without needing to redesign the output criteria. The concept of neural networks, which has its roots in artificial intelligence, is swiftly gaining popularity in the development of trading systems.

A neural network works similarly to the human brain’s neural network. A “neuron” in a neural network is a mathematical function that collects and classifies information according to a specific architecture. The network bears a strong resemblance to 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 into an activation function that may be nonlinear.

A neural network evaluates price data and unearths opportunities for making trade decisions based on the data analysis. The networks can distinguish subtle nonlinear interdependencies and patterns other methods of technical analysis cannot. According to research, the accuracy of neural networks in making price predictions for stocks differs. Some models predict the correct stock prices 50 to 60 percent of the time while others are accurate in 70 percent of all instances. Some have posited that a 10 percent improvement in efficiency is all an investor can ask for from a neural network.

In a development that could scupper the plot of numerous sci-fi movies about an artificial intelligence apocalypse, scientists have created a neural network that is smart enough to know when it shouldn’t be trusted. This self-awareness of trustworthiness feature has been dubbed “deep evidential regression,” and it bases its confidence level on the quality of the available data it has to work with. The scientists tested their network by training it to judge depths in different parts of an image, similar to how a self-driving car might calculate proximity to a pedestrian or another vehicle.

Regards
Sarah Rose
Managing Editor
International Journal of Swarm Intelligence and Evolutionary Computation