Neural Network
What is a Neural Network?
A neural network is a series of algorithms that endeavors 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.
Basics of Neural Networks
Neural networks, in the world of finance, assist in the
development of such process as time-series forecasting, algorithmic trading,
securities classification, credit risk modeling and constructing proprietary
indicators and price derivatives.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.In a
multi-layered perceptron (MLP), perceptrons are arranged in interconnected
layers. The input layer collects input patterns. The output layer has
classifications or output signals to which input patterns may map. For
instance, the patterns may comprise a list of quantities for technical
indicators about a security; potential outputs could be “buy,” “hold” or
“sell.”Hidden layers fine-tune the input weightings until the neural network’s
margin of error is minimal. It is hypothesized that hidden layers extrapolate
salient features in the input data that have predictive power regarding the
outputs. This describes feature extraction, which accomplishes a utility
similar to statistical techniques such as principal component analysis.
Application of Neural Networks
Neural networks are broadly used, with applications for
financial operations, enterprise planning, trading, business analytics and
product maintenance. Neural networks have also gained widespread adoption in
business applications such as forecasting and marketing research solutions,
fraud detection and risk assessment.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.There
will always be data sets and task classes that a better analyzed by using
previously developed algorithms. It is not so much the algorithm that matters;
it is the well-prepared input data on the targeted indicator that ultimately
determines the level of success of a neural network.
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