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 understanding neural networksneural networks in the world of finance assist in the development of such processes 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 known as 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 1history of neural networksthough the concept of integrated machines that can think has existed for centuries there have been the largest strides in neural networks in the past 100 years in 1943 warren mcculloch and walter pitts from the university of illinois and the university of chicago published a logical calculus of the ideas immanent in nervous activity the research analyzed how the brain could produce complex patterns and could be simplified down to a binary logic structure with only true false connections 2frank rosenblatt from the cornell aeronautical labratory was credited with the development of perceptron in 1958 his research introduced weights to mccolloch s and pitt s work and rosenblatt leveraged his work to demonstrate how a computer could use neural networks to detect imagines and make inferences 3even though there was a dry spell of research largely due to a dry spell in funding during the 1970 s paul werbos is often credited with the primary contribution during this time in his phd thesis 4 then jon hopfield presented hopfield net a paper on recurrent neural networks in 1982 in addition the concept of backpropagation resurfaced and many researchers began to understand its potential for neural nets 5most recently more specific neural network projects are being generated for direct purposes for example deep blue developed by ibm conquered the chess world by pushing the ability of computers to handle complex calculations 6 though publicly known for beating the world chess champion these types of machines are also leveraged to discover new medicine identify financial market trend analysis and perform massive scientific calculations recent analysis from the los alamos national library allows analysts to compare different neural networks the paper is considered an important part in moving towards characterizing the behavior of robust neural networks 7multi layered perceptronin 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 89types of neural networksfeed forward neural networks are one of the more simple types of neural networks it conveys information in one direction through input nodes this information continues to be processed in this single direction until it reaches the output mode feed forward neural networks may have hidden layers for functionality and this type of most often used for facial recognition technologies 1011a more complex type of neural network recurrent neural networks take the output of a processing node and transmit the information back into the network this results in theoretical learning and improvement of the network each node stores historical processes and these historical processes are reused in the future during processing this becomes especially critical for networks in which the prediction is incorrect the system will attempt to learn why the correct outcome occurred and adjust accordingly this type of neural network is often used in text to speech applications 12convolutional neural networks also called convnets or cnns have several layers in which data is sorted into categories these networks have an input layer an output layer and a hidden multitude of convolutional layers in between the layers create feature maps that record areas of an image that are broken down further until they generate valuable outputs these layers can be pooled or entirely connected and these networks are especially beneficial for image recognition applications 13deconvolutional neural networks simply work in reverse of convolutional neural networks the application of the network is to detect items that might have been recognized as important under a convolutional neural network these items would likely have been discarded during the convolutional neural network execution process this type of neural network is also widely used for image analysis or processing 14modular neural networks contain several networks that work independently from one another these networks do not interact with each other during an analysis process instead these processes are done to allow complex elaborate computing processes to be done more efficiently similar to other modular industries such as modular real estate the goal of the network independence is to have each module responsible for a particular part of an overall bigger picture 15application of neural networksneural 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 of the time still others have posited that a 10 improvement in efficiency is all an investor can ask for from a neural network 16specific to finance neural networks can process hundreds of thousands of bits of transaction data this can translate to a better understanding of trading volume trading range correlation between assets or setting volatility expectations for certain investments as a human may not be able to efficiently pour through years of data sometimes collected down second intervals neural networks can be designed to spot trends analyze outcomes and predict future asset class value movements 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 advantages and disadvantages of neural networksneutral networks that can work continuously and are more efficient than humans or simpler analytical models neural networks can also be programmed to learn from prior outputs to determine future outcomes based on the similarity to prior inputs neural networks that leverage cloud of online services also have the benefit of risk mitigation compared to systems that rely on local technology hardware in addition neural networks can often perform multiple tasks simultaneously or at least distribute tasks to be performed by modular networks at the same time last neural networks are continually being expanded into new applications while early theoretical neural networks were very limited to its applicability into different fields neural networks today are leveraged in medicine science finance agriculture or security though neutral networks may rely on online platforms there is still a hardware component that is required to create the neural network this creates a physical risk of the network that relies on complex systems set up requirements and potential physical maintenance though the complexity of neural networks is a strength this may mean it takes months if not longer to develop a specific algorithm for a specific task in addition it may be difficult to spot any errors or deficiencies in the process especially if the results are estimates or theoretical ranges neural networks may also be difficult to audit some neural network processes may feel like a black box where input is entered networks perform complicated processes and output is reported it may also be difficult for individuals to analyze weaknesses within the calculation or learning process of the network if the network lacks general transparency on how a model learns upon prior activity can often work more efficiently and for longer than humanscan be programmed to learn from prior outcomes to strive to make smarter future calculationsoften leverage online services that reduce but do not eliminate systematic risk