A neural network is a collection of algorithms that attempt to recognize underlying relationships in a set of data by simulating the workings of the human brain. A neural network in this context refers to a system of neurons, both organic and artificial in nature.
The neural network concept, which originated in artificial intelligence, quickly gained popularity in the development of trading systems because it is capable of adapting to changing inputs, allowing the network to produce the best results without the need to modify the output criteria.
Definition of Neural Network
In the financial sector, neural networks aid in processes such as algorithmic trading, security classification, credit risk modeling, time-series forecasting, and the development of price and volume indicators.
Neural networks, like the neural network of the human brain, have “neurons”, which are mathematical functions that collect and classify information according to their architecture. These networks are similar to statistical methods such as curve fitting and regression analysis.
A neural network consists of different layers of nodes. Similar to multiple linear regression, each node is called a perceptron. The perceptron feeds the signal from multiple linear regression into an activation function that may not be linear.
History of Neural Network
The concept of integrated machines that can think has been around for centuries, but in the last hundred years, there have been the biggest advances in neural networks. “A Logical Calculus of the Ideas Immanent in Nervous Activity” was published in 1943 by Warren McCulloch and Walter Pitts of the University of Illinois and the University of Chicago. This study looked at how the brain can create complex patterns and then simplify them into a binary logical structure with only true/false relationships.
In 1958, Frank Rosenblatt of Cornell Aeronautical Laboratory received credit for the creation of the perceptron. His research reinforced the work of McColloch and Pitt, and Rosenblatt used his work to show how computers could detect shadows and make inferences using neural networks.
After a research dry spell during the 1970s, largely due to lack of funding. Hopfield Net, a paper on neural networks, was first published by Jon Hopfield in 1982. Many researchers began to understand the potential of backpropagation for artificial neural networks as this idea resurfaced. Paul Werbos has been widely credited with his important contributions in his PhD thesis during this time.
More specialized neural network projects for direct purposes have recently been created. For example, Deep Blue, developed by IBM, helps people win at chess by improving the computer’s ability to complete complex calculations. This type of machine is not only known for defeating world chess champions, but is also used to produce new drugs, analyze financial market trends, and perform complex scientific calculations.
Multi-Layered Perceptron
The perceptron is organized in layers that are interconnected in a multilayered perceptron (MLP). Input patterns are collected by the input layer, and the output layer has classification signals or outputs that can be mapped by the input patterns. These patterns can be a list of quantities for technical indicators of securities, with potential outputs such as “buy”, “hold”, or “sell”.
The hidden layer reduces the error margin of the neural network by optimizing the input weights. It is hypothesized that the hidden layer extrapolates important features from the input data, which have predictive power associated with the output. This explains feature extraction, which accomplishes similar utility to statistical methods such as principal component analysis.
Types of Neural Networks
Feed-forward Artificial Neural Network
One of the simpler types of neural networks is the feed-forward neural network. The network’s input nodes transmit data in one direction, and until the output mode, this data continues to be processed in one direction. Feed-forward neural networks may have hidden layers to perform their functions, and this type of network is most commonly used in facial recognition technology.
Recurrent Artificial Neural Network
Recurrent neural networks, a more complex type of neural network, take the output of a processing node and then return it to the network. Theoretically, this results in network improvement and “learning”. Process history is stored at each node, and is reused during future processing.
This is especially important for networks whose predictions are wrong. The system will attempt to find out why the correct result occurred and adjust accordingly. Text-to-speech applications often use this type of neural network.
Convolutional Neural Network
Artificial neural networks, also known as ConvNets and CNNs, consist of various layers that are used to organize categories of data. There is an input layer, an output layer, and many convolutional layers hidden in between in these networks. These layers create a feature map that records areas of the image that are further broken down, resulting in a valuable output. These layers can be unified or entirely connected, and these networks are very beneficial for image recognition applications.
Deconvolutional Neural Network
Simply put, deconvolutional neural networks work just like convolutional neural networks. One of the purposes of implementing this network is to find components that are considered important in convolutional neural networks. These components will most likely be discarded during the execution process of the convolutional neural network. Image analysis and processing are two examples of applications of this type of neural network.
Modular Neural Network
A modular neural network consists of many networks that work separately. During the analysis process, these networks do not interact with each other. Instead, this process is done to allow for more efficient operation of complex and complex computational processes. The goal of network independence, similar to other modular industries, such as modular real estate, is for each module to be responsible for a specific part of the larger scheme.
Neural Network Implementation Method
Neural network applications are very common and are used for financial operations, business planning, trading, business analysis, and product maintenance. Businesses also use neural networks for forecasting solutions and marketing research, risk assessment, and fraud detection.
Neural networks use data analysis to find opportunities to make trading decisions. These networks have the ability to distinguish subtle nonlinear interdependencies of patterns that other analysis techniques cannot. Studies show that the accuracy of neural networks in predicting stock prices varies. Certain models predict the correct stock price fifty to sixty percent of the time, while other models are accurate seventy percent of the time. According to some, an investor can ask for a ten percent increase in the efficiency of a neural network.
Neural networks have the ability to process hundreds of thousands of bits of transaction information in an economic context. This can improve understanding of trading volumes, trading ranges, relationships between assets, or volatility expectations for a particular investment. Neural Networks can be created to spot trends, analyze results, and predict future movements in the value of asset classes because humans cannot efficiently pour over years of data, sometimes in just a few seconds.
Pros and Cons of Neural Network
Advantages of Neural Network
Neural networks can work continuously and more efficiently than humans or simpler analytical models. They can also be programmed to learn from previous outputs to determine future outcomes based on how previous inputs compare to previous outputs.
In addition, neural networks can often perform multiple tasks simultaneously (or, at least, distribute the tasks that modular networks must perform simultaneously), which is another benefit of neural networks that rely on cloud online services.
Finally, neural networks are constantly developing new applications. At first, neural networks theoretically could not be used in many fields, but they are now being used in fields such as medicine, science, finance, agriculture, and security.
Disadvantages of Neural Network
While neural network can rely on online platforms, there are still hardware components required to create a neural network. This creates a physical risk of the network relying on complex systems, setup requirements, and potential physical maintenance.
While the complexity of neural networks is a strength, it may mean it takes months (or even longer) to develop a particular algorithm for a specific task. In addition, it may be difficult to find errors or flaws 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 inputs are entered, the network performs complex processes, and outputs are reported. It may also be difficult for individuals to analyze weaknesses in the network’s calculations or learning process if the network does not have general transparency into how the model learned on previous activities.
