Neural Networks Pave a Way in Finance

This article was written by Megan Gomberg, a student at the University of Illinois.

When I think of neural networks, I typically begin to picture a stream of fast-moving neurons all around the brain. How these neurons interact with each other and the surrounding cells effect our everyday constant decisions and actions taken each day. The neural network system that relates to finance consists of a certain artificial intelligence sequence that attempts to mimic the human brain. 


What it is: 

Neural Networks are computing systems with interconnected nodes that work similar neurons in the brain. This system is developed using an algorithm and continuously recognizes hidden patterns in data in order to classify the data. The machine learning then continues to improve the data process much like the human brain. After the algorithm is trained, the end goal is the ability to create forecasts based on historical information

A Deeper look at Neural Networks: 

Artificial intelligence has different and unique abilities compared to the human mind. Companies all over the world are employing neural networkers to conquer difficult tasks with complex judgement of data patterns that may be solved quicker with machine learning. With no preconceived bias, the analysis is strictly based on data and is able to recognize patterns to draw conclusions based on the past information and forecast the future. Neural networks draw these conclusions by detecting subtle non-linear interdependenciesand patterns that other methods of analysis would not be able to discover. 

Neural networks are by no means a perfect prediction and a miracle worker; however, they can increase efficiency by up to 10%. Since this increase is only by 10% it is important that previous algorithms are used also. 

The most essential part of utilizing neural networks is governing the input layers. The input-weight products are summed and pass through the node where computation happens. In this process the input data is combined with a set of coefficients to amplify or reduce the input. Once the inputs are run through, they are summed and pass through the activation function to determine the extent of the signal. In this process, when a hidden layer is included, this is when the network becomes Deep Learning. 


Direction toward Finance: 

As neural networks developed, researchers routed their work toward more specific directions to complete different and diverse tasks. Some of these include computer vision, speech recognition, machine translation and medical diagnosis. 

Neural networks have developed a certain complexity to solve real-life issues, as I Know First predicts the stock market that is everchanging using multiple models tested over a 15-year historical period of time. The predictive ability of neural networks has assisted in the decision processes, efficiency and speed of the financial predictions in the markets across all industries and commodities. 

Neural Networks in Finance Today: 

As discussed earlier, health, science, manufacturing, bankingand government are a wide range of industries that benefit from the abilities of machine learning. Technology is changing the way we interact, travel, communicate and research is very different. Neural networks attribute to this change in productivity and opening of new doors and solutions in the finance industry. 

Given the unpredictable nature of the stock market, machine learning gives the best opportunity to mitigate this issue and reduce the margin of error between the predicted price and actual day to day price in the market. There is a wide range of problems that the neural networks are working to solve including business bankruptcy predictions, debt risk assessment, security market applications, mortgage, loans, options and commodities. In fact, in the past 10 years 25.4% of neural network applications associated with the financial sector. 

Specifically, neural networks allow I Know First to design a neural network forecasting model that predicts the stock market through Deep Learning as well as other techniques. Long short-term memories (LSTM) is one example of a model that has the ability to remember long term sequences compared to RNNs. Using the artificial neural networks (ANNs), they are able to identify patterns to increase market profits for companies of all sizes. There are six different time horizons that I Know First utilizes to predict and forecast outcomes, so the predictability success is increased as new data flows through and is recorded in the system. Once the systems learn the success and failure of the data, it is able to build and continue predicting the future results. 

I Know First has created a market prediction algorithm to calculate forecasts of the market based on past relationships and movements in the market. Since the algorithm uses artificial intelligence, it continuously builds and learns from past movements in the market. The system outputs a number that indicates whether to trade buy or hold the asset. The empirical model means that it is solely based on historical information, so no investor bias is involved. “Theories” are proposed, and the algorithm tests the theories on past data, and finally confirming it with present data. The input is rejected if it does not improve the model.