Neural networks, also known as artificial neural networks (ANNs) are systems and processes which mimic the networks of the human brain.
Artificial neural networks form the base of deep learning, and similar to how the neural networks in the brain can be trained, the forecasting models in financial neural networks are used to “train” the algorithms so that they can take into account previous data and historical statistics to make a prediction going forward. This could be in the form of a stock entry point, exit point or whether a stock is oversold or overbought.
ANNs in finance are not dissimilar to the neural networks in the human mind, where neural pathways are formed by neurons that are linked to each other. Each individual neuron will send a signal to another, and in turn this neuron then receives and computes the signal before sending a further signal to the next neuron. Financial neural networks use algorithmic modeling to predict and forecast outcomes, and these networks have a multitude of uses in businesses whether it be competitor and market research, financial forecasting and risk analysis.
Regarding risk analysis, although many companies use risk assessors to find issues in their company, in many cases the management of a company are simply told what they already know and no new information is presented to them. This is where neural networks can be implemented and using artificial intelligence, come up with different permutations and combinations that a “normal” risk assessor would miss. The way this would work is information on past experience pertaining to the company’s handling of risk and general risk management would be taken into consideration by discussing this with key figures at the company such as the CEO, CFO and various managers across the spectrum. Using the concept of neural networks a risk assessment model would be created by inputting various data into the formula which we will explore below. This would then be processed and the outputs would reveal the risk level of the company’s activities and where there is a need for improvement.
With financial forecasting in particular, due to the fact that neural networks are trained to continue learning, they can be an effective method to predict the outcomes mentioned above. Although not necessarily immediately accurate, through the process of trial and error and applying a variety of inputs, a neural network can very quickly become a strong financial modeling tool.
Neural Network Formula
Source: Gavril Ognjanovski (www.towardsdatascience.com)
A neural network consists of these individual neurons which are organized by layers with inputs forming the bottom part and the forecast forming the topmost layer. The inputs can also be known as predictors and the forecast is also known as the output. In majority of cases there are “hidden neurons” which form a hidden layer.
The above diagram depicts a multilayer feed-forward network, and these inputs are assigned “weights” which are a numerical value. At this point each input is multiplied by its corresponding weight and the sum of these values “fed forward” as a new input to the hidden layer. From here, the output is fed through a threshold function, and only if the value of this is able to pass the threshold value the neuron is then activated. If this does not occur, the neuron does not fire and is not activated. The activation procedure is completed using the activation function which then creates the final output value. In the above example there are two hidden layers, although in many models only a singular hidden layer forms part of the equation.
Neural Networks in the Stock Market
Due to the fact that neural networks are trained to predict outcomes, they can be highly effective when predicting whether a stock will rise or fall in the future. If we take the example of the Standard and Poors 500 (S&P 500) Index Fund, using neural networks can help both long term investors as well as those investing for the short term with entry and exit point forecasting. If we were to apply neural networks to a single trading day, we could use it to predict the price point at which the S&P 500 index would close. Therefore, the input values would consist of historical information regarding the daily high, daily low, opening price and closing price as well as stock volume. By following the method described above, the “best results” will be determined in the final output giving an accurate prediction of where and when to buy and sell the stock. This could also be applied to other individual stocks too such as Chevron, Microsoft and Berkshire Hathaway. These particular individual stocks have a decades long track record which means there is more data to be computed which would most likely result in a more accurate output result. When comparing this to a relatively new company such as Tesla Inc., there is less empirical evidence and historical data compared to these even more established enterprises so the accuracy of the output figures may be less reliable.
Neural networks have been proven to effective tools when it comes to the financial world, and can be used to predict outcomes in the stock market but also the foreign exchange market. From an investor’s point of view the use of neural network analysis can be extremely beneficial as it adds an extra angle when analyzing stocks and index funds from a solely “traditional” point of view (such as moving averages). It is imperative that neural networks are correctly “trained” so that they can be most effective in predicting outcomes. This is a procedure which does take some fine tuning however can once established it becomes an automated procedure taking into account the various inputs and multiplying by the weights assigned before being passed through the activation stage. Like many financial forecasting tools, neural networks are not necessarily a “one stop shop” and should be used in conjunction with other financial analysis models in order to help an investor come to an informed decision when choosing their investments.