Artificial Neural Network (ANN): Why You Should Care

Yu YaoThis Artificial Neural Network (ANN) article was written by Yu Yao – Financial Analyst at I Know First.


  • Understand what is Artificial Neural Network (ANN) and how it works.
  • What are the applications of ANN in the real world.
  • How I Know First implements ANN and find the most promising investment opportunities.
Source: Commons Wikimedia

What Are Artificial Neural Networks (ANN)?

In the domains of AI, machine learning, and deep learning, Artificial Neural Networks (ANN), or Neural Networks (NN) for short, enable computer programs to identify patterns and resolve common problems by mimicking the behavior of the human brain.

Let’s look at the words separately.

Neural comes from Neurons, which are information-processing units that are fundamental to the operation of neural networks. They use electrical impulses and chemical signals to transmit information between different areas of the brain, and between the brain and the rest of the nervous system.

Source: Commons Wikimedia

Neural Networks refer to networks of neurons that are connected together by axons and dendrites as shown in the figure above. Our brain is a highly complex, nonlinear, and parallel computer (information-processing system).

Artificial Neural Networks (ANN)refer to computing systems with interconnected nodes that work much like neurons in the human brain. Using algorithms, they can recognize hidden patterns and correlations in raw data, cluster and classify it, and – over time – continuously learn and improve.

What Are Inside Artificial Neural Networks (ANN)?

As demonstrated in the figure below, a typical Artificial Neural Network (ANN) has three layers: input layer, hidden layer, and output layer.

The procedure for an artificial neural network starts with the input layer. The input layer is normally composed of artificial input neurons and brings the initial data into the system for further processing by subsequent layers of artificial neurons.

A hidden layer is where artificial neurons take in a set of weighted inputs and produce an output through an activation function. As the name implied, the hidden layer hides the true values of their nodes in the training dataset. In fact, only the input and output are known. Each neural network has at least one hidden layer.

The output layer is the last layer in ANN where predicted output is obtained.  

Source: Commons Wikimedia

How Does Artificial Neural Networks (ANN) Work?

The first step is to determine the input and assign weights to each variable. Different weights represent the levels of importance for the corresponding variables. Variables with larger weights contribute more significantly to the output compared to variables with smaller weights. The weighted values will be passed to the next layer. As an unsupervised model, the ANN model learns to adjust the weights during the training process so that the output is correct.

There are different types of ANN. The five types of ANN are listed in the figure below. The Feedforward Neural Network is the most basic one. In this network, data travels in a single direction from the input layer to the hidden layer if exists and exits through the output layer. The unique characteristic of a Recurrent Neural Network (RNN) is that it remembers its input. RNN is the first algorithm that has internal memory, making it ideal for machine learning problems involving sequential data. The Convolutional Neural Network (CNN) has convolutional layers, pooling layers, and fully-connect (FC) layers. With each layer, CNN increases its complexity, making it better for image data processing. A Modular Neural Network (MNN) can divide a single, huge, unmanageable neural network into smaller and more controllable components, in order words, decreasing complexity. Radial Basis Function Neural Network is used for classification or regression.   

What Are the Applications in the Real World?

Source: Commons Wikimedia

When you wake up in the morning, you unlock your phone with your face ID, open social media and see notifications of people you may know, ask Alexa to play some music, check the weather for the day, and reply to messages by handwriting to text function. In just five minutes, you are enjoying the applications of ANN every second.

As shown in the figure above, ANN is utilized in almost every aspect of our daily life. People should know more about how ANN can help them in their daily life. In the field of finance, ANN can predict the stock market by utilizing historical stock performances, annual returns, operating ratios, etc.

How I Know First Implements ANN?

I Know First is one leading company that has been effectively using machine learning and AI-based algorithms to provide daily forecasts and facilitate trading for over 10 500 financial instruments. Our system is a predictive stock forecast algorithm based on Artificial Intelligence and Machine Learning with elements of Artificial Neural Networks and Genetic Algorithms incorporated into it. This means the algorithm is able to create, modify, and delete relationships between different financial assets. Based on the relationships and the latest market data, the algorithm calculates its forecasts. Since the algorithm learns from its previous forecasts and is continuously adapting the relationships, it adapts quickly to changing market situations.

The I Know First Market Prediction System models and predicts the flow of money between the markets. It separates the predictable information from any “random noise”. It then creates a model that projects the future trajectory of the given market in the multidimensional space of other markets. The system outputs the predicted trend as a number, positive or negative, along with the wave chart that predicts how the waves will overlap the trend. This helps the trader decide which direction to trade, at what point to enter the trade, and when to exit. The model is 100% empirical, meaning it is based on historical data and not on any human-derived assumptions. The human factor is only involved in building the mathematical framework and initially presenting to the system the “starting set” of inputs and outputs. From that point onward, the computer algorithms take over, constantly proposing “theories”, testing them on years of market data, then validating them on the most recent data, which prevents over-fitting. If an input does not improve the model, it is “rejected” and another input can be substituted. This bootstrapping system is self-learning and thus live. The resulting formula is constantly evolving, as new daily data is added and a better machine-proposed “theory” is found. Some stocks are members of several separate modules. Thus, multiple predictions can be obtained based on different data sets. Also, each module consists of a number of sub-modules, each giving an independent prediction. If sub-modules give contradictory predictions, this should be a warning sign. Six different filters are also employed to refine the predictions.

You can access our forecast packages here. Below you can see the investment result of our S&P500 package which was recommended to our clients for the period from January 1st, 2020 to July 13th,2022.

The Investment Result for the period from January 1st, 2020 to July 13th, 2022

The investment strategy that was recommended by I Know First accumulated a return of 81.94%, which exceeded the S&P 500 return by 63.74%.


Because many of the relationships between inputs and outputs in the real world are both non-linear and complex, ANNs have the capacity to learn and simulate these types of relationships. With these advantages, ANN is a great candidate for AI Algorithm in predicting the stock market. I Know First AI Algorithm is capable of finding the most promising investment opportunities in the financial market.

I Know First has used AI outputs to provide an investment strategy for institutional investors that generated a return of 81.94% and exceeded the S&P 500 return by 63.74% for the period from January 1st, 2020 to July 13th, 2022.

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Please note-for trading decisions use the most recent forecast.