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. 

Source: Pixabay.com

What it is: 

Neural Networks are computing systems with interconnected nodes that work

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How Deep Learning Works In The Stock Market And How to Utilize It for Investment Decisions

 

 

The article was written by Yutian Fang, a Financial Analyst at I Know First and Master of Science in Finance candidate at Brandeis International Business School

 

Summary

  • To make informed investment is always what investors are concerned about
  • Solutions saw their limitations and improvements as techniques developed
  • What Deep Learning can do
    -Deep Networks for Unsupervised or Generative Learning
    -Deep Networks for Supervised Learning
    -Hybrid Deep Networks
  • How I Know First utilized Deep Learning for investment decisions

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Stock Market Prediction: AI And Chaos Theory

I Know First Research Team LogoThis article was written by the I Know First Research Team.

Rationality and Chaos

There have been many attempts to model the inner workings that make markets tick the way they do, starting from those as fundamental as the Smithsonian unseen hand correcting all the wrongs. However, when it comes to things less abstract and academic, one of the main questions on everybody’s minds is whether market, and, more specifically, stock market predictions are a possibility or not.

stock market predictions
(Source: Pixabay.com)

In extreme cases, both a simple “Yes” and “No” seem to equally contradict the reality on the ground. On the one hand, the famous Efficient

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Bayesian Neural Networks: Bayes’ Theorem Applied to Deep Learning

 

The article was written by Amber Zhou, a Financial Analyst at I Know First.

Highlights:

  • Vanilla Deep Learning Method: Multilayer Perceptron (MLP)
  • Introduction to Bayes’ Theorem
  • Application of Bayes’ Theorem: Bayesian Inference
  • Combination: Bayesian Neural Networks
  • How I Know First Utilizes Bayesian Neural Networks for Forecasting

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Deep Learning Finance: Revolutionizing The Market Today

This article was written by David Shabotinsky, a Financial Analyst at I Know First, and enrolled at the undergraduate Finance program at the Interdisciplinary Center, Herzliya.

Deep Learning Finance

Summary

  • How Deep Learning developed from AI
  • The evolution of Deep Learning in the market
  • How the finance sector has begun to further take advantage of Deep learning
  • I Know First implementation of Deep Learning to better forecast financial markets

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Why Machine Learning Is Important In The Future

“There is no reason and no way that a human mind can keep up with an artificial intelligence machine by 2035.” —Gray Scott

Summary

  • How Neurons Build A Neural Network
  • Difference Between Unsupervised And Supervised Learning
  • For Google Machine Learning Is The Future
  • I Know First Predictive Algorithm

How Neurons Build A Neural Network

[Image Source: medium.freecodecamp.org]
A Neuron can simply be described as a function. A function is a fancy name for something that takes some input, applies some logic, and outputs the result. A Neuron can also be determined as one learning unit. The crucial part is to understand what a learning unit (function) is, then we will understand the basic building block of a Neural Network. A function is basically a relationship between two variables usually denoted by x and y. The goal is to find a common pattern for these two variables by drawing a straight line with very few errors(see graph above).  The advantage here is, that next you are looking for x, the machine can apply the function and tell you y.

So, a neuron is a function, consequently a Neural Network is a network of functions. This means, that we have many functions (learning units). Now once I gave them many correct inputs and outputs (functions), I can give the Neural Network new input it has never seen before and it can give you the right output.

A neural network usually involves many functions and tiers. The first tier contains receives the input information. After that each successive tier gets the from the previous tier and then the final tier gives the output of the total system. The graph below illustrates the complexity of such a deep neural network.

[Image Source: searchenterpriseai.techtarget.com]
As the amount of data, we are talking about here is immense, the amount of new content coming out every minute is impossible for any human to follow. As the designer of a network you must consider the following questions:

  • How do I model the inputs and outputs? (for example, if the input is some text, can I model it in letters? numbers? vectors?….)
  • What are the functions in each neuron? (are they linear? exponential?…)
  • What is the architecture of the network? (that is, which function’s output is which function’s input?)

Machine Learning

[Image Source: towardsdatascience.com]
In general, Machine Learning is learning from examples. Machine learning is a part of artificial intelligence (AI) that provides systems with ability to learn automatically from experience. The process of learning begins with collection data, such as examples, direct experience, or instruction in order to look at patterns. The primary goal is that the machine learns automatically without any human intervention.

There are two main types of machine learning: supervised learning and unsupervised learning. Supervised machine learning is used by most of the practical machine learning. You have the input variables (X) and the output variable (Y) and the algorithm gives you the function from the input to the output. The goal is to calculate the right function that you can just insert the data (X) and predict the variables , in our case the stock predictions for Y. We call it supervised learning because we know the correct answers and the algorithm makes forecasts for the data and is corrected by the teacher .

The second type is unsupervised machine learning. There you have the input data (X) but no corresponding output variables. The goal is to draw a model of the underlying structure or distribution to learn about the data. As there is no correct answer and no teacher, it is called unsupervised learning. The system can not figure out the right output but can still draw inferences from datasets to describe patterns.

The reasons that I Know First uses a predictive algorithm instead of financial analysts is as follows: The amount of data that must be used to forecast so many different assets, can only be handled by a computer. Secondly the speed that a computer can proceed data, is much faster than any human ever could do. And last is that the algorithm is objective and relies 100% on empirical data, whereas financial analysts are often influenced by their opinion when analyzing a company.

For Google, Machine Learning Is the Future

[Image Source: insidebigdata.com]
Machine learning is about to revolute the world, as we do not have to teach computers how to solve complex tasks. The senior research Greg Corrado, who works for Google as scientist states: “It’s not magic. It’s just a tool. But it’s a really important tool.”  He also said that “Before internet technologies, if you worked in computer science, networking was some weird thing that weirdos did. And now everyone, regardless of whether they’re an engineer or a software developer or a product designer or a CEO understands how internet connectivity shapes their product, shapes the market, what they could possibly build.” He says that in the future most people will know machine learning and that it is going to be a normal thing. Nevertheless, it remains a complex system. On June 16th, Google announced to open a machine learning team in its Zurich engineering office, the largest collection of Google developers outside of the US. The group will be divided into three groups: machine intelligence, natural language processing, and machine perception. Google is highly investing in machine learning, as they consider it to be the future. Google acquired the start-up company named DeepMind in January 2014 for $500 million. Already now machine learning is integrated into many Google product. Google already gave running in-house courses to teach the engineers the machine learning and therefore have a big advance compared to their competitors. Although machine learning has been long part of Google’s technology, the company in 2016 became obsessed with it as the Ceo Sundar Pichai said in a corporate mindset: “Machine learning is a core, transformative way by which we’re rethinking how we’re doing everything. We are thoughtfully applying it across all our products, be it search, ads, YouTube, or Play. And we’re in early days, but you will see us — in a systematic way — apply machine learning in all these areas.” This statement shows the strong commitment of Google to machine learning and its future perspective.

I Know First Predictive Algorithm Based on Artificial Intelligence and Machine Learning

The system is a predictive stock forecast algorithm based on Artificial Intelligence and Machine Learning with elements of Artificial Neural Networks and Genetic Algorithms incorporated in 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.

[Image Source: iknowfirst.com]
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.

 

For further information, please review related articles:

AI Hedge Fund: Artificial Intelligence Taking over Hedge Fund Markets
AI Hedge Fund: Bridgewater Using AI to Increase Profits and Productivity
Portfolio Strategies & Asset Allocation – 86.43% Expected Annual Return Using Algorithmic Allocation
I Know First Live Evaluation Report For The Singaporean Stock Universe: Top 5 Stock Signals Return 2.30% in a 3-Month Investment Horizon

I

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Bayesian Inference and I Know First’s Application

 

 

This article was written by Kwon Sok Oh, a Financial Analyst at I Know First.

 

 

Summary

  1. Introduction of Bayesian Inference and BAC Example
  2. Prior Distribution: Concepts and BAC Example
  3. Likelihood Function: Concepts and BAC Example
  4. Posterior Distribution: Concepts and BAC Example
  5. Posterior Predictive Distribution: Concepts and BAC Example
  6. Conclusion of Discussion and BAC Example
  7. I Know First and Bayesian Neural Networks

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