Big Tech Stocks: AI Outperforms S&P 500 by 15.74% with an Accuracy of 98%

Highlights:

  • The highest average return is 49.97% for the Top 20 Signals on a 1-year time horizon
  • The most impressive out-performance against the S&P 500 index is from the Top 20 signal group in the 1-month horizon with 1.52 times higher return
  • Predictions reach up to 98% hit ratio regardless of economic conditions amid COVID-19
  • Every signal group has hit ratios above 52% for all time horizons
  • I Know First provides an investment strategy for institutional investors that generated a return of 69.98% and exceeded the S&P 500 return by 26.86% for the period from May 31st, 2020 to October 3rd, 2021

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The Conceptual Framework of Applying ML and AI Models to Analyze and Forecast Financial Assets

This article was written by:

Sergey Okun  Sergey Okun – Financial Analyst at I Know First, Ph.D. in Economics.


Eugen KalaidinEugene Kalaidin – Professor, Dept. of Mathematics and Computer science, The Financial University under the Government of the Russian Federation, Ph.D., D. Sci. (Habilitation) in Physics and Mathematics.

Highlights:

  • Knowledge significantly decreases the speculative risk of investment
  • ML and AI technologies allow us to get relevant knowledge about the financial market
  • Information asymmetry is a key factor in getting the arbitrage return
  • Models of nonlinear dynamic systems allow correctly to evaluate financial assets by determining the backbone behavior of assets

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Trade Smartly in the Fractal Stock Market with Machine Learning Power

motek 1This algorithmic article was written by Yutong Li – Analyst at I Know First, Master's candidate at Brandeis University.

Highlights:

  • Although Efficient Market Hypothesis has been a dominated financial theory for years, it fails to give a sensible reason and interpretation of the financial crashes and crises that occurred
  • A more comprehensive financial theory - Fractal Market Hypothesis is capable to explain these crises and provide a clear-cut description of the financial markets
  • Fractal Market Hypothesis puts forward the idea of self-similarity and stability in the market when it consists of investors from a wide range of investment horizons
  • FMH verifies the root of technical analysis under the idea that history can repeat, and this process of pattern-finding can be efficiently attained by machine learning

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The AI Book: Featuring I Know First’s Yaron Golgher, Dr. Lipa Roitman, and Denis Khoronenko

On December 30, 2019, I Know First published an article covering the continued work of the I Know Firsts team, Yaron Golgher, Dr. Lipa Roitman, and Denic Khoronenko, as they write a chapter to be published in The AI Book. This book aggregates multiple expertise through crowd-sourced experts into a uniform volume that enforces the significance of artificial intelligence and how it can be utilized amongst financial services. 

With great pleasure, we can announce the chapter titled “Finding Order In The Chaos: Investment Selection Using AI” has been published alongside various prominent

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What Is An Artificial Neural Network And How I Know First Is Using It?

The article was written by Hieu Nguyen, a Financial Analyst at I Know First.

Artificial Neural Network and I Know First

Summary:

  • Artificial Neural Network Definition – word by word
  • How does Artificial Neural Network work?
  • Application of ANN in real life
  • How ANN transforms the financial market?
  • Artificial Neural Network and I Know First

You come to the office today like every other day, sit down, grab a cup of coffee, and start reading the news. The more you read, the more you realize there are some words that show up more often “Artificial Intelligence”, “Artificial Neural Network”, “Deep Learning”. A decade ago, no one talked about these concepts. You start to ask yourself “What is all of these?” How’re they going to change my world?”

Artificial Neural Network Definition – word by word

Undoubtedly, Artificial Neural Network (ANN) has turned the page of the computer science. Let’s first understand what ANN is.

Network refers to “a system consisting of many similar nodes that connect together by edges to allow movement or communication between or along parts”. One of the most common example of network is the World Wide Web. Web pages represent the nodes and the hyperlinks between them represent edges. As a result, one page can link the one other using the hyperlink. Another example is our social network. Everyone is a node and our relationship are the edge that connect us.

Neural Network: similar to other networks, neural network consists of nodes that connect with each other. The phrase was first used in neuroscience to describe how our brain, the most sophisticated system in the world, works. Our brain has about 100 billion neurons, connecting with each other by synapses. In fact, a child’s brain has a quadrillion of connections, 10 times the connections of the entire Internet. As we grow up, our brain starts pruning the unnecessary connections and strengthening the others. Thus, we are able to learn new things of the world. The same terminology is applied in computer science.

Artificial Neural Network: As we understand more about how effective our brain is in learning new things, scientists have developed the artificial neural network that replicates our brain. Artificial Neural Network has changes the whole computer science industry from a conventional computer to an artificial intelligence computer. Thanks to the new tech, our computers now can perform sophisticate tasks that have never been experienced before.

(Source: wikimedia commons)

How does Artificial Neural Network work?

Artificial Neural Network consists of three types of layer: input layer, hidden layers, and one or more output layers. The main function of the ANN is to build a model that identifies patterns and makes decisions without human intervention. To perform the task, the raw materials (dataset) is imported to the input layer. Then, the data is passed down to the nodes in each of the hidden layers. Finally, the result will appear in the output layer.

What makes ANN different with the conventional program is backpropagation. This algorithm allows the model to adjust the weight of each node and recognize the most accurate pattern. The precision of the model is presented by a cost function, which is a measure of how wrong the model is in term of estimating the relationship between two variables.

The goal of the backpropagation algorithm is to minimize the “cost” of the model by changing the weight and bias of each node. To do so, we have to calculate the gradient of the cost function, which shows the directions to reduce the cost function the most. After adjusting the weight, the model can recognize an efficient pattern to apply in the future.

Application of ANN in real life

Artificial Neural Network seems sophisticated, but does it really work in real life? You may not realize but the application of ANN is every where around you.

Look at your phone. Siri and Google Assistant is one of the apps that use ANN to recognize your voice command and perform the task. In fact, Google has used 1.2 billion gigabytes of data to train Google Assistant. As a result, Google Assistant now can convert a voice command (input) to a text command (output) and then perform the task.

Another application of ANN is face recognition. It’s such a surprise of how accurate Facebook can recognize our friends in a photo. Currently, the face recognition technology can accurately analyze our emotions. The latest product that includes face recognition technology is iPhone X, whose Face ID can allow us to unlock the phone

It will be unfair not to mention self-driving cars. Thanks to Artificial Intelligence in general and ANN in particular, self-driving car now can find the lane on the road using colors, edges, and gradients. Moreover, the car also uses a deep neural network to draw a boundary with the other vehicles and obstacles on the road and make its own decision of whether to accelerate, break, or turn.

(Source: wikimedia commons)

How ANN transforms the financial market?

Artificial Intelligence and ANN also make a huge difference in financial market. In financial market, the data has been collected decades ago. A large amount of data is truly meaningful to train the model. Currently, many big financial institutions have applied AI in their business

One example is fraud detection using by credit cards companies. Late last year, Mastercard introduced a new security platform, Decision Intelligence, that uses AI to improve the accuracy of real-time approvals. The new platform will reduce the fraud probabilities. Hence, it not only reduces the loss on fraud, but also promotes customers to purchase more often.

Besides that, banks are also using AI to predict which customers have higher probability of default or cancel service. Moreover, Bank of America also launches an AI bot on the smartphone app to learn about the customer behaviors and offer suitable financial services.

ANN and I Know First

Although there are a lot of applications of AI and ANN both in real life and in financial market, there has not been a true model that has been effectively built to forecast the market. Many may believe that it is impossible to predict the market, it may turn out to be the opposite with I Know First’s algorithm.

We truly understand that stock market is a chaotic process, which is predictable, rather than a random process. The stock market is driven by three paradigms: Stability, Memory, and Sudden and Drastic Changes. Details are discussed in “Machine Learning Trading, Stock Market, and Chaos”.

Understanding the underlying concepts of the stock market, Dr. Lipa Roitman, the founder and CTO of I Know First, then developed a predictive algorithm based on Artificial Intelligence and Machine Learning with elements of Artificial Neural Networks incorporated in it. The model is trained with 15 years of historical data as well as real-time data. Then I Know First will test the prediction algorithm on years of market data, and validate them with the 2-month recent data to avoid overfitting. Every day, new data is recorded and the system will adjust itself based on the results that it achieved in the past and the new inputs.

For 5 years, I Know First’s algorithm has proved its effectiveness in beating the stock market. The success of I Know First one more time proves how Artificial Intelligence is changing our life as well as the financial market.

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Artificial Intelligence Stock Trading – Now And in The Future

This article was written by Maria Grishaev, Analyst at I Know First.

Executive Summary

In the last decade, the usage of machine learning and artificial intelligence based trading algorithms grew with the rapid development of computational powers. It makes everyone to wonder - do we take the maximum advantage technology gives us or not. In this article I’m going to discuss the current trend of usage and what are the challenges we are going to need to overcome in order to utilize all the benefits of this advanced technology.

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AI-Driven Algorithmic Trading: Self-driving Car For Stock Markets

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

Summary:

  • The AI industry is re-shaping the world, making sci-fi movies inch closer to reality by day.
  • This technology is implemented across very diverse businesses and industries.
  • The same tech that powers your smart trading algorithm today will power your self-driving car tomorrow.

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Machine Learning Trading, Stock Market, and Chaos

taliTali Soroker is a Financial Analyst at I Know First.

Summary

  • There is a notable difference between chaos and randomness making chaotic systems predictable, while random ones are not
  • Modeling chaotic processes are possible using statistics, but it is extremely difficult
  • Machine learning can be used to model chaotic processes more effectively
  • I Know First has employed artificial intelligence and machine learning in order to make predictions in the stock market
  • Definitions for underlined words can be found in the Glossary at the end of the article


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