I Know First Evaluation Report for S&P 500 Index

Executive Summary

In this forecast evaluation report, we will examine the performance of the forecasts generated by the I Know First AI Algorithm for the S&P 500 Index with time horizons ranging from 3 days to 3 months, which were delivered daily to our clients. Our analysis covers the time period from the 1st of January 2019 to 19th of June 2019. Below, we present our key takeaways for checking hit ratios of our predictions.


  • 75% Hit Ratio for 14-day time period of S&P 500 predictions allowing our clients to be able to invest their money with significant less risk
  • Predictions consistently above 60% accurate despite very volatile times in the world economy over the last half year

Interpreting Interpretability in Algorithmic Trading

This article was written by Talia Shakhnovsky, a Financial Analyst at I Know First

Interpreting Interpretability in Algorithmic Trading

“If a machine learning model performs well, why [don’t] we just trust the model and ignore why it made a certain decision?” – Christoph Molnar, author of Interpretable Machine Learning


  • An Anecdote on Algorithmic Interpretability
  • What is Machine Learning?
  • Interpreting Interpretability
  • Is Interpretability Ever Insignificant?
  • The Importance of Algorithmic Interpretability
  • Algorithmic Trading: Interpretability in I Know First’s Forecasts

An Anecdote on Algorithmic Interpretability

Envision the near future. Self-driving vehicles roam the roads, and car accidents are a nightmare from the past. Society questions how people could have driven such dangerous machines they weren’t qualified to control.

Until, one day, a headline reads, “BREAKING: Bicyclist Dead in Hit-and-Run”. Shock

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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



  • 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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I Know First Weekly Review Algorithmic Performance: March 31, 2019

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Investment Selection Using AI Predictive Algorithm
March 31, 2019

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I Know First Weekly Review Algorithmic Performance: March 25, 2019

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Investment Selection Using AI Predictive Algorithm
March 25, 2019

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AI Wealthtech: Ten Key AI Terms and Their Applications in the Wealth Management Industry

Source: Wikimedia Commons

Artificial Intelligence

Artificial intelligence (AI) is a branch of computer science that aims to create intelligent machines that can think and learn for themselves. In 1950 when computers where just starting Alan Turing was asking the question “can machines think?” This question is still debated to this day but there is little doubt that Turing would be incredibly impressed with modern computing and what it has achieved in this field.

Modern AI is able to beat grand masters in chess and be used to predict financial markets. The term is still pretty loose with no real set of clear boundaries defining it but any machine that is able to think intelligently and learn is generally considered to be an Artificial Intelligent machine.

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In The News: I Know First Article on Machine Learning Featured on TechgraByte

I Know First was recently cited in an article on TechgraByte.com, a leading news source for technology, gadget, artificial intelligence, and business topics. The article, which can be found here, discusses how beginners can approach the field of Artificial Intelligence and learn the skills necessary to apply it to projects. When discussing the current state of AI, the article mentions how machine learning techniques are used for searching the web, placing ads, credit scoring, and stock trading.

Source: Flickr

I Know First, and the article cited by TechgraByte, are great resources for understanding how machine learning is being used in the financial sector. The article used by TechgraByte is written by Tali Soroker, a Financial Analyst at I Know First. In it, she writes in-depth about trading in the market using machine learning, and specifically discusses chaos theory in relation to I Know First’s predictive algorithm. Chaos modeling is shown to be difficult to do with statistics alone, and machine learning is an integral part of effectively achieving good results. This technique of using artificial intelligence to complete a statistical model is something that I Know First heavily relies on when formulating its predictions for stocks, indices, commodities, currencies, and more. To learn more about I Know First, and how the algorithm works, read here.

About TechgraByte

TechgraByte is a growing site that covers innovations in the technology sector, especially concerning Artificial Intelligence and Machine Learning. It serves a source for learning fundamentals and applications of cutting-edge data and computer science methods.

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I Know First, Ltd. is a financial technology company that provides daily investment forecasts based on an advanced, self-learning algorithm. Thus, the company’s algorithm predicts over 8,000 securities (and growing). Thus, it has capabilities to discover patterns in large sets of historical stock market data.

The underlying technology of the algorithm based itself on Artificial Intelligence. It also based itself on machine learning and incorporating elements of artificial neural networks and genetic algorithms. Moreover, the algorithm generates daily market predictions for stocks, commodities, ETF’s, interest rates, currencies, and world indices for the short, medium and long-term time horizons.

For more information, visit I Know First.

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


  • 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


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