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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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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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.

Highlights:

  • 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

Summary:

  • 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

 

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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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


I Know First Weekly Newsletter
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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