Machine Learning Methods in Stock Price Prediction

Yuxiao YangThis article was written by Yuxiao Yang – Financial Analyst at I Know First.

Summary:

  • Machine Learning Methods help us find patterns from historical data and then apply them to predictions and algorithmic trading strategies.
  • Major Machine Learning Methods in Stock Price Prediction can be divided into Traditional Machine Learning Methods such as regression methods, Deep Learning methods, Time Series Analysis methods, and Graph-Based methods.
  • The I Know First AI algorithm provides us with the tool to select the most promising stocks.

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Hedge and Safe Haven Financial Assets

Sergey Okun  This article was written by Sergey Okun – Senior Financial Analyst, I Know First, Ph.D. in Economics.

Summary:

  • Gold, short-term treasury bills, long-term treasury bonds, the spread between the long-term treasury and corporate bonds, 5-month volatility, and 1-month volatility are uncorrelated or negatively correlated with the S&P500 making them good candidates for a role of a hedge asset.
  • 5-month and 1-month volatility as exchange trade products, and also the short-term government bills and long-term government bonds able to play a role of a safe haven asset.
  • Correlation analysis of the S&P500 and TNX shows that the correlation structure is not consistent from data frame to data frame.

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The World Indices Package Forecast

Market analysis by indices allows us to determine the forces that lead the financial market and identify the most attractive investment areas at various stages of the economic cycle. The algorithm of I Know First tracks and provides forecasting for a broad range of world indices to help our clients construct an effective portfolio based on their preferences and investment periods from 3-day to 1-year horizons. Currently, we have updated the Indices forecast package which covers now 384 world indices.

Stock Market Forecast Based on Artificial Intelligence for 2022

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Quantum Trading: Econophysics Can Help Predict Financial Markets

dr roitmanThis article was co-authored by Dr. Roitman, Co-Founder & CTO of I Know First Ltd. With over 35 years of research in AI and machine learning. Dr. Roitman earned a Ph.D  from the Weizmann Institute of Science.

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

 

Quantum Trading

Summary:

  • How Econophysics has shaped to become an important field of study for financial markets and policy makers
  • The uncertainty principle and how its used to predict the financial markets, such as bear and bull markets
  • What is phase transition in physics and how can it be applied to the financial market
  • Real applications of Econophysics in finance and policy making today
  • I Know First's self-learning predictive algorithm and its application of phase transition
  • Real differentiated competitive advantages offered by I Know First's algorithmic solutions

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