AI Forecast: Efficient Forecasting Segments of the Financial Market by AI

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

Summary:

  • Utilizing a Machine Learning approach to analyze financial data, enabling the identification of patterns at a level inaccessible to humans.
  • All financial assets, to varying degrees (with the exception of VIX, the volatility index), exhibit long-term patterns, allowing for the application of machine learning methods.
  • Corporate 5-10 years bonds and crude oil are potentially the most predictable financial market segments.

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Modeling Volatility with TGARCH

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

Summary:

  • Volatility has a number of statistical properties that must be taken into account in the modeling process.
  • The TGARCH model is one of the GARCH family models which allows for modeling volatility.
  • I Know First provides volatility predictions for short-term and long-term periods based on the machine learning approach.

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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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World Indices Forecast: AI Beats the S&P500 by 41.85%

Highlights:

  • The highest average return is 57.52% for the Top 5 Signals on a 1-year time horizon
  • The most impressive out-performance against the S&P 500 index is from the Top 5 signal group in the 1-year horizon with 3.67 times higher return
  • Predictions reach up to 88% hit ratio regardless of macroeconomic conditions
  • Every signal group has hit ratios above 51% for all time horizons
  • I Know First provides an investment strategy for institutional investors that generated a return of 142.57% and exceeded the S&P 500 return by 118.93% for the analyzed period

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IWF/IWD Stock Forecast for 2022: Growth vs Value in the Pandemic Realities

Sergey Okun  This "IWF/IWD Stock Forecast for 2022: Growth vs Value in the Pandemic Realities" article was written by Sergey Okun – Senior Financial Analyst, I Know First, Ph.D. in Economics.

Summary:

  • Low-interest rates provide a more favorable environment for the Growth Stocks than for the Value Stocks in the pandemic time.
  • The IWF/IWD spread is equal to 1.67 corresponding to the numbers during the Dotcom bubble, and significantly higher than its historical mean and median numbers of 1.10 and 1.01, respectively.
  • Expected changes in the monetary policy can have a strong impact on the IWF/IWD spread with the increasing strength of the Value Stocks. 

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Volatility Ahead – VIX Index Surges as September Begins

The I Know First September 3rd AI algorithmic prediction was successful in foreseeing that VIX index would surge on this trading day and until today. In reality, the index went up by almost 26% on Thursday to 33.6, and slightly declined to 30.75 on Friday. It’s value remains higher than it’s closing value on Wednesday – around 26.

Source: Yahoo Finance

As more and more uncertainty about the future economic activity rises due to Coronavirus implications and new lock-downs worldwide triggered by the second wave of patients, the VIX predictions lead its way to the top of I Know First’s forecast

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I Know First Weekly Review Algorithmic Performance: April 9th, 2020

I Know First Weekly Newsletter
Investment Selection Using AI Predictive Algorithm
April 9, 2020

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