Stock Filtering by the I Know First Signal and Predictability Indicators

Dario Biasini is a Research Analyst at I Know First.





Summary
We expand on research performed in previous articles by further exploring the effect and interpretation of the I Know First prediction measures and how these can be used for stock filtering. We show that as predictability and signal strength increase the average trade returns based on these indicators grow in a consistent, significant, and robust manner and that by daily selecting stocks with the highest predictabilities and signals average returns significantly above those of S&P500 Index can be achieved.
  • Analysis of the Returns Generated by Filtering S&P500 Stocks using the I Know First Signal and Predictability Indicators
  • Comparison of the Compounded Returns Generated by using the I Know First Signal and Predictability Indicators against those of the whole S&P500 Stock Universe

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Bayesian Inference and I Know First’s Application

   


This article was written by Kwon Sok Oh, a Financial Analyst at I Know First.    




Summary

  1. Introduction of Bayesian Inference and BAC Example
  2. Prior Distribution: Concepts and BAC Example
  3. Likelihood Function: Concepts and BAC Example
  4. Posterior Distribution: Concepts and BAC Example
  5. Posterior Predictive Distribution: Concepts and BAC Example
  6. Conclusion of Discussion and BAC Example
  7. I Know First and Bayesian Neural Networks

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Investment Research: AI-based forecasting system as an integrated DSS


Using I Know First AI-based forecasting system as an integrated DSS within a client’s investment or advisory process – case study with a bank


Key results

Applying screens derived from the I Know First AI-based forecasting system to recommendations list of a very well-known investment research company (=: “InvResCo’”) results in improved trade and portfolio performances:

Stock Prediction: Daily Stock Selection Based On AI Algorithm for January 7th, 2016-April 1st, 2017

Stock prediction

Short-term Trading

Daily Stock Selection Based On a Self-Learning Algorithm

stock prediction Time Period: January 7th, 2017 – April 1st, 2017 for stock forecasts 

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