I Know First Algorithm Evaluation Report for Live ETF Universe Forecasts

June 6, 2019

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 ETF universe, which is sent to our customers on a daily basis. Our analysis covers the time period from 1 April 2019 to 31 May 2019. We will start with an introduction to our asset picking and benchmarking methods and then apply it to the ETF universe. We will then compare returns based on our algorithm with the benchmark performance over the same period. This evaluation is part of our continuous studies of I Know First’s live AI predictive algorithm’s performance.

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Algorithmic Performance ETF's Forecast

Performance Report of I Know First’s AI-Predictive System for ETF Universe – Beating SPY

July 31, 2018

Early upon the launch of I Know First’s forecasting algorithm, we implemented our AI-based ranking and forecasting model for a selection of 100 ETFs in the U.S. market. Since then, I Know First began to issue ETF forecasts for our global subscribed investors.

According to our forecast evaluation results, the predictions generated returns greatly surpassing that of the benchmark we have utilized, namely, the SPDR S&P 500 Trust ETF (NYSE: SPY) which tracks the S&P 500 stock market index.

For each covered ETF, the forecasts are generated daily for 5 main time horizons, expressed in calendar days or months: 3 days, 7 days, 14 days, 1 month and 3 months.

The daily updated forecast consists of two numbers: the signal which indicates the predicted direction and strength of the ETF’s movement in the respective time frame, and the predictability which indicates how predictable the algorithm considers the ETF’s movements to be.

Currently, I Know First’s AI-based forecasting system covers 100 ETFs in total in the U.S. market, which are shown in the table below by their tickers.

Table 1: I Know First’s ETF Coverage

 

AI Added Value to the Investors

The predictive AI system can be used by investors/traders to make smarter investment decisions by:

Identifying promising opportunities in the U.S

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ETF's Forecast Performance Report

ETF Trading Strategies Based on I Know First’s Aggregated Algorithmic Forecasts

March 29, 2018

In the following article we update a set of ETF trading strategies based on I Know First’s aggregated investment signals for the individual stocks contained within the funds. In this article, the ETF forecast is generated by using a weighted aggregation of stock-level forecasts and the performance of this method is tested on the SPDR Sector ETFs. We show that these aggregated forecasts result in ETF Trading strategies which outperform the benchmark and present excellent performance and risk statistics.

Algorithmic Performance

ETF Predictions Based Trading Strategies Using I Know First’s Aggregated ETF Forecast

February 13, 2018

We continue our series of articles analyzing the performance of ETF predictions computed by aggregating I Know First’s algorithmic forecasts for the individual stocks contained within the funds. In this article, a new method to generate an ETF forecast by using a weighted aggregation of stock-level predictions is presented and the performance of this method is tested using Sector ETFs. We show that the computed ETF predictions result in strategies which outperform the benchmark and present excellent performance and risk statistics both on an individual ETF level and for portfolios of ETFs.

Algorithmic Performance

ETF Investing by Aggregating I Know First’s Algorithmic Trading Signals

January 18, 2018

In the following article we continue our analysis on computing sector-level predictions by aggregating I Know First’s stock-level forecasts and using these aggregated predictions to build strategies for sector ETF investing. In particular we:

  1. Update previously presented strategies which compute sector-level predictions by comparing the distributions of the long and short forecasts of the stocks within each sector
  2. Present new ways of aggregating our forecasts using simpler rules and show the performance of various strategies based on these
  3. Show that both the previously developed and the new, simplified decision rules give rise to portfolios that beat the benchmark and register excellent risk statistics

Algorithmic Articles