In this stock market forecast evaluation report, we will examine the performance of the forecasts generated by the I Know First AI Algorithm for Highest Implied Volatility Options for long and short positions which were sent daily to our customers. Our analysis covers the period from May 15th, 2020, to October 7th, 2020.
In this Know First forecast evaluation report, we will examine the performance of the forecasts generated by the I Know First AI Algorithm for the Low P/E Stocks, a subpackage of the Fundamental package, for short and long positions that were sent daily to our customers. Our Analysis covers the period from May 17, 2020, until September 9, 2020. We will start with an introduction to our asset picking for undervalued stocks and benchmarking methods. In this case, it apply to the universe covered by us in the Low PE subpackage.
This performance evaluation for the Under 10 Dollars stocks package was written by Diana Na Kyoung Lee, an Applied Math-Computer Science student at Brown University.
In this forecast evaluation report, we will examine the performance of the forecasts generated by the I Know First AI Algorithm for stocks from the Stocks Under 10 Dollars Package, which is sent to our customers on a daily basis. Our analysis covers the time period from 1 April 2018 to 31 May 2019. We will start with an introduction to our asset picking and benchmarking methods and then apply it to the stock universe of all of the stocks covered by us in the Under 10 Dollars Package. We will then compare returns based on our algorithm with the benchmark performance over the same period.
In this stock forecast evaluation report, we will examine the performance of the forecasts generated by the I Know First AI Algorithm for the US stock market and sent to our customers on a daily basis. Our analysis covers time period from January 1, 2019 to May 23, 2019.
The above results were obtained based on the stock forecast evaluation over the specific time period using consecutive filtering approach – by predictability, then by signal, to give general overview of the forecasting capabilities of the algorithm for specific stock universe. The following sections of this study will develop
As of December 2018, I Know First finished a performance evaluation of the live AI-based predictive forecasts for the new West stock strategy sent to customers. This evaluation clearly demonstrates the consistent out-performance of I Know First’s forecasts vs. the entirety of stocks within the West Market. The West Market is comprised of stocks in Western countries such as the U.S., UK, Canada, and Brazil. This evaluation is part of our continuous studies of live I Know First’s AI predictive algorithm performance.
In this Live Forecast Evaluation Report, we will examine the performance of the forecasts generated by the I Know First AI Algorithm for the West Market stock universe. Our analysis covers the time period from January 1, 2018 – December 1, 2018. We present below the optimal stock picking strategy for this period. This period was chosen since this was a milestone in which I Know First introduced a major new version of the stock picking method for West Market investment universe. It includes analysis of all the predictions generated by the algorithm and sent daily to I Know First’s clients over this time period. The results of this study illustrate the significant positive effects of the algorithm’s continuous improvement utilizing advanced machine learning and AI capabilities.
The asset universe under consideration is the full 10,200 set of assets that I Know First forecasts. These assets include stocks, commodities and currency pairs. Each daily forecast sent to I Know First’s clients includes predictions for the following time horizons: 3 days, 7 days, 14 days, 1 month, 3 month and 1 year.
The I Know First self-learning algorithm analyses, models, and predicts the stock market. The algorithm is based on Artificial Intelligence (AI) and Machine Learning (ML) and incorporates elements of Artificial Neural Networks and Genetic Algorithms.
The system outputs the predicted trend as a number, positive or negative, along with a wave chart that predicts how the waves will overlap the trend. This helps the trader to decide which direction to trade, at what point to enter the trade, and when to exit. Since the model is 100% empirical, the results are based only on factual data, thereby avoiding any biases or emotions that may accompany human derived assumptions. The human factor is only involved in building the mathematical framework and providing the initial set of inputs and outputs to the system. The algorithm produces a forecast with a signal and a predictability indicator. The signal is the number in the middle of the box. The predictability is the number at the bottom of the box. At the top, a specific asset is identified. This format is consistent across all predictions.
Our algorithm provides two independent indicators for each asset – Signal and Predictability.
The Signal is the predicted strength and direction of movement of the asset. Measured from -inf to +inf.
The predictability indicates our confidence in that result. It is a Pearson correlation coefficient between past algorithmic performance and actual market movement. Measured from -1 to 1.
Here is the detailed description of the heatmap.
Our New Stock
Disclaimer:
I Know First-Daily Market Forecast, does not provide personal investment or financial advice to individuals, or act as personal financial, legal, or institutional investment advisors, or individually advocate the purchase or sale of any security or investment or the use of any particular financial strategy. All investing, stock forecasts and investment strategies include the risk of loss for some or even all of your capital. Before pursuing any financial strategies discussed on this website, you should always consult with a licensed financial advisor.
