I Know First Evaluation Report For Undervalued Stocks

Executive Summary

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.

Chart 1: Performance comparison for Top 20, Top 10, and Top 5 signals for Fundamental – Low P/E Stocks vs S&P 500 Highlights for shorter-term horizons from May 17, 2020, until September 9, 2020.
Chart 2: Performance comparison for Top 20, Top 10, and Top 5 signals for Fundamental – Low P/E Stocks vs S&P 500 Highlights for longer-term horizons from May 17, 2020, until September 9, 2020.

Top Undervalued Stocks Highlights

  • The Top 10 and Top 5 signal groups consistently outperformed the S&P 500 Index.
  • All the group returns had a good performance, especially the ones for the 14 days and 1-month time horizon. Every group on those time horizons registered a significant higher return than the S&P 500 Index.
  • The Top 5 signal group significantly outperformed the S&P 500 for all time horizons.

About the I Know First Algorithm


The I Know First self-learning algorithm analyzes, 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.

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. 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 the 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.

You can find a detailed description of our heatmap here.

Evaluating US Stock Forecasts: Fundamental package – Low PE

The Fundamental Package includes our algorithmic forecasts for undervalued stocks screened by fundamental criteria. We choose stocks from the Fundamental package by taking the top 30 most predictable assets, and then we apply a set of signal-based filters: top 20, 10, and 5 based on signals. By doing so we focus on the most predictable assets on the one hand, while capturing the ones with the highest signal on the other. These forecasts are provided to our clients, which include short-term and long-term time horizons, spanning from 3 days to 3 months.

For the analysis we group the forecasts by absolute signals since these strategies are long and short. If the signal is positive, then we buy and if negative, we short.

The Stock Market Forecast Performance Evaluation Method

We perform evaluations on the individual forecast level. It means that we calculate what would be the return of each forecast we have issued for each horizon. Then, we take the average of those results by forecast horizon.

For example, we calculate the return of each trade by using this formula:


This simulates a client purchasing the asset based on our prediction and selling it exactly 1 month in the future.

We iterate this calculation for all trading days in the analyzed period and average the results.

Note that this evaluation does not take a set portfolio and follow it. This is a different evaluation method.

The Hit Ratio Method

The hit ratio helps us to identify the accuracy of our algorithm’s predictions.

Using our Daily Forecast asset filtering, we predict the direction of the movement of different assets. Our predictions are then compared against actual movements of these assets within the same time horizon.

The hit ratio is then calculated as follows:


A 90% hit ratio for predictability implies that the algorithm correctly predicted the movements of 9 out of 10 assets.

The Benchmarking Method: S&P 500

In order to evaluate our algorithm’s performance, we used the S&P 500 index as a benchmark.

The S&P 500 measures the stock performance of 500 of the U.S’ largest publicly traded companies. It is one of the most followed equity indices and is frequently used as the best gauge of large cap US equities. The S&P is often used as a benchmark for the performance of US publicly traded companies, and the US market as a whole. The S&P 500 is a capitalization-weighted index, the weight of each company in the index is determined based on its market cap divided by the aggregate market cap of all the S&P 500 companies.

For each time horizon, we compare the S&P 500 performance with the performance of our forecasts.

Performance Evaluation: Overview

In this report, we conduct testing for the Fundamental – Low P/E Stocks that I Know First cover by its algorithmic forecast. The period for evaluation and testing is from May 17, 2020, until September 9, 2020. During this period, we were providing our clients with daily forecasts for undervalued stocks spanning from 3 days to 3 months which we evaluate in this report.

Undervalued Stocks

undervalued stocks
Table 1: Daily forecasts average performance for time horizons from 3 days to 3 months vs S&P 500 for time horizons 3 days to 3 months May 17, 2020, until September 9, 2020

Top 10 and Top 5 Signals for shorter and longer time horizons performed extremely well between May 17, 2020, until September 9, 2020, especially for the 14 days and 1 month. For the longest time horizons, the top 5 signal group exponentially outperformed the benchmark index. That group had a return of 18.99% for 3 months forecasts, in comparison to the S&P 500’s return of 12.95% and a 11.99% return for 1 month forecast, substantially greater than the S&P 500 gain of 4.20%.

undervalued stocks
Table 2: Daily forecasts hit ratio for time horizons from 3 days to 3 months from May 17, 2020, until September 9, 2020

In addition, our longer time frames signal most of them had a hit ratio of above 50%. Even though those results seem low the average returns are positive and beating the benchmark. This is significant because, in addition to having tremendous returns on a long horizon, we also have demonstrated consistency for positive gains in short time frames.


This evaluation report presented the performance of undervalued stocks from May 17, 2020, until September 9, 2020. It shows the average returns and hit ratios for all time horizons.

The I Know First algorithm has obtained better performance for both short term and long term time horizons. It succeeded in generating significant positive returns for most of the signal group and time horizon. Also, it is important to note that almost every signal group across longer-horizon gave a hit ratio above 50%.  It is advisable for traders to rebalance their portfolio using the most updated forecasts sent by I Know First.