Consumer Stocks’ Performance Evaluation Report – Accuracy Up To 65%

Executive Summary

In this stock market forecast evaluation report, we will examine the performance of the forecasts generated by the I Know First AI Algorithm for the top consumer stocks for long and short positions which were sent daily to our customers. The Consumer Stocks Package is designed for investors and analysts who need predictions of the best-performing stocks for the whole Consumer Industry. Our analysis covers the time period from August 19th, 2019 to November 18th, 2020.

Consumer Stocks’ Performance short horizon
Consumer Stocks’ Performance long horizon
s&p500 historical price

Consumer Stocks Evaluation Highlights:

  • The most impressive returns came from the Top 20 signal group in the 1-year time horizon with a 133% higher return than the S&P 500 Index.
  • All the signal groups generated by I Know First succeeded in outperforming S&P 500 Index in the 2-weeks’ time horizon.
  • The Top 20, Top 10, and 5 signal group generated by I Know First succeeded in outperforming S&P 500 Index on almost every horizon.
  • Every signal group has hit ratios are above 48% for all time horizons amid the COVID-19 crisis.

The above results were obtained based on forecasts’ evaluation over the specific time period using a consecutive filtering approach – by predictability, then by signal, to give an overview of the forecasting capabilities of the algorithm for the specific stock universe.

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 and Genetic Algorithms.

I Know First algorithm

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 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.

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 in the testing period. Then, we take the average of those results by strategy and forecast horizon.

For example, to evaluate the performance of our 1-month forecasts, we calculate the return of each trade by using this formula:

performance evaluation 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 at the individual forecast level.

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:

hit ratio formula

The Benchmarking Method – S&P 500 Index

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

The S&P 500 measures the stock performance of the largest 500 companies by market cap listed on different stock exchanges in the United States. It is one of the most followed equity indices and is frequently used as the best indicator for the overall performance of US public companies, and the US market as a whole. 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 consumer stocks that I Know First covers by its algorithmic forecast. The period for evaluation and testing is from August 18th, 2019 to November 18th, 2020. During this period, we were providing our clients with daily forecasts in time horizons spanning from 3 days to 1 year which we evaluate in this report.

Consumer Stocks average return

As can be seen in the table above, our algorithm provided mostly positive returns for most time horizons. The S&P 500 benchmark was outperformed in many signal groups for most of the time horizon, up to 2 times higher return than the benchmark for the 1 month’ and one-year time periods. Although the algorithm gave some negative returns in the 3-day and 90-day time horizons, it remained consistent and provided mostly positive returns over the S&P 500.

According to the table above, each signal group across every time horizon gave a hit ratio greater than 48%. This shows that the algorithm’s accuracy is consistent and reliable amid the COVID-19 crisis. For example, the Top 20 signal group for the one-year time horizon all had a hit ratio of 63% and a return that outperformed the S&P 500 Index by a large margin, suggesting the consistent accuracy of the algorithm. The Top 20, Top 10, and Top 5 signal group for all time horizons all gave a hit ratio of 48-65% accuracy.


This evaluation report presented the performance of I Know First’s algorithm for the consumer stocks from August 18th, 2019 to November 18th, 2020. It shows the average returns and hit ratios for all time horizons, with the algorithm outperforming the benchmark index in most of the time periods. The I Know First algorithm has obtained better performance for the two-weeks’ time horizon and on the 1-year time horizon. It is also important to note that every signal group across every time horizon gave a hit ratio are above 48% and up to 65%, showing a consistent and reliable accuracy.