Top Stock Picks: Daily US Stocks Forecast Performance Evaluation Report

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 Top 10 US stock picks for long and short positions (20 stocks) and S&P 500 Index which were daily sent to our customers. Our analysis covers the time period from January 1, 2019, to March 15, 2020.

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Chart 1: Performance comparison for Top 20, Top 10 and Top 5 signals by Daily Model vs S&P 500 for Short Term Horizons
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Chart 2: Performance comparison for Top 20, Top 10 and Top 5 signals by Daily Model vs S&P 500 for Long Term Horizons
Chart 3: Hit ratio of Top 5, Top 10 and Top 20 signals forecasting by Daily Model for Short Term Horizons
Chart 4: Hit ratio of Top 5, Top 10, and Top 20 signals forecasting by Daily Model for Long Term Horizons
Chart 5: S&P 500 Index Price (January 1, 2019 – March 15, 2020)

Top 10 US Stock Picks Evaluation Highlights:

  • The Top 10 and Top 5 signal groups generated by I Know First succeeded in outperforming S&P 500 Index in short term horizons.
  • The Top 10 and Top 5 signal groups achieved positive returns for all time horizons.
  • Every signal group has hit ratios higher than 50% for all time horizons

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.

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.

Evaluating US Stocks Forecasts: Top 10 Stock Picks Package

Stock Forecast & S&P 500 Forecast

Top 10 Stock Picks Package is one of I Know First’s systematic trading tools. The package includes a daily prediction for a total of 20 stocks with bullish and bearish signals:

  • Top 10 stocks pick for the long position
  • Top 10 stocks pick for the short position
  • Prediction of the S&P 500 Index

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 id 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 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:


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:


For instance, a 90% hit ratio for a predictability filter with a top 10 signal filter would imply that the algorithm correctly predicted the price movements of 9 out of 10 assets within this particular set of assets.

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 US stocks that I Know First cover by its algorithmic forecast. The period for evaluation and testing is from January 1st, 2019 to March 15th, 2020. During this period, we were providing our clients with daily forecasts for US stocks and S&P 500 in time horizons spanning from 3 days to 1 year which we evaluate in this report.

Top 10 US Stock Picks Results: Average Returns and Hit Ratio

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Table 1: Daily forecasts average performance vs S&P 500 for time horizons from 3 days to 1 year

As can be seen in Table 1, our algorithm provided almost only positive returns. The benchmark was outperformed by almost all signal groups for short term horizons (3 days, 7 days, 14 days). An exceptionally high performance was observed for 365-day time horizon by the Top 10 signal filtering, where the algorithm achieved a return of 12.26%. In addition, the Top 5 signal group outperformed the benchmark almost by 6 times on the 7 days time horizon. Those results indicate that the signal effect on the forecast returns was strong and consistent.

Table 2: Hit Ratio by Daily Forecast Model

According to the table and charts above, the hit ratios were all higher than 50%. That means the accuracy is good and reliable. We can observe that the best performance of 64% is reached on 365-day time horizon by the Top 10 Signals.


This evaluation report presented the performance of I Know First’s algorithm for the Top 10 US Stock Picks and S&P 500 forecast from January 1st, 2019 to March 15th, 2020. It shows the average returns for all time horizons after the signal grouping process. The results of this analysis demonstrated a strong and consistent overall performance of the generated forecasts. Indeed, the analysis demonstrates the efficacy of the algorithm which forecasted assets groups having positive average returns for almost every time horizon. 

The I Know First algorithm has obtained better performance for short term horizons. It succeeded in outperforming the benchmark for all of them, except for Top 20 signals on 14-day time horizon.We can also note that the accuracy was good because all hit ratios were higher than 50%  It is advisable for traders to rebalance their portfolio using the most updated forecasts sent by I Know First.

We look forward to new market data in the following months and will monitor the changes in performance trends that are going to be communicated to our investors and subscribers in the follow-up reports.