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 ETFs for long and short positions (20 ETFs) which were sent daily to our customers. Our analysis covers the time period from May 9 2019 to September 9 2020.

Top ETFs Evaluation Highlights:

• The most impressive out-performance against the S&P 500 index is from the Top 5 signal group in the 3-day horizon with 10 times higher return.
• The Top 20, Top 10 and Top 5 signal groups generated by I Know First succeeded in outperforming S&P 500 index in all short-term horizons.
• The Top 20, Top 10 and 5 signal group generated by I Know First succeeded in outperforming S&P 500 index in the one-year horizon.
• Every signal group has hit ratios above 52% 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 ETFs Forecasts: Top 10 ETFs Package

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

• Top 10 ETFs for the long position
• Top 10 ETFs for the short position

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 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.

Top ETFs Performance Evaluation – Overview

In this report, we conduct testing for the ETFs that I Know First covers by its algorithmic forecast. The period for evaluation and testing is from May 9th, 2019 to September 9th, 2020. During this period, we were providing our clients with daily forecasts for ETFs in time horizons spanning from 3 days to 1 year which we evaluate in this report.

As can be seen in table above, our algorithm provided mostly positive returns. The S&P 500 benchmark was outperformed by every signal group for almost every time horizon, up to 10 times more return than the benchmark for the 3 days’ time period. The Top 5 signal group also outperformed the benchmark index by 21.47% in the 365-day time horizon. Although the algorithm gave some negative returns in the 30 and 90-day time horizons, it remained consistent and provided mostly positive returns over the S&P 500. It is also evident that with each following signal group, the returns increase. The Top 5 signal group has higher returns than the Top 10 signal group which has higher returns than the Top 20 signal group. This shows the algorithm’s increased accuracy with each subsequent forecast narrowing down the best ETFs to trade. The ability to filter by signal can improve the overall performance of the investment. An investment in the top 5 signals would have provided almost a 10 times higher return than an investment in the S&P 500 index and 53% higher than an investment in the top 10 signals.

According to the table above, each signal group across every time horizon gave a hit ratio greater than 52%. This shows that the algorithm’s accuracy is consistent and reliable. For example, the Top 5 signal group for the 1-year horizon all had hit ratio of 73% and 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 2-weeks, 1-month and 3-months’ time horizon all gave the highest hit ratio of 55-59% accuracy.

These results show continuous performance improvement by the I Know First AI predictive algorithm amid COVID-19 crisis in comparison to our previous ETF report for the short-time horizons.

Conclusion

This evaluation report presented the performance of I Know First’s algorithm for the Top ETFs forecast from May 9th, 2019 to September 9th, 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 short-term horizons 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 52% and up to 73%, showing a consistent and reliable accuracy.