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 Global Tech stocks from Western markets for long and short positions which were sent daily to our customers. This Global Tech stocks investment universe covers Western technological sector stocks. Our analysis covers the time period from December 10^th  2019 to September 4^th  2020.

Top Global Tech Stocks Evaluation Highlights:

• The most impressive outperformance of the S&P 500 Index comes from the Top 5 signal in the 2-weeks’ time horizon with 15 times higher return
• The Top 10 and Top 5 signal groups succeeded in outperforming S&P 500 in all time horizons
• Every signal group has hit ratios above 50% for all time horizons amid 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.

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:

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.

During the discussed period, the S&P 500 grew in total by 9.28% despite the drastic market falls caused mainly by the global pandemic crisis. The first quarter of 2020 ended with a -19.6% loss, in the second quarter the losses were recovered (+20.54%) and Q3 ended with a 8.4% gain.

Global Tech Stocks -Performance Evaluation

In this report, we conduct testing for the Global Tech stocks from Western markets that I Know First covers by its algorithmic forecast. The period for evaluation and testing is from December 10^th , 2019 to September 4^th, 2020. During this period, we were providing our clients with daily forecasts in time horizons spanning from 3 days to 3 months which we evaluate in this report.

As can be seen in table above, our algorithm provided positive returns for all but one group for all time horizons. The S&P 500 benchmark was outperformed by every signal group for every time horizon except one, up to 23 times more return than the benchmark for the 1-month’ time period. The Top 5 signal group also outperformed the benchmark index by 6.74% in the 3-months’ time horizon. Although the algorithm gave a negative average return in the 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 increased. 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 stocks to trade by signal value. Hence, there is an ability to filter by signal that can improve the overall performance of the investment within this stock universe. An investment in the top 5 signals would have provided six times higher return than an investment in the S&P 500 index and 4 times higher than an investment in the top 10 signals in the 3 days’ time horizon.

According to the table above, each signal group across every time horizon gave a hit ratio greater than 50%. This shows that the algorithm’s accuracy is consistent and reliable amid the COVID-19 crisis. Per above, the Top 5 signal group for the 3-months horizon all had hit ratio of 64% 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 3-days, 1-week, 2-weeks, and 1-month time horizon all gave the highest hit ratio of 52-57% accuracy.

Conclusion

This evaluation report presented the performance of I Know First’s algorithm for the Global Tech stocks from Western markets forecasts that were issued by the AI algorithm from December 10^th, 2019 to September 4^th, 2020. It shows relatively high average returns and hit ratios for all time horizons, with the algorithm outperforming the benchmark index for most of the time periods. The I Know First algorithm has obtained most noticeable 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 50% and up to 64%, showing a consistent and reliable accuracy.