Executive Summary

The purpose of this report is to present the results of live forecast performance evaluation for I Know First AI Algorithm, specifically for Tech Giants Stocks. The following results were observed when signal and predictability filters were applied in order to pick the best performing stocks out of the most predictable ones. The period under evaluation is from 1st January 2019 to 21st July 2019. The corresponding returns distribution of stock filters for Tech Giants stock universe are below:

Tech Giants Stock Package Highlights:

• The highest return of 3.00% came from the Top 10 Signals stocks on 1-month investment horizon
• There is a clear increasing trend for returns improvement with the time horizon increase for both Top 10 and Top 5 stocks
• Top 10 stocks consistently out-perform the benchmark

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

About the I Know First Algorithm

The I Know First self-learning algorithm analyses, 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 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 the detailed description of our heatmap here.

The Stock Picking Method

The method in this evaluation is as follows:

We take the top X most predictable assets, and from them we pick the top Y highest 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.

For example, a top 30 predictability filter with a top 10 signal filter means that on each day we take only the 30 most predictable assets, and then we pick from them the top 10 assets with the highest absolute signals.

We use absolute signals since these strategies are long and short ones. If the signal is positive, then we buy and, if negative, we short.

The 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 Benchmarking Method

The theory behind our benchmarking method is the “Null hypothesis“, meaning buying every stock in the particular asset universe regardless of our I Know First indicators.

The benchmark used in this report is the average return generated by the stocks in the Tech Giants stock universe for the period from 1 January 2019 to 21 July 2019 in a long position. We measured the returns of our long and short strategy for our forecasts against the benchmark. This helps us to determine the effectiveness of our algorithm by comparing the rate of return of the benchmark with the rate of return of our predictability-based strategy.

Example: A benchmark for the 3 days time horizon means buy every stock in the Tech Giants Universe and sell exactly 3 business days afterwards. We then average the returns to get the benchmark. This is in order to get an apples to apples comparison.

Stock Universe Under Consideration – Tech Giants Stock Market

In this report we conduct testing for Tech Giants stocks that I Know First cover by its algorithmic forecast in “Tech Giants stocks” package. Some Tech Giants Stocks covered are Apple Inc., NVIDIA Corporation, Micron Technology, Inc. and Applied Materials, Inc. The period for evaluation and testing is from 1st January 2019 to 21st July 2019. During this period, we were providing our clients with daily forecasts for Tech Giants stocks and the time horizons which we evaluate in this report are 4 periods spanning from 3 days to 1 month.

Evaluating the Signal Indicator for Tech Giants

In this section we will demonstrate how adding the signal indicator to our stock picking method improves the above performance even further. After filtering by predictability, we applied further filtering by signal strength to investigate potential improvement. The results of the testing showed that there is a significant positive marginal effect on the assets return, especially in the case of the 3-days, 1- and 2-weeks’ investment horizon. We present our findings in the following table and charts.

From table 1, we can see that if we apply signal strength filtering to the Tech Giants stocks’ universe, subsets for 3 days, 1 and 2 weeks time horizons show improvement in the performance as we filter from Top 10 to Top 5 signals. In general, all the above subsets start to produce greater returns than the benchmarks for most time horizons. As soon as we start to consider 2-weeks’ time horizon, we see that the returns of the Top 10 and Top 5 subsets make significant jump comparing to the 3-days or 1-week periods and ultimately reaches 2.37% and 2.42% by Top 10 and Top 5 on 2-weeks’ horizon, respectively. From figure 2, the hit ratio for Top 10 and Top 5 filtered subsets follows the similar pattern – for all of the time horizons the hit ratio are all above 60% and we observe its peak values for both sets on 2-weeks time horizon at 66% for Top 10 Signals and 65% for Top 5 Signals.

Conclusion

In this analysis, we demonstrated the out-performance of our forecasts for the stocks from Tech Giants Stocks picked by I Know First’s AI Algorithm for the period from 1st January 2019 to 21st July 2019. 

Applying our predictability indicator as an investment criterion coupled with filtering by our signal strength, results in even greater performance over the benchmarks comprised of stocks from the Tech Giants stocks universe. That said, the Top 5 stocks from the considered stock universe filtered by predictability and signal yield significantly higher returns than any other asset subset on all considered time horizons spanning from 3 days to 1 month. Therefore, an investor who wants to critically improve the structure of his investments by adding some Tech Giants stocks to his portfolio can do so by simultaneously utilising the I Know First predictability and signal indicators as criteria for picking stocks.