Executive Summary

In this forecast evaluation report, we will examine the performance of the forecasts generated by the I Know First AI Algorithm for best stocks Under 10 universe, which are sent to our customers on a daily basis. Our analysis covers the time period from 1 January 2019 to 23 October 2019. We will start with an introduction to our asset picking and benchmarking methods and then apply it to the stock universe of all of the stocks covered by us in this package. We will then compare returns based on our algorithm with the benchmark performance over the same period. Below, we present our key takeaways from applying signal and volatility filters to pick the best performing aggressive stocks:

Best Stocks Under 10 Highlights:

• Top 5 stocks subset had better returns across almost all time horizons, providing an average return of 14.9% in the 1-year horizon which outperformed the benchmark by 234%.
• In the absence of signal filtering the forecasts provided positive returns above the benchmark for short-term forecasting horizons ranging from 3-days to 14-days, out-performing it by 3-14 times.

Note that the above results were obtained as a result of evaluation conducted over the specific time period and using a sample approach of consecutive filtering by predictability and by signal indicators to give a general presentation of the forecast performance patterns for specific currency pairs. The following report provides extensive explanation on our methodology and detailed analysis of the performance metrics that we obtained during the evaluation. This report is a new I Know First evaluation series illustrating the ability to provide successful short term and flexible forecasting for the currency market.

About the I Know First Algorithm

The I Know First self-learning algorithm analyzes, models, and predicts the capital market, including stocks, bonds, currencies, commodities and interest rates markets. 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. This is 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. This is measured from -1 to 1.

You can find a 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. This means that we calculate the return of each forecast we have issued for each horizon in the testing period. We then take the average of those results based on our positions on different assets 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 on the day we issue our prediction and selling it exactly 1 month in the future from that day.

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 Calculation

The hit ratio helps us to identify the accuracy of our algorithm’s predictions.

Using our asset filtering method based on predictability and signal, we predict the direction of 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 top 30 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

The S&P 500 index is used as a benchmark. Particular assets should be bought (or shorted) when they have been identified to have high signal strength and high predictability. We compare our rate of return based on purchasing (or shorting) the top X assets after applying both the predictability and signal filters with the rate of return of the S&P 500 index in the same time horizon. This helps us to determine the effectiveness of our methodology against the average investor.

It is important to measure the performance of our strategy with respect to the benchmark, and for that, we will apply the below formula:

The out-performance ratio of our trading results (based on our indicators) to benchmark results indicates the quality of the system and our indicators and provides a measure of competitive advantage an investor could get by using our forecasting solution.

Asset Universe Under Consideration: Stocks Under 10 Universe

In this report, we conduct testing for best stocks under 10 universe that I Know First covers in its algorithmic forecast. These stock picks are determined by screening our algorithm daily for stocks that have market price up to 10 USD. These forecasts for assets from Under 10 stocks universe are provided to our clients, which include 10 stock picks for short-term and long-term time horizons, spanning from 3 days to 1 year.

Performance: Evaluating The Predictability Indicator

We conduct our research for the period from 1 January 2019 to 23 October 2019. Using the methodology described in the previous sections, we start our analysis by computing the performance of the algorithm’s signals for time horizons ranging from 3 days to 3 months, considering the predictability indicator solely. The below results reflect Top 30 predictability filter being imposed on the stock universe.

Our findings are summarized in the table below:

From the above table, we can observe that Top 30 assets filtered by predictability generally provided positive returns, especially for short-term horizons, whereas the benchmark provided moderately positive returns. Returns based on predictability did outperform the benchmark on the 3- to 14-days time horizons. The maximum performance was recorded for Top 30 at 1-year horizon with a return of 2.09%. After analyzing the predictability filtering effect, we continue our study in order to identify whether the results could be improved in the case of 30 best stocks under 10 when we filter the set by signal indicators.

Performance Evaluation: Evaluating The Signal Indicator

In this section, we will demonstrate how adding the signal indicator to our asset picking method improves the above performance even further.

We further filtered the assets based on signal strength starting from the Top 30 assets, which were previously already filtered by predictability. The results of the testing showed that there is a significant positive marginal effect on the assets’ return. We present our findings in the following table and charts.

From the above set of charts, after applying the signal strength filtering to the best stocks under 10 universe, the subsets of Top 20, Top 10 and Top 5 stocks started to produce significantly greater returns than the benchmarks for all time horizons, with only exception of 90-days horizon. Our forecasts outperformed the benchmark, especially the Top 5 signals. Our best result came from the Top 5 signal, which outperformed the benchmark by 234% for a time horizon of 1 year.

Hit ratios are important for the investor who uses I Know First’s proprietary AI algorithm. The investor is interested in understanding how a regular portfolio would compare against one that uses the algorithm. It is easy to see from the above that the hit ratios shown above for Top 20, Top 10, Top 5 and all Signals are oscillating around 50% for time periods up to 1 month and make jump up to 67% for 1 year horizon. Essentially, it means that even though the hit ratio was relatively low for shorter periods, the AI algorithm managed to identify the most promising market opportunities correctly and they provided returns significant enough to push the average return far above the benchmark return during the comparable period. As the investment horizon expands, the hit ratios improve along with returns which means that algorithm managed to identify and correctly predict even more profitable trade ideas.

Conclusion

In this analysis, we demonstrated the out-performance of our forecasts for assets from the best stocks under 10 universe picked by I Know First’s AI Algorithm during the period from 1 January 2019 to 23 October 2019. Based on the presented observations, we record significant out-performance returns of the Top 20, Top 10 and Top 5 stocks subsets when our predictability and signal indicators are used together as investment criterion. The Top 5 stocks filtered by predictability and signal tend to yield significantly higher returns than any other asset subset and out-perform S&P 500 benchmark index. Therefore, an investor who wants to critically diversify the structure of his investments into the low price stocks market within his portfolio can do so by simultaneously utilizing the I Know First predictability and signal indicators as criteria for identifying the best performing assets.