Executive Summary

In this forecast evaluation report, we will examine the performance of the forecasts generated by the I Know First AI Algorithm for assets from World Indices asset universe provided as part of the Indexes World Package, which is sent to our customers on a daily basis. Our analysis covers the time period from 1 January 2019 to 30 June 2019. We will start with an introduction to our asset picking and benchmarking methods and then apply it to the world indices asset universe of all of the indices covered by us in the Indexes World 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 world indices:

World Indices Highlights:

• Top 5 signals had better returns in all time horizons than the S&P 500. The best return came from the forecast of a 3 month time horizon which produced a return of 8.3% which outperformed the benchmark by 2.08%.
• Top 10 signals had better returns in all time horizons than the S&P 500. The best return came from the forecast of the 1 month time horizon which produced a return of 4.07% which outperformed the benchmark by 1.92%.

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 assets in the Indexes World package. 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 World indices asset 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 Asset Picking Method

The method in this evaluation is as follows:

To fully utilize information provided by our forecast, we filter out the top X most predictable assets and rank them according to their predictability value. Thereafter, from them, we pick the top Y highest signals and re-adjust the rankings accordingly.

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 from our asset universe, and then we pick from them the top 10 assets with the highest absolute signals. On the other hand, a top 30 predictability filter with a top 30 signal filter would imply that we are solely filtering based on predictability, since we are selecting all assets in this particular set which have already been filtered by predictability.

We use absolute signals since these strategies are long and short ones. If the signal is positive, then we buy assets, i.e. open long position and, if negative, we open short position on such asset. This is to help us to identify the assets with the maximum magnitude of change, which is indiscriminate as to whether one adopts a short or long position.

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

We utilize two benchmarks to determine the effectiveness of our methodology. An internal benchmark is used to determine the effectiveness of filtering assets based solely on predictability. The S&P 500 index is used as a benchmark only after filtering assets based on predictability and signal.

In the first case, the theory behind this benchmarking method is the “Null hypothesis“. This means buying every asset in the particular asset universe regardless of our I Know First indicators. For instance, if we were to identify the top 20 assets from a universe of 50 assets, we would calculate the rate of return of 50 assets, where an equal amount of each asset would be bought at the start of the time horizon and sold at the end of the time horizon. This helps us to determine the effectiveness of our predictability-based asset filtering process by comparing the rate of returns of the benchmark with the rate of return of our predictability-based strategy.

In the second case, 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.

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: World Indices Asset Universe

In this report, we conduct testing for world indices asset universe that I Know First covers in its algorithmic forecast in the Indexes World package. These world indices asset picks are determined by screening the market with our algorithm daily for best performing indices. These forecasts for assets from the world indices asset universe are provided to our clients, which include 10 asset picks for short-term and long-term time horizons, spanning from 3 days to 3 months.

Performance: Evaluating The Predictability Indicator

We conduct our research for the period from 1 January 2019 to 30 June 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. We applied filtering by the predictability indicator for different levels to investigate its sole marginal contribution in terms of return, and we observe how returns change as these different filters are applied. Afterward, we calculated the returns for the same time horizons for the benchmark using the world indices asset universe and compare it against the performance of the filtered sets of assets. Our findings are summarized in the table (Table 1) below:

From the above table, we can observe that Top 30 assets filtered by predictability generally provided all substantially positive returns whereas the benchmark provided moderately positive returns. The return on investment for the Top 30 assets increases by 3.45% from 3 days to 3 months. Returns based on predictability outperform the benchmark significantly, by up to 2.13% on the 3 months time horizon. The maximum performance was recorded for Top 30 World Indices at the 3-month horizon with a return of 3.91%. After analyzing the predictability filtering effect on World indices assets’ return, we continue our study in order to identify whether the results could be improved in the case of Top 30 World indices assets 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. It is important to measure the performance of our strategy with respect to the benchmark, and for that, we will apply the formula:

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, especially in the case of the 1 month and 3-month investment horizons. We present our findings in the following table and charts.

From the above set of charts, after applying the signal strength filtering to the World Indices assets universe, the subsets of Top 10 and Top 5 indices will start to produce greater returns than the benchmarks for all time horizons of 3 days, 7 days, 14 days, a month and 3 months. When we consider 3-month time horizons, returns of the Top 20, Top 10 and Top 5 subsets make significant jump to 4.51%, 6.44% and 8.3% returns respectively, as compared to the shorter periods. Our forecasts also outperformed the benchmark greatly, especially from our Top 5 signals. Our best result came from the Top 5 signal, which outperformed the benchmark by 234% for a time horizon of 1 month.

Hit ratios are important for the investor using I Know First’s proprietary AI algorithm. The investor is interested in understanding how an unadulterated portfolio would compare against one that uses the algorithm. If the hit ratio is 50%, it is merely as good as the flip of a fair coin. Not just any number above 50% is good enough for the layman investor. 60% is probably the fair minimum hit ratio that the investor can accept. The Hit Ratios shown above includes the Top 20, Top 10 and Top 5 Signals with horizons of 3 days, 7 days, 14 days, a month and 3 months. If one takes a closer look, the hit ratios improve as the investment horizon expands. The time horizon commensurates with the hit ratio, which explains why the Top 20 Signal and Top 10 signal has a Hit Ratio of 70% and 68% respectively for a 3 month time horizon. This is by far, better than many forecasts.

Conclusion

In this analysis, we demonstrated the out-performance of our forecasts for assets from the World Indices assets universe picked by I Know First’s AI Algorithm during the period from 1 January 2019 to 30 June 2019. Based on the presented observations, we record significant out-performance returns of the Top 5 and Top 10 indices when our predictability and signal indicators are used together as investment criterion. As shown in the above diagram, the Top 5 indices filtered by predictability and signal tend to yield significantly higher returns than any other asset subset for longer time periods. Not only are the returns higher, the high hit ratio will serve as a larger vote of confidence in the assets the investor is interested in. Therefore, an investor who wants to critically improve the structure of his investments into the world indices 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.

World Indices Asset Universe Coverage