I Know First Live Forecast Evaluation Report For MSCI Stock Universe – Significantly Outperforms MSCI-ACWI Index And ACWI ETF Benchmarks

Executive Summary

In this live forecast evaluation report we will examine the performance of the forecasts generated by the I Know First AI Algorithm for the MSCI stock universe and sent to our customers on a daily basis.Our analysis covers time period from January 2, 2018 to September 30, 2018. We will start with an introduction to our stock picking and benchmarking methods and then apply it to the MSCI stock universe of the MSCI index and compare it to ACWI (iShares MSCI ACWI ETF by Blackrock) performance over the same period. This report continues our investigation of the algorithm’s performance for MSCI stock universe published on September 23, 2018.

MSCI Stocks’ Universe Highlights

  • Signal indicator filter applied to Top 30 most predictable stocks provides the highest return of 6.19% for Top 20 stocks on 3-months’ investment horizon
  • There is a clear increasing trend for returns improvement with the time horizon increase – the delta in returns from 1 month to 3 months is more than 1253% for Top 10 filters.
  • The Top 20 significantly out-perform the ACWI Benchmark by 1896.77%.

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 general overview of the forecasting capabilities of the algorithm for specific stock universe. The following sections of this study will develop the analysis and the data behind the above results and provide you with deeper understanding of the Methodology and the filtering results for different subsets of assets.

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.

Here is the detailed description of the heatmap.

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:

his 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 analysed 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.

In comparison, only when our signals are of high signal strength and high predictability, then the particular stocks should be bought (or shorted).

The ratio of our signals trading results to benchmark results indicates the quality of the system and our indicators.

Example: A benchmark for the 3d horizon means buy on each day and sell exactly 3 business days afterwards. We then average the results to get the benchmark. This is in order to get an apples to apples comparison.

MSCI Stock universe under consideration – MSCI index

In this report we conduct testing for MSCI ACWI emerging stock markets index. MSCI is a market leader in global equity indexes and has over $3.7 trillion in assets benchmarked to the MSCI ACWI Index. The MSCI ACWI captures large and mid-cap representation across 23 Developed Markets (DM[1]) and 24 Emerging Markets (EM) countries[2]. With 2,778 constituents, the index covers approximately 85% of the global investable equity opportunity set. The index is based on the MSCI Global Investable Market Indexes (GIMI) Methodology —a comprehensive and consistent approach to index construction that allows for meaningful global views and cross regional comparisons across all market capitalisation size, sector and style segments and combinations. This methodology aims to provide exhaustive coverage of the relevant investment opportunity set with a strong emphasis on index liquidity, investability and replicability. The index is reviewed quarterly—in February, May, August and November—with the objective of reflecting change in the underlying equity markets in a timely manner, while limiting undue index turnover. During the May and November semi-annual index reviews, the index is rebalanced, and the large and capitalisation cutoff points are recalculated.

For more information on the composition of the MSCI ACWI index please follow this link.

Also, for the benchmarking purposes we take the performance of iShares MSCI ACWI ETF by Blackrock. This ETF seeks to track the investment results of an index composed of large- and mid-capitalisation developed and emerging market equities.

For more information about iShares MSCI ACWI ETF please follow this link.

Evaluating the predictability indicator

We conduct our research for the period from January 1, 2018 to September 30, 2018. Following the methodology described in the previous sections, we start our analysis with computing the performance of the algorithm’s long and short signals for time horizons ranging from 3 days to 3 months without considering the signal indicator. Therefore, we applied filtering by the predictability indicator for 5 different levels to investigate its sole marginal effect in terms of return when different filters are applied. Afterwards, we calculated the returns for the same time horizons of the two benchmarks using the MSCI stocks universe and ACWI asset universe and compared them to the performance of the filtered sets of assets. Our findings are summarised in the table below:

Figure 6 -1 MSCI Predictability Effect On Return


Average returns per time horizon (3 days to 2 weeks), only predictability filter

Average returns per time horizon (1 month to 3 months), only predictability filter

From the above charts we can observe that generally the marginal predictability effect increases with the narrowing of the asset subsets from the Top 50 assets to the Top 30 assets filtered by predictability. We observed this effect for the majority of the considered time horizons and the maximum performance was recorded at 3-months’ horizon. In comparison with the benchmarks based on all assets from each of the considered assets’ universes, we can see that just by applying the predictability indicator as an investment criterion without consideration of the signal strength, already yields positive return and outperforms the benchmarks by more than 4% in case of Top 30 stock by predictability criterion on 3-months’ horizon.

Evaluating the Signal indicator

In this section we will demonstrate how adding the signal indicator to our stock picking method improves the above performance even further. It is also important to measure the outperformance relative to the benchmark and for that we will apply the formula:

Therefore, we applied filtering by signal strength to the Top 30 assets filtered previously 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 3-months’ investment horizon. We present our findings in the following table and charts (Figure 6-2).

Figures 6-2 MSCI Key Performance Indicators Summary


Average returns per time horizon (3 days to 2 weeks), predictability & signal filters

Average returns per time horizon (1 month and 3 months), predictability & signal filters

Out-performance per time horizon (3 days to 3 months), predictability & signal filters

Average hit ratio per time horizon, predictability & signal filters


From the above set of charts, we can clearly see that if we apply signal strength filtering to the MSCI stocks universe, the subsets of Top 20 and Top 10 stocks will start to produce greater returns than the benchmarks with increase of time horizon. As soon as we start to consider 3-months’ time horizon, we see that the returns of the Top 20 and Top 10 subsets make significant jump comparing to the shorter periods and ultimately reaches 4.72% and 6.19% by Top 20 and Top 10 on 3-months’ horizon, respectively. That results in the outperformance over the considered benchmarks by some 980.36% in case of Top 20 stocks and by 1253.92% in case of Top 10 stocks. Finally, the hit ratio follows the similar pattern and we observe its peak values for both Top 20 and Top 10 sets on 3-month time horizon – 65.38% and 69.89%, respectively, comparing to the benchmark’s 46.90%.


In this analysis, we demonstrated the out-performance of our forecasts for the stocks from emerging markets picked by I Know First’s AI Algorithm from the MSCI index universe for the period from January 1, 2018 to September 30, 2018. Based on the presented observations we record significant outperformance of the Top 30 stocks filtered by predictability for all time horizons for the considered asset universe, even without filtering by signal strength.

Applying our predictability indicator as an investment criterion coupled with filtering by our signal strength, results in even greater outperformance over the benchmarks comprised of stocks from the MSCI index and the ACWI stocks universe in general. That said, the Top 10 stocks from the MSCI index universe filtered by predictability and signal yield significantly higher returns than any other asset subset on almost all considered time horizons spanning from 3 days to 3 months. Therefore, an investor who wants to critically improve the structure of his investments by adding stocks being traded on emerging markets to his portfolio can do so by simultaneously utilising the I Know First predictability and signal indicators as criteria for picking stocks.

[1] DM countries include: Australia, Austria, Belgium, Canada, Denmark, Finland, France, Germany, Hong Kong, Ireland, Israel, Italy, Japan, Netherlands, New Zealand, Norway, Portugal, Singapore, Spain, Sweden, Switzerland, the UK and the US.

[2] EM countries include: Brazil, Chile, China, Colombia, Czech Republic, Egypt, Greece, Hungary, India, Indonesia, Korea, Malaysia, Mexico, Pakistan, Peru, Philippines, Poland, Qatar, Russia, South Africa, Taiwan, Thailand, Turkey and United Arab Emirates.