I Know First Live Forecast Evaluation Report For Indian Stock Market – Significantly Beating Nifty200 Index And India Stock Market 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 Indian stock market and sent to our customers on a daily basis. Our analysis covers time period from January 3, 2018 to August 24, 2018.We will start with an introduction to our stock picking and benchmarking methods and then apply it to the stock universe of the Nifty 200 index as well as all of the stocks covered by us in the “Indian Stocks” Package.

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 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.

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.

Evaluating The Predictability Indicator

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 NIFTY 200 stocks universe and entire Indian Stock Market asset universe and compared them to the performance of the filtered sets of assets. Our findings are summarized in the table below:

Figure 5 -1 NIFTY 200 – Predictability Effect On Return


Average returns for 3-days’, 1-week and 2-weeks’ horizons


Average returns for 1-month and 3-months’ horizons

From the above charts we can observe that 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 all considered time horizons and the maximum performance was achieved 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.

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 5-2).

Figure 5-2 NIFTY 200 – Key Performance Indicators Summary

From the above set of charts, we can clearly see that if we apply signal strength filtering to the NIFTY 200 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 1-month and 3-months’ time horizons, we see that the returns of the Top 20 and Top 10 subsets make significant jump comparing to the shorter periods and ultimately reaches 8.41% and 8.81% by Top 20 and Top 10 on 3-months’ horizon, respectively. That results in the outperformance over the considered benchmarks by 228% in case of Top 20 stocks and by 234% 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 – 70.88% and 69.70%, respectively, comparing to the benchmark’s 34.07%.


In this analysis, we demonstrated the out-performance of our forecasts for the Indian Stocks picked by I Know First’s AI Algorithm from the NIFTY 200 index universe. Based on the presented observations, we record significant outperformance of the Top 30 stocks filtered by predictability for all time horizons for both asset universes, 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 NIFTY 200 index and the Indian stock market in general. That said, the Top 10 stocks from the NIFTY 200 index universe filtered by predictability and signal yield significantly higher returns than any other asset subsets on 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 Indian stocks to his portfolio can do so by simultaneously utilizing the I Know First predictability and signal indicators as criteria for picking stocks.

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