Executive Summary

In this stock market forecast evaluation report, we will examine the performance of the forecasts generated by the I Know First AI Algorithm for best Chinese stocks traded on Shanghai stock exchange which were daily sent to our customers. Our analysis covers the time period from January 23, 2019, to March 23, 2020.

Chart 1: Performance comparison for All Signals, Top 20, Top 10 and Top 5 signals with predictability filter vs SSE Composite Index

Chart 2: Performance comparison for All Signals, Top 20, Top 10 and Top 5 signals with predictability filter vs SSE Composite Index

Chart 3: SSE Composite Index Price (June 23, 2019 – March 23, 2020)

Best Chinese Stocks Evaluation Highlights:

• Outperformance until 1526% and the majority of signal groups succeeded in outperforming SSE Composite Index
• Amazing performances with an average return of 24.42%
• I Know First reached an accuracy of 71%
• Most of signal groups have hit ratios higher than 50% for the longest time horizons

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

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 the 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 a detailed description of our heatmap here.

Evaluating Chinese Stocks: The Stock Picking Method

We take the top 30 most predictable assets, and then we apply a set of signal-based filters: top 20, 10 and 5 based on predictability.

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.

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.

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.

The Stock Market Forecast 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 Hit Ratio Method

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

Using our Daily Forecast asset filtering, we predict the direction of the 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 – Shanghai Stock Exchange Composite Index

In order to evaluate our algorithm’s performance in comparison to the Shanghai Stock Exchange, we used the SSE Composite Index as a benchmark. The Shanghai Stock Exchange Composite was developed on December 19, 1990. It is a capitalization-weighted index. The index tracks the daily price performance of all A-shares and B-shares listed on the Shanghai Stock Exchange. A-shares are shares that denominated in Renminbi. B-shares refers to those that are traded in foreign currencies.

For each time horizon, we compare the SSE Composite Index performance with the performance of our forecasts after the filtering processes described above.

Performance Evaluation – The Signal Indicator Effect

Once filtering by predictability is done, we utilize the signal indicator in our asset picking method to achieve the maximum forecast performance. It is important to measure it with respect to the benchmark, i.e. how the selected assets out-perform the benchmark, and for that, we will apply the formula:

We further filter and rank the assets based on absolute signal strength to the Top 20 assets under the Daily Forecast model, which were already filtered by predictability.

In this article, we examine the kind of effect the signal filter has. To do so, we have filtered the Chinese stocks from SSE by predictability, selected the 30 most predictable Chinese stocks from the stock universe. These Chinese stocks are not necessarily all long or short positions, but they are a mix of both. That is because the I Know First algorithm is able to get return for both bull and bear market positions. Therefore, we applied filtering by signal strength to the top 30 assets filtered by predictability.

We further filter and rank the assets based on absolute signal strength to the Top 20 assets under the Daily Forecast model, which were already filtered by predictability.

Performance Evaluation – Overview

In this report, we conduct testing for Chinese stocks traded on SSE that I Know First cover by its algorithmic forecast. The period for evaluation and testing is from 23^th January 2019 to 23^th March 2020. During this period, we were providing our clients with daily forecasts and the time horizons which we evaluate in this report are 6 periods spanning from 3 days to 1 year.

Best Chinese Stocks Results: Average Return and Hit Ratio

Table 1: Average performance VS SSE Composite Index

As can be seen in Table 1, by applying the Top 30 Predictability filter our algorithm provided only positive returns. The SSE Composite Index was beaten by all signal groups for long term horizons (30 days, 90 days, 365 days) and most of them for short terms horizons (3 days 7 days, 14 days). Exceptionally high performances were observed for 365-day time horizon by the Top 10 signal filtering and Top 5 Signal filtering, reaching returns of 24.42% and 23.04% respectively. Those results indicate that the signal effect on forecast return was strong and consistent.

Table 2: Outperformance with predictability filter

We can notice that every signal group outperformed the SSE Composite index on almost all time horizons. We can observe higher outperformances on longest times horizons. Particularly on 90-day horizon, Top 5 and Top 10 Signals achieved amazing outperformances with 1526% and 1032% respectively. Overall, these results demonstrate a very strong and reliability of the algorithm. 

Table 3: Hit Ratio with predictability filter

According to the table above, the accuracy was better for the longest term horizons. Indeed, we can observe that from 14-day horizon, all hit ratios are higher than 50% (except one on 365-day horizon). The best hit ratio reached is 70% by Top 10 Signals on 90-day horizon. Overall, the accuracy is consistent and even high for Top 10 and Top 5 Signals for 90-day and 365-day horizons.

Table 4: Hit Ratio without predictability filter

As the last table, in this table with signals without predictability filter, we can notice that the accuracy is better for longest terms horizons (except one on 365-day horizon) achieving a hit ratio of 71% for Top 5 Signals for 365-day horizon. As we can see on the tables, the difference isn’t not big, but globally the signals filtered by predictability achieved better hit ratios than ones without predictability filter.

Conclusion on Chinese Stocks Forecast

This evaluation report presented the performance of I Know First’s algorithm for the Chinese Stocks traded on SSE from January 23^th, 2019 to March 23^th, 2020. It shows the average returns, hit ratios and outperformances for all time horizons. We also compared the accuracy between a signal grouping process with a predictability filter and one without. 

The results of this analysis showed amazing average returns and outperformances by the I Know First algorithm. Indeed, the algorithm achieved very high average returns: 24.42%, 23.04% and 13.65% on 365-day horizon. It also succeeded to outperform the SSE Composite Index until a percentage of 1526% (Top 5 Signals for 90-day horizon).

Moreover, this evaluation report demonstrated the efficacy of predictability filter. The hit ratios were slightly but overall higher for the signals with predictability filter. We look forward to new market data in the following months and will monitor the changes in performance trends that are going to be communicated to our investors and subscribers in the follow-up reports.