Executive Summary

In this Low P/E evaluation report, we will examine the performance of the forecasts generated by the I Know First AI Algorithm for the Low P/E Stocks, a subpackage of the Fundamental stocks package, for short and long positions that were sent daily to our customers Our analysis covers the time period from May 17th, 2020 to October 1st, 2020.

Fundamental Stocks Evaluation Report Highlights:

• The most impressive out-performance against the S&P 500 index is from the Top 5 signal group in the 3-month horizon with 2.5 times higher return.
• The Top 20, Top 10 and Top 5 signal groups generated by I Know First succeeded in outperforming S&P 500 index in most of the short-term horizons.
• The Top 20, Top 10 and 5 signal group generated by I Know First succeeded in outperforming S&P 500 index in all long term horizons.
• Every signal group has significant hit ratios reaching 62%.

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 Stock Forecasts: Fundamental Package – Low P/E

The Fundamental Package includes our algorithmic forecasts for undervalued stocks screened by fundamental criteria. We choose such fundamental stocks by taking the top most predictable assets, and then we apply a set of signal-based filters: top 20, 10, and 5 based on 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. These forecasts are provided to our clients, which include short-term and long-term time horizons, spanning from 3 days to 3 months.

For the analysis, we group the forecasts by absolute signals since these strategies are long and short. If the signal is positive, then we buy and if negative, we short.

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 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 – S&P 500 Index

In order to evaluate our algorithm’s performance in comparison to the US market, we used the S&P 500 index as a benchmark.

The S&P 500 measures the stock performance of the largest 500 companies by market cap listed on different stock exchanges in the United States. It is one of the most followed equity indices and is frequently used as the best indicator for the overall performance of US public companies, and the US market as a whole. S&P 500 is a capitalization-weighted index, the weight of each company in the index is determined based on its market cap divided by the aggregate market cap of all the S&P 500 companies.

For each time horizon, we compare the S&P 500 performance with the performance of our forecasts.

Performance Evaluation: Fundamental Stocks with Low P/E

In this P/E evaluation report, we conducted testing for the Fundamental stocks with Low P/E ratio that I Know First covers by its algorithmic forecast. The period for evaluation and testing was from May 17th until October 1st, 2020. During this period, we were providing our clients with daily forecasts for fundamental stocks with Low P/E ratio spanning from 3 days to 3 months which we evaluate in this report.

As can be seen in the table above, our algorithm provided positive returns. The S&P 500 benchmark was outperformed by every signal group for almost every time horizon, up to 3.8 times more return than the benchmark for the 1 month period. The Top 5 signal group also outperformed the benchmark index by 13.87% in the 365-day time horizon. The Top 10 and Top 5 Signals for shorter and longer time horizons performed extremely well, especially for the long time periods. It is also evident that with each following signal group, the returns increase. The Top 5 signal group has higher returns than the Top 10 signal group which has higher returns than the Top 20 signal group. This shows the algorithm’s increased accuracy with each subsequent forecast narrowing down the best fundamental stocks with Low P/E ratio to trade. The ability to filter by signal can improve the overall performance of the investment. An investment in the top 5 signals would have provided almost a 5 times higher return than an investment in the S&P 500 index and 39% higher than an investment in the top 10 signals for the 14 days time period.

According to the table above, each signal group across every time horizon gave a hit ratio reaching 62%. This shows that the algorithm’s accuracy is consistent and reliable within the current volatile market context. For example, the Top 5 signal group for the 3-months time horizon had a hit ratio of 62% and return that outperformed the S&P 500 Index by a large margin, suggesting the consistent accuracy of the algorithm. The Top 10 and Top 5 signal group for 1-week, 2-weeks, 1-month and 3-months’ time horizon all gave the highest hit ratio of 51-62% accuracy.

These results show continuous performance improvement by the I Know First AI predictive algorithm in comparison to our previous report for the long-time horizons.

Conclusion

This Low P/E evaluation report presented the performance of I Know First’s algorithm for the Top Low P/E forecast from May 9th, 2019 to October 1st, 2020. It shows the average returns and hit ratios for all time horizons, with the algorithm outperforming the benchmark index in most of the time periods. The I Know First algorithm has obtained better performance for the long-term horizons. It is also important to note that every signal group across every time horizon gave a hit ratio are above 47% and up to 62%, showing a consistent and reliable accuracy.