Dividend Stocks: AI Beats S&P 500 by 34.67%

Executive Summary

The purpose of this Dividend Stocks forecast report is to present the results of the live forecast performance evaluation for the Dividend Stocks package by the I Know First AI Algorithm. The following results were observed when a signal filter was applied. The evaluation period is from 24th June 2020 to 24th October 2021. The corresponding returns distribution of stock signal filters for this package is shown below:

The Dividend Stocks Report Highlights:

  • The highest average return is 67.07% for the Top 5 Signals on the 1-year time horizon
  • The most impressive out-performance against the S&P 500 index is from the Top 5 signal group in the 1 year horizon with 2.07 times higher return
  • Predictions reach up to 100% hit ratio regardless of economic conditions amid COVID-19
  • Every signal group has hit ratios above 54% for all time horizons
  • I Know First provides an investment strategy for institutional investors that generated a return of 64.71% and exceeded the S&P 500 return by 18.61% for the period from June 24th, 2020 to October 24th, 2021

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.

I Know First - Algorithm

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.

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:

forecast performance

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 the actual movements of these assets within the same time horizon.

The hit ratio is then calculated as follows:

hit ratio

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. The 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.

Dividend Stocks Performance Evaluation – Overview

In this report, we conduct testing for Dividend Stocks that I Know First covers by its algorithmic forecast. The period for evaluation and testing is from June 24th, 2020 to October 24th, 2021. During this period, we were providing our clients with daily forecasts in time horizons spanning from 3 days to 1 year which we evaluate in this report.

(Table 1: Average Returns Per Time Horizon for the Dividend Stocks Package)

As can be seen in the table above, our algorithm provided positive returns for all time horizons and had a better return than the S&P 500 in every time horizon analyzed. Other than beating the benchmark in every time horizon analyzed, the difference between the return of the I Know First algorithm and the benchmark grows as the time horizons get longer.

(Figure 1: Average Returns Per Time Horizon Short Term for the Dividend Stocks Package)
(Figure 2: Average Returns Per Time Horizon Long Term for the Dividend Stocks Package)

From the above charts, it is evident that as the forecasting horizon expands, the average returns tend to become higher, and also the delta grows between the S&P500 and the average returns. In every time horizon analyzed, the I Know First algorithm beat the benchmark clearly. When analyzing the Long term horizon, on average, the algorithm provided a return of almost twice the S&P 500. For the 1-year time horizon, the Top 5 Signals group significantly outperformed the benchmark index – by more than 2.07 times resulting in an average return of 67.07% versus the S&P 500’s average return of 32.40%.

(Table 2: Hit Ratio Per Time Horizon for the Dividend Stocks Package)

According to the table above, all the signal groups across all time horizons gave a hit ratio greater than 54%. By increasing the time horizon analyzed, we notice that the hit ratio increases, meaning as the time horizon gets longer the market becomes more predictable. I Know First hit ratios gradually increase from 54% for the short-term horizons to 100% for the 1-year horizon. Even though the Top 10 Signals has a 100% hit ratio, the highest return for the 1-year time horizon is from the Top 5 Signals, which also has a high hit ratio (99%).

(Figure 3: Hit Ratio Per Time Horizon of the Dividend Stocks Package)

Looking at Figure 3, it is clear that at the short term horizons the hit ratios are relatively low at all signal indicators but the hit ratios increase over the long term horizons showing the I Know First Algorithm is able to successfully predict most of the stock movements, reaching almost 100% hit ratio for all signals subsets (Top 20, Top 10 and Top 5 Signals).

I Know First has used algorithmic outputs from the Dividend Stocks package to provide an investment strategy for institutional investors.

hit ratios signals indicators return long term horizon
(Figure 4: The Investment Result for the period from 24th June 2020 to 24th October 2021)

The investment strategy that was recommended to institutional investors by I Know First accumulated a return of 64.71% that exceeded the S&P 500 return by 18.61%.

Dividend Stocks: Conclusion

This report looked at the live performance forecast of I Know First data for the Dividend Stocks Package from June 24th, 2020 to October 24th, 2021. From the above data, we can observe that the I Know First Algorithm is exceeding the S&P 500 benchmark index across all signal filtering subsets and forecasting periods in most groups. Data from Figures 1 and 2 above shows I Know First was able to generate a return that exceeded the S&P 500 return by 34.67% in one year. In the 3 months period, the Top 10 Signals subset average return is 13.82% that exceeds the S&P500 index by 7.14%. Moreover, I Know First has used AI outputs to provide an investment strategy for institutional investors that generated a return of 64.71% and exceeded the S&P 500 return by 18.61% for the period from June 24th, 2020 to October 24th, 2021.