Stock Market Prediction: I Know First provides investment solutions for both individual and institutional investors, utilizing an advanced AI self-learning algorithm to gain a competitive advantage. We offer a personalized approach to our institutional clients, assisting them in their investment process based on their specific needs and preferences. For more details about I Know First solutions for institutional investors, please visit our website.

The Industry Portfolio with Consistent Forecasts and Intra-Month Rebalancing

Stock Prediction: The following trading strategy was developed using I Know First’s AI Algorithm daily forecasts from January 1^st, 2020, to December 31^st, 2023, with a focus on S&P 500 stocks selected based on the signal and predictable filters. This strategy is available to our institutional clients: hedge funds, banks, and investment houses, as a tier 2 service on top of tier 1 (the daily forecast).

The strategy involves trading GICS level 2 ETFs. While, the Level 1 Sectors include broad segments of the economy, such as technology, healthcare, finance, and consumer goods to provide a high-level view of the market. Level 2 Industries goes deep into specific industries. For example, within the technology sector (Level 1), you might find industries like semiconductors, software, and hardware. These industries offer a more detailed perspective on the market.

The strategy involves constructing a count-weighted portfolio with monthly rebalancing. Additionally, we utilize a signal outlier filter to ensure that stocks with signals outside of the selected range, i.e., those exhibiting extreme values, are not included.

Moreover, the strategy controls the majority direction. The term “majority direction” refers to our predictions for stocks, upon which we base our position. This decision is guided by a number of long and short stock forecasts. Therefore, if the count of long stock forecasts surpasses the count of short stock forecasts, the majority direction is to go long and we construct a long portfolio. Conversely, if the count of short stock forecasts is higher, we assume a short portfolio.

Also, we control the consistency of AI forecasts by checking how significantly the current forecast is different from the last one.

In this strategy, we initiate our portfolio based on the prevailing direction of the majority. If the majority direction is long, we choose the top three Level 2 ETFs with the most components from our stock universe. These ETFs are weighted based on the number of stocks from each ETF in our selection. For instance, if among our 50 picks, we have 7 stocks from XHB, 5 from KCE, and 4 from XSD, we purchase these three ETFs and assign weights accordingly. In the case of a short majority direction, we establish a short portfolio and review it weekly to ensure no changes are needed. If a short majority direction switches to long, we close our portfolio and transition to a long position in the SPY until the next rebalancing period.

Furthermore, we conduct a weekly review of our forecasts to perform weight rebalancing for selected ETFs using the sector stocks count method. If the number of stocks from XSD increases significantly, and KCE is no longer among the top 3 ETFs, we increase the weight of XSD. Instead of buying other ETFs to replace KCE, we close this position and move to cash.

Additionally, we monitor forecast consistency. If the majority direction is long/short, we buy/sell three Level 2 ETFs. Every week, we evaluate how many of them changed in the selection. If 2 out of 3 (66%) changed, we exit and buy SPY.

The strategy provides a positive return of 237.53% which exceeded the S&P 500 return by 190.52%. Below we can notice the strategy behavior for each year.

I Know First Algorithm – Seeking the Key &  Generating Stock Market Forecast

The I Know First predictive algorithm is a successful attempt to discover the rules of the market that enable us to make accurate stock market forecasts. Taking advantage of artificial intelligence and machine learning and using insights of chaos theory and self-similarity (the fractals), the algorithmic system is able to predict the behavior of over 13,500 markets. The key principle of the algorithm lies in the fact that a stock’s price is a function of many factors interacting non-linearly. Therefore, it is advantageous to use elements of artificial neural networks and genetic algorithms. How does it work? At first, an analysis of inputs is performed, ranking them according to their significance in predicting the target stock price. Then multiple models are created and tested utilizing 15 years of historical data. Only the best-performing models are kept while the rest are rejected. Models are refined every day, as new data becomes available. As the algorithm is purely empirical and self-learning, there is no human bias in the models and the market forecast system adapts to the new reality every day while still following general historical rules.

Stock Prediction: Conclusion

I Know First offers investment solutions for institutional investors, leveraging our advanced self-learning algorithm to gain a competitive advantage. We provide a personalized approach for our institutional clients, enhancing their investment process according to their specific needs and preferences. In this context, we have evaluated the performance of the industry portfolio with consistent forecasts and intra-month rebalancing during the period from January 1^st, 2020, to December 31^st, 2023.

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Please note-for trading decisions use the most recent forecast.