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.

Stock Market Prediction: Strategies with Duplicate Positions

The following trading strategies were developed using I Know First’s AI Algorithm daily forecasts from January 1^st, 2020, to March 24^th, 2023, with a focus on S&P 500 stocks selected based on the predictability filter. The results of these strategies serve as examples of the trading solutions that I Know First could offer to institutional clients.

Strategy 1

Strategy 1 is to construct an equal-weighted portfolio by the top 30 most predictable stocks in the first week and complete the portfolio with the additional top 30 most predictable stocks in the following week, and do monthly rebalancing based on the predictability filter. At the same time, we drop duplicate stocks if these stocks have already been included in the first-week portfolio.

The strategy provides a positive return of 106.36% which exceeded the S&P 500 return by 86.26%. Below we can notice the strategy behavior for each year.

Strategy 2

Strategy 2 is to construct two signal-weighted portfolios in two weeks in a month by the top 20 most predictable stocks and do monthly rebalancing based on the predictability filter. At the same time, we drop duplicate stocks from the second portfolio if these stocks have already been included in the first portfolio, which we opened early.

The strategy provides a positive return of 219.37% which exceeded the S&P 500 return by 199.26%. Below we can notice the strategy behavior for each year.

Stock Market Prediction: Strategies with Double-Down Positions

Strategy 3

Strategy 3 is to construct an equal-weighted portfolio by the top 50 most predictable stocks in the first week and complete the portfolio with the additional top 50 most predictable stocks in the following week, and do monthly rebalancing based on the predictability filter. At the same time, we double down on stocks if these stocks have already been included in the first-week portfolio.

The strategy provides a positive return of 125.68% which exceeded the S&P 500 return by 105.57%. Below we can notice the strategy behavior for each year.

Strategy 4

Strategy 2 is to construct two equal-weighted portfolios in two weeks in a month by the top 20 most predictable stocks and do monthly rebalancing based on the predictability filter. At the same time, we double-down stocks in the second portfolio if these stocks have already been included in the first portfolio, which we opened early.

The strategy provides a positive return of 174.07% which exceeded the S&P 500 return by 153.97%. Below we can notice the strategy behavior for each year.

Overall, we can notice that the IKF strategy beats the S&P500 in a systematic way for 2020-2023.

Which is Strategy to Choose?

A choice of which strategy to choose depends on an investor’s appetite for return and risk. Above we can notice the comparison between strategies. Strategy 3 provides the highest overall return of 219.37% with daily volatility of 2.33%. At the same time, strategy 1 provides an overall return of 106.36% with the least daily volatility of 1.11%.

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 Market 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 Duplicate and Double-Down trading strategies based on the predictability filter during the period from January 21^st, 2020, to March 24^th, 2023.

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