Stock Market Prediction: The Signal Weighted Strategy with the Signal Outlier Filter
This article “Stock market prediction: The Signal Weighted Strategy with the Signal Outlier Filter” was written by the I Know First Research Team.
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: The Signal Weighted Strategy with the Signal Outlier Filter
The following trading strategy was developed using I Know First’s AI Algorithm daily forecasts from January 21st, 2020, to June 26th, 2023, with a focus on S&P 500 stocks selected based on the signal filter. 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 constructing a signal-weighted portfolio with monthly rebalancing by implementing the signal outlier filter. In this context, stock weights are assigned based on forecasts of signals. The signal outlier filter ensures that stocks with signals outside of the chosen range, i.e., those with extreme values, are not considered.
The strategy provides a positive return of 539.93% which exceeded the S&P 500 return by 507.63%. 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.
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 signal-weighted strategy with the signal outlier filter during the period from January 21st, 2020, to June 26th, 2023.
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Please note-for trading decisions use the most recent forecast.
