This article “Stock Market Forecast: Integration of New Technologies and Traditional Approach” was written by Sergey Okun – Senior Financial Analyst at I Know First, Ph.D. in Economics.

Highlights

• Fundamental analysis allows investors to understand the intrinsic value of stocks and their growth potential.
• Machine learning and artificial intelligence transform the process of forecasting in the stock market by analyzing large amounts of data and making accurate predictions.
• The combination of fundamental analysis and machine learning opens up new opportunities for forecasting and managing investments.

Stock Market Forecast: Early mentions of fundamental analysis date back to the beginning of the 20th century when economists began studying the relationship between economic indicators and stock prices. Benjamin Graham was one of the first known proponents of this approach, developing the principles of ‘value investing.’ Warren Buffett, widely recognized as the most famous follower of Benjamin Graham’s ideas, is renowned for his successful investment strategies based on fundamental principles.

Fundamental analysis in the stock market represents an approach to valuing a company that considers financial reports, economic data, and macroeconomic factors. This method enables investors to understand a stock’s intrinsic value and identify its growth potential. Fundamental analysts evaluate companies based on several key parameters, such as revenues, expenses, assets, liabilities, profits, and dividends. By analyzing these parameters, they attempt to determine the fair value of a share, which may differ from the current market price. If the assessment indicates a higher value than the current price, an analyst might recommend buying shares, anticipating that their value will rise. Conversely, if the evaluation suggests a lower value than the current price, they might advise selling shares, expecting that their value will decline.

Despite its widespread adoption and popularity, fundamental analysis has its critics. They argue that it lacks flexibility and does not always accurately predict market changes. Firstly, it requires significant time and effort to analyze large amounts of information. Secondly, it often relies on subjective judgments and assumptions by analysts, who can make mistakes. Thirdly, fundamental data tends to become obsolete faster than technical data, rendering it less useful for short-term forecasting.

Machine Learning and Artificial Intelligence in Stock Market Prediction

Stock Market Forecast: Machine learning and artificial intelligence are new technologies that are revolutionizing stock market prediction. These methods enable the analysis of vast amounts of data, reveal hidden patterns, and make accurate predictions based on that data. One of the most popular machine learning methods used in finance is neural networks. Neural networks learn from historical data and can predict future events based on that data. They are particularly effective in scenarios where complex dependencies need to be predicted or hidden patterns need to be discovered in large datasets.

Another application of artificial intelligence is the use of machine learning algorithms to optimize trading strategies. These algorithms analyze historical trade data to identify optimal moments for entering and exiting the market, significantly increasing the accuracy and efficiency of trading operations. Machine learning and artificial intelligence offer several advantages over traditional analytical methods. Firstly, they can handle enormous amounts of data that are beyond human capacity. Secondly, they operate in real-time, updating predictions and recommendations based on the latest data. Finally, they can account for far more variables than humans could possibly manage.

Combining Fundamental Analysis and Machine Learning

The combination of fundamental analysis and machine learning opens up new horizons for predicting and managing investments. Traditional fundamental analysts can use machine learning to automate routine tasks such as data collection and analysis. This frees up time for a deeper exploration of the important factors that influence stock prices.

Artificial intelligence can be used to test hypotheses and model various scenarios of event development. This allows analysts to develop more precise and reliable predictions by considering numerous variables and uncertainties. Ultimately, fundamental analysis and machine learning complement each other, creating a more comprehensive and accurate approach to stock market prediction. Investment firms that effectively integrate these two approaches will gain a significant competitive advantage and increase their profitability.

Stock Market Forecast: Finding Investment Opportunities with AI

I Know First provides stock market forecasts based on chaos theory approaches. Previously, we discussed the Conceptual Framework of Applying ML and AI Models to Analyze and Forecast Financial Assets. 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 lays 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.

I Know First has used algorithmic outputs to provide an investment strategy for institutional investors. Below you can see the investment result of our sector ETFs portfolio for the period from January 1^st, 2020, to July 31^st, 2024.

The strategy provides a positive return of 117.80% which exceeded the S&P 500 return by 50.25%.

Conclusion

Both fundamental analysis and machine learning/artificial intelligence techniques are crucial tools for predicting and analyzing the stock market. Each has its own strengths and weaknesses, and combining these approaches can yield the best results. Investors should utilize a combination of these methods to make informed decisions and maximize their investments.

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