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Highlights:

• Utilizing a Machine Learning approach to analyze financial data, enabling the identification of patterns at a level inaccessible to humans.
• All financial assets, to varying degrees (with the exception of VIX, the volatility index), exhibit long-term patterns, allowing for the application of machine learning methods.
• Corporate 5-10 years bonds and crude oil are potentially the most predictable financial market segments.

AI Forecast: The financial market is a dynamic and complex ecosystem in which individuals and institutions trade a wide range of financial instruments, from stocks and bonds to derivatives and commodities. While it offers tremendous opportunities for profit and wealth creation, it is also fraught with risk. Understanding and managing risk in the financial market is essential for investors to make informed decisions and protect their investments.

In our previous article, we estimated the Hurst exponent for the S&P500 and sector ETF SPDRs, identifying that the energy sector (XLE) is the least risky for further comprehensive learning and forecasting by artificial intelligence algorithms. This assertion about the energy sector in the stock market contradicts the classical understanding of energy companies as among the most risky stocks based on volatility or the Beta coefficient. In this article, we shift our focus to various segments of the financial market.

The possibility of effectively forecasting financial markets has intrigued minds for several centuries. Currently, various models have been developed, ranging from fundamental and technical analysis to econometric and macroeconomic models. The latest milestone in the development of financial models is the advancement of artificial intelligence based on machine learning models. These models enable a deeper level of data analysis and pattern identification compared to traditional financial models. Artificial intelligence models can consider and test numerous potential investment scenarios, using their deep learning capabilities to paint a more comprehensive picture of the investment landscape and uncover risks and benefits not available to traditional models. However, the effectiveness of machine learning models in financial markets hinges on the learning aspect. A poorly trained model is more likely to generate losses for investors.

Machine learning focuses on the development of algorithms and models that enable computers to learn from data and make predictions or decisions. However, the effectiveness of training and subsequent forecasting depends on the quality of the training material. If the training material consists of white noise, the effectiveness of the training will be negligible. The Hurst exponent measures the degree of jaggedness in a time series. A smaller Hurst exponent value indicates more noise in the system and a greater resemblance to randomness. Conversely, a larger Hurst exponent value suggests less noise, more persistence, and clearer trends in the data.

There are three possible cases to consider:

• If 0 ≤ H < 0.5, then a time series is characterized as mean-reverting.
• if H = 0.5, then there is a completely uncorrelated time series with normal distribution. Stock returns are random, which is an example of Brownian motion.
• if 0.5 < H ≤ 1, then a time series has positive autocorrelation and clusters in one direction (there is a persistent component). The closer the H value is to 1, the more predictable a time series is.

The persistent time series defined as 0.5<H≤1 is a fractal because it can be described as a generalized Brownian motion. In generalized Brownian motion, there is a correlation between events on the time scale. The Hurst value greater than 0.5 means that today’s events will have significance tomorrow. This means that the received information continues to be considered by the market for some time afterward. It’s not just a short-term correlation where the impact of information quickly diminishes. It’s a function of long-term memory that determines the informational influence over extended periods of time (we discussed features of long-term memory on the different stock markets here, and we analyzed the behavior of the S&P500 in the COVID time here).

Analysis of the Financial Market Segments

In this article, we estimate various segments of the financial market to identify the efficiency with which machine-learning algorithms can be implemented to learn patterns in data and discover investment opportunities. We calculate the Hurst exponent for the stock market, volatility, government and corporate bonds, currency, commodities, and cryptocurrency assets.

Why does the Hurst exponent emerge in capital markets? Price changes are fundamentally based on investors’ perception of fair value. Investors assess assets within a certain price range, influenced partly by fundamental information. The second component of the price range is how investors perceive the willingness to pay from others. This intuitive component is also analyzed, resulting in a specific range around a fair price. If fundamental indicators are favorable, the price approaches fair value. If investors observe a trend aligning with their positive expectations for a particular financial asset, they start buying, following the example of others. Consequently, yesterday’s activity influences today, as the market retains a memory of its previous trend. However, this behavior is not constant, as new information about a financial asset can dramatically change the established price range and reverse the market situation.

Below, we have estimated the Hurst exponent for the analyzed assets based on the returns of the last 1024 data points. Additionally, we calculated the mean using a rolling window of 1024 data points.

According to Table 2, we observe that all assets, except for VIX, have a Hurst exponent value above 0.5. This underscores that their time structure is considered fractal, making it problematic to implement standard statistical tools. Variances are uncertain (or infinite), rendering volatility an inappropriate risk measure. A higher value of Hurst implies less noise, greater persistence, and clearer trends compared to a lower value of Hurst. Larger Hurst values can be interpreted as indicating less risk because they correspond to data containing less noise. Consequently, this suggests that USO or IGIB have fewer risks than the S&P500 or BTC. However, assets with high Hurst values have the risk of a sharp drop. Hurst describes the existence of long-term memory, the presence of which invalidates econometric models.

Volatility holds a special place in our research. Currently, the VIX has a Hurst exponent of 0.5029, which is close to 0.5, meeting the requirements of an efficient market. Simultaneously, the mean is 0.4686, satisfying the conditions for mean-reverting. Specifically, a Hurst exponent less than 0.5 indicates an anti-persistent time series. Volatility is one of the few anti-persistent time series existing in economics. If volatility increased a month ago, it is most likely to decrease next month. Since the Hurst exponent is less than 0.5, there is no mean value in this distribution. Volatility can pose one of the most challenging scenarios for the implementation of machine learning models.

How Our AI Algorithm Works

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

Conclusion

At first glance, financial markets offer enormous opportunities for wealth and allow potential investors to hope for an easy way to increase their wealth. To achieve this goal, one must address what, at first glance from price charts, appears to be a simple task: finding patterns in the price movements of financial assets and using this knowledge for forecasting. However, the simplicity of this task is illusory and requires the use of machine learning and artificial intelligence approaches to analyze financial data, enabling the identification of patterns at a level inaccessible to humans. In this article, we measured the Hurst exponent, an indicator of long-term memory, for various segments of the financial market. Based on our analysis, all financial assets, to varying degrees (with the exception of VIX, the volatility index), exhibit long-term patterns, allowing for the application of machine learning methods.

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