The article explores the core principles of FMH and its application in trading and market price forecasting. FMH offers a new perspective for understanding market dynamics, allowing for the detection of patterns that traditional analysis methods often overlook. Special emphasis is placed on the scaling properties of market data, which enables the use of forecasting models across different time intervals, from short-term to long-term predictions. FMH also considers the probability of extreme events, enhancing risk assessment and improving forecasting accuracy. The article discusses the advantages of the Fractal Market Hypothesis compared to traditional financial hypotheses, such as the Random Walk Hypothesis and the Efficient Market Hypothesis, and its potential for integration with machine learning methods to create more accurate and reliable forecasting models.
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