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Author: [Your Name/AI-Assisted Draft] Affiliation: Quantitative Finance Research Unit Date: April 17, 2026 Abstract Traditional financial models, rooted in the Efficient Market Hypothesis (EMH) and Gaussian statistics, fail to account for extreme events, volatility clustering, and long-range dependence. This paper introduces Fractal Market Analysis (FMA) as a superior framework. We explain the theoretical foundations of fractals, self-similarity, and the Hurst exponent (H). Using the Rescaled Range (R/S) and Detrended Fluctuation Analysis (DFA) methodologies, we demonstrate that financial returns are not random walks but persistent fractal processes. Empirical results on S&P 500 data show H > 0.5, confirming long-term memory. The paper concludes with practical implications for risk management, trading strategies, and option pricing under fractal dynamics.
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Author: [Your Name/AI-Assisted Draft] Affiliation: Quantitative Finance Research Unit Date: April 17, 2026 Abstract Traditional financial models, rooted in the Efficient Market Hypothesis (EMH) and Gaussian statistics, fail to account for extreme events, volatility clustering, and long-range dependence. This paper introduces Fractal Market Analysis (FMA) as a superior framework. We explain the theoretical foundations of fractals, self-similarity, and the Hurst exponent (H). Using the Rescaled Range (R/S) and Detrended Fluctuation Analysis (DFA) methodologies, we demonstrate that financial returns are not random walks but persistent fractal processes. Empirical results on S&P 500 data show H > 0.5, confirming long-term memory. The paper concludes with practical implications for risk management, trading strategies, and option pricing under fractal dynamics.
