Application of Multifractal Analysis in Stock Market

Abstract

Multifractal analysis provides a detailed approach to examine complex systems with varying scaling behaviors across multiple time scales. In this article, multifractal detrended fluctuation analysis (MFDFA) is applied to the index returns of Netflix from 2019 to 2024. The purpose is to uncover the multifractal nature of financial data through analyzing original, shuffled and surrogate time series and to identify sources of multifractality, particularly focusing on the roles of fat-tailed distributions and temporal correlations. This article finds out that even after shuffling, which disrupts time-dependent correlations, the multifractality still remains or even intensifies. Meanwhile, the surrogate data is investigated to study the sources of multifractality. The results show that Netflix stock returns exhibit clear multifractal properties, primarily driven by fat-tailed distributions rather than long-term correlations. In general, multifractal detrended fluctuation analysis provides important insights into the complex dynamics of financial markets, and it demonstrates how multifractal analysis can reveal underlying structures that traditional methods often overlook. These findings have implications for better risk management and market analysis by acknowledging the critical role of extreme events and distributional characteristics in stock market behavior.