This article examines the MF-DFA (Multifractal Detrended Fluctuation Analysis) algorithm developed by J. Kantelhardt, a widely used tool for detecting informational inefficiency in the stock market. Its ability to identify subsets of data with varying degrees of correlation has linked it to the concept of market efficiency, where correlated returns imply inefficiency. However, as demonstrated in this study, the algorithm was originally designed for general purposes and does not account for the unique characteristics of financial data. One of the key features of financial data is volatility clustering, which, by itself, does not violate market efficiency. The article shows that volatility clustering generates a significant portion of the multifractal spectrum, often interpreted as a sign of inefficiency. Thus, applying the MF-DFA algorithm to financial data fails to distinguish between (a) the multifractal effect arising from varying degrees of correlation in price returns, and (b) the multifractal effect induced by clustered volatility. Consequently, the multifractal effect detected via MF-DFA analysis cannot serve as a definitive criterion for assessing whether a market is efficient or inefficient in the weak-form sense.

Philipp Sysoev. The MF-DFA Algorithm as a Tool for Testing Market Efficiency. Academic Journal of Business & Management (2025), Vol. 7, Issue 9: 73-78. https://doi.org/10.25236/AJBM.2025.070911.

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