This study investigates the validity of the Fractal Market Hypothesis (FMH) within the Nigerian Stock Market (NSM) by employing the Exponential Generalized Autoregressive Conditional Heteroskedasticity (EGARCH) model to analyze daily stock price data spanning 2015 to 2024. Unlike the Efficient Market Hypothesis (EMH), which assumes random, memoryless price movements driven by homogeneous investor behavior, the FMH posits that financial markets are inherently fractal, shaped by heterogeneous investor horizons and the presence of long-term memory in price dynamics.

The empirical analysis reveals strong evidence of volatility clustering and asymmetry in stock returns—key features consistent with the FMH. Specifically, the EGARCH model confirms that negative news or shocks have a greater impact on market volatility than positive ones of the same magnitude, indicating the presence of the leverage effect. This asymmetric response to market shocks suggests that the NSM does not fully conform to the assumptions of market efficiency, instead exhibiting persistent, fractal-like structures in its volatility behavior.

These findings have meaningful implications for both investors and regulatory bodies. Investors may benefit from recognizing and adapting to the fractal characteristics of the market, particularly by incorporating volatility patterns into risk management and trading strategies. For policymakers and market regulators, the presence of long-term memory and asymmetric effects underscores the need for improved transparency, market stability mechanisms, and investor education initiatives.

Overall, the study contributes to the ongoing discourse on market behavior in emerging economies by offering empirical support for the fractal nature of stock price movements in Nigeria. Future research could extend this analysis by incorporating macroeconomic variables and applying the FMH framework to other African or emerging markets to evaluate the universality of these findings