Permutation entropy (PE) has been widely used as a nonlinear statistical metric to quantify the evolution of complexity in time series data. PE has certain limitations, although it is conceptually simple and computationally efficient. Specifically, it ignores amplitude variations in the time series, and symbolic sequences generated from equal values are assigned only by order of occurrence. Few works are dedicated to using modified analogs of PEn for analyzing financial systems. Neither has focused on using modified indicators as indicators or precursors of financial collapse. This paper presents a comparative analysis of classical PEn, weighted PEn, amplitude-aware PEn, and uniform quantization-based PEn. The goal is to address the limitations of classical measures in distinguishing between amplitude differences in motifs that correspond to the same order pattern and to filter noisy fluctuations in a more sophisticated manner. To illustrate their prevalence, we compute them in a sliding window for a segment of the 2008 crash in the Dow Jones Industrial Average (DJIA) stock index. Empirical findings indicate that these entropies can capture market fluctuations more effectively and serve as more reliable indicators of financial crashes.