On behavior of Malaysian equities through fractal analysis

Existence of a long memory in many processes including financial time series, documented via different methodologies in previous literatures, has inspired recent work in quantifying the fractal characteristics. This paper questions the accuracy of commonly applied fractal analysis on explaining persistency behavior of time series understudy. Rescaled range (R/S) and power spectrum techniques produce fractal dimensions for daily returns of twelve Malaysian stocks from the most well performed firms in Kuala Lumpur stock exchange. Zipf´s law generates linear and logarithmic power-law distribution plots to evaluate the validity of estimated fractal dimensions on prescribing stocks´ persistency behavior with less ambiguity. Findings from this study suggest that a more thoughtful approach is required when applying fractal analysis for quantifying persistent behavior.