Study on the Random Anomalies in the Stock Market

This paper uses the rescaled range analysis (R/S analysis) method to examine the fractal characteristics of the Index. Due to the possibility of multiple bubbles in the stock market, the log-periodic power law (LPPL) model is used to identify the bubbles in different periods. In order to conduct an investigation of this index, a calculation and analysis of the fractal dimension of the index was used along with LPPL modeling. The fractal dimension can be related to the nature of changes in the price of a stock, which allows for the detection of market bubbles. The LPPL modeling allows for the classification of bubbles as positive, negative, reverse, and reverse negative. Financial bubbles generated by market jumps can result in tremendous economic damage. Given their catastrophic impacts, it is important to accurately detect and identify market jump anomalies and their underlying financial bubbles. Existing research on financial market bubbles can be broadly divided into four categories: herd behavior, bubble theory, LPPL analysis, and the market jump anomaly. Most of these studies analyzed the formation, development, and bursting of financial market bubbles retrospectively.