Application of the LPPL model in the identification and measurement of structural bubbles in the Chinese stock market

Abstract

This study proposes innovative methods for determining the starting point and estimating the parameters of the LPPL model for predicting stock market bubbles. These methods address the challenges in determining the starting point of irregularly staged bubbles and the issues of multiple local optima or no solutions in previous prediction algorithms. The proposed LPPL model, incorporating innovative algorithms, is applied to the Chinese A-share market from 2018 to 2021. The starting points of structural bubbles are determined using the difference and grid search methods, and the sequential quadratic programming (SQP) algorithm is used to estimate the parameters of the LPPL model. The study measures and warns about structural bubbles in the A-share market. The results demonstrate that the LPPL model with the innovative algorithm can accurately measure staged and structural bubbles in the A-share market and provide reasonably accurate warnings of bubble bursts, which have been confirmed by the market multiple times.