Detection of Chinese Stock Market Bubbles with LPPLS Confidence Indicator

TL;DR

This paper introduces an advanced LPPLS-based method for early detection of stock market bubbles in China, demonstrating its effectiveness in predicting bubbles and crashes using daily CSI 300 data from 2002 to 2018.

Contribution

It is the first to apply the LPPLS confidence indicator to Chinese stock market data, enhancing bubble detection accuracy with stricter filtering and statistical tests.

Findings

Successfully identified historical bubbles and crashes in the Chinese stock market.

The probability distribution of bubble start times is skewed towards periods of rapid price growth.

The method allows for early detection, potentially reducing bubble-related damages.

Abstract

We present an advance bubble detection methodology based on the Log Periodic Power Law Singularity (LPPLS) confidence indicator for the early causal identification of positive and negative bubbles in the Chinese stock market using the daily data on the Shanghai Shenzhen CSI 300 stock market index from January 2002 through April 2018. We account for the damping condition of LPPLS model in the search space and implement the stricter filter conditions for the qualification of the valid LPPLS fits by taking account of the maximum relative error, performing the Lomb log-periodic test of the detrended residual, and unit-root tests of the logarithmic residual based on both the Phillips-Perron test and Dickey-Fuller test to improve the performance of LPPLS confidence indicator. Our analysis shows that the LPPLS detection strategy diagnoses the positive bubbles and negative bubbles corresponding to well-known historical events, implying the detection strategy based on the LPPLS confidence indicator has an outstanding performance to identify the bubbles in advance. We find that the probability density distribution of the estimated beginning time of bubbles appears to be skewed and the mass of the distribution is concentrated on the area where the price starts to have an obvious super-exponentially growth. This study is the first work in the literature that identifies the existence of bubbles in the Chinese stock market using the daily data of CSI 300 index with the advance bubble detection methodology of LPPLS confidence indicator. We have shown that it is possible to detect the potential positive and negative bubbles and crashes ahead of time, which in turn limits the bubble sizes and eventually minimizes the damages from the bubble crash.