On the predictability of stock market bubbles: evidence from LPPLS confidence multi-scale indicators

TLDR

The study examines the predictability of positive and negative stock market bubbles using LPPLS Confidence Multi-scale Indicators on the S&P 500, FTSE, and NIKKEI indexes. The authors apply the LPPLS Confidence Multi-scale Indicators to these indices to analyze bubble dynamics. The LPPLS framework successfully captures prominent bubbles across time scales, and short‑selling activity and market liquidity provide robust predictive power for negative and positive bubbles, suggesting that trading‑activity proxies can guide bubble monitoring.

Abstract

We examine the predictability of positive and negative stock market bubbles via an application of the LPPLS Confidence Multi-scale Indicators to the S&P500, FTSE and NIKKEI indexes. We find that the LPPLS framework is able to successfully capture, ex-ante, some of the prominent bubbles across different time scales, such as the Black Monday, Dot-com, and Subprime Crisis periods. We then show that measures of short selling activity have robust predictive power over negative bubbles across both short and long time horizons, in line with the previous studies suggesting that short sellers have predictive ability over stock price crash risks. Market liquidity, on the other hand, is found to have robust predictive power over both the negative and positive bubbles, while its predictive power is largely limited to short horizons. Short selling and liquidity are thus identified as two important factors contributing to the LPPLS-based bubble indicators. The evidence overall points to the predictability of stock market bubbles using market-based proxies of trading activity and can be used as a guideline to model and monitor the occurrence of bubble conditions in financial markets.