On the Predictability of Stock Market Bubbles: Evidence from LPPLS ConfidenceTM Multi-scale Indicators
Riza Demirer (),
Guilherme Demos (),
Rangan Gupta () and
Didier Sornette ()
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Didier Sornette: ETH Zürich, Dept. of Management, Technology and Economics, Zürich, Switzerland and Swiss Finance Institute
No 201752, Working Papers from University of Pretoria, Department of Economics
We examine the predictive power of market-based indicators over the positive and negative stock market bubbles via an application of the LPPLS ConfidenceTM Multi-scale Indicators to the S&P500 index. 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.
Keywords: Financial bubble indicators; LPPL method; Markov switching; Predictability; Short interest (search for similar items in EconPapers)
JEL-codes: C13 C58 G14 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-rmg
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