A reader suggested: “I know you’ve looked at Didier Sornette’s work in the past, but I think it would be worthwhile to look at his work again. His latest is ‘Bubble Diagnosis and Prediction of the 2005-2007 and 2008-2009 Chinese Stock Market Bubbles’, with abstract as follows:”
“By combining (i) the economic theory of rational expectation bubbles, (ii) behavioral finance on imitation and herding of investors and traders and (iii) the mathematical and statistical physics of bifurcations and phase transitions, the log-periodic power law model has been developed as a flexible tool to detect bubbles. The LPPL model considers the faster-than-exponential (power law with finite-time singularity) increase in asset prices decorated by accelerating oscillations as the main diagnostic of bubbles. It embodies a positive feedback loop of higher return anticipations competing with negative feedback spirals of crash expectations. We use the LPPL model in one of its incarnations to analyze two bubbles and subsequent market crashes in two important indexes in the Chinese stock markets between May 2005 and July 2009. Both the Shanghai Stock Exchange Composite and Shenzhen Stock Exchange Component indexes exhibited such behavior in two distinct time periods: 1) from mid-2005, bursting in Oct. 2007 and 2) from Nov. 2008, bursting in the beginning of Aug. 2009. We successfully predicted time windows for both crashes in advance with the same methods used to successfully predict the peak in mid-2006 of the US housing bubble and the peak in July 2008 of the global oil bubble. The more recent bubble in the Chinese indexes was detected and its end or change of regime was predicted independently by two groups with similar results, showing that the model has been well-documented and can be replicated by industrial practitioners. Here we present more detailed analysis of the individual Chinese index predictions and of the methods used to make and test them.”
Some key points from the paper you cite are:
- “In a nutshell, bubbles are identified as ‘super-exponential’ price processes, punctuated by bursts of negative feedback spirals of crash expectations. These works have been translated into an operational methodology to calibrate price time series and diagnose bubbles as they develop.”
- “Here, we present an ex-post analysis of what we identified earlier in their respective epochs as being two significant bubbles…”
- “Our main method for detecting bubbles and predicting the critical time…when the bubble will end either in a crash or change of regime is by fitting observed price time series to a log periodic power law model. This is a stochastic fitting procedure that we complement with other techniques… This philosophy of using multiple measures aids in filtering predictions, in that a candidate prediction must pass all tests to be considered worthy.”
- “…we stress that, notwithstanding the common use of the term ‘crash’ to refer to the aftermath of a bubble, a real crash does not always occur. Rather, the end of a bubble may be the most probable time for a crash to occur, but the bubble may end without a splash and, instead, transition to a plateau or a slower decay. This point is actually crucial in rational expectation models of bubbles in that, even in the presence of investors fully informed of the presence of the bubble and with the knowledge of its end date, it remains rational to stay invested in the market to garner very large returns since the risk of a crash remains finite.”
- “…we predicted both crashes with our techniques before the actual dates of the observed peaks…”
Reactions are:
The methods of the authors appear to be highly empirical and involve considerable experimentation through (1) fitting of data to model through parameter selection and (2) application of other filtering tests. Do the methods described fully mitigate data snooping bias?
Skepticism is in order when forecasters report their own past successes. Confirmation bias (and purposeful deception) can result in overemphasis of past successes and underemphasis of (or silence about) past failures. The iterative failures of related methods described in the blog entry you cite (see also “Testing the Stability of the 2000-2003 US Stock Market ‘Antibubble'”) support skepticism about the accuracy rate for a meaningful sample of forecasts.
The analyses do not apply bubble-related forecasts to returns from any investing strategies. In other words, the authors do not demonstrate any economic value of their forecasts for investors (and imply in the fourth point above that there may be none).
Measurement of investing strategy outcomes for a reasonably large sample of ex ante forecasts of bubble bursts from the authors would be interesting.
