Work detail

Using the log-periodic power-law model to detect bubbles in stock market

Author: Bc. Samuel Maroš Kožuch
Year: 2017 - summer
Leaders: doc. PhDr. Ladislav Krištoufek Ph.D.
Consultants:
Work type: Bachelors
Language: English
Pages: 50
Awards and prizes:
Link: https://is.cuni.cz/webapps/zzp/detail/185527/
Abstract: Stock market crashes were considered as an chaotic even for a long time. However, more
than a decade ago a specific behavior was observed, which accompanied most of the
crashes: an accelerating growth of price and log-periodic oscillations. The log-periodic
power law was found to have an ability to capture the behavior prior to crash and even
predict the most probable time of the crash. The log-periodic power law requires a
complicated fitting method to find the estimated values of its seven parameters. In the
thesis, an alternative simpler fitting method is proposed, which is equally likely to find
the true estimates of parameters, thus generating an equally good fit of log-periodic power
law. Furthermore, four stock indices are fitted to log-periodic power law and examined for
possible log-periodic oscillations in different time periods, including a very recent period
of 2017. In all of the analyzed indices, a log-periodic oscillations could be observed. One
index, analyzed in past period, was fitted to log-periodic power law, which was able to
capture the oscillations and predict the critical time of crash. In the rest of the selected
stocks, which were analyzed in a recent period, the critical time was estimated with
varying results.

Partners

ČSOB
Deloitte
McKinsey & Company

Sponsors

CRIF