Detail práce

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

Autor: Bc. Samuel Maroš Kožuch
Rok: 2017 - letní
Vedoucí: doc. PhDr. Ladislav Krištoufek Ph.D.
Konzultant:
Typ práce: Bakalářská
Jazyk: Anglicky
Stránky: 50
Ocenění:
Odkaz: https://is.cuni.cz/webapps/zzp/detail/185527/
Abstrakt: 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.
VO Martina Jasova

20

Prosinec

VO Martina Jasova

Prosinec 2017
poútstčtsone
    123
45678910
11121314151617
18192021222324
25262728293031

Partneři

ČSOB
Deloitte
McKinsey & Company

Sponzoři

CRIF