Work detail

Predicting stock market crises using investor sentiment indicators

Author: Mgr. Kateřina Havelková
Year: 2020 - summer
Leaders: PhDr. Jiří Kukačka Ph.D.
Consultants:
Work type: Finance, Financial Markets and Banking
Masters
Language: English
Pages: 117
Awards and prizes: Deloitte Outstanding Thesis Award.
Link: https://is.cuni.cz/webapps/zzp/detail/213865/
Abstract: Using an early warning system (EWS) methodology, this thesis analyses the
predictability of stock market crises from the perspective of behavioural fnance.
Specifcally, in our EWS based on the multinomial logit model, we consider investor sentiment as one of the potential crisis indicators. Identifcation of the
relevant crisis indicators is based on Bayesian model averaging. The empirical results reveal that price-earnings ratio, short-term interest rate, current
account, credit growth, as well as investor sentiment proxies are the most relevant indicators for anticipating stock market crises within a one-year horizon.
Our thesis hence provides evidence that investor sentiment proxies should be
a part of the routinely considered variables in the EWS literature. In general,
the predictive power of our EWS model as evaluated by both in-sample and
out-of-sample performance is promising.

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