Detail práce

Extending volatility models with market sentiment indicators

Autor: Mgr. Lenka Röhryová
Rok: 2018 - zimní
Vedoucí: prof. PhDr. Ladislav Krištoufek Ph.D.
Konzultant:
Typ práce: Diplomová
Ekonomická teorie
Jazyk: Anglicky
Stránky: 93
Ocenění:
Odkaz: https://is.cuni.cz/webapps/zzp/detail/185578/
Abstrakt: In this thesis, we aim to improve forecast accuracy of a heterogenous autoregressive
model (HAR) by including market sentiment indicators based
on Google search volume and Twitter sentiment. We have analysed 30 companies
of the Dow Jones index for a period of 15 months. We have performed
out-of-sample forecast and compiled a ranking of the extended models based
on their relative performance. We have identified three relevant variables:
daily negative tweets, daily Google search volume and weekly Google search
volume. These variables improve forecast accuracy of the HAR model separately
or in a Twitter-Google combination. Some specifications improve
forecast accuracy by up to 22% for particular stocks, others impair forecast
accuracy by up to 24%. The combination of daily negative tweets and weekly
search volume is a superior model to the basic HAR for 17 stocks according
to RMSE and for 16 stocks according to MAE and MASE. The daily negative
tweets specification outperforms the basic HAR for 17 and 19 stocks,
respectively. And, the combination of daily negative tweets and daily search
volume outpaces the basic HAR for 15 and 18 stocks, respectively. Based on
the average MASE improvement, the combination of daily negative tweets
and weekly search volume is a clear winner as it lowers the average MASE
by 0.71%.

Partneři

Deloitte

Sponzoři

CRIF
McKinsey
Patria Finance
Česká Spořitelna
EY