Detail práce

Forecasting stock market returns and volatility in different time horizons using Neural Networks

Autor: Bc. Martin Hronec
Rok: 2015 - letní
Vedoucí: doc. PhDr. Jozef Baruník Ph.D.
Konzultant:
Typ práce: Bakalářská
Jazyk: Anglicky
Stránky: 102
Ocenění:
Odkaz: https://is.cuni.cz/webapps/zzp/detail/150416/
Abstrakt: This thesis is focused on multiple-step-ahead forecasting of Nasdaq Composite
index returns and daily range-based volatility. In order to capture the complex
patterns potentially hidden to traditional linear models we use artificial neural
networks as nonlinear, nonparametric and robust forecasting tool. We contribute
to the ongoing discussion about stock market predictability with following empirical
results. In case of Nasdaq Composite returns, all four applied neural networks
fail to outperform benchmark model in all time horizons, suggesting high unpredictability
in accordance with Efficient market hypothesis. Also in case of Nasdaq
Composite daily range-based volatility, 1 day and 1 month ahead predictions are
not significantly more accurate than benchmark model. However, we find 1-week
and 2-weeks-ahead forecasts to be significantly more accurate than benchmark
model and able to capture the predictive patterns.

Partneři

Deloitte

Sponzoři

CRIF
McKinsey
Patria Finance