Detail práce

Predicting Stock Market Volatility with Google Trends

Autor: Bc. Jan Pecháček
Rok: 2016 - letní
Vedoucí: prof. PhDr. Ladislav Krištoufek Ph.D.
Konzultant:
Typ práce: Bakalářská
Jazyk: Anglicky
Stránky: 55
Ocenění:
Odkaz: https://is.cuni.cz/webapps/zzp/detail/166327/
Abstrakt: This thesis aims to investigate the usability of Google Trends data for predicting stock market
volatility. Using daily Google data on tickers of three companies with large market
capitalization, we examine the causal relationship between Google data and volatility proxy.
We employ two common models for volatility, Generalised Autoregressive Conditional
Heteroskedasticity model (GARCH) and Heterogeneous Autoregressive model (HAR) and
we augment them by adding Google data. We studied the performance of in-sample
forecasting and out-sample forecasting. Our results show that Google data Granger-cause
stock market volatility and is able to produce more accurate results in in-sample forecasts
then models without Google data added.

Partneři

Deloitte

Sponzoři

CRIF
McKinsey
Patria Finance