Work detail

Predicting Stock Market Volatility with Google Trends

Author: Bc. Jan Pecháček
Year: 2016 - summer
Leaders: prof. PhDr. Ladislav Krištoufek Ph.D.
Consultants:
Work type: Bachelors
Language: English
Pages: 55
Awards and prizes:
Link: https://is.cuni.cz/webapps/zzp/detail/166327/
Abstract: 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.
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