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. |
