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Resources: financial applications of GARCH and rugarch

Practical Issues in the Analysis of Univariate GARCH Models
Zivot (2008)
http://faculty.washington.edu/ezivot/research/practicalgarchfinal.pdf

Introduction to the rugarch package
Ghalanos (2014)
http://cran.r-project.org/web/packages/rugarch/vignettes/Introduction_to_the_rugarch_package.pdf

Estimation and forecast using rugarch 1.2-2
Pedersen (2013)
https://stat.ethz.ch/pipermail/r-sig-finance/attachments/20130608/91b6cb9d/attachment.pdf

FTSE 100 raw vs. detrended Google Trends data

What can Google Trends tell us about the stock market?

Google Trends is an interesting data source that can be used in a number of applications. But what does our search behaviour say about reality? Here, I summarize how researchers have described Google Trends data in the past. Researchers have suggested that Google Trends data can be viewed as a description of collective behavior, aggregate demand, stock market moves, investor expectations, information demand, attention, and market sentiment.

"Internet search data may offer new possibilities to improve forecasts of collective behavior"

Quantifying the semantics of search behavior before stock market moves
Curme, Preis, Stanley & Moat (2013)
http://www.pnas.org/content/111/32/11600.abstract

"Our findings /.../ suggests that Google data is a promising source of information for nowcasting components of aggregate demand in short-run models"

Nowcasting with Google Trends in an emerging market
Carrière-Swallow & Labbé (2013)
http://onlinelibrary.wiley.com/doi/10.1002/for.1252/full

"By analyzing changes in Google query volumes for search terms related to finance, we find patterns that may be interpreted as 'early warning signs' of stock market moves."

Quantifying trading behavior in financial markets using Google Trends
Preis & Stanley (2013)
http://www.nature.com/srep/2013/130425/srep01684/full/srep01684.html?ftcamp=crm/email/2013426/nbe/AlphavilleNewYork/product

"We use a novelty Google search volume to proxy the market expectation hypothesis according to which firms with an abnormal upward change in Google searches are identified as firms with potential merger activity."

Google attention and target price run ups
Signos (2013)
http://www.sciencedirect.com/science/article/pii/S1057521912001184

"[W]e introduce a new indicator for private consumption based on search query time series provided by Google Trends."

Forecasting private consumption: survey‐based indicators vs. Google trends
Vosen & Schmidt (2011)
http://onlinelibrary.wiley.com/doi/10.1002/for.1213/full

"Demand is approximated in a novel manner from weekly internet search volume time series drawn from the recently released Google Trends database."

Information Demand and Stock Market Volatility
Vlastakis & Markellos (2011)
http://www.sciencedirect.com/science/article/pii/S0378426612000507

"We propose a new and direct measure of investor attention using search frequency in Google"

In search of attention
Da, Engelberg & Gao (2011)
http://onlinelibrary.wiley.com/doi/10.1111/j.1540-6261.2011.01679.x/full

"The objective of this study is to investigate factors that influence investor information demand around earnings announcements and to provide insights into how variation in information demand impacts the capital market response to earnings."

Investor information demand: Evidence from Google searches around earnings announcements
Drake, Roulstone & Thornock (2011)
http://onlinelibrary.wiley.com/doi/10.1111/j.1475-679X.2012.00443.x/full

"We use daily internet search volume from millions of households to reveal market-level sentiment."

The sum of all fears: investor sentiment and asset prices
Da, Engelberg & Gao (2010)
https://www3.nd.edu/~pgao/papers/FEARS_20131007.pdf

"How to use search engine data to forecast near-term values of economic indicators. Examples include automobile sales, unemployment claims, travel destination planning and consumer confidence."

Predicting the Present with Google Trends
Choi & Varian (2009)
http://onlinelibrary.wiley.com/doi/10.1111/j.1475-4932.2012.00809.x/full

Google Trends and stock indexes

The return of a stock index is the sum of the individual returns of the consituents. Is there a similar relationship for search words. I.e. is the searches for "FTSE 100" similar to the average searches for the index constituents?

The black line is the searches for "FTSE 100". The red line is the average search volume for companies in the FTSE 100 index. The data has been detrended and missing values have been interpolated.

There doesn't seem to be much of a correlation between the two. Nevertheless, a regression reveals that the relationship is statistically significant, allthough the magnitude is very small with a coefficeint of 0.039 (16.011).

We can conclude that there is no economically meaningful correlation between the two.

FTSE implied volatility compared to daily Google Searches

One of the findings that Vlastakis and Marekllos (2011) is that the implied volatility of S&P 500 as measured my the VIX index moves closely with what they call "market related information demand", i.e. the number of searches for "S&P 500" per week as measured by Google Trends. Figure one is taken from their research.

Since I'm doing research on FTSE 100, I thought it would be interesting to see if there is a similar relationship there. The graphs below presents my data for two time periods. Especially the second chart seem to indicate a strong relationship between the two.

 

The coefficient for the search volume is 0.18 (15.06) in the above regression.

In the longer time period, the coefficient drops to 0.08 (15.89) but remains significant on a 0.1% level. The R-squared drops more, from 30.15% in the shorter time period to 18.29% above.

The search data has been manipulated in a number of ways before it is used here.

1. Daily data is collected in 90-day intervals and merged based on the weekly time series that is available for the entire period. In this way, the granularity of daily data is combined with the comparability across time periods provided by the weekly data.
2. The linear trend is extracted from the search data.
3. A day-of-the-week trend is also extracted from the data.

The impact on the data of this treatment can be seen here.

Side by side plots of trading volume, volatility, and Google searches for selected companies

The plots below show the time series charts and scatter plots for a selection of compaines from FTSE 100. The charts plot data of trading volume, squared daily returns, and searches on Google (SVI).

FTSE 100

Aggreko

 Anglo American

 Antofagasta

 

 AstraZeneca

 Aviva

 Barclays

BT Group

 Carnival

CRH

 EasyJet

 Royal Dutch Shell

 Shire