Supercell revenue estimate 2014

I've previously used Google Trends to estimate revenues, and I will give it another go here for the Finnish gaming company Supercell. I've gathered the combined search volume for Hay Day, Clash of Clans, and Boom Beach.

Based on the search volume, the new game Boom Beach has not yet exceeded the interest in Supercell's first game Hay Day, and is still a long way off from matching the success of Clash of Clans. The trend is however positive. The interest in Clash of Clans has continued increasing.

The total search volume for all three games has increase 330 % as compared to 2013. Based on the past correlation between revenue and search volume, I estimate Supercell's revenue to be 1.7 billions 2014.




The graph above plots the weekly search volume index summed by year against the official yearly revenue figures. The green dot represents the estimate for 2014.

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.