TransferWise's hockey stick growth according to Google Trends

To get a free transfer from TransferWise, use this link.

Finland's most connected companies

How are the decision makers of listed companies in Finland connected? Using R for collecting decision maker data and D3 for visualizing the results, I have created this network chart (sorry for the ugly iframe):


Connections are the number of connections to other boards from one company to other. KONE's board members are for instance on 13 other boards. The most connected companies on Helsinki's stock exchange are Kone, Sponda and Teleste.

Company	Connections
KONE Oyj	13
Sponda Oyj	12
Teleste Oyj	10
Outotec Oyj	10
Cargotec Oyj	10

The data has been filtered out so that only companies with at least one connection to another company are included. The data is collected from finder.fi and includes the company's management, board members, and other people with the right to sign for the company.

Network chart of board members in Helsinki

The data is scraped from the Finnish business directory finder.fi. The network chart is created using D3.js.

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.

Most googled words 2014

These are the five search terms with the biggest increase in interest during 2014. The new iPhone tops the list, followed by Disney's Frozen, the emerging markets classifieds company OLX, India's Flipkart and Google Drive.

Top 5 search terms with biggest increase 2014 from 321k

Out of these search terms, OLX is has the highest search volume, while the new iPhone was by far the most Googled word out of the list during the month of the launch.

Creating daily search volume data from weekly and daily data using R

In my previous post, I explained the general principle behind using Google Trends' weekly and daily data to create daily time series longer than 90 days. Here, I provide the steps to take in R to achive the same reuslts.


#Start by copying these functions in R. 
#Then run the following code:
#NB! In order for the code to run properly, you will have to specify the download directory of your default browser (downloadDir)

downloadDir="C:/downloads"

url=vector()
filePath=vector()
adjustedWeekly=data.frame()
keyword="google trends"


#Create URLs to daily data
for(i in 1:12){
    url[i]=URL_GT(keyword, year=2013, month=i, length=1)
}

#Download
for(i in 1:length(url)){
    filePath[i]=downloadGT(url[i], downloadDir)
}

dailyData=readGT(filePath)
dailyData=dailyData[order(dailyData$Date),]

#Get weekly data
url=URL_GT(keyword, year=2013, month=1, length=12)
filePath=downloadGT(url, downloadDir)
weeklyData=readGT(filePath)

adjustedDaily=dailyData[1:2]
adjustedDaily=merge(adjustedDaily, weeklyData[1:2], by="Date", all=T)
adjustedDaily[4:5]=NA
names(adjustedDaily)=c("Date", "Daily", "Weekly", "Adjustment_factor", "Adjusted_daily")

#Adjust for date missmatch
for(i in 1:nrow(adjustedDaily)){
    if(is.na(adjustedDaily$Daily[i])) adjustedDaily$Daily[i]=adjustedDaily$Daily[i-1]
}

#Create adjustment factor
adjustedDaily$Adjustment_factor=adjustedDaily$Weekly/adjustedDaily$Daily

#Remove data before first available adjustment factor
start=which(is.finite(adjustedDaily$Adjustment_factor))[1]
stop=nrow(adjustedDaily)
adjustedDaily=adjustedDaily[start:stop,]

#Fill in missing adjustment factors
for(i in 1:nrow(adjustedDaily)){
    if(is.na(adjustedDaily$Adjustment_factor[i])) adjustedDaily$Adjustment_factor[i]=adjustedDaily$Adjustment_factor[i-1]
}

#Calculated adjusted daily values
adjustedDaily$Adjusted_daily=adjustedDaily$Daily*adjustedDaily$Adjustment_factor

#Plot the results
library(ggplot2)
ggplot(adjustedDaily, aes(x=Date, y=Adjusted_daily))+geom_line(col="blue")+ggtitle("SVI for Google Trends")

Creating daily search volume data from weekly and daily data

Risteski & Davcev (2014) refers to my method for getting Google Trends data for more than 90 days in their recent paper. For those interested, I've outlined the key principle in this post.

This post illustrates a method for combining daily and weekly search data from Google Trends in order to create a daily time series for over a period longer than the 90 days of daily data provided by Google Trends. For the sake of simplicity, I have only included two weeks, but the general principle can easily be extended to longer time series. SVI in the tables refers to Google Trends' search volume index.

For an example of how this can be done using R, look here.

Step 1: Collect daily search data from Google Trends and combine it into one array.

Date	Daily SVI
1.1.2014	72
2.1.2014	96
3.1.2014	16
4.1.2014	70
5.1.2014	61
6.1.2014	97
7.1.2014	44
8.1.2014	32
9.1.2014	8
10.1.2014	13
11.1.2014	67
12.1.2014	9
13.1.2014	63
14.1.2014	91

Step 2: Collect weekly search data over the same time period

Date	Daily SVI	Weekly SVI
1.1.2014	72	20
2.1.2014	96
3.1.2014	16
4.1.2014	70
5.1.2014	61
6.1.2014	97
7.1.2014	44
8.1.2014	32	30
9.1.2014	8
10.1.2014	13
11.1.2014	67
12.1.2014	9
13.1.2014	63
14.1.2014	91

Step 3: Adjust the daily data based on the weekly data

The key here is the adjustment factor. It is the weekly SVI divided by the daily SVI for those dates where there are values for both. For other data points, the last available adjustment factor is applied.

Date	Daily SVI	Weekly SVI	Adjustment factor	Adjusted values
1.1.2014	72	20	0,3	20,0
2.1.2014	96		0,3	26,7
3.1.2014	16		0,3	4,4
4.1.2014	70		0,3	19,4
5.1.2014	61		0,3	16,9
6.1.2014	97		0,3	26,9
7.1.2014	44		0,3	12,2
8.1.2014	32	30	0,9	30,0
9.1.2014	8		0,9	7,5
10.1.2014	13		0,9	12,2
11.1.2014	67		0,9	62,8
12.1.2014	9		0,9	8,4
13.1.2014	63		0,9	59,1
14.1.2014	91		0,9	85,3

Time series plot

As can be seen from the graph below, the inter-week changes remain the same, but is adjusted down or up based on the weekly search volumes. This way, the relative volumes between weeks is unchanged and the data is comparable over periods longer than the 90 days provided by Google Trends.

Data artifacts in daily Google Trends data

An important consideration when merging daily Google Trends data together is that the first and last day of a month sometime are shown as zero in the data. This can create serious inconsistencies in the data, as we cannot make a percentage adjustment to zero values. To account for this, I recommend that the the first and last 30 days from the time series is stripped out before merging the data.

To illustrate the issue, I've copied the daily Google Trends data for the search term "CAC40" below. The value for 1 January 2014 is zero in the upper graph, while it's 77 in the upper graph.

January - February 2014

December 2013 - February 2014

David Leinweber writes more on the topic here.