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Proceedings of 7th Asia-Pacific Business Research Conference
25 - 26 August 2014, Bayview Hotel, Singapore ISBN: 978-1-922069-58-0
Investigating the Relationship between Google Trends with
Equity Return and Volume of Commercial Banks in Indonesia
Zahra Azhalia1 and Anggoro Budi Nugroho2
This paper investigates the correlation of people behavior on searching
information based on online news or headline found through Google, as the
most well-known search engine on the web. The data is used to measure
the popularity of the search of five commercial banks in Indonesia which
listed in LQ45 as most frequently traded stocks during 2013 and seeks
whether it affects the stocks movement. The number of searches that have
been done for the terms entered is compared to the total number of Google
searches conducted during that time is gathered from Google Trend and its
correlation with each stock will be compared using a structure analysis with
regression and event analysis for qualitative approach. Results show there
is no statistically significant relationship between five most probable chosen
keyword on Google to the stocks return and traded volume, nevertheless,
qualitative approach delivers better results on representing supply and
demand. Findings in this paper can give different point of view for
investment manager, traders, and investors that involves behavioral
aspects in analyzing or predicting the stock market.
Keywords: Behavioral Finance, Google, Stock Movement, Web Search Engine
Field of Research: Finance
1. Introduction
In a hustle and bustle big city lifestyle, internet is one of the most important part of
everybody’s life. People use internet mostly for communication and information gathering.
The users of internet is still growing as the population keeps increasing each year and the
access of internet is even more easier than before. In terms of information gathering,
people need an effective and efficient way to collect information around the world and
internet provides an easy, fast and inexpensive access to information with no additional
cost needed compared to conventional way such as magazine or newspaper. The most
widely known way of information gathering through internet is by using search engine. One
of the most well-known search engine on the web is Google. Since the first time it’s
established on September 1997 by Larry Page and Sergey Brin, it had become the largest
search engine in the world. It also has many additional features and services to be offered
which make it more preferable than the other search engine like Yahoo! or Bing.
As people consume and invest more, their life surely are associated with bank which
makes it a daily needs to save or withdraw their money. Moreover, Indonesian commercial
bank, in this case, competes each other by offering the newest and best service to the
______________________________________________________________________
1
Zahra Azhalia, School of Business and Management, Institut Teknologi Bandung, Indonesia
Email: [email protected]
2
Anggoro Budi Nugroho, MBA., School of Business and Management, Institut Teknologi Bandung, Indonesia
Email: [email protected]
Proceedings of 7th Asia-Pacific Business Research Conference
25 - 26 August 2014, Bayview Hotel, Singapore ISBN: 978-1-922069-58-0
customer and target customer to earn profit. It directly proportional to Indonesian banking
conditions that were in good condition, with indicators of capital adequacy ratio (CAR)
reached 17.89%, well above Bank Indonesia provision of 8%.This is one of the reason
that this industry is favourable in the eyes of investor as it haven’t stop growing. Moreover,
as Senior Researcher HD Capital Indonesia Yuganur Wijanarko said in his research that
with a lot of positive domestic catalysts ahead, the banking sector is one of sector that is
recommended to be looked at since it is sensitive to economic news.
This study aims to investigate the relationship between Google Trend and five commercial
banks in Indonesia (Bank BNI, Bank BCA, Bank BRI, Bank Danamon, and Bank Mandiri)
because these five commercial banks which are private and government owned
companies is already well-known by Indonesian people. All of them considered to have
good reputation for a long time in the financial sector. They also stay in LQ45 from 2008 to
2014 and as previously said that means that they are frequently traded in stock
market.The author wants to know whether their stock performance has the correlation with
Google Trend during that period. This research will hopefully be useful for investors,
traders, or any portfolio manager for them to help their decision making in trading activity.
2. Literature Review
2.1 Behavioral Finance
Behavioural Finance stands on the perspective that investors’ decision making is affected
by irrational reasoning. Discussion about this topic began to emerge in many academic
journals and business publications since the 1990s, when research conjectured that value
strategies yield higher returns because these strategies exploit the suboptimal behaviour
of the typical investor (Lakonishok, Shleifer, & Vishny, 1994). In fact, the idea of
Behavioural Finance can be traced back over 150 years, though indeed this terms has not
been commonly used in the meantime. One of the example of earlier study is Psychology
of the Stock Market (Selden, 1912) he based the book ‘upon the belief that the
movements of prices on the exchanges are dependent to a very considerable degree on
the mental attitude of the investing and trading public'. This concept is in accordance with
current knowing that pointed out on the key to defining behavioural finance is to first
establish strong definitions for psychology, sociology and finance.
2.2 News and Stock Prices
This paper discussed behavioural finance in a way that online news may be able to reflect
stock return and traded volume. The movement of stock prices change every day, it
makes investor or trader should keep an eye on their portfolio. Media has a big role on
providing news about stock market, issues facing a company, and analysis from the
expert, which make it easier for someone to decide on selling or purchasing stocks.
Though news can be powerful in stock movement, someone have to figure out what news
is positive for a company and what news is negative. Online news is another category that
influences investors. It becomes more critical for investors to stay abreast of investment
news in addition to stock quotes and charts. A research about the impacts between the
Internet stock news (ISN) and the stock price movements reflects today society in the era
of the popular usage of the Internet. It is found that the contents of the ISN are related to
the stock returns based on the XML protocols. Also, the volume of the ISN is associated
Proceedings of 7th Asia-Pacific Business Research Conference
25 - 26 August 2014, Bayview Hotel, Singapore ISBN: 978-1-922069-58-0
with the stock price movements. From both aspects, it illustrates the relationship between
the two (Liang, 2005).
2.3 Noise Trader
News can affect trader as they become what is usually called noise trader-someone who
is not having fundamental data to analyse and usually refer to emotional aspects as they
often overreacting to news whether it is bad or good which make them create an irrational
decision and sometimes project an unusual movements in the price of securities. Robert
Bloomfield, Maureen O’Hara, and Gideon Saar (2007) have conducted research on how
they seek to clarify the role and market impact of noise traders. One of the result of theirs
is that noise traders in their experiment appear to act as irrational contrarian traders. In
contrast with skilful technical traders, the noise traders lose the most money when values
are extreme, because they act as (unwise) contrarians and as a result slow down the
adjustment of prices (worsening informational efficiency exactly when it is needed the
most).
As opposed to the research above, Andrei Shleifer and Lawrence H. Summers (1990)
searched for an alternative to the Efficient Market Hypothesis approach and made two
assumptions implying that changes in investor sentiment are not fully countered by
arbitrageurs and so affect security returns. They argue this approach to financial markets
is in many ways superior to the efficient market paradigm. Moreover, they believed the
argument that noise traders lose money and eventually disappear is not self-evident. If
risk-taking is rewarded in the market, noise traders can earn higher expected returns even
despite buying high and selling low on average.
2.4 Google: search engine
Since the first time search engine has been established in 1990, Google Search is one of
the widely-known and used search engine in the world with 5.9 billion searches per day
and 2.1 trillion searches per year worldwide in 2013. In Indonesia, Alexa Traffic Rank puts
Google in the number one on the top sites visited by the citizens. The rank is calculated
using a combination of average daily visitors and page views over the past month. The
site with the highest combination of visitors and page views is ranked #1. This means that
Indonesian people become familiar and comfortable using this search engine compared to
the others.
2.5 Current Study
This study aims to seek whether there is correlation between google searches using terms
related to five commercial banks in Indonesia which listed in LQ45 especially to the stock
return and volume traded on the market. Banking sector chosen as it is sensitive to the
economic news and also is now closely associated with the technology in expanding their
business. The study that has been conducted by UK and US academics, is the latest
attempt to mine online behaviour patterns for clues about future movements in financial
markets. There is something interested in their result that provided some insight into future
trends in the behaviour of economic actors. As that research gives some significant result,
the author tries to replicate into Indonesian stock market by simplified the methodology
and sample taken.
2.6 Research Gap
There is not many research about Google search correlated with stock market especially
in Indonesia. This study will focus on only one sector of Indonesian stock which listed in
Proceedings of 7th Asia-Pacific Business Research Conference
25 - 26 August 2014, Bayview Hotel, Singapore ISBN: 978-1-922069-58-0
LQ45 and choose one sector. Moreover, it uses daily data in the period of 2013, which is
more updated compared to earlier research that use the period of 2004-2011.
3. The Methodology and Model
For this study, a step by step action is needed to achieve the objective in order to know
whether Google Trend data correlated to Indonesian stock market, in terms of volume
traded and stock return. The study is specifically addressed to the stocks of five
commercial banks in Indonesia which listed in LQ45. After the research objective is
determined, the next step is conducting research variable which will be gathered to be
processed further. Based on the research objective, the determined research variables as
follows:
a. Dependent variable
 The average standardized return and traded volume of BMRI, BBNI, BBCA,
BDMN, and BBRI. The calculation for the standardized return is:
Standardized return = return per day / ((P12/31/2013-P1/1/2013)/P1/1/2013)) …… (eq
3.1)
 The traded volume is calculated by dividing the volume of each day by the mean
traded volume
Traded volume = Volume each day / Average volume from January 1 st –
December 31st ….. (eq 3.2)
b. Independent variables:
For quantitative approach which use linear regression, the independent variables
are number of searches per day of unique keyword that is related to stock market.
All of the keywords chosen are ‘saham bank’, ‘saham’, ‘LQ45’, ‘profit bank’, and
‘bank’
While the second method, event analysis, is qualitative approach which is not necessary
to use dependent and independent variables.
Data used for this research is collected from online source. The historical closing price
and volume is collected from www.finance.yahoo.com and www.duniainvestasi.com, both
data is in the daily basis from January 1 st 2013 to December 31st 2013. The second data
is searches on Google which is gathered from www.trend.google.com. In order to assure
the data represent each stock, there is filtering needed. The author excluded words or
phrases which are not needed, also the location is limited in Indonesia and in Indonesian
language. In total, the author gets searches per year in average as people can search for
24/7 and is converted by Google Trend to a smaller number to reduce noises.
The data will be processed by statistic tool which generates output to further be analysed
and therefore make a conclusion of it. Multiple linear regression is chosen for the
quantitative method as it defined as an approach attempts to model the relationship
between two or more explanatory variables and a response variable by fitting a linear
equation to observed data. Every value of the independent variable x is associated with a
value of the dependent variable y. Linear regression can identify the type of mathematical
relationship that exist between a dependent and independent variable, to quantify the
effect that changes in the independent variable have on the dependent variable, and to
identify unusual observation.
Proceedings of 7th Asia-Pacific Business Research Conference
25 - 26 August 2014, Bayview Hotel, Singapore ISBN: 978-1-922069-58-0
In this study, T-test is used to conduct hypothesis testing into the independent variables.
An independent variable is considered have an influence to dependent variable when it is
significantly reject H0 p-level less than α = 5% is used. The hypothesis is defined as
follows:
H0: b1 = 0: an independent variable is not partially significant
H1: b1 ≠ 0: an independent variable is partially significant
Otherwise, correlation of coefficient (r) measured the strength and the direction of a linear
relationship between independent and dependent variable. It basically states how well a
model to explain variation in the dependent variable (Ghozali, 2011). The higher the value
of R2 is more suitable to explain that the independent variables explain the dependent
variable. The smaller the value of R2 means the less the ability of the independent
variables to explain the dependent variable. The things to note about the coefficient of
determination is as follows:
 R2 value should range from 0 to 1
 When R2 = 1 means there is a perfect match of the independent variables explain
the dependent variable.
 When R2 = 0 means no relationship at all between the independent variable on
the dependent variable.
For the qualitative approach, the author use event analysis to analyse the searches on
Google, the author analyse the interesting point of volume of trade from BMRI, BDMN,
BBCA, BBNI, and BMRI since it reflects supply and demand. This methodology is
conducted to describe and explain people behaviours to particular event situation and
time. To support the analysis, graph of volume traded will be provided to visualize the
situation.
4. The Findings
4.1 Multi Linear Regression
Banking company’s stocks which are included as member of Liquidity (LQ) 45 in the
period of 2013 are being calculated on the average return and traded volume. The period
2013 is deliberately chosen because this paper attempt to examine the relationship
between searches on terms related to financial market to stocks return and traded volume
in daily basis. For this study, the author has selected five keywords in Indonesian
language commonly used for the financial market as independent variables that have
passed the filtering process to be the most suitable predictors which is symbolized by
keyword1 for saham bank, keyword2 for saham, keyword3 for LQ45, keyword4 for bank,
and keyword5 for profit bank. For the dependent variable, y1 represents volume and y2
represent return. Then, the results are generated as follows:
Proceedings of 7th Asia-Pacific Business Research Conference
25 - 26 August 2014, Bayview Hotel, Singapore ISBN: 978-1-922069-58-0
Table 4.1 Value of R2 with Volume as the Dependent Variable
Model Summary
Model
1
R
Adjusted R
Std. Error of the
Square
Estimate
R Square
.389
a
.151
.134
.76625
a. Predictors: (Constant), keyword5, keyword2, keyword1, keyword4,
keyword3
The table above shows that the R squared is 0.15, then 15% of the variation in the
outcome is accounted by the independent variables. After the value of R and R squared
obtained, the author will find out the level of significance of each independent variable and
interpret the result. Predetermined significance level used in the significance tests is 0.05.
If Sig < 0.05, then H0 will be rejected. This means that there is a significant correlation
between independent variables and dependent variable.
Table 4.2 Significance Test with Volume as Dependent Variable
Coefficients
a
Standardized
Unstandardized Coefficients
Model
1
B
Coefficients
Std. Error
Beta
(Constant)
1.043
.667
keyword1
.032
.008
keyword2
-.018
keyword3
t
Sig.
1.564
.119
.286
4.190
.000
.007
-.183
-2.698
.007
-.037
.011
-.417
-3.458
.001
keyword4
.037
.007
.565
5.106
.000
keyword5
-.016
.009
-.197
-1.822
.070
a. Dependent Variable: Volume
Based on the results above, only the fifth variable which is the term ‘profit bank’ does not
partially affect to the dependent variable, volume. Whereas the other variables statistically
significant to the dependent variable. As a whole, it can be concluded that there is no
significant correlation between keyword ‘saham bank’ , ‘saham’, ‘LQ45’, ‘bank’, and ‘profit
bank’ to volume
Table 4.3 Value of R2 with Return as the Dependent Variable
Model Summary
Model
1
R
.464
R Square
a
.215
Adjusted R
Std. Error of the
Square
Estimate
.199
.35888
a. Predictors: (Constant), keyword5, keyword2, keyword1, keyword4,
keyword3
The table above shows that the R squared is 0.215 that means around 21.5% of the
average return of stock BMRI, BBCA, BBRI, BDMN, and BBNI can be explained by the
Proceedings of 7th Asia-Pacific Business Research Conference
25 - 26 August 2014, Bayview Hotel, Singapore ISBN: 978-1-922069-58-0
independent variables. Furthermore, to prove that H0 can be rejected, significance level of
this model must below 0.05 to prove that there is a significant correlation between
independent variables and dependent variable.
Table 4.4 Significance Test with Return as Dependent Variable
Coefficients
a
Standardized
Unstandardized Coefficients
Model
1
B
Std. Error
(Constant)
-1.361
.312
keyword1
-.009
.004
keyword2
.023
keyword3
Coefficients
Beta
t
Sig.
-4.357
.000
-.160
-2.434
.016
.003
.470
7.218
.000
.009
.005
.209
1.798
.074
keyword4
-.010
.003
-.312
-2.938
.004
keyword5
.007
.004
.175
1.689
.093
b. Dependent Variable: Return
With this method, the search in Google with keywords ‘LQ45’ and ‘profit bank’ does not
partially affect to the dependent variable, return. Whereas the other variables statistically
significant to the dependent variable.As a whole, it can be concluded that there is no
significant correlation between keyword ‘saham bank’ , ‘saham’, ‘LQ45’, ‘bank’, and ‘profit
bank’ to return.
4.2 Event Analysis
Unlike the previous method which use average standardized return and traded volume of
the stock BMRI, BDMN, BBRI, BBRI, and BBCA, event analysis will provide the graph of
volume from each stock and highlight point that interest the most. It is because every
stock is predicted to have unique correlation with the news related to the company or
stock itself. Volume is chosen because it reflects supply and demand. The graph below
will show the volume of BMRI, BBNI, BBCA, BDMN, and BBRI from January 1 st, 2013 until
December 31th, 2013.
Proceedings of 7th Asia-Pacific Business Research Conference
25 - 26 August 2014, Bayview Hotel, Singapore ISBN: 978-1-922069-58-0
1. BBNI
Figure 4.1 Volume Traded Even Analysis Graph (BBNI)
Source: Excel Output
In February 1st, 2013 there is significant increase in volume traded for BNI stocks. Turned
out, there are two news in February 1st and 2nd pointed out that BNI debit card has
outperformed Mandiri and BCA, stated that BNI debit card gained 2,273 in score. It is
supported by growth in the number of cards reaching 15.03%. In addition, not a long
before, BNI launched a co-branding with British football club, Chelsea. The breakthrough
seemed to attract customer and became a hot topic discussed in cyberspace which also
affected the stock market.
2. BBCA
Figure 4.2 Volume Traded Even Analysis Graph (BBCA)
Source: Excel Output
Proceedings of 7th Asia-Pacific Business Research Conference
25 - 26 August 2014, Bayview Hotel, Singapore ISBN: 978-1-922069-58-0
In February 11th, 2013 the stock is in its smallest number in the year with 2,294,500 for
volume. In that date, there is news about sale of BBCA shares by the board of directors of
PT Bank Central Asia Tbk, the amount of securities sold by its shareholders reached
2.679 million with a value of IDR 22,687 impacting to the shares dropped as much as 1%
to IDR 9,950. Otherwise, in June 12th it comes to the highest level in 59,032,500 for
volume because board of directors and the commissioner of PT Bank Central Asia Tbk
(BBCA) buy its shares. There are 11 directors and commissioners who increased their
interest. In total, the stock purchased as much as 1,356 million shares. At the end of the
news, it was stated that First Asia Capital analysts, David Sutyanto, said that for long
term, the stock is still very interesting and is right to choose for investment.
3. BBRI
Figure 4.3 Volume Traded Even Analysis Graph (BBRI)
Source: Excel Output
The volume of BBRI jumped to 134,142,000 in June 12 th, 2013 because of news about
Bank BRI which recorded a profit increase of + 18.7%. In that article, the writer suggested
that they were still positive for BBRI based on the following factors: (i) higher ROE, (ii)
prospective micro-business growth, (iii) and better asset quality. Thus, they recommended
th
to buy BBRI. Just a few months later, in August 20 , 2013 the stock volume reached
130,617,500 because of news wrote that banking stocks which focused on microenterprises, like BBRI should be considered by investors. They also added that this
company has the highest profit compared to another banking company amounted as IDR
18.10 trillion or grew 28.04% from 2011. In the last paragraph, it is stated that BBRI is still
promising at that time.
Proceedings of 7th Asia-Pacific Business Research Conference
25 - 26 August 2014, Bayview Hotel, Singapore ISBN: 978-1-922069-58-0
4. BDMN
Figure 4.4 Volume Traded Even Analysis Graph (BDMN)
Source: Excel Output
The highest volume of BDMN is on October 31st, the following news assume to have
impact for the volume; the first news is about collaboration of PT. Bank Danamon
Indonesia Tbk (BDMN) and PT. Asuransi Jiwa Manulife Indonesia that launched their
program to give insurance solution to business owner of micro-enterprise in Indonesia.
The second news is an article about the observation of stock market condition at that time
where LQ45 index weakened 6.51 points (0.86%) to 748.29 and in the end of the article, it
provided recommendation by Head of Research from Riset Trust Securities, Reza
Priyambada, the four stocks to be an options for investors and among the four choices,
BDMN was the only banking stocks that is promising to buy.
5. BMRI
Figure 4.5 Volume Traded Even Analysis Graph (BBRI)
Source: Excel Output
Proceedings of 7th Asia-Pacific Business Research Conference
25 - 26 August 2014, Bayview Hotel, Singapore ISBN: 978-1-922069-58-0
From the graph above, BMRI stock gets to the highest volume level on September 10 th,
2013. Based on search on the web, there are three articles giving the recommendation
about this stock. Infobank magazine in collaboration with PT. Bursa Efek Indonesia (BEI)
reviewed Infobank15-index measured the performance of price from 15 leading stocks in
banking sectors that are listed on the stock exchange. From their review, BMRI is
recommended to be purchased for investors. This is because BMRI accounted for the
increase in the index at 14.2 points with an increases of 6.18% to IDR 9,450. Another
news from www.inilah.com wrote about an analysis from Samuel Utomo, Head of
Research at PT Astronacci International. Based on his analysis, he suggested the reader
to buy stocks like BMRI. The third source stated that BMRI stock was moving shot on the
transaction that morning. As quoted from RTI data, at 10.15 am, BMRI shares rose 7.91%
to IDR 7,500 which made Muhammad Wafi, Indo Premier Securities analyst, giving
recommendations to buy this stock.
From this analysis, it can be said that all of the stocks (BBNI, BBCA, BBRI, BDMN, BMRI)
volume movement has strong association with news around that time. As investors or
traders regularly review their portfolio by checking on the online news, they can be
influenced by the content or the recommendation given in the article which affect the
supply and demand in stock market.
5. Summary and Conclusions
Google as the most broadly used search engine in Indonesia is observed in this study to
find out the correlation of number of searches on Google Trend to stocks of five
commercial bank in Indonesia. Using stocks in daily basis which are included as member
of Liquidity 45 in Indonesia Stock Exchange with observation period 2013 as samples,
findings show that there is no significant relationship between five different keywords that
are most trending in Indonesia related to banking stocks and the return.
When the next step processed to find the correlation whether five different keywords that
are most trending in Indonesia related to banking stocks are the significant factor to
influence the traded volume, results show that almost all of the variable has p-value of <
0.05, still, it is interpreted that there is no significant relationship between traded volume
and trending keywords in Google. This finding implies that number of search on trending
keyword related to stock is not powerful in estimating stock returns and traded volume, at
least not yet. However, event analysis as qualitative approach indicated that there are
positive relationship between the searches on Google to traded volume. Though, this
method cannot fully guarantee that the event is represented by the searches. Still, it
should be considered by investors or trader as their benchmark source of information.
As this study is still relatively new in Indonesia, the author only use simple method on
predicting the result. For future study, there should be improvement in the methodology
and the data collecting process. This research uses only one year period in 2013 and one
sector in the stock market, specifically only in LQ45. For further research, longer period
may obtain statistically better result which will bring deeper understanding on stock
analysis for investment strategy.
Proceedings of 7th Asia-Pacific Business Research Conference
25 - 26 August 2014, Bayview Hotel, Singapore ISBN: 978-1-922069-58-0
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