Dmitry Mitrofanov MT With Original Text

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МАГИСТЕРСКАЯ ДИССЕРТАЦИЯ

MASTER THESIS

Title: Using modern search engines and social networks to predict stock returns and volatility

Название : Новый подход в предсказании волатильности и доходности акций

Студент/ Student:

Дмитрий Митрофанов/Dmitry Mitrofanov

Научный руководитель/ Supervisor:

Олег Шибанов/Oleg Shibanov

Оценка/ Grade:

Подпись/ Signature:

Москва

2012/2013



2

Content

Abstract 3

1. INTRODUCTION…………………………………………………………………………………………………….4

2. THEORETICAL MODEL............................................................................................ 5

3. DATA ………………………………………………………………………………………………………………… 9

4. DESCRIPTIVE STATISTICS…………………………………………………………………………………… 10

5. RESULTS……………………………………………………………………………………………………………. 15

6. CONCLUSION……………………………………………………………………………………………………..

23

7. ACKNOWLEDGEMENTS………………………………………………………………………………………. 24

8. REFERENCES……………………………………………………………………………………………………….. 25

9. APPENDIX ……………………………………………………………………………………………………………26



3

Abstract

In my Master Thesis I analyzed the use of modern search engines and social networks

to predict stock returns and volatility. In particular, I developed a model to predict a future

return and volatility of a particular stock using Google Trends which can provide us with

search

frequencies

for

particular

company

in

Google

search

engine

and

investor

sentiments, measured in Twitter. Recent literature, “Google Search Volume and its

Influence on Liquidity and Returns of German Stocks” by Matthias Bank, Martin Larch; “Can

Internet Search Queries Help to Predict Stock Market Volatility?” by Thomas Dimpfl and

Stephan Jank, shows that an increase in search queries is associated with a rise in trading

activity and stock return. I try to improve the predictive power of stock return and volatility

by taking into account not only Search Volume Index(SVI), search frequencies from Google

Trends for a particular company (as a proxy of investor attention to a particular company),

but also

investor

sentiments.

It comes

from

behavioral

finance:

when

investors

are

in

a

good mood, they tend to be confident and underestimate the risk of investing in the stock

of a particular company. In turn, it will increase the volatility and return of this stock. So,

adding investor sentiment can considerably improve the prediction of stock return and

volatility. I will show it using out‐of ‐sample prediction analysis. Analyzing our model, I

discovered different peculiar effects. For example, the increase of stock volatility due to

increase in SVI of this company is higher when investors are happier. Also, the higher the

market

value

of

the

company

the

less

sensitive

its

stock

volatility

to

SVI

of

this

company.

To

estimate the mood of the society I use Twitter content, the ratio of smiley emoticons “:)” to

frown emoticons “:(“. This Master Thesis add the new dimension to the existent literature,

as both SVI and emotions on Twitter are used to predict stock returns and volatility. It is

worth noting that empirical results are in accordance with theoretical model that is

developed in our article.



4

1. Introduction

In this Master Thesis we use Google search query of firm names as a proxy for

investor attention and study the implications for trading activity and returns of American

stocks. We find that search volume is a good measure of investor recognition. In particular,

an increase

in

Internet

search

volume

in

Google

is related

to

higher

volatility

and

leads

to

higher future returns of particular stock. Merton (1987) introduces the notion of investor

recognition and establishes that investor attention maybe relevant for stock pricing and

liquidity. In practice, however, measuring investor recognition is a difficult task. For

example, Fangand Peress (2009) believes that attention attracted by a companies

approximated by the number of published newspaper articles. Unfortunately, there is no

reliable information as to the extent to which readers of a newspaper pay attention to the

mention of a company in its pages. Other measures of investor attention, such as analyst

coverage

or

advertisement

expenditures,

suffer

from

similar

shortcomings.

As

an

alternative proxy for investor recognition, Daetal. (2009) propose using of information

conveyed by search volume on Google. The number of search queries as an indicator for

public interest has great appeal. First, the importance of the World Wide Web has grown

significantly; it is the largest pool of freely available information, accessible to almost

everyone nearly everywhere. Second, search volume seems appropriate, since an Internet

user will only actively “google” a specific key word if she is interested in the object

underlying

the

search

term.

Our study contributes to the research that focuses on the relation between investor

attention and stock liquidity and return. We try to improve the predictive power of stock

return and volatility by taking into account not only Search Volume Index (SVI) search

frequencies from Google Trends for particular company (as a proxy of investor attention to

a particular company), but also investor sentiments. According to behavioral finance

theory, when investors are in a good mood, they tend to be confident and underestimate

the

risk

of

investing

in

the

stock

of

a

particular

company.

In

turn,

it

increases

the

volatility

and return of this stock. So, the economic intuition upon adding investor sentiment is as

follows: when economy is growing rapidly (investors are in a good mood) the increase in SVI

for a particular company will result in higher volatility and return than when economy is

growing slowly( investors are in a bad mood) . All the recent studies in this field do not take

this factor into account. Our hypothesis is proved by our empirical analyses. So, it seems

logical to find a measure for investor sentiments.

In my Master Thesis I use a measure of investor mood which allows to capture

sentiments, the ratio of smiley emoticons and frown emoticons on Twitter. Using data for



5

all tweeting activity from 2008 till 2010, we find that smiley usage on Twitter has a

statistically and economically significant effect on the stock market returns and volatility.

Our analysis shows that the SVI index, sentiment from Twitter and their cross term are

statistically and economically significant. I chose Twitter to measure investor sentiments for

several reasons. First, Twitter is able to react fast on every event. Second, it is able to

transmit

current

activity

and

current

mood

tweets.

Many

users

write

tweets

about

what

they like or dislike at this moment. Hence, Twitter collects information about the emotional

state of its users. In this Master Thesis I use a mood proxy from this information. Smiley and

frowny emoticons show people's emotions. One of the main advantages of measuring

sentiments by emoticons is that smileys like ":)" corresponds to positive emotions almost in

any language and any context. Hence, it is difficult to misinterpret the mood of tweets with

a smiley icon. Using a ratio of positive to negative emotions has another important

advantage: although the number of positive and negative emoticons rises gradually, their

ratio

remains

stable.

According

to

Nofsinger

(2005),

the

general

level

of

optimism/pessimism in society influences the mood of financial decision makers. Thus, high

level of optimism in the society leads to optimistic mood of decision makers in financial

area. Hence, high ratio smiley emoticons to frown on Twitter corresponds to optimistic

mood of traders. Optimistic investors tend to underestimate risks and thus they are more

likely to buy stock of a particular company. So, it is very reasonable to include investor

sentiment proxy in addition to SVI to predict stock volatility and return for a particular

company.

2. Theoretical Model

The model is based on price‐taking traders. A riskless asset and one risky asset are

exchanged in two rounds of trading at times t=1, t=2. Consumption takes place only at t=3,

when riskless asset pays 1 unit per share and each share of risky asset pays , where 1

( , )v N v h

. The riskless interest rate is assumed to be 0. There are M investors. Thus each

investor correctly

assumes

that

his

own

demand

does

not

affect

prices.

At

t=0

each

trader

has an endowment 0i

f of the riskless asset and0i of the risky asset. In trading round t,

trader i’s demand for the riskless asset and risky asset are ti

f and ti. is the per capita

supply of the risky asset; it is fixed, known to all, and unchanging.t

P is the price of the risky

asset in trading rounds 1,2. Trader i’s wealth is ti ti t ti

W f P x , for t=1,2 and 3 3 3i i i

W f vx .

There is no signal prior to the first round of trading at t=1. Prior to trading at t=2, trader i

receives one of M private signals, ti tm

y v , where 1(0, )tm N h

and

21 22 2, ..., are

mutually independent.

1

/ M

t ti

i

Y y M

is the

average

signal

at

time

t.



6

Each trader believes that the precision of his signal , 1h

. She believes the

precision of other signals to be , 1h

. All traders believe that the precision of is

, 1vh ; that is, traders underestimate, or correctly estimate, the precision of their prior

information. Let 1 2 2 2{}, { P }

T

i i iФ Ф y . Thus, 1i

Ф represents the information available to

trader i (in addition to prior beliefs) at time t.

Every trader when forming her believes pay attention not only on her idiosyncratic

signal; also she takes into account the average signal among all the agents.

(1 )ti tm t

y v , where describes the importance of her individual signal, based on

her own knowledge, 1 is extent to which trader rely on the average signal among

traders.

Trader i’s utility function is exp( )it

aW , thus traders have constant absolute risk

aversion (CARA)

with

a risk

‐aversion

coefficient

a.

Traders

are

assumed

to

be

myopic,

that

is, they look only one period ahead when solving their trading problem. Thus, at times t=1,2

trader i solves:

1 1 1max E[-exp(-a(W )|Ф ] s.t. P P

ti

t i ti t ti ti t t i t i x

x f x f (1)

The traders in this model correctly conjecture that they do not affect prices. When

solving their maximization problems, traders conjecture that prices are linear functions of

the

average

signals:

2 21 22 2 P Y

The conjectures are identical for all traders and the coefficients determine an

equilibrium in which the conjectures are fulfilled. Equilibrium is obtained because traders

believe that they are behaving optimally even though, in fact, they are not.

We first solve the equilibrium for the second round of trading. Trader i believes 2iФ

has a multivariate

normal

distribution.

We

calculate

the

mean

and

the

covariate

matrix

of

this distribution which are

2 21 22( ) [ , ]i E Ф v v

2

2 2 2

1 (1 ) (1 ) ( 1)ar( )i

v

M M V y

h M h M h

22 22

2 2 2 2

(1 )( 1)(1 )

( , )i

v

M M

Cov y P h M h M h



7

2 2 2

22 22 222 2 2

( 1)( )

v

M Var P

h M h M h

2

22 22

2 2 2 2

2 2 2

22 22 22 22 222 2 2 2

(1 )( 1)1 (1 ) (1 ) ( 1) (1 )

(1 )( 1) ( 1)(1 )

v v

v v

M M M M

h M h M h h M h M h

M M M h M h M h h M h M h

222

1cov( , )T

i

v v

A v Фh h

1

2 2 2( | ) ( ( ))i i i E Ф v A Ф E Ф

1

2

1

( | )

T

iv

Var Ф

A Ah

2 2 2 2

2 2 2

( 1) ( 1)( 1)( | ) ( )i

M M E Ф v Y v

2 2 2 2

2 2

1 ( 1) ( 1)( 1)( | )i

v

M M Var Ф

h

22 2 2 2 21 1 1 1 1(

1 2)

v

e e e v e v e v

e v e v

h h M h M h M h h h h M h h M

h h M h h M

We can solve maximization problem (1) following Grossman (1976) and get demand

function:

2 2

22

( | )

( | )

i

ii

E Ф P

x aVar v Ф

(2)

2 2

1

1 M

i

i

x x M

(3)

Then using (2) and (3) we can get:

2 2

2 2 2

( 1) ( 1)( 1)( ) ( ) (4)

v v v

ax M ax M ax P v Y Y

h h h



8

2 2

21

( 1)( 1) ( 1)( )( )

v v

ax M M axv

h h

2 2

22

( 1) ( 1)( 1) M M

2 11

2

( )( )

i E P P xaVar P

1 1

1

1 M

i

i

x x M

2

1 21 22 22 2

1( ) (5)

v

M P ax

h h M

Proposition 1 When M

is sufficiently

high

2 1 P P is an

increasing

function

of

2

2 1 22 2 22 2

1: From 4 and 5 we can get that ( ) ( )

v

M Proof P P Y ax

h h M

2 22Since M is sufficiently high; than Y is close to 0. Since is positively related to , the

statement can be concluded.

v

Proposition 2 2 1( )Var P P is an increasing function of

2

2 1 22 222: From Proposition 1 we can get that Var( ) ( ) . Since is

positively related to , the statement can be concluded.

M Proof P P

h M

Proposition 3 When M is sufficiently high

2 1 2 1

1 1

and var( ) P P P P

P P

are increasing functions

of

1

22

1 22 2

2 1

1

: Since part of P which depends on is( 1)( 1) 1

( ) ( ) ,

which negatively depends on , and using Proposition 1 we can conclude that is positivel

v v

Proof M ax M

P axh h h M

P P

P

y

related to .

2 11

1

Since P is constant, and using the Proposition 2, it can be concluded that Var( ) is

positively related to .

P P

P



9

Proposition 4 When M is sufficiently high

2 1 2 1

1 1

and var( ) P P P P

P P

are increasing functions

of .

2 1 2 1

1 1

: In the given parameter range, the derivative of and var( ) with respect to is

positive, which is in accordance with the article "Volume, volatility, price, and profit when all

tr

P P P P Proof

P P

aders are above average" by Terrance Odean.

So, from our theoretical model we derived that the higher the degree of

overconfidence of traders, measured by , the higher is the return and volatility of the

stock. The higher is the extent to which a trader searches for the information of a particular

stock to form her own believe of the stock, measured by the higher would be the return

and volatility of a particular stock. Next we will see if the data will support this theoretical

model. The proxy for will be SVI. If SVI is high, it means that investors investigate the firm

to invest and mostly form their own believes, based on information provided by search

engine. In this case will be high. On the contrary, when SVI is low, it means that people

rely on common knowledge of society to take investment decisions. In this case will be

low. The proxy for is Twitter Mood Index. When investors are in the good mood, they

tend to be more overconfident.

3. Data

To quantify public interest in a particular stock, we use the number of Internet search

queries of firm names as provided by Google Insights. The search volume for a specific key

word provided by Google Insights is not given in absolute terms, but as a value relative to

the total

number

of

searches

on

Google

in

the

corresponding

time

interval.

For

each

search

term, this relative value is then normalized so that the search volume always varies

between 100 ( period in which the highest relative volume was observed) and 0 (a period in

which search volume does not meet a designated threshold ). The data transformation

done by Google eliminates a general trend in search volume due to a higher popularity of

the Internet, but also inhibits us from making use of information about the absolute

number of search queries . Consequently, variation in the level of search volume among

different companies does not convey any analyzable information for our investigation and

we are thus restricted to analyzing variation in search volume within each firm. Firms that



10

have only very few search records on Google Insights usually have the largest within

variation due to normalization of the data. To ensure that stocks with few observations of

Internet search volume do not drive our results, we include only those firms for which

more than five monthly or more than 20 weekly search volumes are provided. Of the

remaining observations, we drop all values if the search volume in two or more consecutive

months

equals

zero

because

Google

Insights

has

designated

a

threshold

for

search

volume

below which the variable is set to zero. As a result, search terms with very low overall traffic

may have long periods with zero searches. We discard these observations because they do

not provide any analyzable within variation for our investigation. In the analysis, we use the

variable AbSVI (Abnormal SVI) which is the difference between SVI and the average SVI

during a special time period for a given firm.

As to the proxy for investor sentiment from Twitter, I collect data about Twitter

emoticons

usage

from

the

Infochimps

website.

The

Infochimps

database

include

smiley

and

frowny timestamp from the launch of Twitter (March 2006) till April 2010. Twitter was not

popular at the beginning. As I want my mood index to reflect general mood of society, I

excluded early observations (where smiley to frowny ratio reflect only mood of several

thousand people). Thus I restrict data to the interval from 2nd of June 2008 through the 1st

of April 2010. This time period includes 467 working days. The data includes a timestamp

about every emoticon on Twitter for this period.

Financial data for returns and volatility of the stocks was downloaded from Google

Finance.

4. Descriptive statistics

In the Master Thesis we use 40 companies. We examine the influence of company’s

size on the relationship between search volume and volatility by means of analyzing the

effect

of

increase

in

SVI

for

stock

volatility

for

companies

with

different

market

capitalization. The market‐to‐book value, on the other hand, does not seem to be

significantly related to changes in Google search volume.

To convince skeptics that SVI really measures people’s interest in particular word the

frequency for word “New year” is depicted on the picture 1. As anyone can predict, the

interest for New Year is higher close to the 1st January which is in accordance with Google

Trend.



11

Picture 1 To convince skeptics that Twitter index really captures people’s emotional state the

mood of people during the working days and weekends based on Twitter index is depicted

on the picture 2. As anyone can predict, the mood of people gets better during working

days from Monday to Friday. During weekends the mood gets worse from Saturday to

Sunday, the

closer

working

days

are.

Our

Twitter

index

proves

this

intuition.

Picture 2 Mood dynamics during working week



12

Picture 3 Mood dynamics during weekend

Picture 4



13

Picture 5 Smiley to frowny ratio

Picture 6

From the pictures below it can be concluded that SVI and Volume of trade of a

company are related to each other because they have patterns, which are similar to some

extent. The rest of the descriptive statistics is in Appendix.



14

Picture 7



15

5. Results

Volatility prediction

Time series for different stocks Tables

1−

3

present

results

of

regression

of

Volatility

of

the

stocks

to

the

mood

index,

volatility lags, Search Volume Index (SVI) and cross term of first lag of SVI and mood index.

We estimated different models including and omitting different variables. The full

regression model:

113322113322

11332211332211

_ ReReRe SQSenseturnturnturnSQSQ

SQSenseSenseSenseVol Vol Vol Vol

t t t t t

t t t t t t t t

(1)

Where Return− return of the stock for a particular company, Vol − volatility of the stock

for a particular company, Sense− mood index from Twitter, SQ− SVI index for a particular

company,

Sense_SQ−

multiplication of

mood

index

and

SVI

index.

In all models 1 (coefficient of the mood index of investors) is positive and significant at

5 % level. So, the increase of investor’s mood will increase stock volatility. Coefficient

1 (coefficient of the Search Volume Index) is positive and significant at 1 % level. So, the

increase of Search Volume Index for particular company will increase the stock volatility of

this company. The most interesting coefficient is 1 (coefficient of the cross term Sense_SVI)

which is positive and significant. It means that including investor’s mood substantially

changes the influence of SVI increase on the stock volatility. So, the effect of increase of

stock volatility due to increase in SVI of the company is higher when investors are happier.

Comparing coefficient 1 for big (Apple), medium (OBAS) and small (Tlab) firms in tables 1‐

3, we can conclude that the higher the market value of the company the smaller is the

coefficient 1 , and the less sensitive its stock volatility to SVI increase for this company.

In all models we use HAC errors (Newey and West (1987) and Andrews (1991)) in order

to avoid problems with autocorrelation or heteroskedastisity of errors. An important

feature of the regression in this Master Thesis is that a stock return and volatility depends

on values which are known by the exchange opening.

Panel data analysis In the previous section we analyzed time series data for a particular stock. Here we

will investigate relation between Volatility, SVI, TMI and market capitalization using panel

regression with FE. The results are provided in the Table 0. From Table 0 it follows that SVI

(Search Volume Index) positively and significantly influence Volatility. Since the coefficient

of cross term svi_cap is significant and negative; it is again supports the fact that the higher

the market value of the company the less sensitive its stock volatility to SVI increase for this

company. Coefficient

on

Sense is positive

and

significant

which

indicates

that

Twitter

Mood

Index is positively related to Volatility of stocks. Cross term sense_svi is also positive and



16

significant. All in all, the results from panel data analysis are in accordance with previous

section.

APT model to predict stock returns:

Arbitrage pricing theory (APT) holds that the expected return of a financial asset can bemodeled as a linear function of various factors or theoretical market indices, where

sensitivity to changes in each factor is represented by a factor-specific beta coefficient. We

make a three factor model: the first factor is AbSVI – Abnormal Search Volume Index, the

second factor is Twitter Mood Index, the third one is market excess return. Also we make a

2 factor model where the first factor is Twitter Mood Index and the second factor is market

excess return. We will compare these factor models to find out if inclusion of Abnormal

Search Volume index will improve the 2 factor model. For testing the three factor model

with AbSVI

we

use

Fama

‐MacBeth

Procedure.

The

parameters

in

this

procedure

are

estimated in two steps.

First step: We regress each stock on the proposed risk factors to determine that asset's beta for

that risk factor. In the pictures below betta coefficients are provided. With red color are

marked risk‐factors at 1% significance level, with yellow –at 5 % level, with green – at 10%

level. We can see that market_rf ‐ excess market return is almost always very significant,

but other

factors

also

stand

out

very

often.

We

will

see

the

final

results

on

the

second

step,

but even now we can see from tables below that our factor‐ AbSVI is quite reasonable; so, it

can significantly improve our prediction power.



17

Second step: We regress all asset returns for a fixed time period against the estimated betas in the

first step without a constant to determine the risk premium for each factor, for every time

(T=467). Than

we

take

residuals

from

these

cross

section

regressions.

We

have

467

residual

vectors. Than these residuals are used to form a statistic that is distributed as chi‐square.

Than we will see if our hypothesis that we explained returns variation substantially with our

3 factors is rejected or accepted. This statistics equal to 34.13, with p‐value over 0.4. It

means that the null hypothesis that we explained almost all variation with our factor is not

rejected. So, 3 factor model with AbSVI is in accordance with our stock returns.

it t i

ei

t R

' ,

i=1,2,….N

for

each

t

;ˆ1ˆ

1

T

t

t T

;ˆ1

ˆ1

T

t

it iT

;)ˆˆ(1

)ˆ( 2

12

2

T

t

t T

;)ˆˆ(1

)ˆ(1

2

2

2

T

t

it iT

;ˆ

1ˆ

1

T

t t T ;)

ˆˆ)(

ˆˆ(

1

)ˆ

cov( 12

T

t t t T



18

.ˆ)ˆcov('ˆ 2

1

1

N

Relevance of SVI to predict stock returns in APT models:

We will compare models based on their R‐square adjusted from following

regressions. In

2 factor

model

R‐square

adjusted

is taken

from

linear

regression

of

stock

excess returns on market excess return and Twitter mood index; in 3 factor model in

addition to two factors we include Abnormal SVI. In the table below we provide these R‐

square adjusted values for both models (for every company) and compare them. We

depicted the model with higher R‐square with red color. So, we can see that 3 factor APT

model posses more red colors, so Abnormal SVI has an important role in predicting stock

returns.

Relevance of Twitter Mood Index and SVI with macroeconomic data

We showed that SVI and Twitter Mood Index are of big importance in explaining

stock returns. However, one may think that macroeconomic variables may substitute these

indexes, because people can have bad mood in Twitter network or increase they search

volume for

a given

firm

only

because

of

macroeconomic

situation.

So,

we

should

test

if

macroeconomic variables such as unemployment and average hourly earnings can explain



19

variation that is explained by Twitter Mood Index and SVI. Here we will show that the

variables in question, Twitter Mood Index( TMI) and SVI will be still significant almost for all

firms, even if we include macroeconomic variables in the model. Firstly, we regress stock

market returns of different firms on market return, TMI and SVI. Than we add

macroeconomic variables in addition such as unemployment and average hourly earnings

into

our

model.

From

the

table

below

we

can

see

even

with

macroeconomic

components,

TMI and SVI are still significant. So, SVI’s and TMI’s role in explaining stock returns can not

be substituted by macroeconomic variables. From the table below it follows that

significance of TMI and SVI almost remains the same after inclusion of macroeconomic

variables. However, it is not honest enough to use this comparison, since available for us

macroeconomic data frequency is only monthly. I am going to return to this problem in my

further research.



20



21

Out of sample prediction analysis

From the recent literature, it follows that Twitter Mood Index and market return can

explain a significant variation of stock return of a given company. In this section we will

show that out of sample predictive power of the stock return will increase if we include SVI

in addition to Twitter Mood Index and market return explanatory variables. We divide our

sample into

2 parts:

the

first

part

– for

N=1..300

−in

sample

model

selection;

the

second

part – for N=301..467− out of sample checking for predictive ability. We use MSPE (Mean

Square Predictive error) measure to compare model with SVI as an explanatory variable

and the model without SVI. The model with the lowest MSPE is better in stock return

prediction. From the table below we can find out that the model with SVI is better almost

for all companies; in this case the cell is filled with red color. Moreover the difference

between MSPE is significant in 5% significance level.

Endogeneity problems The regressions in our study might bear an endogeneity concerns. There is a probability

that stock market activity determines Twitter mood. But only a negligible percent of tweets

are financial oriented. In order to prove this we collected most popular financial words.

Interest rate, inflation, unemployment are examples of these words. Then we calculated

the frequency of these words. Sum of these frequencies was less than 0.1%. Thus only small

percent of Twitter accounts is financial related. Hence, stock market does not determine

Twitter mood.

Also, there is a probability of reverse causality problem with SVI, because it is possible

that stock market volatility is not influenced by SVI increase; on the contrary, an increase in

stock market volatility influences SVI. To investigate this problem we use Granger causality

test. The results showed in appendix suggest that based on the test we do not have such

kind of

problem.

However,

it is very

important

to

understand

Granger

causality

cannot



22

establish causality in theoretical sense, also Granger causality is not test for strict

exogeneity.

Relation between theoretical model and data The theoretical model developed above posits that the higher the degree of

overconfidence of traders, measured by , the higher is the return and volatility of the

stock. It is in accordance with the data, since from the regression analysis the higher is

Twitter Mood Index, the higher is the return and volatility. The proxy for is Twitter Mood

Index. When investors are in the good mood, they tend to be more overconfident. The

higher is the extent to which a trader searches for the information of a particular stock to

form her own believe of the stock, measured by ,the higher would be the return and

volatility of a particular stock. The proxy for will be SVI. If SVI is high, it means that

investors investigate the firm to invest and mostly form their own believes, based on

information provided

by

search

engine.

In

this

case

will

be

high.

On

the

contrary,

when

SVI is low, it means that people rely on common knowledge of society to choose

investment decisions. In this case will be low. From our empirical analysis we obtained

that SVI is positively related to return and volatility of a particular stock. So, data supports

our theoretical model.



23

6. Conclusion In this Master Thesis we improved the predictive power of stock return and volatility

by taking into account not only SVI (Search Volume Index), search frequencies from Google

Trends for a particular company, but also investor sentiments, measured on Twitter. We

showed that

our

models

have

higher

predictive

power

than

recent

models,

based

only

on

investor emotional state, measured on Twitter, using out‐of ‐sample analyses. So, using SVI

and Twitter Mood Index can considerably improve the prediction power of stock returns

and volatility. Also, we showed that the significance of Search Volume Index and Twitter

Mood Index persist even if we add some macroeconomic variables. Analyzing my model, I

discovered interesting effects, such as: the higher TMI of society and SVI, the higher would

be stock return and volatility; the increase of stock volatility due to increase in SVI of a

given company is higher when investors are happier; the higher the market value of the

company the

less

sensitive

its

stock

volatility

to

SVI

of

this

company.

Also,

it is worth

noting

that it is easy to use SVI and TMI for investment decision since these data are open and the

frequency of these data is very high. So, investors can considerably improve their positions

in the stock market analyzing Twitter Mood Index and Search Volume Index. Since the

theoretical model developed in this article is in accordance with the data; it adds additional

strength to the results of this paper.



24

7. Acknowledgments

The author is deeply grateful to his research advisors Oleg Shibanov and Dmitry

Makarov for responsive guidance and useful comments. It has been a great honor to work

with them. Oleg Shibanov and Dmitry Makarov created special collaborative atmosphere in

the research

seminars;

thank

you

for

your

support.



25

8. References

1. Baker, Malcolm, and Jefrey Wurgler, 2006, “Investor sentiment and the cross‐section

of stock returns”, Journal of Finance 61, 1645‐1680.

2. Baker,

Malcolm,

and

Jefrey

Wurgler,

2007,

“Investor

Sentiment

in

the

Stock

Market”,

Journal ofEconomic Perspectives 21, 129‐151.

3. Barberis, N. and R. Thaler (2003): “A survey of behavioral finance", Handbookof the

Economics of Finance, 1, 1053‐1128.

4. Bollen, J., H. Mao, and X. Zeng (2011): “Twitter mood predicts the stock market",

Journal of Computational Science.

5. Cochrane, John, Asset Pricing, Princeton University Press, 2001

6. Glaser, M. and M. Weber (2007): “Overcondence and trading volume", The Geneva

Risk and

Insurance

Review”,

32,

1‐36.

7. Hirshleifer, D. (2001): “Investor psychology and asset pricing," The Journal of

Finance, 56, 1533‐1597.

8. Matthias Bank, Martin Larch, and Georg Peter, “Google Search Volume and its

Influence on Liquidity and Returns of German Stocks”, working paper.

9. Terrance Odean, “Volume, Volatility, Price, and Profit When All Traders Are Above

Average”.

10. Tetlock, P. (2007): “Giving content to investor sentiment: The role of media in the

stock market", The Journal of Finance, 62, 1139‐1168.

11. Thomas Dimpfl and Stephan Jank, “Can internet search queries help to predict stock

market volatility?

12. Volkova E., “Twitter Emoticons and Stock Market Returns”

13. Zhi Da, Joseph Engelberg and Pengjie Gao “In Search of Attention”.



26

9. Appendix

(1) (2) (3) (4)

VARIABLES volatility volatility volatility volatility

volatility_1 0.672*** 0.673*** 0.673*** 0.673***

(0.00833) (0.00830) (0.00829) (0.00825)sense 0.00126** 0.00133** 0.00126** 0.00144** 0

(0.000634) (0.000632) (0.000631) (0.000591) (

sense_1 0.000157 0.000148 0.000199

(0.000313) (0.000313) (0.000313)

svi 0.000241*** 0.000277*** 0.000275*** 0.000277*** 0

(5.32e-05) (4.41e-05) (4.40e-05) (4.41e-05)

svi_1 3.71e-05

(3.03e-05)

return -0.0150*** -0.0150*** -0.0164*** -0.0150***

(0.00406) (0.00406) (0.00407) (0.00406)

sense_svi 1.10e-05* 1.20e-05* 1.15e-05 1.21e-05* (5.60e-06) (5.96e-06) (8.95e-06) (5.06e-06)

svi_cap -6.35e-06* -7.12e-06* -6.40e-06 -7.16e-06*

(3.73e-06) (4.18e-06) (7.21e-06) (3.91e-06)

return_1 -0.0194***

(0.00407)

volatility_2

Constant -0.0125*** -0.0125*** -0.0124*** -0.0124*** -

(0.00267) (0.00267) (0.00267) (0.00264)

Observations 8,244 8,244 8,244 8,244 Number of id 40 40 40 40

Adjusted R-squared 0.545 0.545 0.546 0.545

Table 0 Panel regression with FE



27

(1) (2) (3) (4) (5) (6) (7) Apple (Big firm) Vol Vol Vol Vol Vol Vol Vol

Vol_1 0.516*** 0.525*** 0.530*** 0.524*** 0.544*** 0.524***

(0.0500) (0.0492) (0.0493) (0.0491) (0.0423) (0.0490)

Vol_2 0.0295 0.0171 0.0206 0.0178 0.0364

(0.0556) (0.0545) (0.0546) (0.0543) (0.0473)

Vol_3 0.0429 0.0331 0.0270 0.0324

(0.0508) (0.0466) (0.0466) (0.0463)Sense_1 0.0161** 0.0164** 0.0166** 0.0165** 0.0178*** 0.0173***

(0.00649) (0.00647) (0.00649) (0.00647) (0.00633) (0.00636)

Sense_2 -9.53e-07 -9.86e-07 -1.68e-06 -9.86e-07 -9.43e-07 -8.26e-07 -

(2.10e-06) (2.10e-06) (2.07e-06) (2.10e-06) (2.08e-06) (2.08e-06) (

Sense_3 -3.27e-07 -3.30e-07 -2.89e-08 -2.67e-07

(2.01e-06) (2.00e-06) (2.00e-06) (1.99e-06) (

SQ_1 0.00277*** 0.00210*** 0.00215*** 0.00210*** 0.00227*** 0.00217*** 0

(0.000683) (0.000359) (0.000359) (0.000358) (0.000321) (0.000346) (

SQ_2 -0.000979

(0.000899)

SQ_3 0.000215

(0.000660)Return_1 -0.304** -0.311** -0.289** -0.313** -0.311** -0.318**

(0.133) (0.133) (0.133) (0.132) (0.132) (0.132)

Return_2 -0.172 -0.165 -0.136 -0.163 -0.151 -0.161

(0.137) (0.137) (0.136) (0.136) (0.136) (0.136)

Return_3 -0.268** -0.259* -0.257* -0.252* -0.251*

(0.135) (0.134) (0.134) (0.134) (0.133)

Sense_SQ_1 0.000300** 0.000328 0.000325 0.000308 0.000346* 0.000358** 0.000326 0

(0.000117) (8.50e-05) (8.54e-05) (8.52e-05) (8.08e-05) (7.97e-05) (8.09e-05) (

Constant -0.0386 -0.0405 -0.0438 -0.0404 -0.0428 -0.0444* -0.0427

(0.0266) (0.0265) (0.0266) (0.0265) (0.0263) (0.0262) (0.0263)

Observations 463 463 463 463 463 463 463

Adjusted R-squared 0.690 0.690 0.688 0.691 0.690 0.691 0.691

Standard errors in parentheses

*** p<0.01, ** p<0.05, * p<0.1Table 1 Big firm



28

(1) (2) (3) (4) (5) (6) (7) OBAS (Medium firm) Vol Vol Vol Vol Vol Vol Vol

Vol_1 0.112** 0.117** 0.117** 0.117** 0.134*** 0.119**

(0.0470) (0.0468) (0.0468) (0.0468) (0.0463) (0.0464)

Vol_2 0.113** 0.114** 0.115** 0.114** 0.116**

(0.0471) (0.0469) (0.0469) (0.0469) (0.0466)

Vol_3 0.0145 0.0124 0.00574 0.0127

(0.0474) (0.0473) (0.0470) (0.0473)Sense_1 0.000104***0.000109***0.000108***0.000108*** 0.000118*** 0.000110***

(3.83e-05) (3.81e-05) (3.81e-05) (3.81e-05) (3.79e-05) (3.77e-05)

Sense_2 -6.65e-09 -7.26e-09 -5.10e-09 -7.27e-09 -7.00e-09 -7.24e-09

(1.19e-08) (1.19e-08) (1.18e-08) (1.19e-08) (1.19e-08) (1.19e-08)

Sense_3 -3.67e-09 -3.85e-09 -3.39e-09 -4.11e-09

(1.16e-08) (1.16e-08) (1.16e-08) (1.16e-08)

SQ_1 0.00474*** 0.00420*** 0.00465*** 0.00413*** 0.00457*** 0.00432***

(0.000683) (0.000359) (0.000359) (0.000358) (0.000321) (0.000346)

SQ_2 -0.000979

(0.000899)

SQ_3 0.000215

(3.05e-06)Return_1 -0.283** -0.291** -0.264** -0.304** -0.302** -0.294** -0.315**

(0.133) (0.133) (0.133) (0.133) (0.133) (0.133) (0.133)

Return_2 -5.79e-05 -5.01e-05 5.02e-05 -4.11e-05 8.09e-05 -3.61e-05

(0.000451) (0.000451) (0.000443) (0.000449) (0.000449) (0.000448)

Return_3 -0.000514 -0.000538 -0.000533 -0.000531 -0.000519

(0.000445) (0.000444) (0.000443) (0.000443) (0.000440)

Sense_SQ_1 0.000300** 0.000328 0.000325 0.000308 0.000346* 0.000358** 0.000326

(0.000117) (8.50e-05) (8.54e-05) (8.52e-05) (8.08e-05) (7.97e-05) (8.09e-05)

Constant -0.000106 -0.000178 -0.000182 -0.000176 -0.000180 -0.000176 -0.000178

(0.000212) (0.000203) (0.000203) (0.000203) (0.000203) (0.000204) (0.000203)

Observations 463 463 463 463 463 463 463



*** p<0.01, ** p<0.05, * p<0.1Table 2 Medium firm



29

(1) (2) (3) (4) (5) (6) (7) TLAB (Small firm) Vol Vol Vol Vol Vol Vol Vol

Vol_1 0.499*** 0.530*** 0.535*** 0.524*** 0.530*** 0.552*** 0.524***

(0.0504) (0.0491) (0.0493) (0.0493) (0.0491) (0.0431) (0.0493)

Vol_2 0.0453 0.0276 0.0327 0.0212 0.0443 0.0375

(0.0552) (0.0543) (0.0545) (0.0545) (0.0484) (0.0486)

Vol_3 0.0607 0.0326 0.0238 0.0319

(0.0497) (0.0480) (0.0481) (0.0483)Sense_3 0.0268*** 0.0259** 0.0235** 0.0259** 0.0254**

(0.0135) (0.0136) (0.0136) (0.0106) (0.0135) (0.0135) (0.0105)

Sense_2 -0.00212 -0.000110 0.00118 -0.00198 0.000314 0.000963 -0.00156

(0.0104) (0.0104) (0.0104) (0.0104) (0.0103) (0.0103) (0.0104)

(0.0102) (0.0103) (0.0103) (0.0103) (0.0102)

SQ_1 0.00598***0.00572***0.00577***0.00576***0.00573*** 0.00581***0.00577***0

(0.000684)(0.000418)(0.000419)(0.000420)(0.000417) (0.000407)(0.000419)(

SQ_2 -0.00121

(0.000895)

SQ_3 -0.000883

(0.000807)Return_1 -0.343** -0.309** -0.275** -0.301** -0.302** -0.268**

(0.133) (0.133) (0.133) (0.132) (0.132) (0.132)

Return_2 -0.146 -0.139 -0.122 -0.168 -0.133 -0.121 -0.162

(0.132) (0.133) (0.133) (0.133) (0.132) (0.132) (0.133)

Sense_SQ_1 0.000300** 0.000328 0.000325 0.0003080.000346* 0.000358** 0.0003260

(0.000117) (8.50e-05) (8.54e-05) (8.52e-05) (8.08e-05) (7.97e-05) (8.09e-05) (

Constant 0.0565** 0.0381 0.0331 0.0313 0.0385 0.0362 0.0317

(0.0281) (0.0269) (0.0270) (0.0269) (0.0269) (0.0268) (0.0269)

Observations 463 463 463 463 463 463 463



*** p<0.01, ** p<0.05, * p<0.1

Table 3 Small firm



30

Granger causality test

svi_1 ALL 1.3214 3 0.724

svi_1 return_rf1 1.3214 3 0.724

return_rf1 ALL 8.0868 3 0.044return_rf1 svi_1 8.0868 3 0.044

Equation Excluded chi2 df Prob > chi2

svi_2 ALL 1.107 3 0.775svi_2 return_rf2 1.107 3 0.775












svi_35 ALL .61458 3 0.893svi_35 return_rf35 .61458 3 0.893





31










svi_38 ALL .45993 3 0.928svi_38 return_rf38 .45993 3 0.928





32

Unit root tests

Smile_ratio:

1 -5.929 -3.480 -2.880 -2.5922 -4.709 -3.480 -2.877 -2.5893 -3.871 -3.480 -2.874 -2.5864 -2.481 -3.480 -2.871 -2.5835 -2.953 -3.480 -2.867 -2.5806 -2.671 -3.480 -2.864 -2.577

7 -2.729 -3.480 -2.860 -2.5748 -2.568 -3.480 -2.857 -2.5719 -2.518 -3.480 -2.853 -2.56810 -2.597 -3.480 -2.849 -2.56411 -2.438 -3.480 -2.846 -2.56112 -2.337 -3.480 -2.842 -2.55713 -2.401 -3.480 -2.838 -2.55414 -2.345 -3.480 -2.834 -2.55015 -2.719 -3.480 -2.829 -2.54616 -2.865 -3.480 -2.825 -2.54217 -2.758 -3.480 -2.821 -2.538

[lags] Test Statistic Value Value Value

DF-GLS tau 1% Critical 5% Critical 10% Critical

Maxlag = 17 chosen by Schwert criterionDF-GLS for total_smile_ra~o Number of obs = 449

MacKinnon approximate p-value for Z(t) = 0.0000 Z(t) -8.276 -3.443 -2.871 -2.570Z(rho) -117.143 -20.473 -14.000 -11.200

Statistic Value Value Value

Test 1% Critical 5% Critical 10% CriticalInterpolated Dickey-Fuller

Newey-West lags = 5Phillips-Perron test for unit root Number of obs = 466



33

Abnormal_SVI:

1 -8.656 -3.480 -2.880 -2.5922 -8.965 -3.480 -2.877 -2.5893 -9.690 -3.480 -2.874 -2.5864 -9.914 -3.480 -2.871 -2.5835 -8.638 -3.480 -2.867 -2.5806 -8.557 -3.480 -2.864 -2.5777 -8.323 -3.480 -2.860 -2.5748 -8.139 -3.480 -2.857 -2.5719 -7.531 -3.480 -2.853 -2.56810 -6.448 -3.480 -2.849 -2.56411 -6.255 -3.480 -2.846 -2.56112 -6.004 -3.480 -2.842 -2.55713 -5.767 -3.480 -2.838 -2.55414 -5.132 -3.480 -2.834 -2.550

15 -4.836 -3.480 -2.829 -2.54616 -4.499 -3.480 -2.825 -2.54217 -4.133 -3.480 -2.821 -2.538

[lags] Test Statistic Value Value Value

DF-GLS tau 1% Critical 5% Critical 10% Critical Maxlag = 17 chosen by Schwert criterionDF-GLS for abnormal_svi Number of obs = 449

MacKinnon approximate p-value for Z(t) = 0.0000 Z(t) -9.100 -3.443 -2.871 -2.570Z(rho) -145.009 -20.473 -14.000 -11.200

Statistic Value Value Value

Test 1% Critical 5% Critical 10% CriticalInterpolated Dickey-Fuller

Newey-West lags = 5

Phillips-Perron test for unit root Number of obs = 466



34

Descriptive statistics

.

svi_48 467 77.14347 9.731523 47 100svi_45 467 73.94861 7.943843 47 93svi_44 467 61.76017 8.176059 45 82svi_43 467 52.82655 15.79502 24 100svi_42 467 43.32334 8.239205 34 100







svi_5 467 50.22912 15.42353 25 100svi_4 467 39.35118 14.84168 18 94

svi_3 467 74.30407 9.242654 53 100svi_2 467 52.25054 12.61426 38 100svi_1 467 38.27409 19.86278 17 100

Variable Obs Mean Std. Dev. Min Max

total_smil~o 467 4.187639 .6214554 2.041884 7.793358


abnormal_svi 467 .0154429 1.34225 -4.393421 4.651316




.

return_rf48 467 .0000742 .043163 -.1764149 .198517return_rf45 467 .0001624 .0209174 -.0902819 .1011238return_rf44 467 .0015661 .0640097 -.1833791 1.02642return_rf43 467 .0000825 .0272384 -.1561908 .0955978return_rf42 467 .0008793 .0203028 -.0966328 .1019176

return_rf41 467 .0013318 .0268214 -.0855479 .1837885return_rf40 467 .0032138 .0883152 -.4357144 .6036124

return_rf39 467 -.0006208 .0265094 -.2738005 .0998596return_rf38 467 .0003352 .0303252 -.1200823 .1353238return_rf37 467 -.000255 .0319928 -.1256925 .1423469

return_rf36 467 -.0002325 .044591 -.1977432 .2770596return_rf35 467 .0000777 .0296379 -.1420076 .1931156return_rf34 467 -.0002265 .0413625 -.198405 .2376889return_rf33 467 .0003168 .0415379 -.1453664 .2188289return_rf32 467 .0005718 .0219979 -.0976991 .089963

return_rf31 467 .0002069 .0282733 -.099457 .1381261return_rf29 467 .0000593 .0322851 -.1111415 .1433967return_rf28 467 -.0000696 .0334741 -.1255732 .1408074return_rf25 467 .0010468 .0471699 -.2002347 .1926847return_rf24 467 .0001064 .0259311 -.1225646 .1443371

return_rf23 467 .0000466 .0208847 -.0847422 .1215368return_rf21 467 .0008793 .0337669 -.0985086 .1073404return_rf20 467 -.0002439 .0400372 -.1705379 .2348329return_rf19 467 .0005939 .0510999 -.1905089 .2468608return_rf18 467 .0000168 .028264 -.1256204 .1484373

return_rf17 467 -.0000192 .037853 -.1587009 .2033972return_rf16 467 .0001318 .0170395 -.079277 .0860319return_rf14 467 -.0000347 .0366088 -.1415909 .1566463return_rf13 467 .0004086 .0303057 -.1544241 .1862102return_rf11 467 .0016937 .0622038 -.2505562 .2642725

return_rf10 467 .0001013 .0155925 -.0769608 .1225484return_rf9 467 -.0005746 .023131 -.0773594 .1630577return_rf8 467 -.000196 .0282216 -.1243882 .2078783

return_rf7 467 .0001966 .0200174 -.0589296 .1147778return_rf6 467 -.0005041 .0358722 -.1108287 .1968073

return_rf5 467 -1.01e-06 .0176226 -.0801369 .1105465return_rf4 467 .0004356 .0269971 -.1169918 .1853826return_rf3 467 .0002506 .025358 -.1155433 .1469867return_rf2 467 -.0002908 .0255207 -.1390229 .1712411return_rf1 467 .0008773 .0286992 -.1786473 .1383857


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