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QAP Analysis of Company Co-Mention Network

S. P. Sidorov, A. R. Faizliev, V. A. Balash, A. A. Gudkov,

A. Z. Chekmareva, M. Levshunov, S. V. Mironov

Saratov State University, Saratov, Russian Federation

WAW-2018

The work was supported by grant RFBR (project 16-01-00507)

Summary 1

1 Introduction to News Analytics

2 Data Description

3 Empirical Results

1. Introduction to News Analytics

Changes in the World of Finance 2

1973 � International Monetary Market 24/7.

1973-2016 � the volume of foreign exchange trading has been growing morethan 100 times.

1992 � electronic trading, CME and Globex.

2001 � algorithmic trading, IBM.

2001 � news analytics tools.

News Analytics 3

The most well-known providers of news analytics and data are:

RavenPack ( http://www.ravenpack.com/).

Media Sentiment (www.mediasentiment.com/).

Thomson Reuters News Analytics (http://thomsonreuters.com).

News Analytics 4

News analytics can be described as a measurement of the following quantitative andqualitative characteristics of news:

1 The nature of news;

2 The impact of news;

3 The relevance;

4 The novelty.

News data can be obtained from various sources:

News sources of news agencies

Pre-news

social media (blogs, social networks, etc.)

News

expected;

unexpected.

Extract from Raven Pack data sheet 5

Figure : Extract from Raven Pack data

2. Company Co-Mention Network

Motivation 6

Company co-mention network � undirected weighted graph.

Two companies are considered in a relationship if there has been published apublicly available news mentioning them.

A company can be a node and the act of mentioning two companies in onenews can be considered as a link between them.

Motivation 7




Motivation 8




Motivation 9




Questions 10

In our research we would like to �nd answers to the following questions.

1 What type of the degree distribution exhibits the company co-mention network? What type of thefunctional form has the clustering-degree relation for the network?Many networks have similar degree distributions (Albert, 2002), (Dorogovtsev, 2002b), (Newman,2003), (Albert, 2005), (Boccaletti, 2006). It turned out that most of real networks have degreedistributions that are scale-free (Albert, 2002).

2 Does the analysis of company co-mention network identify groups? Hypothesis: the networkanalysis of the company co-mention network reproduces the sector structure of the economy. Eachgroup or cluster of companies might be associated with a particular sector, for example, banking,communication or oil production.

3 Do the frequency, degree centrality, closeness centrality, betweenness centrality and eigenvectorcentrality of companies vary within clusters?Within each group (or cluster) of companies, our research is going to �nd those who are morecentral than others. It is assumed that the more central a company is within a network, the morein�uential and more important must be news about it.

Our research questions are close to ones from the paper (Kim and Barnett, 2008) which investigatedwhether the patterns of author co-citation can describe the structure of the �eld of communication.

Questions 11






Questions 12






Questions 13






Data 14

Company co-mention analysis can be carried out as follows:

1 full texts of all economic and �nancial news published during a period of timeare collected;

2 for each news full text, a list of companies it mentions is gathered;

3 for all set of available news, a weighted co-mention count is determined foreach pair of co-mentioned companies;

4 the weighted co-mention counts are accumulated into a symmetricco-mention matrix;

5 the co-mention matrix is analysed statistically, and the results are visualizedand interpreted.

Data 15







Data 16







Data 17







Data 18







Methodology 19

Table : News and its mentioned companies

c1 c2 c3 c4 c5 c6 c7

N1 + + +

N2 + +

N3 + + +

N4 + +

N5 + +

N6 + +

N7 + + +

N8 + +

Methodology 20

Table : The matrix of weights

c1 c2 c3 c4 c5 c6 c7

c1 0 3 1 0 0 0 0

c2 3 0 1 0 0 0 0

c3 1 1 0 1 0 0 0

c4 0 0 1 0 2 3 0

c5 0 0 0 2 0 2 0

c6 0 0 0 3 2 0 1

c7 0 0 0 0 0 1 0

Methodology 21

1

2

3

4

5

6

7

3

1

11

2 2

31

3. Data Description

Data 22

From February 1, 2015 to February 28, 2015 (i.e. 20 trading days)

Table : Descriptive statistics

δ, day 1

n 28

Sum 234736

Mean 8383.43

Minimum 262

Maximum 15069

St. deviation 5415,21

Median 10653

Daily dynamics of amount of news items 23

1 5 10 15 20 25 300

0.5

1

1.5

·104

days

Theamountofnew

sitem

sper

day

Figure : Daily dynamics of the news �ow in February, 2015

Dynamics of news �ow intensity (February 2, 2015) 24

0 516 1032 14400

50

100

150

200

250

min

Figure : Dynamics of news �ow intensity (February 2, 2015) with 1-min window

Empirical Results. Sectors of Economics25

All companies we considered were divided into four sectors of economy in following way:

Products (2007 companies) Consumer discretionary,Consumer staples,Industrials

Resources (1398 companies) Energy,Resources (1398 companies) Energy,Raw materials

Services (2375 companies) Financials,Health care,Telecommunications,Utilities

IT Sectors (548 companies) Information technology

Key company analysis according to sectors of economics 26Empirical Results. Key company analysis according to sectors of economics

Table : Companies with higher frequency for four sectors of economy.Sector: Products.


Table : Companies with higher frequency for four sectors of economy.Sector: Resources


Table : Companies with higher frequency for four sectors of economy.Sector: Services


Table : Companies with higher frequency for four sectors of economy.Sector: IT sector

30Empirical Results. Sectors of Economics

Table : Degree distribution for four sectors of economy

Subgraphs Degree exponent γ R2

Products 1.06 0.82

Resources 0.91 0.79Resources 0.91 0.79

Services 1.14 0.84

Information Technology 0.64 0.65

31Empirical Results. Sectors of Economics

Table : Local clustering coefficient for four sectors of the economy


Products 0.74 0.58

Resources 0.44 0.46Resources 0.44 0.46

Services 0.70 0.61

Information Technology 0.61 0.54

Empirical Results. Stock Exchanges32

We considered separately companies that are trading on theDifferent stock exchanges.

Stock Exchanges Edges, % Co-mentions,%N 36.15 38.23O 21.54 20.02L 7.21 7.85

TO 8.25 7.58

Table : Edges and Co-mentions by SE

TO 8.25 7.58DE 3.79 4.03PA 3.76 3.99MI 1.97 2.16HK 0.66 0.63T 0.76 0.75

AS 1.41 1.39MC 1.01 1.02VX 1.1 1.21NS 0.7 0.4… … …

Other 6.45 10.73

33Empirical Results. Stock exchanges (SE) contiguity

SE N O L TO DE PA MI HK T Total

N 59.1 32.1 1.4 1.8 0.3 0.4 0.2 0.2 0.5 96.0O 53.8 38.3 1.2 1.8 0.2 0.3 0.2 0.2 0.4 96.3L

Table : Ratio of edges that connected vertices by SE, %

L 7.2 3.5 31.7 0.9 8.4 10.9 3.5 0.4 0.4 66.9TO 7.8 4.7 0.8 80.6 0.1 0.1 0.1 0.1 0.2 94.6DE 2.8 1.2 16.0 0.3 40.6 9.2 2.8 0.1 1.1 74.2PA 3.7 1.6 20.9 0.3 9.3 25.5 3.9 0.3 0.4 66.0MI 3.8 2.0 12.7 0.5 5.5 7.5 47.7 0.5 0.3 80.5HK 11.8 6.3 4.0 1.2 0.7 1.7 1.3 42.0 2.0 71.0T 21.7 10.0 3.6 2.3 5.3 2.2 0.9 1.7 31.7 79.4

34Empirical Results. Stock exchanges (SE) contiguity

SE N O L TO DE PA MI HK T Total

N 62.8 29.4 1.5 1.5 0.3 0.3 0.2 0.1 0.5 96.7O 56.1 37.5 1.0 1.4 0.2 0.2 0.2 0.2 0.3 97.0L

Table : Ratio of co-mentions (sum of edges weights) by SE, %

L 7.4 2.6 35.8 0.9 7.8 10.3 3.1 0.5 0.3 68.9TO 7.6 3.8 1.0 82.5 0.1 0.1 0.1 0.1 0.2 95.6DE 3.1 0.8 15.1 0.2 41.9 9.7 3.2 0.1 1.0 75.1PA 3.0 0.9 20.3 0.3 9.8 27.5 4.5 0.2 0.4 66.9MI 3.5 1.5 11.4 0.3 6.0 8.3 50.4 0.3 0.7 82.5HK 8.6 4.8 6.8 1.0 0.5 1.5 1.0 34.4 1.3 59.9T 25.2 9.1 2.7 2.4 5.2 2.2 2.0 1.1 35.4 85.3

35Empirical Results. Stock exchanges

Table : Degree distribution for three stock exchanges


Precisely, we analyzed companies that traded on London SE (488 companies), New-York SE (1,715 companies) and Tokyo SE (586 companies).

Subgraphs Degree exponent γ R

London SE 1.10 0.81

New-York SE 1.18 0.84

Tokyo SE 1.18 0.44

36Empirical Results. Stock exchanges

Table : Local clustering coefficient for three stock exchanges

Subgraphs Exponent β R2

Precisely, we analyzed companies that traded on London SE (488 companies), New-York SE (1,715 companies) and Tokyo SE (586 companies).

London SE 0.68 0.55

New-York SE 0.68 0.74

Tokyo SE 0.78 0.74

37Empirical Results. Degree distribution

Figure : The degree distribution of New-York companies co-mention network

Networked Map of Companies 38Networked Map of Companies of economics

Figure : Networked Map of Companies

Networked Map of Companies 39QAP Correlation and Regression Analysis

We use QAP Correlation Analysis to identify correlations: between the co-mention network and companies’ sector affiliation; between the co-mention network and stock exchange affiliation.

Sector affiliation network

Stock Exchanges network

Table : Quadratic Assignment Procedure. Correlation and p-values, 500 replication

network network

Co-mentions network r=0.053(0.000)

r=0.020(0.000)


Figure : Estimated density after QAP replication for sector affiliation network


We use QAP Correlation Analysis to identify correlations: between the co-mention network and companies’ sector affiliation; between the co-mention network and stock exchange affiliation.

Coefficients: Estimate P-value

Table : QAP Regression analysis

Intercept 0.003 0.000

Sector affiliation 0.015 0.000

Stock exchange affiliation 0.033 0.00


Autocorrelation for co-mention matrix time series

Month January February March April May

Table : Correlation coefficient for subgraps of 200 largest companies

January 1.00

February 0.65 1.00

March 0.53 0.62 1.00

April 0.67 0.58 0.52 1

May 0.42 0.49 0.55 0.61 1


ijij

ijij

SametC

SametC

ije

ety

210

210

)(

)(

1)1(

QAP Logit model

C (t) – # of Co-mentions at time t;Cij(t) – # of Co-mentions at time t;Sameij – Same Stock Exchange.


Coefficients: Estimate P-value

Table : QAP Logit model (Subgraphs of the 500 Largest Companies)

Intercept -2.378 0.000

Co-mentions (January) 0.198 0.000

Same Stock Exchange 0.5983507 0.00

. 45

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