Predicting
Adverse Media Risk using a
Heterogeneous Information Networkhttps://arxiv.org/abs/1811.12166

Ryohei Hisano[1,2],
Didier Sornette[3], Takayuki Mizuno[4,2][1] Social ICT Research Center, Graduate School of Information
Science and Technology, The University of Tokyo,[2] The Canon Institute for Global Studies,[3] ETH Zürich [4] National Institute of Informatics
Dec 4, 2018
1

The two keywords

AdverseMediaRisk

HeterogeneousInformation
Network2

Adverse Media Risk
 Negative media coverage may lead to huge risk
Facebook–Cambridge Analytica
data scandal

Sanctioned firms
Among the companies targeted
By the US include GAZ Group …

3

Database on adverse media risk
 Factiva (Dow and Jones), RepRisk etc
 Gathered for financial investment
 Jan 2012 – May 2018
Num firms under our watch list: 35657, 17 Label
DateName

2012/1/3FCA
2012/1/3Daimler Trucks North America
2012/1/10Atlas Fibre
2012/1/11Tokyo Electric Power Company
2012/1/16Air India Regional…
…

Adverse Media
Label
Management
Product/Service
Regulatory
WorkplaceManagement…

BUT WHY CARE TO PREDICT?
4

Measuring the effect of media on returns
 For all US stocks in the list, we gather

price for the period 2012.1-2018.5.
– 1,139 stocks in total
 For each date in the adverse media label list, employ a 10-day

window centered on the specified date. We then take a log returnof the start and end dates (10 trading days difference).
 We compare the above log return to that of 10 trading days log
return outside the windows.
𝑃(𝑡)log
𝑃(𝑡 − 10)

TrustCo Bank
Corp NY

News date, Calc log

𝑃(𝑡+5)
𝑃(𝑡−5)

Ignore5

Result

Indeed there is an effect Left: a histogram of log returns inside the time windows.

Middle: same thing outside the time windows.
 We could see that the negative tail distribution is morestretched while the positive tail is shrunk compared tothe middle.
Frequency of news

With NewsN

0.01
News8685-0.233Rest
1667616-0.2182 sample KS-test p-value=7*10^-7

Rest

0.05
-0.102-0.109

0.5
0.005
0.005

0.95
0.098
0.110

0.99Skewness
0.191-6.521
0.207
0.165

6

Other reasons(2) Watchdog adversarial role of the press Media plays a central role in monitoring powerful
institutions and identifying any activities harmful tothe public.
– Identifying problems = adverse media– Social responsible investment(3) Human nature People tend to prioritize negative information more.
– Psychology: Impression formulation voting behavior– Economics: Loss aversion macroeconomic behavior

9Can we predict future adverse media?
Obviously past label info is not enough to predict future patterns
DateName
2012/1/3FCA
2012/1/3Daimler Trucks North America
2012/1/10Atlas Fibre
2012/1/11Tokyo Electric Power Company
2012/1/16Air India Regional…
…

Adverse Media LabelManagement
Product/Service
Regulatory
WorkplaceManagement…

Our approach: Construct a heterogeneous information network combining
data from different sources and perform label propagation utilizing this HIN

11

Overview of the HIN we have more than just a

network of major firms
Top 25 / 216 relation types

Nodes:50 mil, Edges: 400 mil
Core: 35,000, Edges: 320,000

12Schematic Figure Using adverse media label occurrence patterns and HIN we wantto learn how to propagate labels to predict future occurrences

AdverseMedia Label?

source-target split train-test split
From learned
model predict
future occurrence

2017.215

Two ingredients of the model (1) Propagation model– that could adaptively adjust to each label– Slight variation of LP with edge weight learning (2) Edge features

16Propagation Model We model edge weights 𝑤𝑖𝑗 = 𝑓𝜃 𝑥𝑖𝑗

using edge features. We
enforce 𝟎 ≤ 𝒘𝒊𝒋 ≤ 𝟏 by using a sigmoid function.

𝐷𝑖𝑖 ← Σ𝑗 𝑤𝑖𝑗

LearnedEdge weights
17

Edge features 1: core-relation
 Relation types in the network among firms in thewatch list01𝑓𝜃 ( )0001𝑓𝜃 ( )1018

Edge features 2: path
 Path Ranking Algorithm [Lao,Cohen2010]

We also focus on paths that
could be reached ignoring
nodes that we reached in theprevious path lengths

uses path to perform knowledge graph competition
 We use the top 3,000 frequent paths and use them as an one hotfeatures– We use path length up to 4
 For example if firm A and firm B has

The following relationships– Path length 1: (A,supplies,B)– Path length 2: (A,is_in,c,is_in,B)– Path length 3: (A,makes,x,is_made_of,y,imports,B)(A,supplies,B)(A,is_in,c,is_in,B)(A,makes,x,is_made_of,y,imports,B)edge A-B [1,0,…,0,0,1,0,0,…,0,0,…,1,0,…,0,…]Path length 1 Path length 2 Path length 3

19

Edge features 3:path-segment
 We record the occurrence of relation

types along the path’s segments

We distinguish relation
types occurring along pathsegments. However, since
our network is undirected
there is a symmetry.

– We use path length up to 4
 For example if firm A and firm B has

3:2

3:1

the following relationships––––

Path length 1: (A,supplies,B)
Path length 2: (A,is_in,c,is_in,B)
Path length 3: (A,makes,x,is_made_of,y,makes,B)
Path length 3: (A,supplies,C,supplies,D,supplies,B)

 We record it in a binary format as follows(supplies)(is_in)(supplies) (makes)

3:1(supplies)(is_made_of)edge A-B [1,0,…,0,0,1,0,0,…,0,0,…,1,0,…,1,0,…,1,0,…,1,0,…]Path length 1 Path length 2 Path length 3:1 Path length 3:220

Train test split time

No Info from the future We split our data using 2017.1.31 as our last day of training Because we want to avoid any information coming from the future

to contaminate our HIN
 The problem here is half of the edges in our database has notimestamp. So in order to really ensure that all the edges ourfrom the past, we set the test date after the latest date when weacquired the data (which is Jan 2017)21

Summary of Compared Methods
Information
LowEdge
Learning Label Labelweights Patterns
Correlation

Methods
RandomForestApproach Features
NonNetwork Country and Industry Classification -YesNo

LP-fixed

Network-FixedNoNoLP-mult
LP-corerelation

NetworkRelation types among watch list
firms

FixedNoYes

Learned Yes

NoLP-pathHINNo

LP-pathsegment

HINPaths relating two nodes
Learned Yes
Occurrence of relation types
among path segments relating twonodes
Learned Yes

NetworkNo

High
It’s enough to prove that the HIN approach beat other methods

22

Results as figures
Black：random guessing, Purple: random forest
Light blue:LP-fixed, Green:LP-mult, Blue:LP-core-relation,
Orange: LP-path, Red:LP-path-segment
AUC-PR

AUC-ROC

23

Interpreting the learned model Too many correlated features making it difficult toanalyze what our models have learned directly.
 Thus, we reduce the number of features using
nonnegative matrix factorization to 50 and perform theusual partial dependency analysis along the basis of thematrix obtained by binary NMF
50

About 500

edge A-B [ 1, 0,…,0,0,1,0,0,…,0,0
edge A-C 0, 1,0,…,1,0,…,1,0,0,…~…
0, …,1,0,…,1,1,1,0,…]Basis vectors

Reduced representation25

Product/Service Label Basis 4: Top Negative effect Basis 13: Top Positive effectBasis 13: buyer-seller

Basis 4：license
In-licensing
Out-licensing

In-licensing

Distributor
In-licensing
Out-licensing

CustomerReceive goods
International shipping

PartnerManufacture

franchise

CustomerIn-licensing

Receive goods
Internationalshipping

International shipping26

Why does our method work?
 (1) When a problem occurs, it is likely that similar firms arealso in trouble.
– Similarity: closeness in information network– Moreover, we adjust for the closeness measure using
past adverse media label patterns
 (2) Media does not look for news at random. They search
for nearby firms for follow-up stories– Watchdog role of the press– “All the news that’s fit to sell”

Adverse MediaPrediction

Heterogeneous
Info Net27

Significance Finance: Many “news -> financial impact”, but very

few focusing on predicting news itself– 35,657 -> 8,795(firms with ticker)/46,583 (world total) CS/Network: New frontier of HIN (knowledge graph) Management: Adverse media risk score– (a) Firms could plan counter measure (CSR/PR)– (b) Journalism to find next possible target– (c) (Social Responsible) Investment
 Media studies: Adverse media prediction

 Societyin general: Created ways to monitor
dominant multinational institutions in the era ofinformation technology (Cyber watchdog)28