Network Analytics in Finance

Prof. Dr. Daning Hu

Department of Informatics

University of Zurich

Nov 14th, 2014

Outline

Introduction: Network Analytics in Finance

Stock Correlation Networks

Stock Ownership Networks

Board Director Networks

Systemic Risk in Bank Networks

Ref Book: The Network Challenge: Strategy, Profit, and Risk in an

Interlinked World, Paul R. Kleindorfer, Yoram J Wind, Robert E.

Gunther

Introduction: Network Analytics in Finance Various types of Financial Networks that are based on

relationships like:

Stock correlations

Stock ownerships

Board of Directors

Interbank Payments

The most complex system is human behavior! In financial

markets, stock price movements are largely influenced by

behaviors of different trading parties (human beings).

Network Analytics in Finance studies the impacts of

transactional behaviors among connected market participants

including individual and institutional investors, banks, and

regulators, etc.

Modeling Market Risk: Stock Correlation Networks

Based on Modern Portfolio Theory, the market risk of a portfolio of

stocks can be

calculated as the sum of the co-variance of each stock pairs

return of an individual stock i is calculated as

The correlation between the returns of i and j can be calculated as

The link distance between stock i and j can be calculated as

rij=rirj - ri rj

ri2

- ri2( ) rj

2- rj

2

( )

)1(ln)(ln)( iii PPr

)1(2 ,, jijid

Created using financial network analytics platform: http://www.fna.fi/

Stock Correlation Networks

Nodes are colored based on sectors and

link length indicates correlation between

stocks - a short link length indicates high

correlation.

Garlaschelli et al. Physica A, 350 491 (2005).

Stock Ownership Networks

Bipartite Network:

• two different types of nodes

• links between different groups.

Projection

Link vertices of the same group

Consider Weight !

Multiple Interlock

Weight

Various, see Ref in Battiston et al. EPJB, 350 491 (2005).

Board Director Network

De Masi et al. In preparation.

Interbank Payment/Loan Networks

Financial Network Analytics Financial Networks Analytics can help

in distinguishing behaviors of different market participants

in visualizing important features such as chains of control

in testing the validity of different theories and models

Many of these financial networks are scale-free networks.

Thus different network topological measures can be tested

against empirical financial networks to discover their

topologies.

Case I: The Small-World of Investing

Social Networks on Information Transfer in Stock Market

connections between mutual fund managers and corporate

board members via shared education networks.

academic institutions attended for both undergraduate and

graduate degrees as our network measure

The major findings are

portfolio managers place larger bets on firms they are

connected to through their network

perform significantly better on these holdings relative to

their non-connected holdings. (7.8% per year)

positive returns concentrated on news announcements

Journal of Political Economy, 2008, vol. 116, no. 5

Correlated risk

exposures in banks’

financial asset

portfolios:

Interbank payments

A B C

X ZY

Banks

Case II: Network Analysis of Bank Systemic Risk In a banking system, systemic risk is the risk of contagious bank

failures that leads to the system-wide breakdown.

A close call: 2008 financial tsunami

Two major sources: interbank payment and correlated financial portfolios

Figure.5. An example of contagious bank failures

MIS Quarterly, 2012, vol. 36, no.

4

Research Questions and Design Research Questions:

How to predict contagious bank failures in a given crisis scenario?

Which banks to inject capital first to stop contagion in a given scenario?

We propose a Network Approach to Risk Management (NARM)A Network Model of

Systemic riskSystemic Risk Estimation

Algorithm

Model correlated portfolio

links

(Financial principles)

Model interbank

payment links (Network

principles)

Financial Crisis

Simulation

Simulated

economic shocks

Simulated capital

injection

High systemic risk

scenarios

Predict contagious

bank failures

Recommend banks

for capital injection

The network

model of

systemic risk

Modeling Systemic Risk (Financial Principles ) Based on Modern Portfolio Theory, the systemic risk a bank i

contributes to the banking system from correlated portfolios:

calculated as

proxy of risk from correlated portfolios: return covariance with linked banks

We then construct the correlated portfolio links between i and j by

calculating

Interbank clearing payment vector: (i.e., i’s ability to pay).*

ip

N

j ijjii

N

j ijjiii

N

j ijjii

N

j iijjiii

i

epFif

epFdifepF

depFifd

p

1

*

1

*

1

*

1

*

*

00

0

G(i) = wiw js is jrijjÎL

å

rij=rirj - ri rj

ri2

- ri2( ) rj

2- rj

2

( )rij > 0.5

Correlative Rank-In-Network Principle (CRINP): The importance

of a node i depends on

the number of i’s incoming links

the importance of the nodes that links to i

Nodes use links to transmit their influences/recognitions to others; such

recognitions in turn build up the importance of linked nodes.

Citation impact factor, Webpage ranking (e.g., Google’s PageRank)

Systemic risk in banking systems

Hyperlink-Induced Topic Search (HITS) Algorithm

Authority score estimates the importance of a web page p

Hub score estimates the influences of page p’s outgoing links

Au(p) = Hub(i)i=1

n

å

Hub(p) = Au(i)i=1

n

å

Modeling Systemic Risk (Network Principles)

BI Algorithm Development

Research Design

15

A Network Model of

Systemic riskSystemic Risk Estimation

Algorithm

Model correlated portfolio

links

(Financial principles)

Model interbank

payment links (Network

principles)

Financial Crisis

Simulation

Simulated

economic shocks

Simulated capital

injection

High systemic risk

scenarios

Predict contagious

bank failures

Recommend banks

for capital injection

The network

model of

systemic risk

Link-Aware Systemic Estimation of Risks (LASER) Algorithm

Based on HITS, we integrated MPT-based systemic risk and

CRINP by iteratively calculating

Authority score of bank i measures the relative systemic risk i receives from

its linked banks:

Hub score of i measures the relative systemic risk bank i contributes to its

linked banks:

represents the systemic risk originating from the

correlated financial portfolios

, models the impacts of different amounts of

outgoing/incoming payments

Aui = G( j)Oji

OuiuÎU

åHubj

jÎAå

Hubi = G( j)Iij

IivvÎV

åAu j

jÎCå

G(i) = wiw js is jrijjÎL

å

Oji

OuiuÎU

åIij

IivvÎV

å

A Simulation-based Evaluation Experiment Research Design

17

A Network Model of

Systemic riskSystemic Risk Estimation

Algorithm

Model correlated portfolio

links

(Financial principles)

Model interbank

payment links (Network

principles)

Financial Crisis

Simulation

Simulated

economic shocks

Simulated capital

injection

High systemic risk

scenarios

Predict contagious

bank failures

Recommend banks

for capital injection

The network

model of

systemic risk

A Simulation-based Evaluation Experiment: Dataset

Federal Deposit Insurance Corporation (FDIC) dataset: quarterly reports of major U.S. banks’ condition and income

balance sheets, income statements, and other supervisory data

Simulated interbank payments for each scenario based on

empirical findings on the Fed Wire network topology (Soramäki 2007)

density (0.30%), average path length (2.62), average degree (15.2), etc.

quarterly statistics of U.S. Fed wire Interbank settlement service.

http://www.federalreserve.gov/paymentsystems/fedfunds_qtr.htm

Time SpanNo. of

Quarters

No. of Reporting

Banks

No. of

Reports

Average number of reporting

banks per quarter

2001-2010 38 7,822 281,401 7,405

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5

Av

erag

e B

ank

Fai

lure

Rat

e γ

Shock Rate β

Average Bank Failure Rates of Generated Scenarios

at Different Shock Rates

Results: Average Bank Failure Rates

, ranges from

4.9% to 12.5%.

, increases

drastically.

, ,

System-wide

breakdown

4.1

b ³1.5

b =1.7 g = 54%

The U.S. banking system can sustain mild simulated economic

shocks until the shock rate .

focusing on

b ³1.5

1.9 ³ b ³1.5

Results: Predicting Contagious Bank Failures

LASER Authority score outperforms other benchmark methods

Capital Adequacy Ratio (CAR)

Results: Determine Capital Injection Priority

LASER Hub score outperforms other benchmark methods in

stopping further contagious failures.

22

Applications: Stress Testing in Banking Systems

NARM (LASER and the simulation methods) can be implemented

as stress testing information systems.

Intended users:

Early warning on possible contagious bank failures

Decision support in “Bailout” policy making

Screenshots of an

prototype for NARM