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Quick Analysis of Financial-Industry

Quick Analysis of Financial-Industry

Big-Data Analytic Needs

DRAFT George Fisher July 24, 2017

Abstract

Databricks intends to create a Finance Vertical position to support the Sales and SA teams when working with financial-industry organiza- tions. This article attempts to describe the structure of the worldwide financial industry, who the major players are and what their needs might be in the context of Apache Spark and Databricks offerings.

Contents

1 Executive Summary 2

2 Introduction 2

3 Risk Mitigation 3

4 Opportunity Discovery 5

5 Finance-Industry Sponsored Kaggle Contests 6

6 Spark and Finance on YouTube 11

7 APPENDIX 15


1 Executive Summary

The opportunities in the finance sector lie on a wide spectrum: at one end are the quant funds for whom large-scale analytics are the entire business, at the other are traditional depositories for many of whom a daily batch cycle and a quarterly book closing have long sufficed. Quite often both extremes exist in the same company.

For this entire spectrum the easy-to-use, streaming, multi-source, big-data ana- lytics offered by Databricks can offer advantages. Perhaps with quick adoption by the quants and slower adoption by the others. Early adoption may involve a lot of discovery but a growing collection of proven use cases will ease later sales.

1

. streaming will supplant batch

. predictive analytics will replace BI

. easy multi-sourcing can unite stove pipes

. pooling can dramatically reduce operational complexity and cost

In addition, in the larger companies, the pressure to comply with data- related regulations company-wide has become almost overwhelming and nearly all are struggling with multitudes of incompatible systems that Spark might unite.

2 Introduction

The finance industry is vast, far too large and diverse to make a comprehensive enumeration of all the functions performed or of the firms that perform them. The Economist Intelligence Unit [14] might be a good source to begin with for such a survey.

The Appendix of this report contains lists of the major financial organizations grouped by function starting on page 15.

The questions of interest to Databricks are (1) which finance firms are most likely to benefit from the manipulation and analysis of large datasets and (2) what are the types of manipulation and analysis of interest?

The two main concerns for the finance industry are:

. Risk Mitigation

. Opportunity Discovery

1 I wonder if the entirely cloud-based solution offered by Databricks does not leave a lot on the table given the pervasiveness of proprietary datacenters in this world. IBM mainframes, at that.


3 Risk Mitigation

[7]

Simply put, risk mitigation means don’t lose money, don’t go out of business and don’t go to jail.

Risk Categories

1. Business Risk Risks undertaken by the business itself to maximize share- holder value and profits. For example: the cost to launch a new product. Risk mitigation takes the form of competent management controls.

2. Exogenous Risk Political upheaval, natural disaster, economic disrup- tion. Insurance is the most-common risk mitigation tool in these cases.

3. Financial Risk Financial risk arises from volatility in equities, deriva- tives, currencies, interest rates etc. In the case of financial firms these risks are also Business Risks since finance is the business.

. Market Risk Changes in prices, their magnitude, direction and volatility.

. Credit Risk The effect of counter-party default or the repercussions of providing services to bad actors.

. Liquidity Risk The inability to make timely payment. Margin calls often precipitate this when illiquid securities cannot be sold or col- lateralized.


. Operational Risk Failures of judgment, integrity, controls, proce- dures or technology.

Cyber Security An aspect of Operational Risk that gains clar- ity at senior levels with every report of the losses incurred and chaos engendered by widespread sophisticated hacking.

Financial-firm financial-risk mitigation is a field of study unto itself. For example, there is a rigorous, multi-partFinancial Risk Manager (FRM) Certification [5] created by Global Association of Risk Professionals (GARP).

4. Regulatory Compliance While perhaps not a risk per se this is a huge concern to financial firms, particularly since the Financial Crisis of a decade ago and the rules promulgated as a response.

For example, one of the main tenets of BCBS 239 [15] is that all ‘material risk data’ must be automatically aggregated and analyzed across the en- tire banking group on a near-real-time basis while facing severe economic stresses. Multitudes of incompatible systems are a huge barrier.

[11]


4 Opportunity Discovery

If Risk Mitigation is Operations, Opportunity Discovery is Research & Devel- opment.

An inexhaustive list:

• F undamental Analysis The study of the financial characteristics of in- dividual firms, seeking undiscovered value. Warren Buffett is the world’s most-famous fundamental analyst.

• Macro The study of economy-wide signals. George Soros’ famous short of the UK Pound is an example [12]. The ‘Big Short’ of 2007-2008 is another [22].

• Relative The study of relative movements of securities. Long/Short hedge funds are an example.

• T ec hnical Analysis The study of trendlines.

• Quantitative Analysis The intersection of big data and machine learn- ing. Jim Simons’ Renaissance Capital [16] is the most successful example I know of but there are many others; some are listed in the appendix be- ginning on page 17. Some Kaggle contests focused on this, see Section

5.

• Product Development Swaps are an example of building a product to meet very specific customer needs. Even more sophisticated products are possible with analytical support using all available data.

• Customer Enhancement Using machine learning to reduce customer churn; using predictive analytics for product-customer targeting; consis- tent customer support across multiple access channels; etc. . . . using Ama- zonian techniques in a banking environment to take on the characteristics of the fintechs.

• Cost Control Route optimization for filling ATMs; redundant process identification; risk reduction not just as a regulatory requirement, but as a cost saver and a profit enhancer

• Risk System Integration The regulators are forcing the larger firms to create “living wills” which has resulted in a much better understanding the the numerous piece parts. The Basel risk data requirements are now forcing a near-real-time integration of numerous disparate systems. This seems like fertile ground for innovation both for compliance and to build upon the results.


5 Finance-Industry Sponsored Kaggle Contests

Over the past several years a number of financial firms have sponsored Kaggle contests. Someone at these firms thought that these subjects were worth paying for crowd-sourced analysis and was willing to go to the considerable trouble of setting up and monitoring a contest with thousands of participants lasting three months or more.

Two Sigma is a quant fund, listed in the appendix on pages 17 and 23. The challenge was to predict daily price changes. (In this contest I earned a Kaggle Silver Medal for coming in 37th out of 2,070 contestants. [9])

Opportunity Di sco v ery

Improve credit risk models by predicting the probability of default on consumer credit.

Risk Mitigation

Improve the quality of information within transaction data.

Risk Mitigation

Predict which customers will leave an insurance company in the next 12 months.

Risk Mitigation

Given a dataset of 2D dashboard camera images, State Farm is challenging Kag-


glers to classify each driver’s behavior. Are they driving attentively, wearing their seatbelt, or taking a selfie with their friends in the backseat?

Risk Mitigation

Santander (Spain-based bank) is challenging Kagglers to predict which products their existing customers will use in the next month based on their past behavior and that of similar customers.

Opportunity Di sco v ery

Santander Bank is asking Kagglers to help them identify dissatisfied customers early in their relationship.

Risk Mitigation , Opportunity Disco very

Using terabytes of noisy, non-stationary data Winton Capital is looking for data scientists who excel at finding the hidden signal in the proverbial haystack, and who are excited by creating novel statistical modeling and data mining tech- niques.

Opportunity Di sco v ery

Using a customers shopping history, can you predict what insurance policy they will end up choosing?

Opportunity Di sco v ery


Claims management may require different levels of check before a claim can be approved and a payment can be made. With the new practices and behaviors generated by the digital economy, this process needs adaptation thanks to data science to meet the new needs and expectations of customers. Kagglers are challenged to predict the category of a claim based on features available early in the process.

Risk Mitigation , Opportunity Disco very

The life insurance application process is antiquated. Customers provide exten- sive information to identify risk classification and eligibility, including scheduling medical exams, a process that takes an average of 30 days.

The result? People are turned off. Thats why only 40% of U.S. households own individual life insurance. Prudential wants to make it quicker and less la- bor intensive for new and existing customers to get a quote while maintaining privacy boundaries.

Opportunity Di sco v ery

Predict a transformed count of hazards or pre-existing damages using a dataset of property information. This will enable Liberty Mutual to more accurately identify high risk homes that require additional examination to confirm their insurability.

Risk Mitigation

Fire losses account for a significant portion of total property losses. High sever- ity and low frequency, fire losses are inherently volatile, which makes modeling them difficult. In this challenge, your task is to predict the transformed ratio of loss to total insured value. This will enable more accurate identification of each policyholders risk exposure and the ability to tailor the insurance coverage for


their specific operation.

Risk Mitigation

The Benchmark Bond Trade Price Challenge is a competition to predict the next price that a US corporate bond might trade at.

Opportunity Di sco v ery

Determine whether a loan will default and the loss incurred. We are building a bridge between traditional banking, where we are looking at reducing the con- sumption of economic capital, to an asset-management perspective, where we optimize on the risk to the financial investor.

Risk Mitigation

Develop models to predict the stock market’s short-term response following large trades. Contestants are asked to derive empirical models to predict the behavior of bid and ask prices following such “liquidity shocks”.

Modeling market resiliency will improve trading strategy evaluation methods by increasing the realism of back testing simulations, which currently assume zero market resiliency.

Risk Mitigation , Opportunity Disco very

Bodily Injury Liability Insurance covers other peoples bodily injury or death for which the insured is responsible. The goal of this competition is to predict Bod- ily Injury Liability Insurance claim payments based on the characteristics of the insureds vehicle.

Risk Mitigation


Allstate is currently developing automated methods of predicting the cost, and hence severity, of claims. Kagglers are invited to create an algorithm which accurately predicts claims severity.

Risk Mitigation


6 Spark and Finance on YouTube

• Apache Spark on IBM z Systems Demo for Finance

https://www.youtube.com/watch?v=yw0dQFMyxFQ

References to IMS, CICS and VSAM makes me think this is Spark on an IBM mainframe. Considering the fact that IBM mainframes are still quite widely used, this might be worth understanding.

Opportunity Discovery , Risk Mitigation

• Using Spark to Analyze Activity and Performance in High Speed

T rading En vironmen ts

https://www.youtube.com/watch?v=zdz9Cj1-hjA

Corvil: Irish data monitoring and analytics for financial data using Spark. Non-intrusive low-latency electronic trading monitoring, regulatory com- pliance through the use of streaming telemetry.

Risk Mitigation

• Spark in Finance Quantitative Investing

https://www.youtube.com/watch?v=WPc-DoSeCpU&t=7s

Reading historical and live tick data, determine a trend and propose trades.

Opportunity Disc o v ery

• Financial Modeling Using Apache Spark

https://www.youtube.com/watch?v=jCXOa6doXEs

Blackrock mortgage analysis of mortgage data. Using Spark, Scala and D3 to visualize a large loan-level mortgage dataset, extract distributions and cluster boundaries. Also use K-Means to reveal similar borrower groups and corresponding discriminant attributes.

Opportunity Disc o v ery

• Estimating Financial Risk with Spark

https://www.youtube.com/watch?v=0OM68k3np0E

VaR with Monte Carlo using market risk factors explained by Cloudera

Risk Mitigation


• Apache Spark in Financial Modeling at BlackRock https://www.youtube.com/watch?v=wLJi8YQcWjc&t=2881s Blackrock mortgage security analysis: Why Scala? Why Spark? Opportunity Discovery

• A Distributed Time Series Analysis Framework for Spark

https://www.youtube.com/watch?v=x2iM5he2gAU

Two Sigma equity price prediction with mutli-terabyte datasets; built own time series system on top of Spark

Opportunity Disc o v ery

• Credit Fraud Prevention with Spark and Graph Analysis

https://www.youtube.com/watch?v=q5HFMVoN_rc

Capital One fraud detection

Risk Mitigation

• Stratio’s Big Data - Use case in finance

https://www.youtube.com/watch?v=wmuG3nU9fiY

Catroon marketing video for Stratio Big Data Inc. which I did not inves- tigate

• Estimating Financial Risk with Spark https://www.youtube.com/watch?v=t2RmlshHBvI Duplicate Cloudera VaR presentation

• IBM LinuxONE Scalable Financial Trading Analysis & Insight

https://www.youtube.com/watch?v=Uw2ZioWa-Ak

Combine streaming market data, Twitter, news feed using Spark on an

IBM Linux ONE machine. Did not investigate IBM Linux ONE.

• Streaming Stock Market Data with Apache Spark and Kafka

https://www.youtube.com/watch?v=0tSZo8I2924&t=3139s

MapR presentation: high velocity streaming processing post-Hadoop at NYSE 20 Megabytes per second time windowing. One Kafka topic per stock, parallelized.

Risk Mitigation , Opportunity Disco very

• An Example Application for Processing Stock Market Trade

Data


https://www.youtube.com/watch?v=CXJK4SII0IY MapR presentation on streaming NYSE data pub/sub Opportunity Discovery

• Time Series Stream Processing with Spark and Cassandra

https://www.youtube.com/watch?v=fBWLzB0FMX4

Cloudance Ltd: multi-station weather data, group by on petabytes in operational setting. not trade data but similar structure.

• Realtime Risk Management Using Kafka, Python, and Spark

Streaming

https://www.youtube.com/watch?v=ObBdwhbyv1M

• D AT A & ANALYTICS: Analyzing 25 billion stock market events in an hour with NoOps on GCP https://www.youtube.com/watch?v=fqOpaCS117Q



7 APPENDIX

Global Financial Services Companies by Revenue

[20]

Berkshire Hathaway

Conglomerate

210.8

United States

AXA

Insurance

147.5

France

Allianz

Insurance

140.3

Germany

ICBC

Banking

134.8

China

Fannie Mae

Investment Services

131.9

United States

ING

Banking

130.0

Netherlands

BNP Paribas

Banking

126.2

France

Generali Group

Insurance

116.7

Italy

China Construction Bank

Banking

113.1

China

Banco Santander

Banking

108.8

Spain

JP Morgan Chase

Banking

108.2

United States

Socit Gnrale

Banking

107.8

France

HSBC

Banking

104.9

United Kingdom

Agricultural Bank of China

Banking

103.0

China

Bank of America

Banking

100.1

United States

Bank of China

Banking

98.1

China

Wells Fargo

Banking

91.2

United States

Citigroup

Banking

90.7

United States

Prudential

Insurance

90.2

United Kingdom

Munich Re

Insurance

88.0

Germany

Prudential Financial

Insurance

84.8

United States

Freddie Mac

Investment Services

80.6

United States

Banco Bradesco

Banking

78.3

Brazil

Lloyds Banking Group

Banking

75.6

United Kingdom

Ita Unibanco Holding

Banking

70.5

Brazil

Zurich Insurance Group

Insurance

70.4

Switzerland

Aviva

Insurance

69.0

United Kingdom

Banco do Brasil

Banking

69.0

Brazil

MetLife

Insurance

68.2

United States

American International Group

Insurance

65.7

United States

China Life Insurance

Insurance

63.2

China

Mitsubishi UFJ Financial Group

Banking

59.0

Japan

Legal & General Group

Insurance

56.9

United Kingdom

Dai-ichi Life

Insurance

56.5

Japan

Barclays

Banking

55.7

United Kingdom

Aegon

Insurance

55.2

Netherlands

Deutsche Bank

Banking

55.0

Germany

UniCredit

Banking

54.2

Italy

CNP Assurances

Insurance

53.2

France

BBVA

Banking

52.1

Spain

Credit Agricole

Banking

51.2

France


Ping An Insurance Group

Insurance

51.1

China

National Australia

Banking

49.2

Australia

Commonwealth Bank

Banking

47.8

Australia

Intesa Sanpaolo

Banking

47.7

Italy

UBS

Investment Services

47.7

Switzerland

Sumitomo Mitsui Financial Group

Banking

47.3

Japan

Westpac Banking Group

Banking

43.9

Australia

Bank of Communications

Banking

43.5

China

Credit Suisse Group

Investment Services

42.5

Switzerland

MS&AD Insurance Group

Insurance

42.2

Japan

Royal Bank of Scotland

Banking

42.1

United Kingdom

Goldman Sachs

Investment Services

41.7

United States

People’s Insurance Company

Insurance

41.3

China

Tokio Marine Holdings

Insurance

39.4

Japan

Royal Bank of Canada

Banking

38.3

Canada

ANZ

Banking

37.5

Australia

Manulife Financial

Insurance

37.3

Canada

Sberbank

Banking

36.1

Russia

State Bank of India

Banking

35.1

India

Talanx

Insurance

34.9

Germany

Power Corporation of Canada

Insurance

34.2

Canada

Swiss Re

Insurance

33.6

Switzerland

American Express

Financial Services

33.4

United States

Allstate

Insurance

33.3

United States

Mizuho Financial Group

Banking

32.8

Japan

Old Mutual

Investment Services

32.2

United Kingdom

Morgan Stanley

Investment Services

32.0

United States

Standard Life

Insurance

31.2

United Kingdom

Sompo Holdings

Insurance

30.9

Japan

TD Bank Group

Banking

30.6

Canada

China

Banking

28.4

China

China

Banking

27.9

China

Bank of Nova Scotia

Banking

27.6

Canada

Onex

Investment Services

27.4

Canada

China

Insurance

27.3

China

Mapfre

Insurance

27.1

Spain

Standard Chartered

Banking

26.9

United Kingdom

Dexia

Banking

26.6

Belgium

Hartford Financial Services

Insurance

26.4

United States

Travelers Cos

Insurance

25.7

United States

Commerzbank

Banking

25.5

Germany

Aflac

Insurance

25.4

United States

Shanghai Pudong Development

Banking

25.4

China


Major Stock Exchanges

[21]

New York Stock Exchange

United States

New York

NASDAQ

United States

New York

London Stock Exchange Group

United Kingdom

London

Japan Exchange Group

Japan

Tokyo

Shanghai Stock Exchange

China

Shanghai

Hong Kong Stock Exchange

Hong Kong

Hong Kong

Euronext

European Union

Amsterdam, Brussels, Lisbon, London, Paris

Shenzhen Stock Exchange

China

Shenzhen

Toronto Stock Exchange

Canada

Toronto

Deutsche Brse

Germany

Frankfurt

Bombay Stock Exchange

India

Mumbai

National Stock Exchange of India

India

Mumbai

SIX Swiss Exchange

Switzerland

Zurich

Australian Securities Exchange

Australia

Sydney

Korea Exchange

South Korea

Seoul

OMX Nordic Exchange

Sweden

Stockholm

JSE Limited

South Africa

Johannesburg

BME Spanish Exchanges

Spain

Madrid

Taiwan Stock Exchange

Taiwan

Taipei

BM&F Bovespa

Brazil

So Paulo

Quant F unds

[13]

• D. E. Shaw (New York, NY)

• Renaissance Technologies (East Setauket, NY)

• Morgan Stanley PDT (New York, NY)

• Point72 Asset Management (SAC Capital)

• AQR Capital

• Two Sigma Investments (New York, NY)

• Citadel (Chicago, IL)

• Jane Street Capital (New York and London)

• RG Niederhoffer

• Jump Trading


• KCG Holdings

• Bridgewater Associates

• Hudson River Trading

• Man Group AHL

• Highbridge

• Millennium/WorldQuant

• Winton

• Bluecrest

• Ellington Capital

• Tower Research Capital

• Parametrica Global Master Ltd

• Camox Ltd

• Voloridge Trading

• Senvest Partners Ltd

• BlackRock European Hedge

Credit Card Issuers

[1]

1. Visa - 323M Cardholders

2. MasterCard - 191M Cardholders

3. Chase - 93M Cardholders

4. American Express - 58M Cardholders

5. Discover - 57M Cardholders

6. Citibank - 48M Cardholders

7. Capital One - 45M Cardholders

8. Bank of America - 32M Cardholders

9. Wells Fargo - 24M Cardholders

10. US Bank - 18.5M Cardholders


11. USAA - 10M Cardholders

12. Credit One - 6M Cardholders

13. Barclaycard US 418K Cardholders

14. First PREMIER Bank (subprime)

15. PNC

Mortgage Risk

[10]

Prior to the financial collapse of 2007-2008 mortgage securitization was the hot thing. Many institutions and individuals got burned and a residual fear of securitization remains.

The result is that for jumbo and subprime mortgages, the originators are now holding many more of the loans. This reduces the systematic risk but an unanticipated consequence is that Fannie Mae and Freddie Mac [3] are now holding 50% of $11 trillion outstanding in the middle market.

Therefore the US government has undertaken a huge amount of default and interest-rate risk.


Insurance Companies by Premium Income

[8]

Property/Casualty Insurance

State Farm Mutual Automobile Insurance

62,189,311

Berkshire Hathaway Inc.

33,300,439

Liberty Mutual

32,217,215

Allstate Corp.

30,875,771

Progressive Corp.

23,951,690

Travelers Companies Inc.

23,918,048

Chubb Ltd.

20,786,847

Nationwide Mutual Group

19,756,093

Farmers Insurance Group of Companies

19,677,601

USAA Insurance Group

18,273,675

Life Insurance/Annuities

MetLife Inc.

95,110,802

Prudential Financial Inc.

45,902,327

New York Life Insurance Group

30,922,462

Principal Financial Group Inc.

28,186,098

Massachusetts Mutual Life Insurance Co.

23,458,883

American International Group

22,463,202

Jackson National Life Group

22,132,278

AXA

21,920,627

AEGON

21,068,180

Lincoln National Corp.

19,441,555

Homeowners Insurance

State Farm Mutual Automobile Insurance

17,516,715

Allstate Corp.

7,926,984

Liberty Mutual

5,993,803

Farmers Insurance Group of Companies

5,284,511

USAA Insurance Group

5,000,407

Travelers Companies Inc.

3,305,427

Nationwide Mutual Group

3,249,456

American Family Insurance Group

2,609,366

Chubb Ltd. (4)

2,485,193

Erie Insurance Group

1,471,544

Private P assenger Auto Insurance


State Farm Mutual Automobile Insurance

39,194,660

Berkshire Hathaway Inc.

25,531,762

Allstate Corp.

20,813,858

Progressive Corp.

19,634,834

USAA Insurance Group

11,691,051

Liberty Mutual

10,774,426

Farmers Insurance Group of Companies

10,304,622

Nationwide Mutual Group

7,640,558

American Family Insurance Group

4,005,549

Travelers Companies Inc.

3,896,786

Commercial Auto Insurance

Progressive Corp.

2,625,929

Travelers Companies Inc.

2,124,182

Nationwide Mutual Group

1,735,614

Zurich Insurance Group

1,624,621

Liberty Mutual

1,604,461

Old Republic International Corp.

1,123,042

Berkshire Hathaway Inc.

951,775

American International Group (AIG)

867,567

Auto-Owners Insurance Co.

739,495

Chubb Ltd.

695,210

Commercial Lines Insurance

Chubb Ltd.

16,528,891

Travelers Companies Inc.

16,463,566

Liberty Mutual

15,056,251

American International Group (AIG)

13,144,961

Zurich Insurance Group

12,554,597

CNA Financial Corp.

9,763,122

Nationwide Mutual Group

8,335,275

Hartford Financial Services

7,679,737

Berkshire Hathaway Inc.

7,650,236

Tokio Marine Group

6,256,196

W orkers’ Compensation Insurance

Travelers Companies Inc.

4,467,425

Hartford Financial Services

3,324,361

AmTrust Financial Services

2,972,901

Zurich Insurance Group

2,851,695

Liberty Mutual

2,481,479

Berkshire Hathaway Inc.

2,479,354

State Insurance Fund Workers’ Comp (NY)

2,437,325

Chubb Ltd.

2,368,918

American International Group

2,345,247

State Compensation Insurance Fund (CA)

1,638,849


Global Asset Management Firms by Revenue

[18]

BlackRock

United States

4,890

The Vanguard Group

United States

3,149

UBS

Switzerland

2,716

State Street Global Advisors

United States

2,460

Fidelity Investments

United States

2,025

Allianz

Germany

1,949

J.P. Morgan Asset Management

United States

1,760

BNY Mellon Investment Management

United States

1,740

PIMCO

United States

1,590

Credit Agricole Group

France

1,527

Global Investment Banks by Revenue

[2]

JPMorgan

3,361

Goldman Sachs

2,858

Bank of America Merrill Lynch

2,684

Morgan Stanley

2,501

Citi

2,378

Barclays

1,884

Credit Suisse

1,760

Deutsche Bank

1,387

RBC Capital Markets

994

UBS

904

Wells Fargo Securities

871

HSBC

793

Jefferies LLC

750

BNP Paribas

619

Lazard

565

BMO Capital Markets

448

Nomura

445

Mizuho

435

Sumitomo Mitsui Financial Group

413

Evercore Partners Inc

407


Hedge Funds By Assets Under Management

[6]

OrgCRD

PrimaryBusinessName

May2017AUM

110814

NOMURA ASSET MANAGEMENT CO., LTD.

367.6

105129

BRIDGEWATER ASSOCIATES, LP

239.3

158117

MILLENNIUM MANAGEMENT LLC

207.6

158319

SAMSUNG ASSET MANAGEMENT COMPANY, LTD.

182.2

148826

CITADEL ADVISORS LLC

152.7

143161

APOLLO CAPITAL MANAGEMENT, L.P.

125

140074

PICTET ASSET MANANGEMENT SA.

122.8

110997

NIKKO ASSET MANAGEMENT CO LTD

120.6

282598

VANGUARD ASSET MANAGEMENT, LIMITED

120.2

111128

THE CARLYLE GROUP

101.9

106661

RENAISSANCE TECHNOLOGIES LLC

97

144533

KOHLBERG KRAVIS ROBERTS

90

168122

ANNALY MANAGEMENT COMPANY

87.9

152719

ALPHADYNE ASSET MANAGEMENT PTE. LTD.

84.6

133720

PINE RIVER CAPITAL MANAGEMENT L.P.

82.8

159732

TPG GLOBAL ADVISORS, LLC

79.5

138111

BALYASNY ASSET MANAGEMENT L.P.

75.1

144603

EASTSPRING INVESTMENTS (SINGAPORE) LIMITED

74.5

155587

FIELD STREET CAPITAL MANAGEMENT, LLC

63.3

107580

BLACKSTONE ALTERNATIVE ASSET MANAGEMENT LP

62.3

148823

BLUECREST CAPITAL MANAGEMENT LIMITED

62.2

142979

BLACKSTONE REAL ESTATE ADVISORS L.P.

60.1

160795

APG ASSET MANAGEMENT US, INC

59.3

130074

ARES MANAGEMENT LLC

58.4

136979

BLACKSTONE MANAGEMENT PARTNERS L.L.C.

57.4

161600

AGNC MANAGEMENT, LLC

56.9

129612

FORTRESS INVESTMENT GROUP

56.9

156601

ELLIOTT MANAGEMENT CORPORATION

56

160309

ELEMENT CAPITAL MANAGEMENT LLC

55.9

139345

MACQUARIE FUNDS MANAGEMENT

54.7

160188

MOORE CAPITAL MANAGEMENT, LP

53.8

107913

OZ MANAGEMENT LP

51.7

159738

TPG CAPITAL ADVISORS, LLC

51.6


137137

TWO SIGMA INVESTMENTS, LP

49.3

152254

TWO SIGMA ADVISERS, LP

48.7

110338

MACKENZIE INVESTMENTS

48.6

156078

HUDSON AMERICAS L.P.

48.4

160000

LONE STAR NORTH AMERICA ACQUISITIONS, LLC

48.1

152175

CERBERUS CAPITAL MANAGEMENT, L.P.

48

173355

CANDRIAM LUXEMBOURG S.C.A.

47.1

156934

3G CAPITAL PARTNERS LP

46.3

143158

APOLLO MANAGEMENT, L.P.

46.2

157589

CAPULA INVESTMENT US LP

45.8

156945

WARBURG PINCUS LLC

45.7

132272

VIKING GLOBAL INVESTORS LP

43.4

160679

ADAGE CAPITAL MANAGEMENT, L.P.

42

146629

KKR CREDIT ADVISORS (US) LLC

41.5

159215

ALPINVEST PARTNERS B.V.

41.2

108679

D. E. SHAW

37

Largest private equity firms by PE capital raised

[17]

The Carlyle Group

Washington D.C.

$30,650.33

Kohlberg Kravis Roberts

New York City

$27,182.33

The Blackstone Group

New York City

$24,639.84

Apollo Global Management

New York City

$22,298.02

TPG

Fort Worth/San Francisco

$18,782.59

CVC Capital Partners

Luxembourg

$18,082.35

General Atlantic

New York City

$16,600.00

Ares Management

Los Angeles

$14,113.58

Clayton Dubilier & Rice

New York City

$13,505.00

Advent International

Boston

$13,228.09

EnCap Investments

Houston

$12,400.20

Goldman Sachs Principal Investment Area

New York City

$12,343.32

Warburg Pincus

New York City

$11,213.00

Silver Lake

Menlo Park

$10,986.40

Riverstone Holdings

New York City

$10,384.26

Oaktree Capital Management

Los Angeles

$10,147.28

Onex

Toronto

$10,097.21

Ardian (formerly AXA Private Equity)

Paris

$9,805.25

Lone Star Funds

Dallas

$9,731.81


In v estmen t Banking Private Equity Groups

[19]

ABN AMRO AAC Capital Partners Barclays Capital Equistone Partners Europe BNP Paribas PAI Partners

CIBC World Markets Trimaran Capital Partners

Citigroup Court Square; CVC; Welsh, Carson, Anderson & StoweBruckmann, Rosser, S Deutsche Bank MidOcean Partners

Globus Capital Holdings Globus Capital Banca

Goldman Sachs Goldman Sachs Capital Partners JPMorgan Chase CCMP Capital; One Equity Partners Lazard Lazard Alternative Investments Merrill Lynch Merrill Lynch Global Private Equity

Morgan Stanley Metalmark Capital; Morgan Stanley Capital Partners New York

National Westminster Bank Bridgepoint Capital

Nomura Group Terra Firma Capital Partners

UBS UBS Capital; Affinity Equity Partners; Capvis; Lightyear Capital

Wells Fargo Pamlico Capital

William Blair & Company William Blair Capital Partners


F ederal Reserve System

{Privateers}

The St. Louis Fed is well known among economics geeks as a fantastic source of data, analysis and commentary. [4] In fact, all the Fed banks are avid consumers of data, analysis and risk-management metrics.


References

[1] cardrates.com. 15 Largest Credit Card Issuers. http://www.cardrates. com/news/credit-card-companies/ , 2017.

[2] dealogic. Global IB Revenue Ranking. http://fn.dealogic.com/fn/ IBRank.htm , July 2017.

[3] Federal Housing Finance Agency. About Fannie Mae and Fred- die Mac. https://www.fhfa.gov/SupervisionRegulation/ FannieMaeandFreddieMac/Pages/About-Fannie-Mae---Freddie-Mac. aspx .

[4] Federal Reserve Bank of St. Louis. FRED Economic Data. https://

fredblog.stlouisfed.org/ .

[5] Global Association of Risk Professionals (GARP). Financial Risk Manager

(FRM) Certification. http://www.garp.org/.

[6] Raynor Gobran. Biggest hedge funds by assets under man- agement may 2017. https://www.raynergobran.com/2017/05/ biggest-hedge-funds-by-assets-under-management-may-2017/ ,

May 2017.

[7] h ttps://www.simplilearn.com/. Financial Risk and its Types. https://

www.simplilearn.com/financial-risk-and-types-rar131-article ,

2016.

[8] Insurance Information Institute. Insurance Comapny Rankings. http:// www.iii.org/fact-statistic/insurance-company-rankings , Decem- ber 2016.

[9] Kaggle. Two Sigma Financial Modeling Challenge. https://www.kaggle. com/c/two-sigma-financial-modeling/leaderboard , 2017.

[10] MarketWatch. http://www.marketwatch.com/story/

why-the-federal-government-now-holds-nearly-50-of-all-residential-mortgages-2015-10-16

2015.

[11] McKinsey & Company. Living with BCBS 239. http:

//www.mckinsey.com/business-functions/risk/our-insights/

living-with-bcbs-239 , May 2017.

[12] Priceonomics. The Trade of the Century: When George Soros Broke the British Pound. https://priceonomics.com/ the-trade-of-the-century-when-george-soros-broke/ .

[13] Quora. What are the best quant funds? https://www.quora.com/ What-are-the-best-quant-hedge-funds .


[14] The Economist of London. The Economist Intelligence Unit. https://

www.eiu.com/home.aspx .

[15] Wikipedia. BCBS 239. https://en.m.wikipedia.org/wiki/BCBS_239 . [16] Wikipedia. James Harris Simons. https://en.wikipedia.org/wiki/

James_Harris_Simons .

[17] Wikipedia. Largest private equity firms by PE capital raised. https:

//en.wikipedia.org/wiki/List_of_private_equity_firms .

[18] Wikipedia. List of asset management firms. https://en.wikipedia.org/

wiki/List_of_asset_management_firms .

[19] Wikipedia. List of investment banking private equity groups. https://

en.wikipedia.org/wiki/List_of_private_equity_firms .

[20] Wikipedia. List of largest financial services companies by rev- enue. https://en.wikipedia.org/wiki/List_of_largest_financial_ services_companies_by_revenue .

[21] Wikipedia. Major Stock Exchanges. https://en.wikipedia.org/wiki/ List_of_stock_exchanges .

[22] Wikipedia. The Big Short.

https://en.wikipedia.org/wiki/The_Big_ Short

.

 

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