3 Volumes
Computers, Websites, and other Digital Gadgetry
What is novel today is old-hat tomorrow; but what is old-hat to someone today is still novel for someone else. These are our own thoughts about a variety of electronic novelties, for whoever finds them of interest.
The Age of the Philadelphia Computer
Computers have a long slow history. The computer industry, however, had an abrupt start and sudden decline, in Philadelphia.
Website Development
The website technology supporting Philadelphia Reflections is PHP, MySQL and DHTML. The web hosting service is Internet Planners. The development of this website has provided an opportunity to learn new technology, to try out different techniques for getting noticed by the search engines and the trials and tribulations of dealing with malicious hackers and spammers who range from the annoying to the abusive. This collection of articles documents some of our experiences and we hope that people surfing the web looking for solutions to problems we've encountered will benefit.
The primary purpose of this website is to deliver high quality content on the subjects of Philadelphia, Philadelphia History, medicine, medical economics and other subjects of interest to its author, Dr. George R. Fisher.
However, early in 2006 the site was attacked by spammers who broke in using security holes in the previous implementation of PHP. In the subsequent reconstruction of the site, there's been an opportunity to try out lots of new technology and techniques, some of which are detailed here.
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 organizations. 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 analytics 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 A 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 see 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 entire banking group on a near-real-time basis while facing severe economic stresses. Multitudes of incompatible systems are a huge barrier.
![]() |
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-
guess 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 techniques.
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 the check before a claim can be approved and 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 extensive 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. That's why only 40% of U.S. households own individual life insurance. Prudential wants to make it quicker and less labor 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 an additional examination to confirm their insurability.
Risk Mitigation
Fire losses account for a significant portion of total property losses. High severity 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 the 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 consumption 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 empirically models to predict the behavior of bid and ask prices following such “liquidity shocksâ€.
Modeling market resiliency will improve trading strategy evaluation methods by increasing the realism of backtesting 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 Bodily 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 make 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 compliance 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 multi-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
![]() |
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. https://www.cardrates. com/news/credit-card-companies/ , 2017.
[2] dealogic. Global IB Revenue Ranking. https://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://
[5] Global Association of Risk Professionals (GARP). Financial Risk Manager
(FRM) Certification. https://www.garp.org/.
[6] Raynor Gobran. Biggest hedge funds by assets under management 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. https:// 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. https://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. https:
//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://
[15] Wikipedia. BCBS 239. https://en.m.wikipedia.org/wiki/BCBS_239 . [16] Wikipedia. James Harris Simons. https://en.wikipedia.org/wiki/
[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 revenue. 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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Quick Analysis of Financial-Industry
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