A term borrowed from the banking world seems to explain the recent decline of local government, local clubs, and local news sources.
|
|
|
The growing speed of communication, especially the electronic sort, exacts its price. Western civilization spent several centuries building up valuable social structures intended to unite citizen opinion with that of their leaders. A lot of that now seems unnecessary. Most people now know how to read, write, type and press enter. A dozen systems attempt to catch up with Google in the art of telling people what they say they want to know. C-span lets us hear our leaders speak, more or less in person, and then answers our phone call, sometimes.
Quite a change from the days when people knew nothing and knew they knew nothing. Benjamin Franklin formed dozens of little clubs and societies for people of like minds to learn what was going on, and to magnify the force of their collective opinion to influence it. That's essentially why Philadelphia remains a city of clubs, but the diminishing need for such megaphones also goes a long way toward explaining the decline of clubs. The Bar Association has less importance for lawyers, the AMA less for doctors. One consequence that is noticeable is an ascension to power within such declining organizations of minority groups, fringe opinions, and other elements still desperately searching for a voice. The power elites now prefer to aspire to befriend and influencing national power centers directly, and in the process unconsciously augment the importance of centralized power. The upper layers of the government bureaucracy have become infiltrated with educated and high-minded graduates of elite schools, and toward them often go the appeals of former classmates with less plausible motivations. Quite rapidly for a social revolution, people are changing political sides, and the consequence is polarization.
Regardless of laments for the systems and institutions of the past, polarization is dissolving the old glue that binds the nation together, heedless of the new glue of electronics and instant communication with like-minded strangers. It's hard to know what people really believe about the polarizing effect of gerrymandering congressional and legislative districts because it brings people of like opinion together and people generally enjoy that. But professional analysts of the political scene focus on the effect of each ten-year census and claim that the elections of the next decade are easily predictable once you know how the revised census was gerrymandered. Contrast the difference in deportment between the scruffy members of the U.S. Congress with those of the U.S. Senate, where gerrymandering is impossible. The consequence often goes unnoticed, because gerrymandering means that people of the same opinion are more likely to find that everyone they know -- agrees with them. It's not entirely a new phenomenon. When Franklin Roosevelt defeated Alfred Landon in the greatest landslide in our history, many voices were raised that the election must have been fixed because everyone they knew voted for Landon. Something like that misperception affects many who voted in the two elections of George W. Bush, differing in these essentially tied elections only that both sides believe they were cheated. The buffering organizations, the clubs, ethnic groups, and even the political parties either no longer survive, or are dominated by die-hards.
How much of all this is just temporary disorientation, how much is a growing trend predicting the future, is unclear. The harsh and thoughtless oaths and demands which have become so disagreeably common may pass away when people get a grip on themselves, or they may escalate into our normal level of public discourse. Negative campaigning, experts say, is effective. Political campaigns get progressively harsher and dirtier as they approach election day. Money talks, and it talks by buying professional assistance to say what the buyer is ashamed to say. A political party wants to win elections above all else; those who lose elections are quickly hounded into oblivion. And yet, and yet. A slogan or two can still turn this sort of thing around. Just tell a loudmouth that he sounds like a junkyard dog, and see how quickly the listeners quiet down. It's a vicious thing to do, but it works, using vile attacks to silence vile remarks.
To a considerable degree it works because it draws attention to how little substance is to be found in these shouting matches. Someone who heard a major general gives a talk may be emboldened to offer a different opinion on combat strategy, but he still knows how little he knows and retreats at the first sound of answering fire. The person who just listened to the Chairman of the Federal Reserve talking about interest rates may claim to disagree but soon looks a fool if asked to document that opinion. The barroom orator, unrestrained by association with local opinion makers in person, is emboldened to rise to combat with the champions of the opposition. Most of us soon learn not to pick fights with the varsity, and there is at least some small hope that civility will eventually return when a few more noses get bloodied.
You can try soft reasoned analysis if you wish, but at the moment it isn't very popular.
|
|
Banking
|
Banks have long operated in a dual system of regulation, state and federal, which permits some shifting back and forth between regulators. Mergers sometimes confuse matters further, and a system of one-bank holding companies adds to the stew. Local banks, waving the red shirt of domination by Wall Street at their state legislatures, have resisted interstate banking in a wide variety of ways. Sometimes a customer finds that funds transfer between two branches of the same bank must be treated as out-of-state action, and so on. Inevitably there is a certain amount of dealing by subsidiaries which are not recorded on the books of the home bank of a bank conglomerate in ways prescribed by the subsidiary's regulator, or not recorded at all. Equally inevitable is the accusation of off-the-books illegality by competitors, politicians, or the merely captious. Fine points of these legal and accounting arguments must be left to experts, peer review, and courts. Muttering Enron at every opportunity, accusers may be right that some of these arrangements have stepped over the line; partisans in Congress and the legislatures, on the other hand, may be correct that existing law is bad law. This is not a good place to debate either point.
|
|
Credit Crunch
|
It does seem appropriate to notice that banking has long been massively inefficient and that much of this inefficiency has been imposed by regulators. Regulators represent the public, more or less, and the public is rightly nervous about stewardship of its assets. Dual regulation offers refuge from the ancient fear of confiscation by the sovereign and is worth a certain amount of inefficiency if it works. But it does create loopholes, and it does impair transparency. In the case of the credit crunch of 2007, it sequestered bad debt in off-the-books ways, perhaps creating tax avoidance, but mainly creating distrust among counterparties. In those days of awful turmoil, no one knew what was going on, multi-billion dollar losses were being confessed by premier institutions, so transactions were delayed, avoided, or rejected. Transactions with anybody. When the time comes to reconsider regulations, it should be emphasized that by far the most damaging component of the whole mess was lack of transparency. Once more, a massive computer programming effort is entirely capable of restoring transparency to the existing regulatory structure, highly pigglety though it may be. After we achieve transparency we might consider achieving efficient transparency, and after that perhaps ponder fairness in transparency. When a trader calls another and asks to buy a zillion shares, the happy recipient of the call likes to glance up at his screen to see what the other fellow is worth, before he shouts, "You got it!"
What the Federal Reserve might well call the highest priority calls for respect, as well. Ever since we began the century-long transition from a gold standard for money, there has been a concern that the Fed might not be able to determine how much money, or credit, or liquidity -- is actually in existence. We have reached a point in this process where the Fed has largely stopped trying to measure monetary aggregates, and merely adjusts its tools to keep the money supply sailing between the rocks of inflation and recession; if neither rock is in sight, the amount of money is about right. That system has served us for eighteen years, long enough to spark hope that it can be permanent. But when a rocky shore does make an appearance, the Captain of the ship must know how much slack he has, and how reliable his sonar. For huge sums to be obscured within bank subsidiaries or delayed marking to market, is to increase the chance we will run up on the rocks when it might have been avoided. He too needs transparency, but he also needs prompt obedience to his orders. The rest of us passengers are rightly concerned when he appears before Congress and admits he is not sure what the situation is. As long as that is the case, fairness -- and dogma -- be damned.
Taking a step backward, the whole credit crunch has brought to the world's attention that real estate transactions are both immense, and immensely inefficient; a great deal of money is to be made if any step in the chain can be streamlined. Therefore, real estate agents, real estate lawyers, title insurance, surveyors, advertisers, inspectors and everyone else who makes a living from real estate sales -- can expect to be drawn into an annoying process of inspecting the premises, promises, kickbacks, referral fees and marketing costs of a whole expensive process, first blasted open to inspection by implementation defects while computerizing the mortgage step. It appears to be high time for it.
Two "new" revenue sources, which we need to discuss, are really quite old. But widespread use of third parties to pay medical bills diminished consumers' attention to their value. Patients become like Queen Victoria, indifferent to what it costs to run a household, even forgetting how to do it. We fit some details into the discussion of Health and Retirement Savings Accounts, but they are capsulized here for descriptive convenience, in an era when personal management has largely moved from junior high schools to the curriculum of graduate business schools. In the process, we have forgotten a timeless message: never let an agent manage your checkbook for you.
1. Compound Interest. Aristotle complained it gets more expensive to repay debts, the longer you take to pay them off. That's the debtor's viewpoint, of course. The creditor's view of it is, the longer the better. But restated as a neutral mathematical comment, an essential feature of compound interest is that both principal and effective interest, rise over time. To repeat: income rates (and/or borrowing costs) from a debt, increase with duration. About half the capital of every major corporation consists of debt, so even owning common stock has some of the quality of being a debtor. Furthermore, this effect is seen sooner, with quite small rises in nominal interest rates. A graph of sample interest rates demonstrates this simple truth with greater clarity:
|
|
|
As a result of centuries of haggling and experimentation, most modern loans charge interest rates of 5-15%. That's an enormous swing, but only for long-term investing. It makes little difference whether this range of rates reflects the supply of money in the economy, or the vigor of the economy, or something else macroeconomic. So long as rates remain steady, or even if they are changing at a slow steady rate, borrowers and lenders can reach an agreement and negotiate a long-term loan. If there is uncertainty about rates in general, they may rise precipitously, so all borrowers know to keep loans as short as possible, and creditors quickly raise rates when they must cover longer time periods.
The moral is, as you become older you tend to become a creditor, so adjust your mentality from borrowing short to lending long. For centuries, nobody thought much about this invisible equilibrium, because life expectancy was stable at the Biblical threescore and ten -- and in fact only twoscore. But suddenly around 1900, life expectancy at birth began to rise, and starting in 1950 it entered a steep climb from forty-seven to eighty-four years. Thirty-year loans remained the extreme, however, because the proportion of those who would chisel you doesn't seem to change much. Stagecoach robberies went away, but inflation took their place. Underneath it all, governments prefer to expand the currency supply rather than raise interest rates, printing repayments rather than repaying them. Interest rates are, as they say, volatile. Within limits, they are also malleable.
Nevertheless, the expansion of longevity created a new opportunity. The long-term investment was more profitable for everybody. The upturn in interest rates was relatively negligible for the first forty years of compound interest, but progressively quite handsome after that. In practical terms, buy-and-hold became a better strategy. The difference of a tenth of a percent means little in a ten-year loan, but it can create a stupendous profit in a ninety-year loan. One suspects the interest rate on a bank loan has more to do with the debtor's working life (the period available for confident repayment) than his life on earth. In this book, we concentrate on the creditor, whose lifespan should not affect interest rates as much as it affects his opportunity to enjoy money, so long as he has some of it. But a long life without money at the end of it is a fearsome prospect, indeed.
2. Equity Index Investing. The stock of only one company (General Electric) was a member of the Dow-Jones Industrial Average a century ago. By definition, the DJII always contains thirty leading stocks; others have been replaced many times. It takes a long time to become a household name, and by the time an investor has heard the name, it is often ready to decline. Active investing, meaning sell one to buy another, was once quite necessary for success. Unless fading leaders are replaced by new leaders, however, the average would fall behind, But it is easy to see the average has moved steadily upward, so it must be actively managed by someone.
If you are careful to avoid the spongers and the fly-by-nights, the investment world is rapidly changing, mostly for the better. To some extent, this reflects a flight from the bond market which governments deal with, but most investors now think total market index funds are safer. When the Federal Reserve forces banks to buy its bonds through "Quantitative Easing", the supply of bonds goes up and so the price goes down. "Passive" investing is certainly easier for the small investor to deal with, and investors are responding.
Later we will try to take advantage of one obvious flaw in such investing. If a single investment represents thousands of companies, investor control is diluted to meaninglessness. The only effective control over management then resides in the shares which are not held by funds; and even there, more and more corporate control rests with insiders and managers. The effect of such a trend is not merely that manager salaries are inflated, but the corporation becomes less responsive to the consumer public. Its legitimate business plan is to make a profit, but to make a short-term profit at the expense of long-term profits is not so defensible. Because of the corporate shield, many corporations borrow too much, risk too much, and collapse too often, but their managers often walk away with riches. If Health and Retirement Savings Accounts really get popular (at last count, they only had thirty billion dollars invested), its counterweight of stock ownership should help restrain consumer prices. Nevertheless, experience seems to show that competition between companies has been a more effective guardian of public interest, than stockholder control of individual competitors.
HSAs collect money when it is not needed, spend it decades later when it is badly needed, and invest the money during the interval, tax-free. The longer the interval, the more it earns. And with careful application of the principles of compound interest and index investing, the earnings are considerably magnified. If your Christmas Savings Fund earns more money, it reduces the effective cost of what you buy. But if you are careless, investment fees and inflation will ruin everything. So that, in sum, is another message.
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.
[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 willsâ€
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
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.
[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 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
.
Originally the "lower counties" of Pennsylvania, and thus one of three Quaker colonies founded by William Penn, Delaware has developed its own set of traditions and history.
Start in Philadelphia, take two days to tour around Delaware Bay. Down the New Jersey side to Cape May, ferry over to Lewes, tour up to Dover and New Castle, visit Winterthur, Longwood Gardens, Brandywine Battlefield and art museum, then back to Philadelphia. Try it!
Millions of eye patients have been asked to read the passage from Franklin's autobiography, "I walked up Market Street, etc." which is commonly printed on eye-test cards. Here's your chance to do it.
In 1751, the Pennsylvania Hospital at 8th and Spruce was 'way out in the country. Now it is in the center of a city, but the area still remains dominated by medical institutions.
Grievances provoking the American Revolutionary War left many Philadelphians unprovoked. Loyalists often fled to Canada, especially Kingston, Ontario. Decades later the flow of dissidents reversed, Canadian anti-royalists taking refuge south of the border.
