|
|
Judge Edwin O. Lewis
|
Judge Edwin O. Lewis
finally got his way, the Pennsylvania State Government acquired four
blocks of Chestnut Street stretching to the East of Independence Hall,
and the Federal Government acquired four blocks stretching to the
North. Judge Lewis was determined that a real revival of historic
Philadelphia required the clearance of a lot of lands. Those who heard
him describe it will remember the emphasis, "It must be BIG if it is to
serve its purpose."
The
open land is rapidly filling in but for a time the movers and shaker
of this town had to scratch a little to find something to put there.
That's fundamentally why the historic district has a Mint, a Federal Reserve, a Court Houses, a Jail,
and a big Federal Building to house various local offices of the
landlord, the federal government. It's where you go to visit your
congressman, or to renew your passport, or to argue with the Internal
Revenue Service. If you have certain kinds of business, there's an
office for the FBI and the U.S. Secret Service. The mission of the Secret Service is a little hard to explain with logic.
The
Secret Service is a federal police organization, charged with
protecting the President of the United States, and enforcing the laws
against counterfeiting money. In unguarded moments, the Secret Service
officers will tell you they only have one function: to guard
three-dollar bills. The President only comes to town from time to time,
but the mandate extends to the President's family, and to the extended
family of official candidates for election to that office. So, there is
usually always a certain amount of activity relating to running behind
limousines with one hand on the fender, or poking around rooftops near
the speaker's platform at Independence Hall, or talking apparently to a
blank wall, using the microphones hidden in their ear canals. The rest
of the time is taken up with counterfeiters, but even then the
excitement is only occasional, depending on business.
A few years ago, the buzz around the office was that some very good, even
exceptionally good, fake hundred dollar bills were in circulation in
our neighborhood. The official stance of The Service is that all
counterfeits are of very poor quality, easily detected and no threat to
the conduct of trade. Unfortunately, some counterfeits are of very good
quality, not easily detected, and when that happens, The Service is
made to feel a strong sense of urgency by its employers. These
particular hundred dollar fakes were of very good quality.
One
evening, a call came in. Don't ask me who I am, don't ask me why I am
calling. But I can tell you that a very large bag of hundred dollar
wallpaper has just been tossed over the side of the Burlington Bristol Bridge, near the Southside on the Jersey end. Goodbye.
Very soon indeed, boats, divers, searchlights, ropes, and hooks
discovered that it was true. A pillowcase stuffed with hundred dollar
wallpaper of the highest quality was pulled out of the river. By the
time the swag was located and spread out for inspection, it was clear
that several million dollars were represented, but they were soaked
through and through. Most of the jubilant crew were sent home at
midnight, and two officers were detailed to count the money and turn it
in by 7 AM. The strict rule about these things is that all of the money
confiscated in a "raid" was to be counted to the last penny before it
could be turned over to the day shift and the last officers could go
home to bed. After an hour or so, it was clear that counting millions
of dollars of soggy wet sticky paper was just not possible by the
deadline. So, partly exhilarated by the successful treasure hunt, and
partly exhausted by lack of sleep, the counters began to struggle with
their problem. One of them had the idea: there was an all-night
laundromat in Pennsauken. Why not put the bills in the automatic drier, so they could be more easily handled and counted? Away we go.
|
|
Burlington Bristol Bridge
|
At four in the morning, there aren't very many people in a public
laundromat, but there was one. A little old lady was doing her wash in
the first machine by the door. It was a long narrow place, and the two
officers took their bag of soggy paper past the old lady, and down to
the very last drying machine on the end. Stuffed the bills into the
machine, slammed the door, and turned it on. Most people don't know
what happens when you put counterfeit money in a drier, but what
happens is they swell up and sort of explode with a terribly loud
noise. The machine becomes unbalanced, and the vibration makes even
more noise. The little old lady came to the back of the laundromat to
see what was going on.
As soon as she got close, she could see hundred dollar bills
plastered against the window, and that was all she stopped to see. She
headed for the pay telephone near the front of the door. The secret
Servicemen followed quickly with waving of hands and earnest
explanations, but within minutes there were sirens and flashing lights
on the roof of the Pennsauken Police car. Out came wallets and badges,
everyone shouting at once, and then everything calmed down as the
bewildered local cop was made to understand the huge social distance
between a municipal night patrolman and Officers of the U.S. Secret
Service. Now, he quickly became a participant in the great adventure and was delegated the job of finding something to do with armloads of
(newly dried) counterfeit hundred dollar bills. He had an idea: the
local supermarket was also open all night, and they carried plastic
garbage bags for sale. Just the thing. But who was going to pay the
supermarket for the bags? Immediately, everyone was thinking the same
thing.
Fortunately for law and order, the one who first suggested the
the obvious idea of passing one the counterfeits was the little old lady.
At that, everyone came to his senses. Wouldn't do at all, quite
unthinkable. The local cop was sent off for the bags, relying on his
ability to persuade the supermarket clerk. And, yes, they did get the
money all counted by 7 A.M.
|
|
Mr. Alan Greenspan
|
When there is inflation, the value of money goes down, so you might expect interest rates -- the rental cost of money -- to go down, too. However, people anticipate higher prices, so lenders build a premium into the interest rate structure to compensate for the value of the money to be lower when it is repaid. That raises interest rates, and the Federal Reserve will generally raise them even higher to put a stop to inflation. So, buying and selling bonds is a zero-sum game, far riskier than it sounds. Consequently, there is a flight toward common stock, thus raising its price. Meanwhile, inflation usually hurts business, tending to lower the stock prices. As a consequence of all these moving parts, long-term investors are urged to buy at a "fair" price and never sell, no matter what. Even that strategy fails for any given stock because somehow corporations seldom thrive for more than seventy-five years. So, the advice is to diversify into a basket of stocks, and the cheapest way to get that basket is to buy an index fund. In a sense, you can forget about the stock market and let someone else manage the index, for about 7 "basis points", that is, seven-hundredths of a percent. All of this explains the choice suggested for Health Savings Accounts of buying total market index funds. Limiting the universe to American stocks is based on a political hunch that it reduces the chances of harmful Congressional protectionism. Having said that, a Health Savings Account must raise cash from time to time, and to guard against forced selling in a down market, some average amount of U.S. Treasury bonds will have to be maintained. Ideally, the number of Treasuries would be small for young people, and grow as they get older, and therefore more likely to get sick. Pregnancy is the one universal cost risk for younger people, and they know better than anyone what the chances of that would be in their own case.
This approach is greatly strengthened by reference to the modern theory of a "natural" interest rate, to which the whole system has a tendency to revert, if only we knew what the natural rate is. It is not entirely constant, but over time it seems to be something like 2%. If we knew for certain what it was, we could set a goal for perpetuities like the Health Savings Account to be "2% plus inflation". Since inflation is targeted by the Federal Reserve as 2%, that would amount to an investment goal of 4%. If you can buy an American total market index fund consistently gaining at 4.007 % per year, you should buy and hold. If it rains less than that, it is either run by incompetents, or it is a bargain which will eventually revert to 4.007% and pay a bonus. If, on the other hand, it gains more than that, there exists a risk it will revert to the mean. That it is being run by a genius is sales hype to be ignored. We suggest buying into it in twenty yearly installments, which should balance out the ups and downs, so then you can forget about even this issue.
But don't count the same issue twice. In order to assure a 2% real return, it is necessary to obtain 4% in the real world of 2% inflation, and the compounded income of 4% accounts for both in equal measure. A compound income of 6%, however, is two-thirds inflation / one third "real", so artificially raising interest rates to control inflation can progressively overstate the requirement, and hence overdo the deflationary intent. Conversely, when the Federal Reserve fails to raise interest rates as Mr. Greenspan did, the result can be an inflationary bubble. The central flaw in adjusting prevailing rates to current natural rates is that we do not know precisely what the natural rate is. To go a step further for immediate purposes, we are also uncertain how much deviation there is between medical inflation and general inflation. As a result, the best we can expect is to make as much income on the deposits as we safely can, and continuously monitor whether the premium contributions to Health Savings Accounts might need to be adjusted. And the safest way to do that is to have two insurance systems side-by-side, one of them a pay-as-you-go conventional policy for basic needs during the working years, and a second one whose entire purpose is to over-fund the heavy expenses at the end of life and the retirement years, permitting any surpluses to be spent for non-medical purposes. With luck, the beneficiary might retain a choice between increased premiums, and increased (or decreased) benefits.
If these calculations are even approximately close, the financial savings would be several percents of GDP, a windfall so large that mid-course adjustments could be tolerated.
Starting with N-HSA We have just described the general outline of New Health Savings Accounts (N-HSA). Essentially, it consists of individual HSA funds for children, connected to Medicare by permitting the funds to sit in escrow from age 21 to age 66. However, the amount which can be accumulated during childhood is small, and the task it is asked to perform is large. Because children are so lacking in income, they can't be expected to accumulate much, even though their grandparents may have helped out. Consequently, that small amount multiplied by compounded income for 45 years, will probably only pay for one designated segment of the Medicare program, and it is unlikely it would be able to pay off much of Medicare's accumulated debt.
So, although it can be shown to be workable, it would look like a long run for a short slide, to an economically illiterate family. Meanwhile, its political enemies would likely describe it as meddling with Medicare, and its chances of achieving the necessary enablements would shrink. However, the grand discovery is, the Health Savings Account idea resembles how President John Adams once described his native Boston -- Every goose is a swan. Every problem we encounter, that is, seems to suggest an unexpected new improvement. Let's explain the three accompanying graphs.
Three Graphs. The top graph shows the situation, without either a bridge around or participation in, the Affordable Care Act. The HSA escrow comes to a halt for 45 years and then resumes with Medicare. There are two savings accounts, but each starts at zero and lasts two decades. One is an escrow account, unspendable until age 66.
The middle graph imagines the situation with a dormant escrow gathering interest during the 45 years. Notice the thickened blue escrow.
The bottom graph is a cutout enlargement of the transfer point for grandpa's gift, showing how easy it would be to adjust the escrow transfer from zero to $29,000. The difference between the extremes added to the escrow is the difference between solvency and riches. To imagine a small deposit spiraling out of control is probably a little fanciful, but for those who worry, here is a ready solution.
Adding Obamacare. If we achieve political consensus, and thereby add the subscribers from age 21 to 66 (the only age group which reliably produces real new wealth), the arithmetic suddenly transforms. The complete system from cradle to grave generates enormous surpluses. After studying this paradox for some time, I came to realize that what distinguished it from Lifetime Health Savings Accounts (L-HSA) was the two, eventually three breaks between programs, where the escrow fund could drop to zero, without some agreement to transfer it between insurance programs. If it drops to zero, the effect of compound interest rising at its far end is chopped off, and overall returns are much reduced. The whole idea unfortunately then becomes politically precarious and runs the risk of some small glitch somewhere unraveling it. To use our own descriptive terms, three Classical (C-HSA) funds are nice, but one Lifetime (L-HSA) is so far superior it raises grandiose questions of starting an inflationary spiral. But in a sense, the radical Right is correct. The changes to the Affordable Care Act must be drastic enough to generate public support for merging the radical plan of the left with a radical plan of the right, essentially making both of them unrecognizable. I'm no politician, but I can easily imagine the difficulties of that negotiation.
|
|
|
The Goose is a Swan. But I came to see that what makes it impractical is the same as what makes it so glamorous. The possibility of linking the healthcare fund to the stock market would likely be brushed aside by the explosions of a money machine -- the system as originally envisioned for L-HSA generates almost any amount of money you please. That's a pretty intolerable effect of inflation heedlessly disregarding any monetary standard, even a return of a gold standard.
But if the HSA is more or less denominated in index funds, it essentially has a monetary standard built in and could maintain it if someone held a meat ax in reserve. Some impregnable threat is needed to control the monster, and it is provided at the three linkage points, where the three existing insurance programs connect.
Three Meat Axes. The connection after the children's escrow fund is the most leveraged and therefore the most sensitive since we have already demonstrated how the difference between zero transfer between two funds, and the transfer of $27,000, is the difference between marginally paying Medicare bills, and having money to burn. If some totally reliable monetary angel could be discovered and put in charge of it, the discretion about inflationary consequences could be placed in one pair of hands.
But the history of inflation has been that even Kings, Popes and Emperors have succumbed to the temptations of such power. Remember, this fund is truly generating $350,000 of new wealth per person (in a nation of 300 million inhabitants) if it operates precisely as hoped, so it starts with some latitude. There are several Presidents of the nations of the world, who might fairly be suspected of raiding their own currency right at this moment, however. Wisdom suggests more caution is necessary. For example, Congress could permit a discretionary band within which the Executive branch could operate, perhaps in consultation with the Federal Reserve. That might permit Congress to create some very difficult hurdle for the process to jump, for widening the limits of the band, such as a Constitutional Amendment.
There's an End in Sight. And also remember, my colleagues in the research department are busy looking for a cure for cancer and Alzheimer's Disease, and I feel confident they will eventually have success. Just cure diabetes, schizophrenia, or birth defects, and our problem with Health Savings Accounts would transform into how to turn them off. In the meantime, we must modulate the ups and downs of medical costs which are steadily becoming less urgent. Take warning from the recent example of the price of tetracycline, which a year or two ago was 35 cents retail for fifty capsules, and suddenly jumped to $3.50 for a single capsule. And then with a new owner, jumped to thousands of dollars. If things like that continue to happen, we might be ready for another pet scheme of mine, the limitation of health insurance coverage to covering the first year of life, and the last year of life, by eliminating most of the disease in-between. Because of the helplessness of both these population groups, and the universality of the need for their coverage, in their case alone drastic interference with market mechanisms might appear justified, to those who are injured by them. The rest of us ought to have a say in something like that. But that's another book, for another time.
We're some way from seriously having that type of problem, so let's get back to details. For this purpose, paying patients arrange themselves into only three groups, children, working folks, and Medicare recipients. Thus there exist three breakpoints between these three programs for different ages, assuming Congress authorizes transfers between them, especially from grandparent generation to grandchildren, incidentally relieving the middle generation of a lot of cost-shifting. There is now so much (necessary) cost shifting, it is nearly impossible to sort out the cost numbers. So I won't try to do it, except in a sort of general way. Rationing is a sort of a lip-service concession to the wide-spread liberal endorsement of a single payer system, endorsing but without facing the resultant deficits in every direction. Instead, we encounter the worrisome potential for generating too much money, even though that is hard to believe without endorsing galloping inflation. There is little difference between external transfers -- between insurance plans, and internal transfers -- within one mega-institution -- except, in this case, one approach creates impossible deficits, and the other approach raises a real concern about inflation. A compromise might be devised, but it requires some sort of conciliatory response from both sides, for even a beginning.
Meanwhile, I don't scoff at the legal issues of who is responsible for those bills, if we destroy the family unit with exciting new social liberties. And I haven't forgotten the problem of corporate finance officers, who have run a confidence game for eighty years, making money for the stockholders by giving away health insurance to employees, as long as they can conceal what they are really doing. We've suggested in this book, we should offer the business a reduction of their corporate income tax to levels comparable to individual tax levels, in return for getting them out of the health insurance business. In a sense, it returns the favor of making a profit by giving away a service benefit, by -- generating revenue for the public sector in return for reduced taxes in the private sector. I'm entirely serious about offering major corporations a one percent cut in corporate taxes for each two percent reduction in fringe benefits tax exemption, down to the point where the top corporate income tax rate is equal to the average individual tax rate. That benchmark is selected because of the temptation otherwise created, to elect Subchapter C to S inter-conversions, exploiting such tax differences. The international corporate flight is another serious consideration. Meanwhile, it is always possible to equalize employee tax exemption by allowing HSAs to purchase catastrophic insurance through the HSA itself, if the law would permit it.
Inflation Protection. Q. Now, wait a minute. If we permit a money machine to be built, what is to prevent it from resembling the galloping inflation which ruined the Weimar Republic? And if we devise a way to keep the United States from going down that road, how do we prevent a hundred small foreign states (Zimbabwe, for instance) from doing it deliberately in order to use their sovereign status to acquire the index funds held by Health Savings Accounts?
A. You've almost answered your own question about Zimbabwe. Even without freely floating currencies, the markets are quick to detect changes in the value of the foreign currency. Zimbabwe can force its own people to accept pennies disguised as trillion-dollar bills, but everybody else avoids them, whereas bitcoins don't even have sovereign power. And as for our own domestic currency, I propose we enact a band of fluctuation in consultation with the Federal Reserve, within which the dollar can float, and beyond which the band may not be expanded without a Constitutional Amendment, again in consultation with the Federal Reserve. In two hundred years, the amendment process has only let one matter (Prohibition of alcohol) slip past, which had to be revoked after the experience with it. Almost every other indiscretion has proved to crumble in spite of the temptation to raid the cookie jar.
Watchdogs. Three breakpoints, one between each age group, with wildly different medical needs and financial viewpoints, need watchdogs. Since going to zero between any two of the three insurance programs could bring inflation to a halt, and since venality knows no political boundaries, I suggest each breakpoint be governed by a different political entity, composed of a board nominated by a different branch of government, and each ratified by a different process. It may or may not be necessary for them to share the same information agency, since think tanks are very popular right now, but may not continue to be. We will need another conference in a resort hotel to work out a paper but keep in mind that foreign powers will be anxious to infiltrate and subvert it. So maybe we need two conferences, one to review the other. After all, we are talking about 18% of the gross domestic product, and Benjamin Franklin isn't available anymore.
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.
