Listen When This Man Speaks (about his business)

I think he’s the greatest non-founder executive to have walked the earth, and Jim Lynch points us to an extended treatment of Hank Greenberg’s management style (the technical stuff, not the bombast), including an interview:

Greenberg said, “You don’t want to roll your company up with undefinable risk. You have to understand the risk. The insurance industry is the only industry where you never really know the results at the end of the year. You may think you know, but you don’t. The tail on a risk could be 10 years, so you don’t really know.”

So how do you mitigate that seeming lack of understanding? “Experience is very valuable to be able to predict those costs,” he said.

“I don’t want to wake up one morning and say ‘What happened?’ ” Greenberg said.

Felix Kloman, a former Towers Perrin partner and a well-known commenter on the subject of risk, said, “Organizations can easily become risk averse. You want them to take on risk in the future and too often risk management defines risk as a negative outcome.”

Kloman said that Greenberg is the exception. “Hank is much more of a risk-taker. The CEO coordinates and encourages intelligent risk-taking.”

Here’s how insurance works: clients hand insurers money and time-bombs, which they toss into a warehouse. Luckily, most time-bombs are duds and, when they do go off, the walls of the warehouse are strong enough to withstand the bang.

Obviously you want as many time-bombs as you can get because you want the money, too. You can use that money to build thicker walls on your warehouse, allowing you to stuff more bombs in there. The problem is that, all too often, insurers don’t find out they’ve overbought time-bombs until it’s too late.

All you can do then is sit there and watch them go off.

Striking that balance between growth and risk management absolutely boggles the mind and, frankly, gets the best of many, many executives.

Hank Greenberg was/is better at that balance than anyone else on earth.

A Piece of The Puzzle

Loving the Sector & Sovereign Blog.

One of my most enduring frustrations with the insurance industry is that there is this bizarre cycle:

For those that don’t want to read about this graph: the industry loses money when the lines cross the horizontal blue line.

This insurance cycle is somewhat related to the business cycle, but the relationship isn’t terribly strong. What the hell is going on then? Some of it is pricing, where rates are cut. But S&S suggest that this masks a shadowy increase in exposure, by way of loosening terms and conditions (T&C) [emphasis in original]:

Rather, we think price declines are concurrent with deteriorating policy term & conditions, and that this is the main source of loss trend deterioration. In other words, we think the industry contributes more to its own loss trend experience than external inflation

We test this theory using loss trend data for work comp, available from the NCCI. We model frequency, medical severity, and indemnity severity separately as well as together. In every case, pricing from 3 years ago matters more than any possible macroeconomic factor.

Now that’s a cool idea. And probably a correct one.

A problem, of course, is that it’s not a terribly useful idea, from the perspective of making money. The market stays stupid for longer than you can stay liquid, after all.

And this isn’t directly observable or measurable, even for reinsurers. People will conceal this kind of T&C deterioration and, because of its lag, the villains have good reason to believe they will get away with it in advance. And for good reason: everyone else in history has.

I’m still ruminating on my critique of S&S’s compelling but (I believe) flawed theory of supply and demand in the insurance market.

Build vs. Buy

Celent is releasing a report soon on build vs buy. I find this debate frustrating because I feel like it isn’t a difficult decision.

Insurance companies are made of three things: money, a processing system and an underwriting system. Money is money and at current regulatory margins I don’t believe there is an advantage to be gained for insurers having more or less of it. Underwriting systems exist to sniff out moral hazard so humans have to handle those (committees, referral, etc).

100 years ago, the processing was done by humans, too. Today, you choose between build or buy.

If you find yourself among the small minority of (re)insurers that write volatile, hard-to-price insurance, you don’t really need a rock-solid processing system. You probably write big deals and work more like a hedge fund than a retail bank. You can buy.

As for the rest: if you don’t build, the risks are simple and the money isn’t yours, your job is to provide a commodity on the cheap. If you aren’t building your own system, what on EARTH do you get paid to do?

Over time technology improves, making new systems better regardless of whether you build or buy. This will, however, rarely (never?) affect whether a company chooses picking clients or shaving margins as its business model.

Third Point, LLC Destroys $100m

Sorry about the big quote, but I need to set the stage here:

Daniel Loeb, the founder of Third Point LLC, started a reinsurance company that can invest in his $8 billion hedge fund, joining rival David Einhorn in seeking more permanent capital.

Third Point Re, which is based in Bermuda, hired John Berger as chief investment officer and has about $500 million in capital, according to two investors familiar with the plan. New York-based Third Point wants to raise $250 million to $500 million more and plans to eventually sell shares of the reinsurer to the public, said the investors, who asked not to be identified because the firm is private.

Here’s more.

First, let’s clear one thing up: the insurance market is soft because there’s too much capacity. Reinsurers are running break-even and, to the extent that there is any price increases in catastrophe-driven lines, it’s in tiny little corners of the market (New Zealand EQ, Japan EQ and other non-US, non-EU catastrophe zones).

And these price increases don’t matter globally because they are being completely captured by incumbent players. Until a company or two dies by the sword and the market reprices the business that killed them (ie policies with lots of claims and fat renewals), nobody is getting rich.

So the market would instantly value a startup at 80% of book. In this case that destroys 100m. Why does Third Point want to lean into these headwinds?

Well, he wants to create a little walled garden where he can play by himself:

Loeb follows Einhorn, head of New York-based Greenlight Capital Inc., in creating a reinsurer as a way to raise capital for his hedge fund that isn’t subject to client redemptions. Reinsurers, which help insurers shoulder risk, earn premiums that they invest to make a profit.

When you put money into a regulated entity, it’s stuck. This is why AIG policyholders had nothing to fear from all the bond insurance shenanigans. Loeb is locking down a chunk of capital into a straight-up illiquid private equity bet.

What’s more, cat reinsurers do not invest for profit. Their liabilities have an 18-to-20-month duration at best and last time I checked, nobody’s getting rich on 2-year paper.

From a straight-up financial perspective, this move is lunacy. But Loeb gets his sandbox, even if he’s trading liquidity for nothing.

Good luck, Danny-boy.

Pricing Power

Looks like Buffet invested in Verisk Analytics, which is an organization that I am familiar with. Here is the real point:

Verisk is an American company that was founded in the 1970s by the major US property and casualty insurance companies. These companies collectively provided Verisk with their claims data in order to create a centralized database that would allow the industry to analyze risk better.

The analyst gets the point right but the names wrong. The Insurance Services Office (ISO) was created as an information mutual for the industry. It’s now a subsidiary of Verisk.

There’s no doubt Buffet’s onto something, though. This is a company with a gigantic ‘moat’, as he likes to call it.

ISO pulls off the confidence trick that I’ve seen before. They poll member companies for data, aggregate it and then sell it back to them. For quite a price, I’m told.

ISO data gives every insurance company a benchmark for claims costs and trends in various lines of business. It is literally the only way people have of guessing whether they can make a profit in a particular line of business if they aren’t currently in it.

Imagine a mining company that pulls zinc out of the ground and is mulling over the possibility of opening up a copper mine next door. How would this company make this decision? Well, they’d probably check the pricing history for copper and see whether they think they can make money on it. They know their costs of production, but they don’t know the price.

Insurance companies don’t have this information. They literally do not know how much their policies cost up front, which means that they need intimate knowledge of a market before they can decide whether investing in new products is a good idea or not. ISO is the only way they can get this data out of their competitors’ hands.

I’ve often toyed with the idea of what it would take to start a company that would compete with ISO. The problem is that ISO benefits from gigantic network effects. Without any scale you’re just one insurance company’s data. And I know that the biggest insurance companies (like AIG) guard their data jealously, so you HAVE to rely on the little guys banding together.

What I’d need to identify is a blind spot for ISO. A line of business that they don’t serve very well and probably won’t start serving soon. I’m sure it exists. The other thing I’ve heard about ISO is that it’s an antiquated, backwards organization. Doesn’t surprise me. They’re practically the insurance government. What incentive do they have of serving someone well?

None.

Holy Cow, Lots Going On

Some links:

Here’s a scathing review of S&P’s conduct, generally:

To say that S&P analysts aren’t the sharpest tools in the drawer is a massive understatement.

Naturally, before meeting with a rating agency, we would plan out our arguments — you want to make sure you’re making your strongest arguments, that everyone is on the same page about the deal’s positive attributes, etc. With S&P, it got to the point where we were constantly saying, “that’s a good point, but is S&P smart enough to understand that argument?” I kid you not, that was a hard-constraint in our game-plan. With Moody’s and Fitch, we at least were able to assume that the analysts on our deals would have a minimum level of financial competence.

Yikes. And imagine what the real regulators are like.

As for S&P’s downgrade, here’s Sumner’s take, under the title of 1.07% on the 5-year and falling fast.

The markets this morning gave a massive vote of no confidence to S&P ratings service, as yields plunged on Treasuries.

The real after-tax rate of return on the 30 year Treasury is now negative, assuming a 30% MTR.  That means the tax rate on capital now exceeds 100% in real terms over the next thirty years, which doesn’t seem particularly conducive to capital formation.

Yikes.

But what’s the ultimate signal that things are bad? Berkshire’s getting the itch.

Here’s Ajit swooping in and demolishing the incumbent bidders’ stock/cash offers with an all-cash, thank-you-very-much email.

Mr. Robert Orlich
President & CEO
Transatlantic Holdings, Inc.
80 Pine Street
New York, NY 10005

Dear Bob:

As you can imagine, subsequent to our telephone conversation yesterday, I have been watching the screen all morning. With your stock trading at $45.83, I have to believe that you will find our offer to buy all of Transatlantic shares outstanding at $52.00 per share to be an attractive offer. As such, I am now writing to formally inform you of National Indemnity’s commitment to do so at $52.00 per share under customary terms for a stock purchase agreement of a publicly traded company to be agreed (but not subject to any due diligence review or financing condition of any nature [emphasis DW’s]). This commitment is subject to:

  • A formal response from you no later than the close of business, Monday, August 8, 2011.
  • Should you decide to accept this offer, your agreement that should the deal not close for any reasons that are under your control by December 31, 2011, a break-up fee of $75.0 million would be paid to us.
  • Your commitment that until the deal closes, you will continue to manage the affairs of the company in a manner that is consistent with how you have managed it historically.

I have deliberately tried to be brief and to the point. I will be happy to discuss any details that you would like at your convenience. I can be reached at [number withheld] (work), [number withheld] (cell) or [number withheld] (home).

Regards,

Ajit Jain

TRC probably figured there wasn’t any point in summarizing the offer and just published it in whole online. Also gives us our “parenthetical statement of the day”.

Note that this values TRC’s stock at something close to the incumbents’ bids BUT the value of the stock portions of the bids have diminished substantially since they were made.

Timing is everything, n’est-ce pas?

update:

Here’s a neat post speculating on who got the margin call today:

You see the desperate selling of the biggest liquid names is a sign of margin calls.

The market is not puking. Some prime broker is puking the stocks held by one or more very large hedge funds.

So lets play the game: guess who got the margin call!

How to Improve

This caught my eye this morning: “10 ways to improve your programming skills”

Since I’m learning how to program (has it been two months!?), and I want to get better at it, I should try to follow some of this advice. Here’s the list:

1. Learn a new programming language

Um. It’s all new!

2. Read a good, challenging programming book

Ok, bit advanced.

3. Join an open source project

Yeah, right.

4. Solve programming puzzles

Possible candidate here. Sounds like a lot of work, though.

5. Program

Got enough of that to do!

6. Read and study code

Ugh.. no time.

7. Hang out at programming sites and read blogs

No “hanging out”, but I read.

8. Write about coding

Hmm….

9. Learn low-level programming

Nope.

10. Don’t rush to StackOverflow. Think!

Meh.

-=-=-=-=-

So maybe writing about programming is the low-hanging fruit here.

Ok, so here’s what I did today at work.

There’s this company called AM Best who are an insurance-specialist rating agency, like S&P but much narrower in focus. I go there periodically for financial information on our clients and markets and for the industry in general.

Anyway, I noticed that there is a press release archive going back to 2000. The thought struck me that it would be neat to have a database of all these press releases to crunch and see if there are any patters in the rating actions taken on companies.

THEN it would be neat to link these rating actions to stock prices, to see whether the ratings actually, um, you know, work.

For instance: how good of a predictor are they of default? Is there an immutable ‘snowball effect’ where a rated entity just keeps getting downgraded until it fails or merges with someone else?

So this project has been bubbling around in my head for a few weeks and this morning I finally had enough spare time in which to implement it.

I’ve put together a scraping routine (busily ‘scraping’ as I type) that is pulling down all 10,000+ press releases and dropping them into a database.

I considered doing all the actual data mining today, too, but that’s going to take a bit too much time. I’m happy with just sitting on the data for now.

My next objectives:

1. Parse the text to figure out what the various categories of press releases are.

I know there are downgrades and upgrades of companies, but what about actions against subsidiaries only? What about debt ratings? Most of this crap is useless to me.

2. Figuring out a system for identifying companies that matter.

There is going to be a ton of mergers, defaults, spin-offs and goodness knows what going on that I’ll need to work out. That will be tricky.

3. Isolating the rating actions associated with corporates and building a more ordered database of actions over time.

This is the ‘real work’, obviously. How ironic that it’s going to by far be the easiest step once everything’s organized. Regular Expressions, baby. Cinch.

3. Figuring out how money has been made or lost in this process.

I want to link these names to stock symbols and see if there is any perceived contagion (by the market) and, even more importantly, whether there is any ACTUAL contagion. I suspect not.

An Anonymous Rant Against A Professional Writer

PC360 gives us this. It’s so sticky with jargon to be barely readable.

Let me summarize the (2,300 word) article:

Claims data can teach underwriters about where claims come from and expose new drivers of claims cost. Analyzing claims databases is a good way of testing new hypotheses but,  for organizational reasons, most companies aren’t great at this.

Yawn. Could have been written at any point in the last 300 years.

Next is a big discussion about how automated computer programs can correlate variables without the burden of actually ‘understanding’ the data.

[shields up! BS ALERT!]

My old man once spent some time learning about a stock picking technique which, to be perfectly honest, looked like garbage to me. But sometimes it worked!

I’d argue it’s complete luck. As they say, “even a broken watch is right twice a day”.

Narrative validation a powerful test for statistical conclusions: correlation is useless without a deep understanding for the causal mechanism. Unexplained, ‘dumb’ empirical relationships (describes all too much of medical research, imo) are too unreliable for me to back with cash.

If you don’t know how it works, how on earth do you know when it breaks?

For The Insurance Geeks In The Crowd

This is a deeply powerful point about the insurance business:

There are ten lines in that graph..

The straight blue line is the 1-1 line, which is the measurement of a year’s performance 1 year out. This is a pure fudge figure because the insurer doesn’t have enough information to measure the cost yet.

The fact that this line is at 1.00 is important. 1.00 means that the insurer expects to pay out 100% of its premium in claims. Nominal Revenue = Nominal Cost. 10 years of interest makes this possible.

As you look back at the year over time (1-2, 1-3, etc), the amplitude of the ‘wave’ increases. This happens because, over time, insurers gain information about how well that year is going and absorb the volatility in the relationship between revenue and costs.

Workers’ Compensation business is the most ‘long tail’ of insurance businesses. This means that the claims cost of comp policies take the longest to resolve.

In fact, insurers have very little idea for the ultimate cost when they write a comp policy. Workers’ comp is notorious for this and many, many insurance companies avoid it entirely because of this uncertainty.

The cycle is present in all insurance businesses, though. Once people figure out they’re losing money, they pull capacity and rates go up. The difference with comp is that there is more risk of finding out too late.

Think of that realization like a tsunami. When they’re out to sea, small waves look like big waves because very few have enough power to displace the entire vertical distance of water from the ocean floor all the way to the surface. Good years and bad years and company-killing years look pretty similar.

But once the sea floor shortens up and you hit the shore, you find out how much energy was in the sucker.

And with comp, those suckers can be big.