Listen When This Man Speaks (about his business)

Posted on by David Wright

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.

Traction

Posted on by David Wright

Haven’t written much on Sumner’s NGDP world but there has been a rather gigantic shift among the economic elite, I think, on replacing inflation targeting with NGDP targeting (and level targeting?).

NGDP targeting is when the fed ignores inflation figures and concentrates on how incomes are doing, which is NGDP. It combines real GDP and inflation measures which, in the real world, are combined.

I’ve been reading Sumner’s blog for some time now and he’s the guy that’s had THE big idea right from the start of this whole crisis. History will judge him well.

Here is Krugman:

My beef with market monetarism early on was that its proponents seemed to be saying that the Fed could always hit whatever nominal GDP level it wanted; this seemed to me to vastly underrate the problems caused by a liquidity trap. My view was always that the only way the Fed could be assured of getting traction was via expectations, especially expectations of higher inflation –a view that went all the way back to my early stuff on Japan. And I didn’t think the climate was ripe for that kind of inflation-creating exercise.

At this point, however, we seem to have a broad convergence. As I read them, the market monetarists have largely moved to an expectations view. And now that we’re almost four years into the Lesser Depression, I’m willing, out of a combination of a sense that support is building for a Fed regime shift and sheer desperation, to support the use of expectations-based monetary policy as our best hope.

And one thing the market monetarists may have been right about is the usefulness of focusing on nominal GDP. As far as I can see,the underlying economics is about expected inflation; but stating the goal in terms of nominal GDP may nonetheless be a good idea, largely as a selling point, since it (a) is easier to make the case that we’ve fallen far below where we should be and (b) doesn’t sound so scary and anti-social

Here is a Sumner Summary.

Here is another Sumner Summary.

If this kind of thing interests you in the least, make a careful study of Scott’s blog. You’ll learn a LOT.

A Piece of The Puzzle

Posted on by David Wright

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.

When The Chips Are Down: It’s All About #1

Posted on by David Wright

I’m just going to quote this whole MR post:

QUOTE

Support for redistribution, surprisingly enough, has plummeted during the recession. For years, the General Social Survey has asked individuals whether “government should reduce income differences between the rich and the poor.” Agreement with this statement dropped dramatically between 2008 and 2010, the two most recent years of data available.  Other surveys have shown similar results.

…the change is not driven by wealthy white Republicans reacting against President Obama’s agenda: the drop is if anything slightly larger among minorities, and Americans who self-identify as having below average income show the same decrease in support for redistribution as wealthier Americans.

Here is more.  The researchers, Ilyana Kuziemko and Michael I. Norton, attribute this to “last place aversion,” namely the desire to always have someone below you in the income pecking order:

Which group was the most opposed [to an increase in the minimum wage]? Those making just above the minimum wage, between $7.26 and $8.25.

For the pointer I thank The Browser.

END QUOTE

Here’s my comment on MR:

Maybe people automatically think that “the poor” is an unemployed person (ie somebody else, most people have jobs). When times are good, you probably don’t mind risking a tax increase for the sake of supporting a cause that signals your magnanimity.

When the ship is sinking, though, @#$@ the women and children, I need to watch out for #1.

My theory of political discourse is that people affiliate with causes when they perceive it to be relatively costless for them to do so. You can yammer on about the poor all you like; actually, you can yammer on about ANYTHING you like, but as soon as shit gets real, the decision-making process changes rapidly.

Talk is cheap: this is why prediction markets are the best way of figuring out what people really think.

-=-

Following a comment exchange below, please be sure to take the title to be a bit of artistic license (ie not, strictly speaking, true).

Stanford Machine Learning Notes

Posted on by David Wright

Wow, I’m really loving this class. Lecture4 slides.

[I should probably point out to regular readers that these notes aren’t really fit for mass consumption. I’m not going to bother even trying to build a complete understanding of each of the concepts so they’re really just for personal use.

That being said, they’re on here and if you’re interested in seeing what I spend just about all my spare time doing, read on!]

Ok, today we covered multi-variate regression and we’re venturing into some virgin territory for me, now. It’s pretty awesome stuff.

So we started out with the insight that we could express a multivariate regression as a transposed matrix multiplication. What a mouthful. Believe me, it’s simpler than it sounds.

The idea is that you have a sec of values (slope values for the dependent variable) and a set of inputs (independent variables) and matrix multiplication just gives us a clean way of grouping them and then mashing them all together at once. This is clearly a programming optimization. If you did it by hand, it wouldn’t really be any easier.

The second idea is to express the error function of the gradient descent algorithm as a matrix. I’m barely holding on at this point, actually, and am looking forward to my first actual exercise.

Feature and mean scaling are next. These are neat little tricks to optimize the program. The idea is that if you have two features, sq footage and age of a house for example, which take on values of massively different magnitudes, your application of a uniform transformation of the slopes (the alpha term) will really frig up the algorithm’s progress.

So let’s say the slope of the sq. footage term is 350 and age is 5. If you apply a 0.01 modification to adjust the algorithm, you’ll barely move the age term. If you apply 0.5, you’ll be blazing away on the sq footage.

There’s some talk about graphing the error term of the error function so you can see your progress. I like visuals, so I’m on board.

There’s also a neat discussion about how to use the variables supplied to build your own variables. Using length and depth to compute sq footage, for example. Also arbitrarily raising some variables to some power: price of a house being related to the sq footage and negatively related to the sq of the sq footage. This is a nice way of introducing non-linearities.

We closed with a discussion of a closed-form solution for some of these problems. I finally lost my grip and will need to spend more time learning about the ‘normalized’ equation, which involves transposing the coefficient matrix and multiplying it by the training vector.

I totally get that there are tradeoffs to this approach versus the iterative gradient descent solution, though. Specifically, the trick is transposing that matrix of coefficients. Once you start transposing 10,000 x 10,000 matrices, it takes quite some time. I wonder if the transpose function in Octave is just an iterative function itself?

Back to the drawing board to deepen my understanding…

How Square Roots are Calculated in Quake

Posted on by David Wright

This has been sitting in my drafts folder, waiting for me to read the article, learn about it and summarize it here.

I took a quick scan today to make sure I wasn’t biting off more than I could chew when I stuck it in the queue. Unfortunately, I don’t think I understand it better than the linked-to-writer and I’m not interested in spending the time to become so.

Here’s the attention-grabbing part:

My Understanding: This incredible hack estimates the inverse root using Newton’s method of approximation, and starts with a great initial guess.

The trick has to do with how floating point numbers are stored in a computer, something I’ve actually blogged about.

Who said math wasn’t useful!

I Agree

Posted on by David Wright

Eventually, maybe everyone will truly have to be able to code to effectively do any office job.

More here.

I love reading about people picking up coding later in life. I consider myself a member of that group. Learning coding when I should rightly be doing other stuff.

David Haye Retires

Posted on by David Wright

Let’s put aside the question of whether this is a real retirement or not. Let’s take him at his word.

Professional athletes have a strange fate. The most successful are the most tough mentally: they train harder, smarter and longer than their equally (or more highly) talented peers.

I actually believe success in any walk of life depends on experience and sustained mental strength. Sports, business, science, family life, friendship: it all takes work and intelligence and effort.

I say this because in all things except sports, you get to use your experience and knowledge and constantly improve for as long as you choose. If success grants you one thing it’s the ability to control your fate. The most successful keep at it right up until and beyond where social norms tell you you should stop.

In sports, though, you work at something from childhood and, just as you’re beginning to reach true mental and intellectual maturity, your physical abilities begin their decline. As an athlete, you have dedicated your LIFE to this activity and just as start to get it, you have to stop it.

They probably feel the same as they did when they were 20. How could those feelings be wrong?! This must be unimaginably frustrating.

Maybe you become a coach. Maybe you go get an MBA. Who knows. But the allure of un-retirement is immense. In contact sports like boxing there is a powerful disincentive, though. Here’s David Haye:

I didn’t want my speech to become any more slurred than it was when I first entered the ring, and was keen not to one day look like an extra from Michael Jackson’s ‘Thriller’ video.

This happens to a lot of boxers. This happens to even more football and hockey players, because those sports employ more people. Concussions destroy lives.

I think that boxers should retire before 30. I think that guys like Floyd Mayweather Jr. got into their 30s with relatively little physical punishment because of technique built on talent. Talent fades, though, and Floyd’s going to start getting hit.

These people train their minds to push their bodies beyond where the limits ‘should be’. This is a skill that begets extraordinary success and wealth.

A more valuable skill, for the sake of their lives, is turning it off.