To Become a Better Investor, Think Like Darwin

On financial innovations — read derivatives and securitizations — and the need for more collaboration :

People respond to incentives, and so if we want to take on much bigger challenges, we need to collaborate across thousands and in some cases hundreds of thousands of people. How do you get 100,000 people to work together? It’s not that easy. In the old days, it was religion and before that it was simple fiat rules, tyranny. The Egyptians built some beautiful pyramids, but they did that with hundreds of thousands of slaves over decades. If we rule out slavery as a possible means of societal advances, there really isn’t any other choice. If we need 100,000 people to cure cancer, to deal with Alzheimer’s, to figure out fusion energy and climate change…I don’t know of any other way to do that other than financial markets: equity, debt, proper financing and proper payout of returns. I think that in many cases [finance] probably is the gating factor. That, to me, is the short answer to the question about why finance is so important.

→ Nautilus

In Defense Of The Gaussian Copula

The Gaussian copula is not an economic model, but it has been similarly misused and is similarly demonised. In broad terms, the Gaussian copula is a formula to map the approximate correlation between two variables. In the financial world it was used to express the relationship between two assets in a simple form. This was foolish. Even the relationship between debt and equity changes with the market conditions. Often it has a negative correlation, but other times it can be positive.

That does not mean it was useless. The Gaussian copula provided a convienent way to describe a relationship that held under particular conditions. But it was fed data that reflected a period when housing prices were not correlated to the extent that they turned out to be when the housing bubble popped. You can have the most complicated and complete model in the world to explain asset correlation, but if you calibrate it assuming housing prices won’t fall on a national level, the model cannot hedge you against that happening.

→ The Economist

What I Learned from Losing $200 Million

That was one hell of a trade. Boy, what a wild ride.

The Sunday after Lehman fell, pacing my empty trading floor, I realized once and for all that my models and reports could no longer tell me what to do. The one unmistakable fact was that my risks would increase if oil continued its decline. I decided that when I came in on Monday, I’d place a big bet that WTI would do just that.

And on a Saturday morning bike ride up the Hudson, it occurred to me that Mexico might be willing to restructure its deal—selling us back the option it owned, and buying a new one—in a way that would lock in billions of profits for the country, while giving me a much needed windfall too. I dropped my bike in a bush and texted our salesperson about the idea.

There were many other decisions and guesses, some made alone, others with help from my team, and still others made by my boss. All were guesswork, none could I have anticipated in stress testing, and all involved abandoning my original strategy along with the illusion of control it gave me.

→ Nautilus

Is the Economy Really in Trouble? A Debate

¯\_(ツ)_/¯ :

But right now I’m stuck. I have no idea how the United States economy is doing. And the closer I look at the data, the more contradictory it looks.

• • •

I’ve tried several times in the last few weeks to convince myself that one of those stories is correct, but just can’t decide between them. And because The New York Times is not fond of headlines that include the “shruggie” emoticon (for the uninitiated, that would be ¯\_(ツ)_/¯), I have held off writing anything.

Why am I telling you all this? Because sometimes the most accurate portrayal of a situation revolves around uncertainty — and because we journalists aren’t always honest about that. This is my effort to be a little more honest.

→ The New York Times

Black Monday Really Did Look Like 1929 Again

A short and informative recollection of what happened roughly three decades ago, from Barry Ritholtz :

Where were you on Monday, Oct. 19, 1987?

Today is the first time since 2009 that Oct. 19 has fallen on a Monday, and that has me thinking about that day. I recall exactly where I was — in graduate school, walking between classes, when I passed a television broadcasting the collapse.

New York Magazine had a great piece too on February 2008 :

It all started, of course, on Wall Street. On Black Monday, October 19, 1987, the Dow Jones index, for reasons still being debated, fell 508 points, almost a quarter of its total. (The current equivalent, for comparison’s sake, would be a 3,200-point loss on one day.) The drop turned out to be a “black swan event,” a weirdly poetic economist’s term meaning, basically, a fluke (though few people remember it, the Dow still eked out a positive finish for the year). Still, the hiccup seemed to foretell the instability to come. Over the next two years, with the economy perceived to be overheating, the Fed repeatedly jacked up interest rates, which made bonds and T-bills sexier than stocks, which triggered an epidemic of unscrupulous bond peddling, which further destabilized the market—leading to a slowdown. (If that all sounds disturbingly like the recent subprime-debt mess, well, that’s because it is. But more on that later.) And a slowdown on Wall Street, which provides over 20 percent of the city’s cash income, spells a slowdown for New York.

→ Bloomberg View