"But the larger failure, they say, was human"

On this blog, I have been putting forward what I call a Human Agency explanation of this crisis versus what I have called a Mechanistic explanation of this crisis. My view focuses on:

1) Incentives ( e.g. ) Implicit and explicit government guarantees
2) Unethical behavior ( e.g. ) Fraud, negligence, fiduciary malpractice
3) Human weakness ( e.g. ) wishful thinking, lack of due diligence

As opposed to, what I would call, conditions that are acted upon or taken advantage of:

1) Low interest rates
2) Sloshing pool of money
3) Complex investments
4) Complex analysis

Today, an excellent article by Steve Lohr in the NY Times entitled "In Modeling Risk, the Human Factor Was Left Out". This is a version of everything that I have been saying.

"Today’s economic turmoil, it seems, is an implicit indictment of the arcane field of financial engineering — a blend of mathematics, statistics and computing. Its practitioners devised not only the exotic, mortgage-backed securities that proved so troublesome, but also the mathematical models of risk that suggested these securities were safe.

What happened?

The models, according to finance experts and economists, did fail to keep pace with the explosive growth in complex securities, the resulting intricate web of risk and the dimensions of the danger.

But the larger failure, they say, was human — in how the risk models were applied, understood and managed. Some respected quantitative finance analysts, or quants, as financial engineers are known, had begun pointing to warning signs years ago. But while markets were booming, the incentives on Wall Street were to keep chasing profits by trading more and more sophisticated securities, piling on more debt and making larger and larger bets."

Absolutely. The models are merely a condition of the crisis, but not the cause.

“Complexity, transparency, liquidity and leverage have all played a huge role in this crisis,” said Leslie Rahl, president of Capital Market Risk Advisors, a risk-management consulting firm. “And these are things that are not generally modeled as a quantifiable risk.”

Math, statistics and computer modeling, it seems, also fell short in calibrating the lending risk on individual mortgage loans. In recent years, the securitization of the mortgage market, with loans sold off and mixed into large pools of mortgage securities, has prompted lenders to move increasingly to automated underwriting systems, relying mainly on computerized credit-scoring models instead of human judgment.

So lenders had scant incentive to spend much time scrutinizing the creditworthiness of individual borrowers. “If the incentives and the systems change, the hard data can mean less than it did or something else than it did,” said Raghuram G. Rajan, a professor at the University of Chicago. “The danger is that the modeling becomes too mechanical.”

Rather, the view of the market and investing becomes too mechanical, forgetting that human agency is behind all of these decisions. I dispute that the investments were too complex to understand their true nature. Rather, the complexity was used to obfuscate risk. Take any of these investments, for example, credit default swaps, and they are not impossible to understand. On the contrary, the risk is apparent.

"Besides, the formation of a housing bubble was well under way. Until 2003, prices moved in line with employment, incomes and migration patterns, but then they departed from the economic fundamentals."

See, human agency is not factored into this mechanistic view. It appears to be a mathematical relationship between various figures.

"The Wall Street models, said Paul S. Willen, an economist at the Federal Reserve in Boston, included a lot of wishful thinking about house prices. But, he added, it is also true that asset price trends are difficult to predict. “The price of an asset, like a house or a stock, reflects not only your beliefs about the future, but you’re also betting on other people’s beliefs,” he observed. “It’s these hierarchies of beliefs — these behavioral factors — that are so hard to model.”

Indeed, the behavioral uncertainty added to the escalating complexity of financial markets help explain the failure in risk management. The quantitative models typically have their origins in academia and often the physical sciences. In academia, the focus is on problems that can be solved, proved and published — not messy, intractable challenges. In science, the models derive from particle flows in a liquid or a gas, which conform to the neat, crisp laws of physics

.Not so in financial modeling. Emanuel Derman is a physicist who became a managing director at Goldman Sachs, a quant whose name is on a few financial models and author of “My Life as a Quant — Reflections on Physics and Finance” (Wiley, 2004). In a paper that will be published next year in a professional journal, Mr. Derman writes, “To confuse the model with the world is to embrace a future disaster driven by the belief that humans obey mathematical rules.”

That last line is exactly what I've been saying.

"Yet blaming the models for their shortcomings, he said in an interview, seems misguided. “The models were more a tool of enthusiasm than a cause of the crisis,” said Mr. Derman, who is a professor at Columbia University."

Bingo. The models were a condition, but not a cause.

"Better modeling, more wisely applied, would have helped, Mr. Lindsey said, but so would have common sense in senior management."

I know that we want some big scary villain, but it does just come down to human agency like common sense.

"The dismissive response, Mr. Lo said, was not really surprising because Wall Street was going to chase profits in the good times. The path to sensible restraint, he said, will include not only better risk models, but also more regulation. Like others, Mr. Lo recommends higher capital requirements for banks and the use of exchanges or clearinghouses for the trade of exotic securities, so that prices and risks are more visible. Any hedge fund with more than $1 billion in assets, he added, should be compelled to report its holdings to regulators.

Financial regulation, Mr. Lo said, should be seen as similar to fire safety rules in building codes. The chances of any building burning down are slight, but ceiling sprinklers, fire extinguishers and fire escapes are mandated by law.

“We’ve learned the hard way that the consequences can be catastrophic, even if statistically improbable,” he said."

The recommendations made are fine:
1) higher capital
2) a clearinghouse
3) better regulation

I've already agreed with and talked about all of them. However, the regulations need to be based on human agency and not simply measurable factors. That's why I believe only simple and broad principles can work in this forthcoming regulation. Otherwise:

“Innovation can be a dangerous game,” said Andrew W. Lo, an economist and professor of finance at the Sloan School of Management of the Massachusetts Institute of Technology. “The technology got ahead of our ability to use it in responsible ways.”

This is just another way of saying that if the regulations are too specific, investors will find a way around them. You need a set of principles that can funnel riskier investments into a screening process where they can be examined. The actual amount of government regulation should be based on that assessment. Unfortunately, that will be done by human agency, and has its own set of conditions and risks. There's no simple answer.