16 December 2008

Software Models Doomed the Markets

After the Crash: How Software Models Doomed the Markets
Overreliance on financial software crafted by physics and math PhDs helped to precipitate the Wall Street collapse

By The Editors

If Hollywood makes a movie about the worst financial crisis since the Great Depression, a basement room in a government building in Washington will serve as the setting for a key scene. There investment bankers from the largest institutions pleaded successfully with Securities and Exchange Commission (SEC) officials during a short meeting in 2004 to lift a rule specifying debt limits and capital reserves needed for a rainy day. This decision, a real event described in the New York Times, freed billions to invest in complex mortgage-backed securities and derivatives that helped to bring about the financial meltdown in September.

In the script, the next scene will be the one in which number-savvy specialists that Wall Street has come to know as quants consult with their superiors about implementing the regulatory change. These lapsed physicists and mathematical virtuosos were the ones who both invented these oblique securities and created software models that supposedly measured the risk a firm would incur by holding them in its portfolio. Without the formal requirement to maintain debt ceilings and capital reserves, the commission had freed these firms to police themselves using risk tools crafted by cadres of quants.

The software models in question estimate the level of financial risk of a portfolio for a set period at a certain confidence level. As Benoit Mandelbrot, the fractal pioneer who is a longtime critic of mainstream financial theory, wrote in Scientific American in 1999, established modeling techniques presume falsely that radically large market shifts are unlikely and that all price changes are statistically independent; today’s fluctuations have nothing to do with tomorrow’s—and one bank’s portfolio is unrelated to the next’s. Here is where reality and rocket science diverge. Try Googling “financial meltdown,” “contagion” and “2008,” a search that reveals just how wrongheaded these assumptions were.

This modern-day tragedy could be framed not only as a major motion picture but also as a train wreck or plane crash. In aviation, controlled flight into terrain describes the actions of a pilot who, through inattention or incompetence, directs a well-functioning airplane into the side of a mountain. Wall Street’s version stems from the SEC’s decision to allow overreliance on risk software in the middle of a historic housing bubble. The heady environment permitted traders to enter overoptimistic assumptions and faulty data into their models, jiggering the software to avoid setting off alarm bells.

The causes of this fiasco are multifold—the Federal Reserve’s easy-money policy played a big role—but the rocket scientists and geeks also bear their share of the blame. After the crash, the quants and traders they serve need to accept the necessity for a total makeover. The government bailout has already left the U.S. Treasury and Federal Reserve with extraordinary powers. The regulators must ensure that the many lessons of this debacle are not forgotten by the institutions that trade these securities. One important take-home message: capital safety nets (now restored) should never be slashed again, even if a crisis is not looming.

For its part, the quant community needs to undertake a search for better models—perhaps seeking help from behavioral economics, which studies irrationality of investors’ decision making, and from virtual market tools that use “intelligent agents” to mimic more faithfully the ups and downs of the activities of buyers and sellers. These number wizards and their superiors need to study lessons that were never learned during previous market smashups involving intricate financial engineering: risk management models should serve only as aids not substitutes for the critical human factor. Like an airplane, financial models can never be allowed to fly solo.


1 comment:

andreas said...

It is easy to create a software model if the rules of the game are defined. If the rules are not known, the model has to use historic data for prediction.

But historic data can not be used if the rules are not only unknown, but also change. Thus the future can't be modeled without full transparency.

The problem was never the models. The problem is the continuous invention of dodgy financial instruments DESIGNED to "fool" the models. That is how you win at any game, you cheat.

If Kasparov had constantly introduced new chess pieces during the game, Deep Blue would have choked.